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Remove delete doc CI
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6471). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005573 / 0.011353 (-0.005780) | 0.003449 / 0.011008 (-0.007559) | 0.063323 / 0.038508 (0.024815) | 0.049369 / 0.023109 (0.026260) | 0.254280 / 0.275898 (-0.021618) | 0.267721 / 0.323480 (-0.055759) | 0.002894 / 0.007986 (-0.005092) | 0.002646 / 0.004328 (-0.001683) | 0.049284 / 0.004250 (0.045033) | 0.037947 / 0.037052 (0.000895) | 0.251654 / 0.258489 (-0.006836) | 0.279729 / 0.293841 (-0.014112) | 0.028022 / 0.128546 (-0.100525) | 0.010653 / 0.075646 (-0.064993) | 0.208567 / 0.419271 (-0.210704) | 0.035863 / 0.043533 (-0.007670) | 0.248522 / 0.255139 (-0.006617) | 0.270274 / 0.283200 (-0.012925) | 0.019683 / 0.141683 (-0.122000) | 1.136342 / 1.452155 (-0.315812) | 1.206757 / 1.492716 (-0.285960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094682 / 0.018006 (0.076676) | 0.304092 / 0.000490 (0.303602) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018606 / 0.037411 (-0.018805) | 0.060568 / 0.014526 (0.046042) | 0.074067 / 0.176557 (-0.102490) | 0.118979 / 0.737135 (-0.618156) | 0.075676 / 0.296338 (-0.220663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290452 / 0.215209 (0.075243) | 2.848868 / 2.077655 (0.771213) | 1.534932 / 1.504120 (0.030812) | 1.386717 / 1.541195 (-0.154478) | 1.416645 / 1.468490 (-0.051845) | 0.569020 / 4.584777 (-4.015757) | 2.421168 / 3.745712 (-1.324545) | 2.781358 / 5.269862 (-2.488503) | 1.758495 / 4.565676 (-2.807182) | 0.063851 / 0.424275 (-0.360424) | 0.004968 / 0.007607 (-0.002639) | 0.339198 / 0.226044 (0.113154) | 3.356392 / 2.268929 (1.087464) | 1.858145 / 55.444624 (-53.586479) | 1.589000 / 6.876477 (-5.287477) | 1.569175 / 2.142072 (-0.572897) | 0.650571 / 4.805227 (-4.154657) | 0.120288 / 6.500664 (-6.380376) | 0.042489 / 0.075469 (-0.032980) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939963 / 1.841788 (-0.901824) | 11.493612 / 8.074308 (3.419304) | 10.353780 / 10.191392 (0.162388) | 0.141945 / 0.680424 (-0.538479) | 0.014397 / 0.534201 (-0.519804) | 0.286971 / 0.579283 (-0.292312) | 0.266787 / 0.434364 (-0.167577) | 0.330385 / 0.540337 (-0.209952) | 0.438542 / 1.386936 (-0.948394) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005360 / 0.011353 (-0.005993) | 0.003720 / 0.011008 (-0.007288) | 0.048790 / 0.038508 (0.010282) | 0.050256 / 0.023109 (0.027147) | 0.275445 / 0.275898 (-0.000453) | 0.297725 / 0.323480 (-0.025755) | 0.004077 / 0.007986 (-0.003909) | 0.002759 / 0.004328 (-0.001569) | 0.047653 / 0.004250 (0.043403) | 0.040205 / 0.037052 (0.003153) | 0.281028 / 0.258489 (0.022539) | 0.304682 / 0.293841 (0.010841) | 0.030158 / 0.128546 (-0.098388) | 0.010957 / 0.075646 (-0.064689) | 0.058193 / 0.419271 (-0.361079) | 0.033277 / 0.043533 (-0.010256) | 0.279501 / 0.255139 (0.024362) | 0.295381 / 0.283200 (0.012181) | 0.017889 / 0.141683 (-0.123794) | 1.121354 / 1.452155 (-0.330801) | 1.225702 / 1.492716 (-0.267014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093385 / 0.018006 (0.075378) | 0.304642 / 0.000490 (0.304152) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021456 / 0.037411 (-0.015955) | 0.068536 / 0.014526 (0.054010) | 0.080867 / 0.176557 (-0.095689) | 0.119093 / 0.737135 (-0.618042) | 0.081875 / 0.296338 (-0.214464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304434 / 0.215209 (0.089225) | 2.990303 / 2.077655 (0.912649) | 1.616959 / 1.504120 (0.112839) | 1.493256 / 1.541195 (-0.047939) | 1.542857 / 1.468490 (0.074367) | 0.575517 / 4.584777 (-4.009260) | 2.455165 / 3.745712 (-1.290547) | 2.810089 / 5.269862 (-2.459773) | 1.756502 / 4.565676 (-2.809175) | 0.064801 / 0.424275 (-0.359475) | 0.004969 / 0.007607 (-0.002638) | 0.360227 / 0.226044 (0.134183) | 3.575029 / 2.268929 (1.306100) | 1.989955 / 55.444624 (-53.454669) | 1.705306 / 6.876477 (-5.171171) | 1.688523 / 2.142072 (-0.453550) | 0.663266 / 4.805227 (-4.141962) | 0.121852 / 6.500664 (-6.378812) | 0.041853 / 0.075469 (-0.033616) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983535 / 1.841788 (-0.858252) | 11.827656 / 8.074308 (3.753348) | 10.663265 / 10.191392 (0.471873) | 0.145942 / 0.680424 (-0.534482) | 0.016004 / 0.534201 (-0.518197) | 0.288907 / 0.579283 (-0.290376) | 0.279100 / 0.434364 (-0.155264) | 0.328061 / 0.540337 (-0.212276) | 0.570253 / 1.386936 (-0.816683) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b52cbc18919869460557e15028e7f489eae8afc7 \"CML watermark\")\n" ]
2023-12-05T12:37:50
2023-12-05T12:44:59
2023-12-05T12:38:50
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2,024,724,319
I_kwDODunzps54rtdf
6,470
If an image in a dataset is corrupted, we get unescapable error
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2023-12-04T20:58:49
2023-12-04T20:58:49
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### Describe the bug Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1 ### Steps to reproduce the bug ``` from datasets import load_dataset, VerificationMode dataset = load_dataset( 'sasha/birdsnap', split="train", verification_mode=VerificationMode.ALL_CHECKS, streaming=True # I recommend using streaming=True when reproducing, as this dataset is large ) for idx, row in enumerate(dataset): # Iterating to 9287 took 7 minutes for me # If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287] pass # error at 9287 OSError: image file is truncated (45 bytes not processed) # note that we can't avoid the error using a try/except + continue inside the loop ``` ### Expected behavior Able to escape errors in casting to Image() without killing the whole loop ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0
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2,023,695,839
PR_kwDODunzps5hC6xf
6,469
Don't expand_info in HF glob
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004998 / 0.011353 (-0.006355) | 0.003523 / 0.011008 (-0.007486) | 0.064932 / 0.038508 (0.026424) | 0.050107 / 0.023109 (0.026998) | 0.253715 / 0.275898 (-0.022183) | 0.275364 / 0.323480 (-0.048116) | 0.003902 / 0.007986 (-0.004084) | 0.002716 / 0.004328 (-0.001612) | 0.048458 / 0.004250 (0.044208) | 0.037802 / 0.037052 (0.000750) | 0.262328 / 0.258489 (0.003839) | 0.285911 / 0.293841 (-0.007930) | 0.027112 / 0.128546 (-0.101435) | 0.010780 / 0.075646 (-0.064867) | 0.206447 / 0.419271 (-0.212824) | 0.035771 / 0.043533 (-0.007761) | 0.255031 / 0.255139 (-0.000108) | 0.270530 / 0.283200 (-0.012670) | 0.017152 / 0.141683 (-0.124530) | 1.094734 / 1.452155 (-0.357421) | 1.163480 / 1.492716 (-0.329237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092944 / 0.018006 (0.074938) | 0.301042 / 0.000490 (0.300553) | 0.000238 / 0.000200 (0.000038) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019090 / 0.037411 (-0.018321) | 0.061046 / 0.014526 (0.046520) | 0.073330 / 0.176557 (-0.103227) | 0.121124 / 0.737135 (-0.616012) | 0.080544 / 0.296338 (-0.215795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.323866 / 0.215209 (0.108657) | 2.797727 / 2.077655 (0.720072) | 1.502994 / 1.504120 (-0.001126) | 1.376177 / 1.541195 (-0.165018) | 1.422741 / 1.468490 (-0.045749) | 0.562990 / 4.584777 (-4.021786) | 2.431781 / 3.745712 (-1.313931) | 2.783226 / 5.269862 (-2.486635) | 1.788055 / 4.565676 (-2.777621) | 0.064206 / 0.424275 (-0.360069) | 0.004989 / 0.007607 (-0.002618) | 0.338282 / 0.226044 (0.112237) | 3.356226 / 2.268929 (1.087297) | 1.855644 / 55.444624 (-53.588980) | 1.580876 / 6.876477 (-5.295601) | 1.617418 / 2.142072 (-0.524655) | 0.636816 / 4.805227 (-4.168411) | 0.117680 / 6.500664 (-6.382985) | 0.042560 / 0.075469 (-0.032909) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956410 / 1.841788 (-0.885377) | 11.764886 / 8.074308 (3.690578) | 10.535801 / 10.191392 (0.344409) | 0.137797 / 0.680424 (-0.542627) | 0.014368 / 0.534201 (-0.519833) | 0.286213 / 0.579283 (-0.293070) | 0.267093 / 0.434364 (-0.167271) | 0.334802 / 0.540337 (-0.205535) | 0.441866 / 1.386936 (-0.945070) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005348 / 0.011353 (-0.006005) | 0.003551 / 0.011008 (-0.007458) | 0.049226 / 0.038508 (0.010718) | 0.052072 / 0.023109 (0.028963) | 0.268025 / 0.275898 (-0.007873) | 0.289968 / 0.323480 (-0.033512) | 0.004034 / 0.007986 (-0.003952) | 0.002675 / 0.004328 (-0.001653) | 0.048099 / 0.004250 (0.043848) | 0.040141 / 0.037052 (0.003089) | 0.272974 / 0.258489 (0.014485) | 0.296097 / 0.293841 (0.002256) | 0.028972 / 0.128546 (-0.099575) | 0.010689 / 0.075646 (-0.064957) | 0.057853 / 0.419271 (-0.361418) | 0.032488 / 0.043533 (-0.011045) | 0.272018 / 0.255139 (0.016879) | 0.287179 / 0.283200 (0.003980) | 0.018446 / 0.141683 (-0.123237) | 1.140346 / 1.452155 (-0.311809) | 1.247743 / 1.492716 (-0.244974) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091987 / 0.018006 (0.073980) | 0.300527 / 0.000490 (0.300037) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021390 / 0.037411 (-0.016021) | 0.068768 / 0.014526 (0.054242) | 0.080798 / 0.176557 (-0.095759) | 0.119081 / 0.737135 (-0.618054) | 0.082461 / 0.296338 (-0.213878) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286631 / 0.215209 (0.071422) | 2.804633 / 2.077655 (0.726978) | 1.574122 / 1.504120 (0.070002) | 1.459994 / 1.541195 (-0.081201) | 1.499739 / 1.468490 (0.031249) | 0.579595 / 4.584777 (-4.005182) | 2.426407 / 3.745712 (-1.319306) | 2.917994 / 5.269862 (-2.351868) | 1.846439 / 4.565676 (-2.719238) | 0.063274 / 0.424275 (-0.361001) | 0.005028 / 0.007607 (-0.002579) | 0.341114 / 0.226044 (0.115070) | 3.402677 / 2.268929 (1.133748) | 1.940980 / 55.444624 (-53.503645) | 1.651902 / 6.876477 (-5.224575) | 1.677037 / 2.142072 (-0.465036) | 0.651576 / 4.805227 (-4.153651) | 0.116398 / 6.500664 (-6.384266) | 0.041060 / 0.075469 (-0.034409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973278 / 1.841788 (-0.868509) | 12.248332 / 8.074308 (4.174024) | 10.830627 / 10.191392 (0.639235) | 0.143146 / 0.680424 (-0.537278) | 0.016249 / 0.534201 (-0.517952) | 0.298563 / 0.579283 (-0.280720) | 0.278643 / 0.434364 (-0.155721) | 0.338206 / 0.540337 (-0.202132) | 0.589485 / 1.386936 (-0.797451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]
2023-12-04T12:00:37
2023-12-15T13:18:37
2023-12-15T13:12:30
MEMBER
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false
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Finally fix https://github.com/huggingface/datasets/issues/5537
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https://api.github.com/repos/huggingface/datasets/issues/6469/timeline
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https://github.com/huggingface/datasets/pull/6468
2,023,617,877
PR_kwDODunzps5hCpbN
6,468
Use auth to get parquet export
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n" ]
2023-12-04T11:18:27
2023-12-04T17:21:22
2023-12-04T17:15:11
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added `token` to the `_datasets_server` functions
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New version release request
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[ "We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)", "Thanks!" ]
2023-12-04T07:08:26
2023-12-04T15:42:22
2023-12-04T15:42:22
CONTRIBUTOR
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### Feature request Hi! I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0. To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us? Thanks very much! ### Motivation . ### Your contribution .
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6,466
Can't align optional features of struct
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[ "Friendly bump, I would be happy to work on this issue once I get the go-ahead from the dev team. ", "Thanks for the PR!\r\n\r\nI'm struggling with this as well and would love to see this PR merged. My case is slightly different, with keys completely missing rather than being `None`:\r\n\r\n```\r\nds = Dataset.from_dict({'speaker': [{'name': 'Ben'}]})\r\nds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})\r\nprint(concatenate_datasets([ds, ds2]).features)\r\nprint(concatenate_datasets([ds, ds2]).to_dict())\r\n```\r\n\r\nI would expect this to work as well because other Dataset functions already handle this situation well. For example, this works just as expected:\r\n\r\n```\r\nds = Dataset.from_dict({'n': [1,2]})\r\nds_mapped = ds.map(lambda x: {\r\n 'speaker': {'name': 'Ben'} if x['n'] == 1 else {'name': 'Fred', 'email': 'abc@aol.com'}\r\n})\r\nprint(ds_mapped)\r\n```", "@vova-cyberhaven can you check with the new release if it fixes your issue? " ]
2023-12-03T15:57:07
2024-02-15T15:19:33
2024-02-08T14:38:34
CONTRIBUTOR
null
null
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### Describe the bug Hello! I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional. I have a column named `speaker`, and this holds some information about a speaker. ```python @dataclass class Speaker: name: str email: Optional[str] ``` If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features` ### Steps to reproduce the bug You can run the following script: ```python ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) concatenate_datasets([ds, ds2]) >>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null"). ``` ### Expected behavior I think this should work; if two top-level columns were in the same situation it would properly cast to `string`. ```python ds = Dataset.from_dict({'email': [None, None]}) ds2 = Dataset.from_dict({'email': ['abc@aol.com', 'one@yahoo.com']}) concatenate_datasets([ds, ds2]) >>> # Works! ``` ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35 - Python version: 3.9.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 - `fsspec` version: 2023.6.0 I would be happy to fix this issue.
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I_kwDODunzps54iIN0
6,465
`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset
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[ "Hi, thanks for reporting! https://github.com/huggingface/datasets/pull/6459 will fix this.", "I meet a similar problem as using loading scripts. I have to set download_mode='force_redownload' to load the latest script." ]
2023-12-02T21:35:17
2024-08-20T08:32:11
null
NONE
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### Describe the bug When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset ### Steps to reproduce the bug Here is a minimal example script to 1. create an initial dataset and upload 2. download it so it is stored in cache 3. change the dataset and re-upload 4. redownload ```python import time from datasets import Dataset, DatasetDict, DownloadMode, load_dataset username = "YOUR_USERNAME_HERE" initial = Dataset.from_dict({"foo": [1, 2, 3]}) print(f"Intial {initial['foo']}") initial_ds = DatasetDict({"train": initial}) initial_ds.push_to_hub("test") time.sleep(1) download = load_dataset(f"{username}/test", split="train") changed = download.map(lambda x: {"foo": x["foo"] + 1}) print(f"Changed {changed['foo']}") changed.push_to_hub("test") time.sleep(1) download_again = load_dataset(f"{username}/test", split="train") print(f"Download Changed {download_again['foo']}") # >>> gives the out-dated [1,2,3] when it should be changed [2,3,4] ``` The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset ```python download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) print(f"Force Download Changed {download_again_force['foo']}") # >>> [2,3,4] ``` ### Expected behavior I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match ### Environment info - `datasets` version: 2.15.0 │ - Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │ - Python version: 3.8.17 │ - `huggingface_hub` version: 0.19.4 │ - PyArrow version: 13.0.0 │ - Pandas version: 2.0.3 │ - `fsspec` version: 2023.6.0
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Add concurrent loading of shards to datasets.load_from_disk
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[ "If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.\r\n\r\nHave you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report", "I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. \r\nOk I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. ", "I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20 \r\n\r\nThe CI fail is unrelated to this PR - I'm trying to fix it on `main` right now", "> Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20\r\n> \r\n> The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now\r\n\r\nThank you! I'll let him know :)", "great work guys! letting you know here too", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005268 / 0.011353 (-0.006085) | 0.003520 / 0.011008 (-0.007488) | 0.063247 / 0.038508 (0.024739) | 0.032337 / 0.023109 (0.009228) | 0.243251 / 0.275898 (-0.032647) | 0.265816 / 0.323480 (-0.057664) | 0.002960 / 0.007986 (-0.005025) | 0.002733 / 0.004328 (-0.001595) | 0.048965 / 0.004250 (0.044715) | 0.044341 / 0.037052 (0.007289) | 0.260352 / 0.258489 (0.001863) | 0.288546 / 0.293841 (-0.005295) | 0.027903 / 0.128546 (-0.100643) | 0.010897 / 0.075646 (-0.064749) | 0.210852 / 0.419271 (-0.208419) | 0.036302 / 0.043533 (-0.007231) | 0.247440 / 0.255139 (-0.007699) | 0.263024 / 0.283200 (-0.020176) | 0.017732 / 0.141683 (-0.123951) | 1.144206 / 1.452155 (-0.307949) | 1.206135 / 1.492716 (-0.286581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098404 / 0.018006 (0.080398) | 0.310268 / 0.000490 (0.309778) | 0.000231 / 0.000200 (0.000031) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018342 / 0.037411 (-0.019070) | 0.060620 / 0.014526 (0.046094) | 0.074248 / 0.176557 (-0.102308) | 0.121025 / 0.737135 (-0.616110) | 0.075331 / 0.296338 (-0.221008) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293721 / 0.215209 (0.078512) | 2.854259 / 2.077655 (0.776605) | 1.520735 / 1.504120 (0.016615) | 1.393490 / 1.541195 (-0.147705) | 1.494905 / 1.468490 (0.026415) | 0.573812 / 4.584777 (-4.010965) | 2.418383 / 3.745712 (-1.327329) | 2.803916 / 5.269862 (-2.465945) | 1.741646 / 4.565676 (-2.824030) | 0.063341 / 0.424275 (-0.360934) | 0.004950 / 0.007607 (-0.002658) | 0.341758 / 0.226044 (0.115714) | 3.392918 / 2.268929 (1.123989) | 1.867037 / 55.444624 (-53.577587) | 1.571381 / 6.876477 (-5.305096) | 1.582883 / 2.142072 (-0.559190) | 0.663660 / 4.805227 (-4.141567) | 0.119587 / 6.500664 (-6.381077) | 0.042071 / 0.075469 (-0.033398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940976 / 1.841788 (-0.900811) | 11.841958 / 8.074308 (3.767650) | 10.510954 / 10.191392 (0.319562) | 0.131927 / 0.680424 (-0.548497) | 0.015373 / 0.534201 (-0.518828) | 0.294245 / 0.579283 (-0.285038) | 0.269355 / 0.434364 (-0.165009) | 0.330173 / 0.540337 (-0.210165) | 0.436809 / 1.386936 (-0.950127) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005609 / 0.011353 (-0.005744) | 0.003800 / 0.011008 (-0.007208) | 0.055693 / 0.038508 (0.017185) | 0.032606 / 0.023109 (0.009497) | 0.302372 / 0.275898 (0.026474) | 0.370530 / 0.323480 (0.047050) | 0.004291 / 0.007986 (-0.003694) | 0.002783 / 0.004328 (-0.001546) | 0.049351 / 0.004250 (0.045101) | 0.048186 / 0.037052 (0.011133) | 0.290022 / 0.258489 (0.031533) | 0.323358 / 0.293841 (0.029517) | 0.053929 / 0.128546 (-0.074617) | 0.011251 / 0.075646 (-0.064395) | 0.058885 / 0.419271 (-0.360387) | 0.033833 / 0.043533 (-0.009699) | 0.283546 / 0.255139 (0.028407) | 0.292416 / 0.283200 (0.009216) | 0.017682 / 0.141683 (-0.124001) | 1.141791 / 1.452155 (-0.310364) | 1.202540 / 1.492716 (-0.290177) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101240 / 0.018006 (0.083233) | 0.313274 / 0.000490 (0.312784) | 0.000255 / 0.000200 (0.000055) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023144 / 0.037411 (-0.014268) | 0.078418 / 0.014526 (0.063892) | 0.089716 / 0.176557 (-0.086840) | 0.129065 / 0.737135 (-0.608070) | 0.090976 / 0.296338 (-0.205362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294585 / 0.215209 (0.079376) | 2.921350 / 2.077655 (0.843695) | 1.600977 / 1.504120 (0.096857) | 1.483218 / 1.541195 (-0.057977) | 1.533599 / 1.468490 (0.065109) | 0.580064 / 4.584777 (-4.004712) | 2.463501 / 3.745712 (-1.282211) | 2.905853 / 5.269862 (-2.364009) | 1.799701 / 4.565676 (-2.765975) | 0.065057 / 0.424275 (-0.359218) | 0.005080 / 0.007607 (-0.002527) | 0.352292 / 0.226044 (0.126248) | 3.429664 / 2.268929 (1.160735) | 1.970752 / 55.444624 (-53.473872) | 1.697151 / 6.876477 (-5.179326) | 1.751678 / 2.142072 (-0.390394) | 0.679264 / 4.805227 (-4.125963) | 0.118197 / 6.500664 (-6.382467) | 0.041834 / 0.075469 (-0.033635) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985756 / 1.841788 (-0.856032) | 13.335160 / 8.074308 (5.260852) | 11.524807 / 10.191392 (1.333415) | 0.134892 / 0.680424 (-0.545532) | 0.016855 / 0.534201 (-0.517346) | 0.294599 / 0.579283 (-0.284685) | 0.285988 / 0.434364 (-0.148376) | 0.331423 / 0.540337 (-0.208914) | 0.418765 / 1.386936 (-0.968171) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65434e449b6bb6c57121d9518d92abe9a97e0bb0 \"CML watermark\")\n" ]
2023-12-01T13:13:53
2024-01-26T15:17:43
2024-01-26T15:10:26
CONTRIBUTOR
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In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads. - Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252). - I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config. - I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`. ### Tests on luster file system (on a shared partial node): Loading 1231 shards of ~2GBs. The files were pre-loaded in another process before the script runs (couldn't get a fresh node). ```python import logging from time import perf_counter import datasets logger = datasets.logging.get_logger(__name__) datasets.logging.set_verbosity_info() logging.basicConfig(level=logging.DEBUG, format="%(message)s") class catchtime: # context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time def __init__(self, debug_print="Time", logger=logger): self.debug_print = debug_print self.logger = logger def __enter__(self): self.start = perf_counter() return self def __exit__(self, type, value, traceback): self.time = perf_counter() - self.start readout = f"{self.debug_print}: {self.time:.3f} seconds" self.logger.info(readout) dataset_path="" # warmup with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=16 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=32 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=32) # num_proc=1 with catchtime("Loading in conseq", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=1) ``` #### Run 1 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s] Loading in parallel: 88.690 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s] Loading in parallel: 109.339 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s] Loading in parallel: 66.931 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s] Loading in conseq: 309.792 seconds ``` #### Run 2 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s] Loading in parallel: 98.831 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s] Loading in parallel: 121.669 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s] Loading in parallel: 68.192 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s] Loading in conseq: 319.759 seconds ``` #### Run 3 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s] Loading in parallel: 96.936 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s] Loading in parallel: 120.761 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s] Loading in parallel: 68.666 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s] Loading in conseq: 335.777 seconds ``` fix #2252
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[ "It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005357 / 0.011353 (-0.005996) | 0.003295 / 0.011008 (-0.007713) | 0.062354 / 0.038508 (0.023846) | 0.054207 / 0.023109 (0.031098) | 0.240030 / 0.275898 (-0.035869) | 0.267863 / 0.323480 (-0.055617) | 0.002925 / 0.007986 (-0.005061) | 0.002634 / 0.004328 (-0.001695) | 0.047952 / 0.004250 (0.043702) | 0.038424 / 0.037052 (0.001372) | 0.248059 / 0.258489 (-0.010430) | 0.271923 / 0.293841 (-0.021918) | 0.027513 / 0.128546 (-0.101034) | 0.010344 / 0.075646 (-0.065302) | 0.210864 / 0.419271 (-0.208407) | 0.035911 / 0.043533 (-0.007622) | 0.245166 / 0.255139 (-0.009973) | 0.260914 / 0.283200 (-0.022285) | 0.016709 / 0.141683 (-0.124974) | 1.098324 / 1.452155 (-0.353830) | 1.162638 / 1.492716 (-0.330079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094419 / 0.018006 (0.076413) | 0.303209 / 0.000490 (0.302719) | 0.000214 / 0.000200 (0.000014) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018350 / 0.037411 (-0.019061) | 0.060625 / 0.014526 (0.046099) | 0.072545 / 0.176557 (-0.104012) | 0.120905 / 0.737135 (-0.616231) | 0.073858 / 0.296338 (-0.222480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282011 / 0.215209 (0.066802) | 2.758741 / 2.077655 (0.681086) | 1.431691 / 1.504120 (-0.072429) | 1.315883 / 1.541195 (-0.225312) | 1.344235 / 1.468490 (-0.124255) | 0.562117 / 4.584777 (-4.022660) | 2.385641 / 3.745712 (-1.360071) | 2.785402 / 5.269862 (-2.484460) | 1.753912 / 4.565676 (-2.811764) | 0.064054 / 0.424275 (-0.360221) | 0.005050 / 0.007607 (-0.002557) | 0.336452 / 0.226044 (0.110407) | 3.302481 / 2.268929 (1.033553) | 1.794105 / 55.444624 (-53.650519) | 1.519346 / 6.876477 (-5.357131) | 1.514911 / 2.142072 (-0.627161) | 0.655779 / 4.805227 (-4.149449) | 0.117913 / 6.500664 (-6.382751) | 0.042229 / 0.075469 (-0.033240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935196 / 1.841788 (-0.906591) | 11.490113 / 8.074308 (3.415805) | 10.542446 / 10.191392 (0.351054) | 0.129614 / 0.680424 (-0.550810) | 0.014919 / 0.534201 (-0.519282) | 0.288448 / 0.579283 (-0.290835) | 0.266929 / 0.434364 (-0.167435) | 0.328830 / 0.540337 (-0.211507) | 0.475510 / 1.386936 (-0.911426) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005469 / 0.011353 (-0.005884) | 0.003798 / 0.011008 (-0.007210) | 0.049129 / 0.038508 (0.010621) | 0.055490 / 0.023109 (0.032380) | 0.265828 / 0.275898 (-0.010070) | 0.286031 / 0.323480 (-0.037448) | 0.004075 / 0.007986 (-0.003910) | 0.002668 / 0.004328 (-0.001660) | 0.047823 / 0.004250 (0.043573) | 0.041946 / 0.037052 (0.004894) | 0.270359 / 0.258489 (0.011869) | 0.294287 / 0.293841 (0.000446) | 0.029643 / 0.128546 (-0.098903) | 0.010523 / 0.075646 (-0.065123) | 0.057370 / 0.419271 (-0.361902) | 0.033149 / 0.043533 (-0.010384) | 0.264408 / 0.255139 (0.009269) | 0.280413 / 0.283200 (-0.002787) | 0.018313 / 0.141683 (-0.123370) | 1.105982 / 1.452155 (-0.346173) | 1.182486 / 1.492716 (-0.310230) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092643 / 0.018006 (0.074637) | 0.301320 / 0.000490 (0.300831) | 0.000221 / 0.000200 (0.000021) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021253 / 0.037411 (-0.016158) | 0.068052 / 0.014526 (0.053527) | 0.080821 / 0.176557 (-0.095736) | 0.119320 / 0.737135 (-0.617816) | 0.081952 / 0.296338 (-0.214387) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288536 / 0.215209 (0.073327) | 2.819900 / 2.077655 (0.742245) | 1.545210 / 1.504120 (0.041090) | 1.422047 / 1.541195 (-0.119147) | 1.439158 / 1.468490 (-0.029332) | 0.564910 / 4.584777 (-4.019867) | 2.430474 / 3.745712 (-1.315238) | 2.763979 / 5.269862 (-2.505882) | 1.732203 / 4.565676 (-2.833474) | 0.062692 / 0.424275 (-0.361583) | 0.004936 / 0.007607 (-0.002671) | 0.341626 / 0.226044 (0.115582) | 3.366623 / 2.268929 (1.097694) | 1.917198 / 55.444624 (-53.527426) | 1.637635 / 6.876477 (-5.238842) | 1.625953 / 2.142072 (-0.516119) | 0.634936 / 4.805227 (-4.170291) | 0.115336 / 6.500664 (-6.385328) | 0.040946 / 0.075469 (-0.034524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964865 / 1.841788 (-0.876922) | 12.077233 / 8.074308 (4.002925) | 10.664120 / 10.191392 (0.472728) | 0.132084 / 0.680424 (-0.548340) | 0.015931 / 0.534201 (-0.518270) | 0.289181 / 0.579283 (-0.290102) | 0.276943 / 0.434364 (-0.157420) | 0.324884 / 0.540337 (-0.215453) | 0.552570 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e \"CML watermark\")\n" ]
2023-12-01T11:35:30
2023-12-01T12:09:09
2023-12-01T12:03:04
MEMBER
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In order to keep PR pages less spammy / more readable. Having the benchmarks on commits on `main` is enough imo
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005594 / 0.011353 (-0.005759) | 0.003672 / 0.011008 (-0.007337) | 0.062796 / 0.038508 (0.024288) | 0.059432 / 0.023109 (0.036323) | 0.253976 / 0.275898 (-0.021922) | 0.281155 / 0.323480 (-0.042325) | 0.003023 / 0.007986 (-0.004962) | 0.003320 / 0.004328 (-0.001008) | 0.049059 / 0.004250 (0.044809) | 0.040252 / 0.037052 (0.003200) | 0.259526 / 0.258489 (0.001037) | 0.318798 / 0.293841 (0.024957) | 0.027883 / 0.128546 (-0.100663) | 0.010883 / 0.075646 (-0.064763) | 0.206948 / 0.419271 (-0.212323) | 0.036335 / 0.043533 (-0.007198) | 0.253209 / 0.255139 (-0.001930) | 0.275173 / 0.283200 (-0.008026) | 0.020365 / 0.141683 (-0.121318) | 1.121630 / 1.452155 (-0.330524) | 1.174680 / 1.492716 (-0.318036) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098372 / 0.018006 (0.080366) | 0.309949 / 0.000490 (0.309460) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019495 / 0.037411 (-0.017916) | 0.062321 / 0.014526 (0.047795) | 0.074525 / 0.176557 (-0.102031) | 0.121832 / 0.737135 (-0.615303) | 0.077612 / 0.296338 (-0.218727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288156 / 0.215209 (0.072947) | 2.816411 / 2.077655 (0.738756) | 1.497926 / 1.504120 (-0.006193) | 1.378137 / 1.541195 (-0.163058) | 1.446466 / 1.468490 (-0.022024) | 0.566195 / 4.584777 (-4.018582) | 2.391933 / 3.745712 (-1.353780) | 2.929290 / 5.269862 (-2.340572) | 1.828215 / 4.565676 (-2.737462) | 0.063312 / 0.424275 (-0.360963) | 0.005199 / 0.007607 (-0.002408) | 0.342883 / 0.226044 (0.116838) | 3.378388 / 2.268929 (1.109459) | 1.865710 / 55.444624 (-53.578915) | 1.573442 / 6.876477 (-5.303035) | 1.631228 / 2.142072 (-0.510845) | 0.651614 / 4.805227 (-4.153613) | 0.118177 / 6.500664 (-6.382487) | 0.043303 / 0.075469 (-0.032166) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950694 / 1.841788 (-0.891094) | 12.559851 / 8.074308 (4.485543) | 10.751123 / 10.191392 (0.559731) | 0.143107 / 0.680424 (-0.537317) | 0.014469 / 0.534201 (-0.519732) | 0.289531 / 0.579283 (-0.289752) | 0.267316 / 0.434364 (-0.167047) | 0.327748 / 0.540337 (-0.212590) | 0.437758 / 1.386936 (-0.949178) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005669 / 0.011353 (-0.005684) | 0.003831 / 0.011008 (-0.007177) | 0.049096 / 0.038508 (0.010588) | 0.061408 / 0.023109 (0.038299) | 0.274571 / 0.275898 (-0.001327) | 0.299978 / 0.323480 (-0.023501) | 0.004216 / 0.007986 (-0.003769) | 0.002848 / 0.004328 (-0.001480) | 0.048755 / 0.004250 (0.044504) | 0.042576 / 0.037052 (0.005524) | 0.276781 / 0.258489 (0.018292) | 0.300903 / 0.293841 (0.007062) | 0.030243 / 0.128546 (-0.098303) | 0.010967 / 0.075646 (-0.064679) | 0.057879 / 0.419271 (-0.361392) | 0.033206 / 0.043533 (-0.010327) | 0.277620 / 0.255139 (0.022481) | 0.296263 / 0.283200 (0.013064) | 0.019022 / 0.141683 (-0.122660) | 1.125615 / 1.452155 (-0.326539) | 1.278016 / 1.492716 (-0.214700) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096836 / 0.018006 (0.078830) | 0.307491 / 0.000490 (0.307001) | 0.000230 / 0.000200 (0.000030) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021552 / 0.037411 (-0.015859) | 0.071099 / 0.014526 (0.056573) | 0.082432 / 0.176557 (-0.094124) | 0.121826 / 0.737135 (-0.615310) | 0.084902 / 0.296338 (-0.211437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.328113 / 0.215209 (0.112904) | 2.989613 / 2.077655 (0.911959) | 1.604904 / 1.504120 (0.100784) | 1.485459 / 1.541195 (-0.055735) | 1.524829 / 1.468490 (0.056339) | 0.580589 / 4.584777 (-4.004188) | 2.440087 / 3.745712 (-1.305625) | 2.944697 / 5.269862 (-2.325164) | 1.832728 / 4.565676 (-2.732949) | 0.064423 / 0.424275 (-0.359852) | 0.004991 / 0.007607 (-0.002616) | 0.357878 / 0.226044 (0.131834) | 3.515415 / 2.268929 (1.246487) | 1.964492 / 55.444624 (-53.480132) | 1.684058 / 6.876477 (-5.192418) | 1.730294 / 2.142072 (-0.411778) | 0.661228 / 4.805227 (-4.143999) | 0.122894 / 6.500664 (-6.377770) | 0.041776 / 0.075469 (-0.033693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969849 / 1.841788 (-0.871939) | 12.897067 / 8.074308 (4.822758) | 10.908200 / 10.191392 (0.716808) | 0.141139 / 0.680424 (-0.539285) | 0.015377 / 0.534201 (-0.518824) | 0.288625 / 0.579283 (-0.290658) | 0.279020 / 0.434364 (-0.155344) | 0.328386 / 0.540337 (-0.211951) | 0.590833 / 1.386936 (-0.796103) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39ea60eaabb05d8ee38c072f375816cf87fce1a9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004986 / 0.011353 (-0.006367) | 0.003070 / 0.011008 (-0.007938) | 0.062433 / 0.038508 (0.023925) | 0.050639 / 0.023109 (0.027530) | 0.241807 / 0.275898 (-0.034091) | 0.262517 / 0.323480 (-0.060963) | 0.003826 / 0.007986 (-0.004160) | 0.002602 / 0.004328 (-0.001727) | 0.048508 / 0.004250 (0.044257) | 0.037276 / 0.037052 (0.000224) | 0.245757 / 0.258489 (-0.012732) | 0.272969 / 0.293841 (-0.020871) | 0.027139 / 0.128546 (-0.101407) | 0.010265 / 0.075646 (-0.065381) | 0.207279 / 0.419271 (-0.211992) | 0.035312 / 0.043533 (-0.008221) | 0.247535 / 0.255139 (-0.007604) | 0.260668 / 0.283200 (-0.022532) | 0.016496 / 0.141683 (-0.125187) | 1.137510 / 1.452155 (-0.314645) | 1.167870 / 1.492716 (-0.324847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091743 / 0.018006 (0.073736) | 0.298649 / 0.000490 (0.298159) | 0.000208 / 0.000200 (0.000009) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019053 / 0.037411 (-0.018359) | 0.060300 / 0.014526 (0.045774) | 0.072154 / 0.176557 (-0.104402) | 0.120293 / 0.737135 (-0.616842) | 0.073923 / 0.296338 (-0.222415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283058 / 0.215209 (0.067849) | 2.769503 / 2.077655 (0.691849) | 1.457016 / 1.504120 (-0.047104) | 1.335753 / 1.541195 (-0.205441) | 1.325986 / 1.468490 (-0.142504) | 0.562553 / 4.584777 (-4.022224) | 2.406144 / 3.745712 (-1.339568) | 2.778063 / 5.269862 (-2.491799) | 1.782199 / 4.565676 (-2.783477) | 0.062490 / 0.424275 (-0.361785) | 0.004912 / 0.007607 (-0.002695) | 0.338500 / 0.226044 (0.112456) | 3.309746 / 2.268929 (1.040818) | 1.819693 / 55.444624 (-53.624931) | 1.510295 / 6.876477 (-5.366182) | 1.578402 / 2.142072 (-0.563671) | 0.637517 / 4.805227 (-4.167710) | 0.117018 / 6.500664 (-6.383647) | 0.048149 / 0.075469 (-0.027320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939424 / 1.841788 (-0.902364) | 11.494891 / 8.074308 (3.420583) | 10.115194 / 10.191392 (-0.076198) | 0.126751 / 0.680424 (-0.553673) | 0.013567 / 0.534201 (-0.520634) | 0.282501 / 0.579283 (-0.296782) | 0.260594 / 0.434364 (-0.173770) | 0.325940 / 0.540337 (-0.214397) | 0.426186 / 1.386936 (-0.960750) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005405 / 0.011353 (-0.005948) | 0.003557 / 0.011008 (-0.007451) | 0.051139 / 0.038508 (0.012631) | 0.053446 / 0.023109 (0.030337) | 0.268051 / 0.275898 (-0.007847) | 0.292343 / 0.323480 (-0.031136) | 0.004716 / 0.007986 (-0.003269) | 0.002677 / 0.004328 (-0.001651) | 0.047634 / 0.004250 (0.043384) | 0.041062 / 0.037052 (0.004009) | 0.269225 / 0.258489 (0.010736) | 0.297462 / 0.293841 (0.003621) | 0.029292 / 0.128546 (-0.099254) | 0.010947 / 0.075646 (-0.064699) | 0.057845 / 0.419271 (-0.361426) | 0.032793 / 0.043533 (-0.010740) | 0.265308 / 0.255139 (0.010169) | 0.288242 / 0.283200 (0.005043) | 0.018311 / 0.141683 (-0.123372) | 1.140957 / 1.452155 (-0.311197) | 1.204883 / 1.492716 (-0.287833) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091375 / 0.018006 (0.073368) | 0.285922 / 0.000490 (0.285432) | 0.000238 / 0.000200 (0.000038) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021277 / 0.037411 (-0.016134) | 0.068853 / 0.014526 (0.054328) | 0.081002 / 0.176557 (-0.095555) | 0.120998 / 0.737135 (-0.616138) | 0.082741 / 0.296338 (-0.213598) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299398 / 0.215209 (0.084189) | 2.909622 / 2.077655 (0.831967) | 1.624381 / 1.504120 (0.120261) | 1.501683 / 1.541195 (-0.039512) | 1.523045 / 1.468490 (0.054555) | 0.548960 / 4.584777 (-4.035817) | 2.413297 / 3.745712 (-1.332415) | 2.817852 / 5.269862 (-2.452010) | 1.754407 / 4.565676 (-2.811270) | 0.061912 / 0.424275 (-0.362363) | 0.004880 / 0.007607 (-0.002727) | 0.353989 / 0.226044 (0.127944) | 3.496147 / 2.268929 (1.227219) | 2.003026 / 55.444624 (-53.441598) | 1.702013 / 6.876477 (-5.174463) | 1.680935 / 2.142072 (-0.461137) | 0.630183 / 4.805227 (-4.175044) | 0.113786 / 6.500664 (-6.386878) | 0.040061 / 0.075469 (-0.035408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957218 / 1.841788 (-0.884569) | 11.914469 / 8.074308 (3.840160) | 10.488896 / 10.191392 (0.297504) | 0.129292 / 0.680424 (-0.551132) | 0.016603 / 0.534201 (-0.517598) | 0.287367 / 0.579283 (-0.291916) | 0.271332 / 0.434364 (-0.163032) | 0.325577 / 0.540337 (-0.214761) | 0.560553 / 1.386936 (-0.826383) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d31e434bbeafdf6a70cb80539342d8fe5f5fd27 \"CML watermark\")\n" ]
2023-11-30T18:09:43
2023-11-30T18:36:40
2023-11-30T18:30:30
MEMBER
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continuation of https://github.com/huggingface/datasets/pull/6431 this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too
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https://api.github.com/repos/huggingface/datasets/issues/6462/timeline
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https://github.com/huggingface/datasets/pull/6461
2,018,850,731
PR_kwDODunzps5gykvO
6,461
Fix shard retry mechanism in `push_to_hub`
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[ "@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872", "We could but I'm not sure to have witness a 504 on S3 before. The issue reported in https://github.com/huggingface/datasets/issues/3872 is a 504 on the `/upload` endpoint on the Hub and this is not an endpoint that is retried on [this line](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300).", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005110 / 0.011353 (-0.006243) | 0.003307 / 0.011008 (-0.007701) | 0.062601 / 0.038508 (0.024093) | 0.049644 / 0.023109 (0.026534) | 0.243195 / 0.275898 (-0.032703) | 0.273543 / 0.323480 (-0.049936) | 0.003862 / 0.007986 (-0.004123) | 0.002624 / 0.004328 (-0.001705) | 0.048273 / 0.004250 (0.044023) | 0.037820 / 0.037052 (0.000768) | 0.249134 / 0.258489 (-0.009355) | 0.319359 / 0.293841 (0.025518) | 0.027816 / 0.128546 (-0.100730) | 0.010422 / 0.075646 (-0.065225) | 0.206607 / 0.419271 (-0.212665) | 0.035719 / 0.043533 (-0.007814) | 0.250300 / 0.255139 (-0.004839) | 0.290377 / 0.283200 (0.007177) | 0.018459 / 0.141683 (-0.123224) | 1.114664 / 1.452155 (-0.337490) | 1.171429 / 1.492716 (-0.321288) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091483 / 0.018006 (0.073477) | 0.302770 / 0.000490 (0.302281) | 0.000203 / 0.000200 (0.000003) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018870 / 0.037411 (-0.018541) | 0.062692 / 0.014526 (0.048166) | 0.075381 / 0.176557 (-0.101176) | 0.122338 / 0.737135 (-0.614797) | 0.075608 / 0.296338 (-0.220730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288115 / 0.215209 (0.072906) | 2.816183 / 2.077655 (0.738528) | 1.535601 / 1.504120 (0.031481) | 1.409546 / 1.541195 (-0.131648) | 1.438569 / 1.468490 (-0.029921) | 0.561797 / 4.584777 (-4.022980) | 2.373921 / 3.745712 (-1.371791) | 2.739437 / 5.269862 (-2.530424) | 1.750921 / 4.565676 (-2.814755) | 0.062114 / 0.424275 (-0.362161) | 0.004965 / 0.007607 (-0.002642) | 0.348614 / 0.226044 (0.122569) | 3.519631 / 2.268929 (1.250703) | 1.910797 / 55.444624 (-53.533827) | 1.610541 / 6.876477 (-5.265936) | 1.617972 / 2.142072 (-0.524100) | 0.639421 / 4.805227 (-4.165806) | 0.117371 / 6.500664 (-6.383293) | 0.041851 / 0.075469 (-0.033618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945563 / 1.841788 (-0.896224) | 11.362399 / 8.074308 (3.288090) | 10.468468 / 10.191392 (0.277075) | 0.128925 / 0.680424 (-0.551499) | 0.013892 / 0.534201 (-0.520309) | 0.285487 / 0.579283 (-0.293796) | 0.269295 / 0.434364 (-0.165069) | 0.324843 / 0.540337 (-0.215495) | 0.438452 / 1.386936 (-0.948484) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005303 / 0.011353 (-0.006050) | 0.003162 / 0.011008 (-0.007846) | 0.048177 / 0.038508 (0.009669) | 0.048708 / 0.023109 (0.025599) | 0.271663 / 0.275898 (-0.004235) | 0.289948 / 0.323480 (-0.033532) | 0.003955 / 0.007986 (-0.004030) | 0.002616 / 0.004328 (-0.001713) | 0.047510 / 0.004250 (0.043260) | 0.039938 / 0.037052 (0.002886) | 0.277449 / 0.258489 (0.018960) | 0.300315 / 0.293841 (0.006474) | 0.029263 / 0.128546 (-0.099283) | 0.010403 / 0.075646 (-0.065244) | 0.056682 / 0.419271 (-0.362590) | 0.032757 / 0.043533 (-0.010776) | 0.273291 / 0.255139 (0.018152) | 0.289023 / 0.283200 (0.005824) | 0.017843 / 0.141683 (-0.123840) | 1.124762 / 1.452155 (-0.327393) | 1.176646 / 1.492716 (-0.316070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004568 / 0.018006 (-0.013438) | 0.300715 / 0.000490 (0.300225) | 0.000212 / 0.000200 (0.000012) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021528 / 0.037411 (-0.015883) | 0.068317 / 0.014526 (0.053792) | 0.081358 / 0.176557 (-0.095199) | 0.119297 / 0.737135 (-0.617838) | 0.082445 / 0.296338 (-0.213893) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289681 / 0.215209 (0.074472) | 2.843862 / 2.077655 (0.766208) | 1.574257 / 1.504120 (0.070137) | 1.454026 / 1.541195 (-0.087169) | 1.478379 / 1.468490 (0.009889) | 0.558259 / 4.584777 (-4.026518) | 2.513261 / 3.745712 (-1.232451) | 2.759751 / 5.269862 (-2.510111) | 1.730335 / 4.565676 (-2.835341) | 0.063805 / 0.424275 (-0.360470) | 0.004991 / 0.007607 (-0.002616) | 0.346586 / 0.226044 (0.120542) | 3.369163 / 2.268929 (1.100234) | 1.934734 / 55.444624 (-53.509890) | 1.658864 / 6.876477 (-5.217613) | 1.645621 / 2.142072 (-0.496452) | 0.636633 / 4.805227 (-4.168594) | 0.116839 / 6.500664 (-6.383825) | 0.040863 / 0.075469 (-0.034606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960925 / 1.841788 (-0.880863) | 11.769189 / 8.074308 (3.694881) | 10.713662 / 10.191392 (0.522270) | 0.140510 / 0.680424 (-0.539914) | 0.015424 / 0.534201 (-0.518777) | 0.288039 / 0.579283 (-0.291244) | 0.277623 / 0.434364 (-0.156741) | 0.322622 / 0.540337 (-0.217716) | 0.539805 / 1.386936 (-0.847131) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07ad81c15bd3b954defe779fc37ba5f432f5ff2a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005501 / 0.011353 (-0.005852) | 0.003754 / 0.011008 (-0.007254) | 0.062628 / 0.038508 (0.024120) | 0.059951 / 0.023109 (0.036842) | 0.254851 / 0.275898 (-0.021047) | 0.272133 / 0.323480 (-0.051347) | 0.003962 / 0.007986 (-0.004024) | 0.002759 / 0.004328 (-0.001569) | 0.048412 / 0.004250 (0.044161) | 0.039349 / 0.037052 (0.002297) | 0.253093 / 0.258489 (-0.005397) | 0.287048 / 0.293841 (-0.006793) | 0.027197 / 0.128546 (-0.101349) | 0.010828 / 0.075646 (-0.064819) | 0.206371 / 0.419271 (-0.212901) | 0.035881 / 0.043533 (-0.007652) | 0.254905 / 0.255139 (-0.000234) | 0.273819 / 0.283200 (-0.009381) | 0.018041 / 0.141683 (-0.123642) | 1.103970 / 1.452155 (-0.348185) | 1.166340 / 1.492716 (-0.326377) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093196 / 0.018006 (0.075190) | 0.302690 / 0.000490 (0.302200) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019552 / 0.037411 (-0.017860) | 0.062337 / 0.014526 (0.047811) | 0.074070 / 0.176557 (-0.102486) | 0.120998 / 0.737135 (-0.616137) | 0.076265 / 0.296338 (-0.220074) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.272637 / 0.215209 (0.057427) | 2.693350 / 2.077655 (0.615696) | 1.398020 / 1.504120 (-0.106100) | 1.285706 / 1.541195 (-0.255488) | 1.342810 / 1.468490 (-0.125680) | 0.565378 / 4.584777 (-4.019399) | 2.390131 / 3.745712 (-1.355581) | 2.892137 / 5.269862 (-2.377725) | 1.819840 / 4.565676 (-2.745836) | 0.062789 / 0.424275 (-0.361486) | 0.004920 / 0.007607 (-0.002687) | 0.329281 / 0.226044 (0.103237) | 3.261664 / 2.268929 (0.992735) | 1.775102 / 55.444624 (-53.669523) | 1.514341 / 6.876477 (-5.362136) | 1.530805 / 2.142072 (-0.611267) | 0.641009 / 4.805227 (-4.164218) | 0.118626 / 6.500664 (-6.382038) | 0.042732 / 0.075469 (-0.032737) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933179 / 1.841788 (-0.908609) | 12.085247 / 8.074308 (4.010939) | 10.541596 / 10.191392 (0.350204) | 0.140141 / 0.680424 (-0.540283) | 0.014646 / 0.534201 (-0.519555) | 0.289640 / 0.579283 (-0.289643) | 0.281042 / 0.434364 (-0.153322) | 0.326462 / 0.540337 (-0.213876) | 0.441981 / 1.386936 (-0.944955) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005259 / 0.011353 (-0.006094) | 0.003766 / 0.011008 (-0.007242) | 0.048782 / 0.038508 (0.010273) | 0.064946 / 0.023109 (0.041836) | 0.264529 / 0.275898 (-0.011369) | 0.289675 / 0.323480 (-0.033805) | 0.004057 / 0.007986 (-0.003928) | 0.002805 / 0.004328 (-0.001523) | 0.047709 / 0.004250 (0.043459) | 0.041149 / 0.037052 (0.004096) | 0.271254 / 0.258489 (0.012765) | 0.296685 / 0.293841 (0.002844) | 0.029486 / 0.128546 (-0.099060) | 0.010608 / 0.075646 (-0.065038) | 0.056392 / 0.419271 (-0.362879) | 0.033181 / 0.043533 (-0.010352) | 0.267029 / 0.255139 (0.011890) | 0.284987 / 0.283200 (0.001787) | 0.018045 / 0.141683 (-0.123637) | 1.137358 / 1.452155 (-0.314796) | 1.184007 / 1.492716 (-0.308709) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004603 / 0.018006 (-0.013403) | 0.303901 / 0.000490 (0.303411) | 0.000225 / 0.000200 (0.000025) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021957 / 0.037411 (-0.015454) | 0.069427 / 0.014526 (0.054901) | 0.082394 / 0.176557 (-0.094163) | 0.120745 / 0.737135 (-0.616390) | 0.084571 / 0.296338 (-0.211767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292832 / 0.215209 (0.077623) | 2.824295 / 2.077655 (0.746640) | 1.563273 / 1.504120 (0.059153) | 1.440202 / 1.541195 (-0.100992) | 1.489810 / 1.468490 (0.021320) | 0.561120 / 4.584777 (-4.023657) | 2.439045 / 3.745712 (-1.306667) | 2.867139 / 5.269862 (-2.402722) | 1.793812 / 4.565676 (-2.771865) | 0.062797 / 0.424275 (-0.361478) | 0.005033 / 0.007607 (-0.002574) | 0.343648 / 0.226044 (0.117604) | 3.432285 / 2.268929 (1.163357) | 1.918175 / 55.444624 (-53.526449) | 1.637245 / 6.876477 (-5.239232) | 1.709246 / 2.142072 (-0.432826) | 0.634744 / 4.805227 (-4.170483) | 0.115782 / 6.500664 (-6.384882) | 0.041228 / 0.075469 (-0.034241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962369 / 1.841788 (-0.879418) | 12.750819 / 8.074308 (4.676511) | 10.927356 / 10.191392 (0.735964) | 0.143454 / 0.680424 (-0.536970) | 0.015348 / 0.534201 (-0.518853) | 0.291207 / 0.579283 (-0.288076) | 0.276924 / 0.434364 (-0.157440) | 0.327287 / 0.540337 (-0.213050) | 0.577439 / 1.386936 (-0.809497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#544ad95f6b6da7fee44a2bc838e15a5e0156c946 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005070 / 0.011353 (-0.006283) | 0.003475 / 0.011008 (-0.007533) | 0.061985 / 0.038508 (0.023477) | 0.048539 / 0.023109 (0.025430) | 0.229935 / 0.275898 (-0.045963) | 0.255247 / 0.323480 (-0.068233) | 0.003919 / 0.007986 (-0.004066) | 0.002664 / 0.004328 (-0.001664) | 0.048892 / 0.004250 (0.044642) | 0.037381 / 0.037052 (0.000328) | 0.238517 / 0.258489 (-0.019972) | 0.284069 / 0.293841 (-0.009772) | 0.027513 / 0.128546 (-0.101033) | 0.010778 / 0.075646 (-0.064868) | 0.205004 / 0.419271 (-0.214268) | 0.035553 / 0.043533 (-0.007980) | 0.230117 / 0.255139 (-0.025022) | 0.251150 / 0.283200 (-0.032050) | 0.017951 / 0.141683 (-0.123732) | 1.145548 / 1.452155 (-0.306607) | 1.191659 / 1.492716 (-0.301057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092335 / 0.018006 (0.074329) | 0.300264 / 0.000490 (0.299774) | 0.000206 / 0.000200 (0.000006) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018608 / 0.037411 (-0.018804) | 0.060376 / 0.014526 (0.045850) | 0.073551 / 0.176557 (-0.103006) | 0.118840 / 0.737135 (-0.618295) | 0.074447 / 0.296338 (-0.221892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287033 / 0.215209 (0.071824) | 2.770958 / 2.077655 (0.693303) | 1.443986 / 1.504120 (-0.060134) | 1.314627 / 1.541195 (-0.226567) | 1.342287 / 1.468490 (-0.126203) | 0.559607 / 4.584777 (-4.025170) | 2.409678 / 3.745712 (-1.336034) | 2.772566 / 5.269862 (-2.497295) | 1.743511 / 4.565676 (-2.822165) | 0.062277 / 0.424275 (-0.361998) | 0.004952 / 0.007607 (-0.002655) | 0.330581 / 0.226044 (0.104537) | 3.280385 / 2.268929 (1.011456) | 1.809599 / 55.444624 (-53.635025) | 1.532186 / 6.876477 (-5.344290) | 1.529689 / 2.142072 (-0.612383) | 0.645213 / 4.805227 (-4.160014) | 0.117564 / 6.500664 (-6.383100) | 0.041657 / 0.075469 (-0.033812) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943912 / 1.841788 (-0.897876) | 11.414317 / 8.074308 (3.340009) | 10.394915 / 10.191392 (0.203523) | 0.129271 / 0.680424 (-0.551153) | 0.013934 / 0.534201 (-0.520267) | 0.288217 / 0.579283 (-0.291066) | 0.267171 / 0.434364 (-0.167193) | 0.327112 / 0.540337 (-0.213225) | 0.446680 / 1.386936 (-0.940256) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005200 / 0.011353 (-0.006152) | 0.003453 / 0.011008 (-0.007555) | 0.048736 / 0.038508 (0.010228) | 0.051073 / 0.023109 (0.027964) | 0.276591 / 0.275898 (0.000693) | 0.294495 / 0.323480 (-0.028985) | 0.004069 / 0.007986 (-0.003917) | 0.002945 / 0.004328 (-0.001383) | 0.047090 / 0.004250 (0.042839) | 0.040445 / 0.037052 (0.003393) | 0.278464 / 0.258489 (0.019975) | 0.304020 / 0.293841 (0.010179) | 0.028811 / 0.128546 (-0.099736) | 0.010388 / 0.075646 (-0.065259) | 0.057214 / 0.419271 (-0.362057) | 0.032588 / 0.043533 (-0.010945) | 0.277694 / 0.255139 (0.022555) | 0.294979 / 0.283200 (0.011779) | 0.018384 / 0.141683 (-0.123299) | 1.162332 / 1.452155 (-0.289822) | 1.188355 / 1.492716 (-0.304361) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090501 / 0.018006 (0.072495) | 0.303122 / 0.000490 (0.302632) | 0.000222 / 0.000200 (0.000022) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022536 / 0.037411 (-0.014876) | 0.068452 / 0.014526 (0.053926) | 0.080932 / 0.176557 (-0.095625) | 0.119185 / 0.737135 (-0.617950) | 0.081513 / 0.296338 (-0.214825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291522 / 0.215209 (0.076313) | 2.849467 / 2.077655 (0.771812) | 1.597395 / 1.504120 (0.093275) | 1.512872 / 1.541195 (-0.028323) | 1.488144 / 1.468490 (0.019654) | 0.572436 / 4.584777 (-4.012341) | 2.440129 / 3.745712 (-1.305583) | 2.788045 / 5.269862 (-2.481817) | 1.754246 / 4.565676 (-2.811430) | 0.066706 / 0.424275 (-0.357569) | 0.005035 / 0.007607 (-0.002573) | 0.336621 / 0.226044 (0.110576) | 3.322820 / 2.268929 (1.053891) | 1.940494 / 55.444624 (-53.504130) | 1.670022 / 6.876477 (-5.206454) | 1.666353 / 2.142072 (-0.475720) | 0.646180 / 4.805227 (-4.159047) | 0.116676 / 6.500664 (-6.383988) | 0.040559 / 0.075469 (-0.034910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971396 / 1.841788 (-0.870392) | 11.782426 / 8.074308 (3.708118) | 10.672034 / 10.191392 (0.480642) | 0.137658 / 0.680424 (-0.542766) | 0.016210 / 0.534201 (-0.517991) | 0.288302 / 0.579283 (-0.290981) | 0.280775 / 0.434364 (-0.153589) | 0.326962 / 0.540337 (-0.213375) | 0.558511 / 1.386936 (-0.828425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76020180407d7ea9a0b535758d8d1b241fd19d8c \"CML watermark\")\n" ]
2023-11-30T14:57:14
2023-12-01T17:57:39
2023-12-01T17:51:33
COLLABORATOR
null
false
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When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that. Fix https://github.com/huggingface/datasets/issues/6392
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I_kwDODunzps54P5kr
6,460
jsonlines files don't load with `load_dataset`
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[ "Hi @serenalotreck,\r\n\r\nWe use Apache Arrow `pyarrow` to read jsonlines and it throws an error when trying to load your data files:\r\n```python\r\nIn [1]: import pyarrow as pa\r\n\r\nIn [2]: data = pa.json.read_json(\"train.jsonl\")\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-14-e9b104832528> in <module>\r\n----> 1 data = pa.json.read_json(\"train.jsonl\")\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0\r\n```\r\n\r\nI think it has to do with the data structure of the fields \"ner\" (and also \"relations\"):\r\n```json\r\n\"ner\": [\r\n [\r\n [0, 4, \"Biochemical_process\"], \r\n [15, 16, \"Protein\"]\r\n ], \r\n```\r\nArrow interprets this data structure as an array, an arrays contain just a single data type: \r\n- when reading sequentially, it finds first the `0` and infers that the data is of type `number`;\r\n- when it finds the string `\"Biochemical_process\"`, it cannot cast it to number and throws the `ArrowInvalid` error\r\n\r\nOne solution could be to change the data structure of your data files. Any other ideas, @huggingface/datasets ?", "Hi @albertvillanova, \r\n\r\nThanks for the explanation! To the best of my knowledge, arrays in a json [can contain multiple data types](https://docs.actian.com/ingres/11.2/index.html#page/SQLRef/Data_Types.htm), and I'm able to read these files with the `jsonlines` package. Is the requirement for arrays to only have one data type specific to PyArrow?\r\n\r\nI'd prefer to keep the data structure as is, since it's a specific input requirement for the models this data was generated for. Any thoughts on how to enable the use of `load_dataset` with this dataset would be great!", "Hi again @serenalotreck,\r\n\r\nYes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n\r\nAs this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n\r\nLet's continue the discussion there! :hugs: ", "> Hi again @serenalotreck,\r\n> \r\n> Yes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n> \r\n> As this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n> \r\n> Let's continue the discussion there! 🤗\r\n\r\nThis is really terrible. My JSONL format data is very simple, but I still report this error\r\n![image](https://github.com/huggingface/datasets/assets/58240629/e3fed922-ced4-406c-b5bc-90a4b891c4ee)\r\nThe error message is as follows:\r\n File \"pyarrow/_json.pyx\", line 290, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column(/inputs) changed from string to number in row 208\r\n" ]
2023-11-29T21:20:11
2023-12-29T02:58:29
2023-12-05T13:30:53
NONE
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### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare **download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 8.0.0 - Pandas version: 1.3.5
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005292 / 0.011353 (-0.006061) | 0.003811 / 0.011008 (-0.007197) | 0.064912 / 0.038508 (0.026404) | 0.061199 / 0.023109 (0.038090) | 0.242953 / 0.275898 (-0.032945) | 0.271789 / 0.323480 (-0.051691) | 0.003994 / 0.007986 (-0.003991) | 0.002723 / 0.004328 (-0.001606) | 0.049952 / 0.004250 (0.045701) | 0.039489 / 0.037052 (0.002437) | 0.261143 / 0.258489 (0.002654) | 0.288800 / 0.293841 (-0.005041) | 0.028130 / 0.128546 (-0.100416) | 0.010724 / 0.075646 (-0.064922) | 0.208218 / 0.419271 (-0.211054) | 0.036224 / 0.043533 (-0.007309) | 0.247189 / 0.255139 (-0.007950) | 0.274702 / 0.283200 (-0.008498) | 0.019714 / 0.141683 (-0.121969) | 1.134853 / 1.452155 (-0.317301) | 1.192655 / 1.492716 (-0.300062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096391 / 0.018006 (0.078385) | 0.303802 / 0.000490 (0.303312) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019530 / 0.037411 (-0.017881) | 0.061588 / 0.014526 (0.047062) | 0.075122 / 0.176557 (-0.101434) | 0.120980 / 0.737135 (-0.616155) | 0.075807 / 0.296338 (-0.220532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281672 / 0.215209 (0.066463) | 2.779884 / 2.077655 (0.702229) | 1.502026 / 1.504120 (-0.002094) | 1.369474 / 1.541195 (-0.171721) | 1.402694 / 1.468490 (-0.065796) | 0.559120 / 4.584777 (-4.025657) | 2.355320 / 3.745712 (-1.390393) | 2.823987 / 5.269862 (-2.445875) | 1.763888 / 4.565676 (-2.801788) | 0.061715 / 0.424275 (-0.362560) | 0.005015 / 0.007607 (-0.002592) | 0.342669 / 0.226044 (0.116625) | 3.360651 / 2.268929 (1.091722) | 1.887277 / 55.444624 (-53.557348) | 1.555613 / 6.876477 (-5.320864) | 1.614126 / 2.142072 (-0.527946) | 0.643797 / 4.805227 (-4.161430) | 0.118365 / 6.500664 (-6.382299) | 0.042596 / 0.075469 (-0.032873) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951383 / 1.841788 (-0.890405) | 13.169812 / 8.074308 (5.095504) | 10.772460 / 10.191392 (0.581068) | 0.133248 / 0.680424 (-0.547176) | 0.014597 / 0.534201 (-0.519604) | 0.289758 / 0.579283 (-0.289525) | 0.266324 / 0.434364 (-0.168040) | 0.334811 / 0.540337 (-0.205526) | 0.445566 / 1.386936 (-0.941370) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005668 / 0.011353 (-0.005684) | 0.003583 / 0.011008 (-0.007425) | 0.050681 / 0.038508 (0.012173) | 0.063244 / 0.023109 (0.040135) | 0.279624 / 0.275898 (0.003726) | 0.308030 / 0.323480 (-0.015450) | 0.004160 / 0.007986 (-0.003826) | 0.002633 / 0.004328 (-0.001696) | 0.048475 / 0.004250 (0.044225) | 0.043106 / 0.037052 (0.006054) | 0.283678 / 0.258489 (0.025189) | 0.309730 / 0.293841 (0.015889) | 0.030290 / 0.128546 (-0.098256) | 0.011112 / 0.075646 (-0.064534) | 0.058234 / 0.419271 (-0.361038) | 0.033553 / 0.043533 (-0.009979) | 0.279902 / 0.255139 (0.024763) | 0.298041 / 0.283200 (0.014841) | 0.019367 / 0.141683 (-0.122316) | 1.142438 / 1.452155 (-0.309717) | 1.197305 / 1.492716 (-0.295411) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090875 / 0.018006 (0.072869) | 0.301174 / 0.000490 (0.300685) | 0.000216 / 0.000200 (0.000016) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021544 / 0.037411 (-0.015867) | 0.071371 / 0.014526 (0.056846) | 0.080821 / 0.176557 (-0.095736) | 0.120054 / 0.737135 (-0.617082) | 0.082611 / 0.296338 (-0.213728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293787 / 0.215209 (0.078578) | 2.862610 / 2.077655 (0.784955) | 1.597282 / 1.504120 (0.093162) | 1.485094 / 1.541195 (-0.056101) | 1.507384 / 1.468490 (0.038893) | 0.558470 / 4.584777 (-4.026307) | 2.414137 / 3.745712 (-1.331575) | 2.863342 / 5.269862 (-2.406520) | 1.776973 / 4.565676 (-2.788704) | 0.062296 / 0.424275 (-0.361979) | 0.004954 / 0.007607 (-0.002653) | 0.346037 / 0.226044 (0.119993) | 3.441864 / 2.268929 (1.172935) | 1.969842 / 55.444624 (-53.474783) | 1.714878 / 6.876477 (-5.161599) | 1.738141 / 2.142072 (-0.403931) | 0.645929 / 4.805227 (-4.159298) | 0.117332 / 6.500664 (-6.383332) | 0.041963 / 0.075469 (-0.033507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983229 / 1.841788 (-0.858559) | 13.186932 / 8.074308 (5.112624) | 11.220549 / 10.191392 (1.029157) | 0.142105 / 0.680424 (-0.538319) | 0.015210 / 0.534201 (-0.518991) | 0.290055 / 0.579283 (-0.289228) | 0.274513 / 0.434364 (-0.159851) | 0.346834 / 0.540337 (-0.193504) | 0.575897 / 1.386936 (-0.811039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005308 / 0.011353 (-0.006045) | 0.003135 / 0.011008 (-0.007873) | 0.061820 / 0.038508 (0.023312) | 0.052005 / 0.023109 (0.028895) | 0.233507 / 0.275898 (-0.042391) | 0.257790 / 0.323480 (-0.065690) | 0.002848 / 0.007986 (-0.005138) | 0.002645 / 0.004328 (-0.001683) | 0.048379 / 0.004250 (0.044128) | 0.038320 / 0.037052 (0.001268) | 0.245470 / 0.258489 (-0.013019) | 0.274854 / 0.293841 (-0.018987) | 0.027335 / 0.128546 (-0.101211) | 0.010349 / 0.075646 (-0.065297) | 0.205872 / 0.419271 (-0.213400) | 0.035896 / 0.043533 (-0.007637) | 0.241645 / 0.255139 (-0.013494) | 0.260033 / 0.283200 (-0.023167) | 0.020325 / 0.141683 (-0.121358) | 1.116768 / 1.452155 (-0.335387) | 1.188067 / 1.492716 (-0.304649) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092622 / 0.018006 (0.074616) | 0.302663 / 0.000490 (0.302173) | 0.000227 / 0.000200 (0.000027) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018633 / 0.037411 (-0.018778) | 0.060117 / 0.014526 (0.045592) | 0.072713 / 0.176557 (-0.103844) | 0.119955 / 0.737135 (-0.617180) | 0.074698 / 0.296338 (-0.221640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277157 / 0.215209 (0.061948) | 2.699650 / 2.077655 (0.621995) | 1.413625 / 1.504120 (-0.090494) | 1.295900 / 1.541195 (-0.245295) | 1.306280 / 1.468490 (-0.162210) | 0.555354 / 4.584777 (-4.029423) | 2.386866 / 3.745712 (-1.358847) | 2.794069 / 5.269862 (-2.475793) | 1.736275 / 4.565676 (-2.829401) | 0.061812 / 0.424275 (-0.362464) | 0.004957 / 0.007607 (-0.002650) | 0.334533 / 0.226044 (0.108488) | 3.251096 / 2.268929 (0.982168) | 1.768193 / 55.444624 (-53.676431) | 1.473752 / 6.876477 (-5.402724) | 1.476320 / 2.142072 (-0.665753) | 0.642485 / 4.805227 (-4.162742) | 0.116986 / 6.500664 (-6.383678) | 0.042083 / 0.075469 (-0.033386) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941364 / 1.841788 (-0.900424) | 11.587408 / 8.074308 (3.513100) | 10.500198 / 10.191392 (0.308806) | 0.129126 / 0.680424 (-0.551298) | 0.015206 / 0.534201 (-0.518995) | 0.286580 / 0.579283 (-0.292703) | 0.263566 / 0.434364 (-0.170798) | 0.331662 / 0.540337 (-0.208676) | 0.431423 / 1.386936 (-0.955513) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005151 / 0.011353 (-0.006202) | 0.003425 / 0.011008 (-0.007583) | 0.049301 / 0.038508 (0.010793) | 0.052005 / 0.023109 (0.028895) | 0.289594 / 0.275898 (0.013696) | 0.312630 / 0.323480 (-0.010849) | 0.003988 / 0.007986 (-0.003998) | 0.002705 / 0.004328 (-0.001624) | 0.048529 / 0.004250 (0.044279) | 0.039645 / 0.037052 (0.002592) | 0.293430 / 0.258489 (0.034941) | 0.311697 / 0.293841 (0.017856) | 0.029044 / 0.128546 (-0.099502) | 0.010282 / 0.075646 (-0.065364) | 0.057641 / 0.419271 (-0.361630) | 0.032733 / 0.043533 (-0.010800) | 0.293553 / 0.255139 (0.038414) | 0.308850 / 0.283200 (0.025651) | 0.018452 / 0.141683 (-0.123231) | 1.147931 / 1.452155 (-0.304224) | 1.173093 / 1.492716 (-0.319623) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100862 / 0.018006 (0.082856) | 0.309286 / 0.000490 (0.308796) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021365 / 0.037411 (-0.016046) | 0.068987 / 0.014526 (0.054461) | 0.081092 / 0.176557 (-0.095465) | 0.119852 / 0.737135 (-0.617283) | 0.082850 / 0.296338 (-0.213489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288477 / 0.215209 (0.073268) | 2.833766 / 2.077655 (0.756111) | 1.576670 / 1.504120 (0.072550) | 1.431643 / 1.541195 (-0.109552) | 1.442132 / 1.468490 (-0.026358) | 0.556079 / 4.584777 (-4.028698) | 2.465042 / 3.745712 (-1.280670) | 2.786329 / 5.269862 (-2.483532) | 1.779428 / 4.565676 (-2.786249) | 0.062278 / 0.424275 (-0.361997) | 0.004867 / 0.007607 (-0.002740) | 0.348444 / 0.226044 (0.122399) | 3.389824 / 2.268929 (1.120896) | 1.919141 / 55.444624 (-53.525484) | 1.635411 / 6.876477 (-5.241066) | 1.654869 / 2.142072 (-0.487204) | 0.634467 / 4.805227 (-4.170761) | 0.114330 / 6.500664 (-6.386334) | 0.039900 / 0.075469 (-0.035569) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970851 / 1.841788 (-0.870937) | 11.951660 / 8.074308 (3.877352) | 10.571115 / 10.191392 (0.379723) | 0.131040 / 0.680424 (-0.549384) | 0.015299 / 0.534201 (-0.518902) | 0.287851 / 0.579283 (-0.291432) | 0.278366 / 0.434364 (-0.155998) | 0.326468 / 0.540337 (-0.213870) | 0.552288 / 1.386936 (-0.834648) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n" ]
2023-11-29T16:56:15
2024-03-25T13:55:42
2024-03-25T13:55:42
MEMBER
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true
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I drafted the logic to retrieve a no-script dataset in the cache. For example it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/*/*/0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp. DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` fix https://github.com/huggingface/datasets/issues/3547 ## Implementation details (EDITED) I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there TODO: - [x] tests - [ ] compatible with https://github.com/huggingface/datasets/pull/6458
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005097 / 0.011353 (-0.006256) | 0.003523 / 0.011008 (-0.007485) | 0.062827 / 0.038508 (0.024319) | 0.051677 / 0.023109 (0.028568) | 0.248919 / 0.275898 (-0.026980) | 0.275892 / 0.323480 (-0.047588) | 0.003908 / 0.007986 (-0.004077) | 0.002622 / 0.004328 (-0.001706) | 0.048634 / 0.004250 (0.044383) | 0.037903 / 0.037052 (0.000850) | 0.255754 / 0.258489 (-0.002735) | 0.283343 / 0.293841 (-0.010498) | 0.027886 / 0.128546 (-0.100660) | 0.010849 / 0.075646 (-0.064797) | 0.208255 / 0.419271 (-0.211017) | 0.035664 / 0.043533 (-0.007869) | 0.254661 / 0.255139 (-0.000478) | 0.274366 / 0.283200 (-0.008834) | 0.017240 / 0.141683 (-0.124443) | 1.092952 / 1.452155 (-0.359203) | 1.148373 / 1.492716 (-0.344344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091592 / 0.018006 (0.073586) | 0.301926 / 0.000490 (0.301436) | 0.000207 / 0.000200 (0.000007) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018525 / 0.037411 (-0.018887) | 0.060539 / 0.014526 (0.046014) | 0.073812 / 0.176557 (-0.102745) | 0.120655 / 0.737135 (-0.616480) | 0.076931 / 0.296338 (-0.219407) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282797 / 0.215209 (0.067588) | 2.746573 / 2.077655 (0.668918) | 1.477652 / 1.504120 (-0.026468) | 1.349922 / 1.541195 (-0.191273) | 1.374347 / 1.468490 (-0.094143) | 0.574096 / 4.584777 (-4.010681) | 2.383317 / 3.745712 (-1.362395) | 2.809320 / 5.269862 (-2.460541) | 1.758947 / 4.565676 (-2.806729) | 0.064029 / 0.424275 (-0.360246) | 0.004936 / 0.007607 (-0.002672) | 0.331403 / 0.226044 (0.105358) | 3.260908 / 2.268929 (0.991980) | 1.817670 / 55.444624 (-53.626954) | 1.525863 / 6.876477 (-5.350613) | 1.542017 / 2.142072 (-0.600055) | 0.638900 / 4.805227 (-4.166327) | 0.119485 / 6.500664 (-6.381179) | 0.042588 / 0.075469 (-0.032881) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951583 / 1.841788 (-0.890205) | 11.621917 / 8.074308 (3.547609) | 10.511062 / 10.191392 (0.319670) | 0.130137 / 0.680424 (-0.550287) | 0.014048 / 0.534201 (-0.520153) | 0.290621 / 0.579283 (-0.288662) | 0.271665 / 0.434364 (-0.162699) | 0.331260 / 0.540337 (-0.209077) | 0.441621 / 1.386936 (-0.945316) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005272 / 0.011353 (-0.006081) | 0.003656 / 0.011008 (-0.007352) | 0.049245 / 0.038508 (0.010737) | 0.054130 / 0.023109 (0.031021) | 0.274775 / 0.275898 (-0.001123) | 0.296664 / 0.323480 (-0.026816) | 0.004870 / 0.007986 (-0.003115) | 0.002728 / 0.004328 (-0.001601) | 0.048087 / 0.004250 (0.043837) | 0.041448 / 0.037052 (0.004396) | 0.279110 / 0.258489 (0.020621) | 0.303660 / 0.293841 (0.009819) | 0.029767 / 0.128546 (-0.098779) | 0.010799 / 0.075646 (-0.064848) | 0.058650 / 0.419271 (-0.360622) | 0.033088 / 0.043533 (-0.010445) | 0.274456 / 0.255139 (0.019317) | 0.290206 / 0.283200 (0.007007) | 0.017259 / 0.141683 (-0.124424) | 1.176501 / 1.452155 (-0.275654) | 1.197552 / 1.492716 (-0.295165) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092865 / 0.018006 (0.074859) | 0.302437 / 0.000490 (0.301947) | 0.000209 / 0.000200 (0.000009) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021211 / 0.037411 (-0.016200) | 0.068858 / 0.014526 (0.054332) | 0.081783 / 0.176557 (-0.094773) | 0.120472 / 0.737135 (-0.616663) | 0.083900 / 0.296338 (-0.212438) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295157 / 0.215209 (0.079948) | 2.910979 / 2.077655 (0.833324) | 1.575772 / 1.504120 (0.071652) | 1.456955 / 1.541195 (-0.084239) | 1.468982 / 1.468490 (0.000492) | 0.560309 / 4.584777 (-4.024468) | 2.460171 / 3.745712 (-1.285541) | 2.805713 / 5.269862 (-2.464149) | 1.754074 / 4.565676 (-2.811603) | 0.063333 / 0.424275 (-0.360942) | 0.004940 / 0.007607 (-0.002667) | 0.346141 / 0.226044 (0.120097) | 3.463431 / 2.268929 (1.194502) | 1.929135 / 55.444624 (-53.515490) | 1.660191 / 6.876477 (-5.216286) | 1.668327 / 2.142072 (-0.473746) | 0.644183 / 4.805227 (-4.161044) | 0.115738 / 6.500664 (-6.384926) | 0.041347 / 0.075469 (-0.034122) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961565 / 1.841788 (-0.880222) | 12.232589 / 8.074308 (4.158281) | 10.778774 / 10.191392 (0.587382) | 0.132709 / 0.680424 (-0.547715) | 0.015964 / 0.534201 (-0.518237) | 0.286944 / 0.579283 (-0.292340) | 0.279740 / 0.434364 (-0.154624) | 0.333024 / 0.540337 (-0.207314) | 0.438819 / 1.386936 (-0.948117) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51002cb0325772adaf46d6f3ce01d41c01b51079 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6458). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005317 / 0.011353 (-0.006036) | 0.003936 / 0.011008 (-0.007072) | 0.063122 / 0.038508 (0.024614) | 0.061274 / 0.023109 (0.038165) | 0.251764 / 0.275898 (-0.024134) | 0.274849 / 0.323480 (-0.048631) | 0.004059 / 0.007986 (-0.003927) | 0.002874 / 0.004328 (-0.001455) | 0.048716 / 0.004250 (0.044465) | 0.038281 / 0.037052 (0.001228) | 0.265224 / 0.258489 (0.006735) | 0.285962 / 0.293841 (-0.007878) | 0.028522 / 0.128546 (-0.100024) | 0.011150 / 0.075646 (-0.064496) | 0.208362 / 0.419271 (-0.210910) | 0.038900 / 0.043533 (-0.004633) | 0.254113 / 0.255139 (-0.001026) | 0.276721 / 0.283200 (-0.006478) | 0.018372 / 0.141683 (-0.123311) | 1.121336 / 1.452155 (-0.330818) | 1.189548 / 1.492716 (-0.303168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097633 / 0.018006 (0.079627) | 0.304443 / 0.000490 (0.303953) | 0.000218 / 0.000200 (0.000018) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021757 / 0.037411 (-0.015654) | 0.061978 / 0.014526 (0.047453) | 0.076296 / 0.176557 (-0.100260) | 0.122320 / 0.737135 (-0.614816) | 0.076738 / 0.296338 (-0.219601) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284328 / 0.215209 (0.069119) | 2.793071 / 2.077655 (0.715417) | 1.504768 / 1.504120 (0.000648) | 1.386083 / 1.541195 (-0.155111) | 1.457593 / 1.468490 (-0.010897) | 0.575887 / 4.584777 (-4.008890) | 2.419396 / 3.745712 (-1.326316) | 2.931305 / 5.269862 (-2.338556) | 1.840759 / 4.565676 (-2.724917) | 0.063801 / 0.424275 (-0.360474) | 0.004966 / 0.007607 (-0.002641) | 0.341612 / 0.226044 (0.115568) | 3.402842 / 2.268929 (1.133913) | 1.860521 / 55.444624 (-53.584103) | 1.603156 / 6.876477 (-5.273321) | 1.665835 / 2.142072 (-0.476237) | 0.655299 / 4.805227 (-4.149929) | 0.124527 / 6.500664 (-6.376137) | 0.044021 / 0.075469 (-0.031449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972068 / 1.841788 (-0.869720) | 12.393202 / 8.074308 (4.318894) | 10.420876 / 10.191392 (0.229484) | 0.140684 / 0.680424 (-0.539740) | 0.014442 / 0.534201 (-0.519759) | 0.288182 / 0.579283 (-0.291101) | 0.265029 / 0.434364 (-0.169334) | 0.327133 / 0.540337 (-0.213204) | 0.443403 / 1.386936 (-0.943533) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005559 / 0.011353 (-0.005794) | 0.004046 / 0.011008 (-0.006962) | 0.048991 / 0.038508 (0.010483) | 0.059576 / 0.023109 (0.036467) | 0.273596 / 0.275898 (-0.002302) | 0.296658 / 0.323480 (-0.026822) | 0.004089 / 0.007986 (-0.003897) | 0.002777 / 0.004328 (-0.001551) | 0.048216 / 0.004250 (0.043966) | 0.043200 / 0.037052 (0.006148) | 0.276815 / 0.258489 (0.018326) | 0.300570 / 0.293841 (0.006729) | 0.030250 / 0.128546 (-0.098296) | 0.011322 / 0.075646 (-0.064324) | 0.057843 / 0.419271 (-0.361429) | 0.033366 / 0.043533 (-0.010167) | 0.275636 / 0.255139 (0.020497) | 0.293750 / 0.283200 (0.010550) | 0.018551 / 0.141683 (-0.123132) | 1.160919 / 1.452155 (-0.291236) | 1.214519 / 1.492716 (-0.278197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100074 / 0.018006 (0.082068) | 0.308434 / 0.000490 (0.307944) | 0.000232 / 0.000200 (0.000032) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022600 / 0.037411 (-0.014811) | 0.070506 / 0.014526 (0.055980) | 0.081185 / 0.176557 (-0.095371) | 0.120688 / 0.737135 (-0.616448) | 0.082897 / 0.296338 (-0.213441) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.306661 / 0.215209 (0.091452) | 2.989656 / 2.077655 (0.912001) | 1.618868 / 1.504120 (0.114749) | 1.485045 / 1.541195 (-0.056149) | 1.549359 / 1.468490 (0.080869) | 0.593596 / 4.584777 (-3.991181) | 2.466215 / 3.745712 (-1.279497) | 2.956570 / 5.269862 (-2.313292) | 1.823160 / 4.565676 (-2.742516) | 0.063442 / 0.424275 (-0.360833) | 0.004928 / 0.007607 (-0.002679) | 0.358464 / 0.226044 (0.132419) | 3.566345 / 2.268929 (1.297417) | 2.006784 / 55.444624 (-53.437840) | 1.687091 / 6.876477 (-5.189386) | 1.729464 / 2.142072 (-0.412609) | 0.655656 / 4.805227 (-4.149572) | 0.119044 / 6.500664 (-6.381620) | 0.042782 / 0.075469 (-0.032687) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974937 / 1.841788 (-0.866850) | 12.992888 / 8.074308 (4.918580) | 10.893713 / 10.191392 (0.702321) | 0.133853 / 0.680424 (-0.546570) | 0.016055 / 0.534201 (-0.518145) | 0.289342 / 0.579283 (-0.289941) | 0.286094 / 0.434364 (-0.148270) | 0.328670 / 0.540337 (-0.211667) | 0.444605 / 1.386936 (-0.942331) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a5bb38bcc71ea21f2d7304aab374fdb81ded463 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005705 / 0.011353 (-0.005648) | 0.003519 / 0.011008 (-0.007489) | 0.062009 / 0.038508 (0.023501) | 0.053481 / 0.023109 (0.030372) | 0.262669 / 0.275898 (-0.013229) | 0.280290 / 0.323480 (-0.043189) | 0.002957 / 0.007986 (-0.005029) | 0.002587 / 0.004328 (-0.001741) | 0.047876 / 0.004250 (0.043626) | 0.038868 / 0.037052 (0.001815) | 0.267854 / 0.258489 (0.009365) | 0.290430 / 0.293841 (-0.003411) | 0.028120 / 0.128546 (-0.100427) | 0.011042 / 0.075646 (-0.064605) | 0.206113 / 0.419271 (-0.213158) | 0.036039 / 0.043533 (-0.007494) | 0.257715 / 0.255139 (0.002576) | 0.281279 / 0.283200 (-0.001921) | 0.019790 / 0.141683 (-0.121893) | 1.114472 / 1.452155 (-0.337683) | 1.192219 / 1.492716 (-0.300497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091049 / 0.018006 (0.073043) | 0.300846 / 0.000490 (0.300356) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018569 / 0.037411 (-0.018843) | 0.060075 / 0.014526 (0.045549) | 0.073877 / 0.176557 (-0.102680) | 0.120337 / 0.737135 (-0.616799) | 0.075454 / 0.296338 (-0.220884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290084 / 0.215209 (0.074875) | 2.805712 / 2.077655 (0.728057) | 1.459393 / 1.504120 (-0.044727) | 1.327356 / 1.541195 (-0.213838) | 1.384734 / 1.468490 (-0.083756) | 0.574532 / 4.584777 (-4.010245) | 2.419696 / 3.745712 (-1.326016) | 2.805449 / 5.269862 (-2.464412) | 1.764127 / 4.565676 (-2.801549) | 0.063256 / 0.424275 (-0.361020) | 0.004954 / 0.007607 (-0.002653) | 0.344246 / 0.226044 (0.118202) | 3.396050 / 2.268929 (1.127121) | 1.807621 / 55.444624 (-53.637004) | 1.536627 / 6.876477 (-5.339850) | 1.552450 / 2.142072 (-0.589623) | 0.651156 / 4.805227 (-4.154071) | 0.119358 / 6.500664 (-6.381306) | 0.042810 / 0.075469 (-0.032660) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930646 / 1.841788 (-0.911142) | 11.830454 / 8.074308 (3.756146) | 10.615315 / 10.191392 (0.423923) | 0.130617 / 0.680424 (-0.549807) | 0.014081 / 0.534201 (-0.520120) | 0.285027 / 0.579283 (-0.294256) | 0.267296 / 0.434364 (-0.167068) | 0.331478 / 0.540337 (-0.208859) | 0.442676 / 1.386936 (-0.944260) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005340 / 0.011353 (-0.006013) | 0.003745 / 0.011008 (-0.007264) | 0.049011 / 0.038508 (0.010503) | 0.051342 / 0.023109 (0.028233) | 0.272482 / 0.275898 (-0.003416) | 0.292816 / 0.323480 (-0.030663) | 0.003977 / 0.007986 (-0.004008) | 0.002642 / 0.004328 (-0.001687) | 0.048213 / 0.004250 (0.043963) | 0.040341 / 0.037052 (0.003289) | 0.275176 / 0.258489 (0.016687) | 0.301098 / 0.293841 (0.007257) | 0.029052 / 0.128546 (-0.099495) | 0.010796 / 0.075646 (-0.064850) | 0.057654 / 0.419271 (-0.361618) | 0.032914 / 0.043533 (-0.010619) | 0.271235 / 0.255139 (0.016096) | 0.289883 / 0.283200 (0.006684) | 0.018548 / 0.141683 (-0.123135) | 1.134072 / 1.452155 (-0.318083) | 1.208228 / 1.492716 (-0.284488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094524 / 0.018006 (0.076518) | 0.310162 / 0.000490 (0.309672) | 0.000237 / 0.000200 (0.000037) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021090 / 0.037411 (-0.016321) | 0.068351 / 0.014526 (0.053825) | 0.082370 / 0.176557 (-0.094186) | 0.121648 / 0.737135 (-0.615487) | 0.083433 / 0.296338 (-0.212906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294616 / 0.215209 (0.079407) | 2.894194 / 2.077655 (0.816539) | 1.619739 / 1.504120 (0.115619) | 1.492466 / 1.541195 (-0.048729) | 1.511662 / 1.468490 (0.043172) | 0.557179 / 4.584777 (-4.027597) | 2.400669 / 3.745712 (-1.345043) | 2.781363 / 5.269862 (-2.488499) | 1.769144 / 4.565676 (-2.796533) | 0.063996 / 0.424275 (-0.360279) | 0.004922 / 0.007607 (-0.002685) | 0.354483 / 0.226044 (0.128438) | 3.474795 / 2.268929 (1.205867) | 1.985743 / 55.444624 (-53.458881) | 1.693173 / 6.876477 (-5.183303) | 1.695857 / 2.142072 (-0.446216) | 0.654800 / 4.805227 (-4.150427) | 0.117316 / 6.500664 (-6.383348) | 0.040708 / 0.075469 (-0.034761) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977678 / 1.841788 (-0.864109) | 12.214098 / 8.074308 (4.139790) | 10.741857 / 10.191392 (0.550465) | 0.130308 / 0.680424 (-0.550116) | 0.015053 / 0.534201 (-0.519148) | 0.295496 / 0.579283 (-0.283787) | 0.276348 / 0.434364 (-0.158015) | 0.326568 / 0.540337 (-0.213769) | 0.441902 / 1.386936 (-0.945034) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#214a3e6dcb66e9c1a8ff586553e8eee0f1c70710 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005218 / 0.011353 (-0.006135) | 0.003270 / 0.011008 (-0.007738) | 0.062380 / 0.038508 (0.023872) | 0.052896 / 0.023109 (0.029787) | 0.233060 / 0.275898 (-0.042838) | 0.259194 / 0.323480 (-0.064286) | 0.002880 / 0.007986 (-0.005106) | 0.002643 / 0.004328 (-0.001686) | 0.048084 / 0.004250 (0.043833) | 0.038807 / 0.037052 (0.001755) | 0.244925 / 0.258489 (-0.013564) | 0.269619 / 0.293841 (-0.024222) | 0.026901 / 0.128546 (-0.101646) | 0.010150 / 0.075646 (-0.065497) | 0.206854 / 0.419271 (-0.212417) | 0.035618 / 0.043533 (-0.007915) | 0.239577 / 0.255139 (-0.015562) | 0.259684 / 0.283200 (-0.023516) | 0.019823 / 0.141683 (-0.121860) | 1.074472 / 1.452155 (-0.377682) | 1.142911 / 1.492716 (-0.349805) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092616 / 0.018006 (0.074610) | 0.301974 / 0.000490 (0.301485) | 0.000201 / 0.000200 (0.000002) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018864 / 0.037411 (-0.018548) | 0.061007 / 0.014526 (0.046481) | 0.073228 / 0.176557 (-0.103328) | 0.120719 / 0.737135 (-0.616416) | 0.075686 / 0.296338 (-0.220653) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281404 / 0.215209 (0.066195) | 2.777671 / 2.077655 (0.700017) | 1.464689 / 1.504120 (-0.039431) | 1.345357 / 1.541195 (-0.195838) | 1.384273 / 1.468490 (-0.084217) | 0.560298 / 4.584777 (-4.024479) | 2.389877 / 3.745712 (-1.355835) | 2.755564 / 5.269862 (-2.514297) | 1.737754 / 4.565676 (-2.827922) | 0.063025 / 0.424275 (-0.361251) | 0.004975 / 0.007607 (-0.002632) | 0.346741 / 0.226044 (0.120697) | 3.321918 / 2.268929 (1.052989) | 1.815700 / 55.444624 (-53.628924) | 1.547333 / 6.876477 (-5.329144) | 1.564809 / 2.142072 (-0.577263) | 0.638645 / 4.805227 (-4.166582) | 0.118157 / 6.500664 (-6.382507) | 0.041605 / 0.075469 (-0.033864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942515 / 1.841788 (-0.899273) | 11.400386 / 8.074308 (3.326078) | 10.208763 / 10.191392 (0.017370) | 0.138144 / 0.680424 (-0.542280) | 0.014354 / 0.534201 (-0.519847) | 0.288289 / 0.579283 (-0.290994) | 0.265973 / 0.434364 (-0.168391) | 0.327703 / 0.540337 (-0.212634) | 0.435474 / 1.386936 (-0.951462) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005163 / 0.011353 (-0.006190) | 0.003307 / 0.011008 (-0.007701) | 0.048885 / 0.038508 (0.010377) | 0.049044 / 0.023109 (0.025935) | 0.261408 / 0.275898 (-0.014490) | 0.284625 / 0.323480 (-0.038855) | 0.003970 / 0.007986 (-0.004015) | 0.002754 / 0.004328 (-0.001575) | 0.048271 / 0.004250 (0.044021) | 0.039849 / 0.037052 (0.002797) | 0.266898 / 0.258489 (0.008409) | 0.291445 / 0.293841 (-0.002396) | 0.028477 / 0.128546 (-0.100069) | 0.010656 / 0.075646 (-0.064990) | 0.057732 / 0.419271 (-0.361539) | 0.033298 / 0.043533 (-0.010235) | 0.297773 / 0.255139 (0.042634) | 0.281894 / 0.283200 (-0.001305) | 0.018595 / 0.141683 (-0.123088) | 1.168849 / 1.452155 (-0.283306) | 1.183493 / 1.492716 (-0.309224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092683 / 0.018006 (0.074677) | 0.300387 / 0.000490 (0.299897) | 0.000221 / 0.000200 (0.000021) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021356 / 0.037411 (-0.016055) | 0.068095 / 0.014526 (0.053569) | 0.079806 / 0.176557 (-0.096750) | 0.118965 / 0.737135 (-0.618170) | 0.082066 / 0.296338 (-0.214273) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293105 / 0.215209 (0.077896) | 2.842800 / 2.077655 (0.765146) | 1.572052 / 1.504120 (0.067932) | 1.450156 / 1.541195 (-0.091038) | 1.464227 / 1.468490 (-0.004263) | 0.561215 / 4.584777 (-4.023562) | 2.456117 / 3.745712 (-1.289596) | 2.739766 / 5.269862 (-2.530095) | 1.730354 / 4.565676 (-2.835323) | 0.062636 / 0.424275 (-0.361639) | 0.004933 / 0.007607 (-0.002674) | 0.345800 / 0.226044 (0.119756) | 3.415858 / 2.268929 (1.146929) | 1.937288 / 55.444624 (-53.507336) | 1.661975 / 6.876477 (-5.214502) | 1.660347 / 2.142072 (-0.481726) | 0.642780 / 4.805227 (-4.162448) | 0.116643 / 6.500664 (-6.384021) | 0.041282 / 0.075469 (-0.034187) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976629 / 1.841788 (-0.865159) | 11.900319 / 8.074308 (3.826011) | 10.574198 / 10.191392 (0.382806) | 0.129689 / 0.680424 (-0.550735) | 0.015390 / 0.534201 (-0.518811) | 0.286543 / 0.579283 (-0.292741) | 0.277676 / 0.434364 (-0.156688) | 0.325053 / 0.540337 (-0.215284) | 0.439663 / 1.386936 (-0.947274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a9674e17156ff10124632ba705125288de7442 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005382 / 0.011353 (-0.005971) | 0.003606 / 0.011008 (-0.007402) | 0.063234 / 0.038508 (0.024726) | 0.053738 / 0.023109 (0.030629) | 0.250405 / 0.275898 (-0.025493) | 0.272244 / 0.323480 (-0.051236) | 0.002896 / 0.007986 (-0.005090) | 0.002684 / 0.004328 (-0.001644) | 0.048394 / 0.004250 (0.044143) | 0.039017 / 0.037052 (0.001964) | 0.259554 / 0.258489 (0.001065) | 0.287215 / 0.293841 (-0.006626) | 0.028290 / 0.128546 (-0.100257) | 0.011482 / 0.075646 (-0.064164) | 0.214264 / 0.419271 (-0.205007) | 0.036257 / 0.043533 (-0.007276) | 0.252873 / 0.255139 (-0.002266) | 0.271269 / 0.283200 (-0.011931) | 0.017173 / 0.141683 (-0.124510) | 1.137474 / 1.452155 (-0.314681) | 1.161499 / 1.492716 (-0.331217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092424 / 0.018006 (0.074418) | 0.283703 / 0.000490 (0.283213) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018307 / 0.037411 (-0.019105) | 0.060780 / 0.014526 (0.046254) | 0.073984 / 0.176557 (-0.102573) | 0.120824 / 0.737135 (-0.616311) | 0.074724 / 0.296338 (-0.221615) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297682 / 0.215209 (0.082473) | 2.853267 / 2.077655 (0.775612) | 1.567643 / 1.504120 (0.063523) | 1.437218 / 1.541195 (-0.103976) | 1.467187 / 1.468490 (-0.001304) | 0.560552 / 4.584777 (-4.024225) | 2.387848 / 3.745712 (-1.357864) | 2.718946 / 5.269862 (-2.550916) | 1.724107 / 4.565676 (-2.841570) | 0.061923 / 0.424275 (-0.362352) | 0.004828 / 0.007607 (-0.002779) | 0.353916 / 0.226044 (0.127871) | 3.404477 / 2.268929 (1.135548) | 1.906078 / 55.444624 (-53.538546) | 1.629686 / 6.876477 (-5.246791) | 1.640839 / 2.142072 (-0.501233) | 0.641082 / 4.805227 (-4.164145) | 0.118078 / 6.500664 (-6.382586) | 0.041881 / 0.075469 (-0.033588) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936062 / 1.841788 (-0.905726) | 11.397678 / 8.074308 (3.323370) | 10.385159 / 10.191392 (0.193766) | 0.127337 / 0.680424 (-0.553087) | 0.013562 / 0.534201 (-0.520639) | 0.290817 / 0.579283 (-0.288466) | 0.259377 / 0.434364 (-0.174987) | 0.324829 / 0.540337 (-0.215508) | 0.434344 / 1.386936 (-0.952592) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005134 / 0.011353 (-0.006219) | 0.003404 / 0.011008 (-0.007604) | 0.048281 / 0.038508 (0.009772) | 0.050952 / 0.023109 (0.027842) | 0.277553 / 0.275898 (0.001655) | 0.298855 / 0.323480 (-0.024625) | 0.003928 / 0.007986 (-0.004058) | 0.002642 / 0.004328 (-0.001687) | 0.047374 / 0.004250 (0.043123) | 0.039883 / 0.037052 (0.002831) | 0.279808 / 0.258489 (0.021318) | 0.301604 / 0.293841 (0.007763) | 0.028708 / 0.128546 (-0.099838) | 0.010949 / 0.075646 (-0.064697) | 0.057090 / 0.419271 (-0.362181) | 0.032438 / 0.043533 (-0.011095) | 0.274690 / 0.255139 (0.019551) | 0.290912 / 0.283200 (0.007712) | 0.017556 / 0.141683 (-0.124127) | 1.111091 / 1.452155 (-0.341064) | 1.166063 / 1.492716 (-0.326653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090557 / 0.018006 (0.072551) | 0.298661 / 0.000490 (0.298171) | 0.000228 / 0.000200 (0.000028) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021712 / 0.037411 (-0.015699) | 0.068682 / 0.014526 (0.054156) | 0.080108 / 0.176557 (-0.096449) | 0.119480 / 0.737135 (-0.617655) | 0.082703 / 0.296338 (-0.213636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294095 / 0.215209 (0.078886) | 2.884758 / 2.077655 (0.807103) | 1.598312 / 1.504120 (0.094192) | 1.480050 / 1.541195 (-0.061145) | 1.488611 / 1.468490 (0.020121) | 0.556052 / 4.584777 (-4.028724) | 2.435484 / 3.745712 (-1.310228) | 2.741592 / 5.269862 (-2.528270) | 1.706223 / 4.565676 (-2.859454) | 0.062214 / 0.424275 (-0.362061) | 0.004901 / 0.007607 (-0.002706) | 0.346301 / 0.226044 (0.120257) | 3.474516 / 2.268929 (1.205587) | 1.995205 / 55.444624 (-53.449419) | 1.726349 / 6.876477 (-5.150128) | 1.659600 / 2.142072 (-0.482472) | 0.643560 / 4.805227 (-4.161667) | 0.115222 / 6.500664 (-6.385442) | 0.041137 / 0.075469 (-0.034332) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974566 / 1.841788 (-0.867221) | 11.872479 / 8.074308 (3.798171) | 10.496919 / 10.191392 (0.305527) | 0.129087 / 0.680424 (-0.551337) | 0.014627 / 0.534201 (-0.519574) | 0.289070 / 0.579283 (-0.290213) | 0.269609 / 0.434364 (-0.164755) | 0.327785 / 0.540337 (-0.212553) | 0.444634 / 1.386936 (-0.942302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e0960ea165a9481b1ff6eed31771475120cb38 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005080 / 0.011353 (-0.006273) | 0.003782 / 0.011008 (-0.007226) | 0.062816 / 0.038508 (0.024308) | 0.056338 / 0.023109 (0.033229) | 0.251317 / 0.275898 (-0.024581) | 0.269414 / 0.323480 (-0.054066) | 0.003984 / 0.007986 (-0.004001) | 0.002749 / 0.004328 (-0.001580) | 0.048126 / 0.004250 (0.043876) | 0.038516 / 0.037052 (0.001464) | 0.253809 / 0.258489 (-0.004680) | 0.283309 / 0.293841 (-0.010532) | 0.027015 / 0.128546 (-0.101531) | 0.010610 / 0.075646 (-0.065037) | 0.213024 / 0.419271 (-0.206247) | 0.035734 / 0.043533 (-0.007799) | 0.247909 / 0.255139 (-0.007230) | 0.263539 / 0.283200 (-0.019660) | 0.018408 / 0.141683 (-0.123275) | 1.104366 / 1.452155 (-0.347789) | 1.169668 / 1.492716 (-0.323048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.114366 / 0.018006 (0.096360) | 0.317674 / 0.000490 (0.317184) | 0.000227 / 0.000200 (0.000027) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018955 / 0.037411 (-0.018457) | 0.060716 / 0.014526 (0.046190) | 0.072963 / 0.176557 (-0.103593) | 0.121671 / 0.737135 (-0.615464) | 0.073785 / 0.296338 (-0.222554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292349 / 0.215209 (0.077140) | 2.832049 / 2.077655 (0.754394) | 1.504488 / 1.504120 (0.000368) | 1.403418 / 1.541195 (-0.137777) | 1.449223 / 1.468490 (-0.019267) | 0.563846 / 4.584777 (-4.020931) | 2.376726 / 3.745712 (-1.368986) | 2.823304 / 5.269862 (-2.446558) | 1.774858 / 4.565676 (-2.790818) | 0.063229 / 0.424275 (-0.361046) | 0.004923 / 0.007607 (-0.002684) | 0.347240 / 0.226044 (0.121195) | 3.486563 / 2.268929 (1.217634) | 1.890516 / 55.444624 (-53.554109) | 1.570620 / 6.876477 (-5.305857) | 1.600842 / 2.142072 (-0.541231) | 0.644287 / 4.805227 (-4.160940) | 0.116931 / 6.500664 (-6.383733) | 0.042068 / 0.075469 (-0.033401) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935662 / 1.841788 (-0.906126) | 11.950247 / 8.074308 (3.875939) | 10.636225 / 10.191392 (0.444833) | 0.139137 / 0.680424 (-0.541287) | 0.014473 / 0.534201 (-0.519728) | 0.294213 / 0.579283 (-0.285070) | 0.273413 / 0.434364 (-0.160951) | 0.325930 / 0.540337 (-0.214407) | 0.444265 / 1.386936 (-0.942671) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005448 / 0.011353 (-0.005904) | 0.003155 / 0.011008 (-0.007853) | 0.048626 / 0.038508 (0.010117) | 0.057427 / 0.023109 (0.034318) | 0.270412 / 0.275898 (-0.005486) | 0.290816 / 0.323480 (-0.032664) | 0.004744 / 0.007986 (-0.003241) | 0.002776 / 0.004328 (-0.001552) | 0.047953 / 0.004250 (0.043703) | 0.041126 / 0.037052 (0.004073) | 0.276046 / 0.258489 (0.017557) | 0.297548 / 0.293841 (0.003707) | 0.029308 / 0.128546 (-0.099238) | 0.010516 / 0.075646 (-0.065131) | 0.056982 / 0.419271 (-0.362290) | 0.032922 / 0.043533 (-0.010611) | 0.271342 / 0.255139 (0.016203) | 0.288963 / 0.283200 (0.005763) | 0.019048 / 0.141683 (-0.122635) | 1.130453 / 1.452155 (-0.321702) | 1.206462 / 1.492716 (-0.286254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099249 / 0.018006 (0.081242) | 0.312409 / 0.000490 (0.311919) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021992 / 0.037411 (-0.015419) | 0.068377 / 0.014526 (0.053851) | 0.080749 / 0.176557 (-0.095807) | 0.120534 / 0.737135 (-0.616602) | 0.082549 / 0.296338 (-0.213790) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299634 / 0.215209 (0.084425) | 2.943496 / 2.077655 (0.865841) | 1.602842 / 1.504120 (0.098722) | 1.462140 / 1.541195 (-0.079055) | 1.511082 / 1.468490 (0.042592) | 0.574148 / 4.584777 (-4.010629) | 2.492158 / 3.745712 (-1.253554) | 2.921695 / 5.269862 (-2.348166) | 1.812416 / 4.565676 (-2.753260) | 0.064145 / 0.424275 (-0.360130) | 0.005133 / 0.007607 (-0.002475) | 0.357935 / 0.226044 (0.131891) | 3.543728 / 2.268929 (1.274800) | 1.948676 / 55.444624 (-53.495948) | 1.664960 / 6.876477 (-5.211517) | 1.678703 / 2.142072 (-0.463370) | 0.645867 / 4.805227 (-4.159360) | 0.117671 / 6.500664 (-6.382993) | 0.040887 / 0.075469 (-0.034582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979127 / 1.841788 (-0.862661) | 12.363904 / 8.074308 (4.289596) | 10.673725 / 10.191392 (0.482333) | 0.143358 / 0.680424 (-0.537066) | 0.015375 / 0.534201 (-0.518825) | 0.287590 / 0.579283 (-0.291694) | 0.284742 / 0.434364 (-0.149622) | 0.326901 / 0.540337 (-0.213437) | 0.443962 / 1.386936 (-0.942974) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68099ca55294bfc12a34781835dd73c533a764bd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004994 / 0.011353 (-0.006359) | 0.003368 / 0.011008 (-0.007640) | 0.062803 / 0.038508 (0.024295) | 0.050778 / 0.023109 (0.027669) | 0.255955 / 0.275898 (-0.019943) | 0.278215 / 0.323480 (-0.045265) | 0.003801 / 0.007986 (-0.004184) | 0.002703 / 0.004328 (-0.001626) | 0.048369 / 0.004250 (0.044119) | 0.037795 / 0.037052 (0.000743) | 0.255634 / 0.258489 (-0.002855) | 0.284226 / 0.293841 (-0.009615) | 0.027252 / 0.128546 (-0.101294) | 0.010686 / 0.075646 (-0.064961) | 0.206139 / 0.419271 (-0.213133) | 0.035543 / 0.043533 (-0.007990) | 0.257167 / 0.255139 (0.002028) | 0.277784 / 0.283200 (-0.005416) | 0.016938 / 0.141683 (-0.124745) | 1.108595 / 1.452155 (-0.343560) | 1.188542 / 1.492716 (-0.304175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090938 / 0.018006 (0.072932) | 0.298463 / 0.000490 (0.297973) | 0.000203 / 0.000200 (0.000003) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027762 / 0.037411 (-0.009649) | 0.060539 / 0.014526 (0.046014) | 0.075986 / 0.176557 (-0.100570) | 0.133851 / 0.737135 (-0.603285) | 0.074669 / 0.296338 (-0.221670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285614 / 0.215209 (0.070405) | 2.810529 / 2.077655 (0.732874) | 1.537092 / 1.504120 (0.032973) | 1.412211 / 1.541195 (-0.128983) | 1.446395 / 1.468490 (-0.022095) | 0.559008 / 4.584777 (-4.025769) | 2.343445 / 3.745712 (-1.402267) | 2.748113 / 5.269862 (-2.521748) | 1.733593 / 4.565676 (-2.832083) | 0.061720 / 0.424275 (-0.362555) | 0.004930 / 0.007607 (-0.002677) | 0.330646 / 0.226044 (0.104602) | 3.314999 / 2.268929 (1.046071) | 1.854527 / 55.444624 (-53.590098) | 1.605819 / 6.876477 (-5.270657) | 1.591406 / 2.142072 (-0.550667) | 0.624239 / 4.805227 (-4.180988) | 0.115352 / 6.500664 (-6.385312) | 0.041600 / 0.075469 (-0.033869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933179 / 1.841788 (-0.908608) | 11.456372 / 8.074308 (3.382064) | 10.578042 / 10.191392 (0.386650) | 0.128045 / 0.680424 (-0.552379) | 0.014212 / 0.534201 (-0.519989) | 0.284795 / 0.579283 (-0.294488) | 0.266210 / 0.434364 (-0.168153) | 0.344468 / 0.540337 (-0.195869) | 0.434414 / 1.386936 (-0.952522) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005142 / 0.011353 (-0.006211) | 0.003607 / 0.011008 (-0.007401) | 0.048770 / 0.038508 (0.010262) | 0.051147 / 0.023109 (0.028038) | 0.277329 / 0.275898 (0.001430) | 0.300863 / 0.323480 (-0.022617) | 0.004005 / 0.007986 (-0.003980) | 0.002624 / 0.004328 (-0.001705) | 0.047740 / 0.004250 (0.043489) | 0.040811 / 0.037052 (0.003759) | 0.280020 / 0.258489 (0.021531) | 0.303758 / 0.293841 (0.009918) | 0.028273 / 0.128546 (-0.100274) | 0.010379 / 0.075646 (-0.065267) | 0.057503 / 0.419271 (-0.361768) | 0.032717 / 0.043533 (-0.010816) | 0.277560 / 0.255139 (0.022421) | 0.300622 / 0.283200 (0.017422) | 0.018142 / 0.141683 (-0.123541) | 1.121890 / 1.452155 (-0.330265) | 1.251481 / 1.492716 (-0.241235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091523 / 0.018006 (0.073517) | 0.300173 / 0.000490 (0.299683) | 0.000216 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011025) | 0.078710 / 0.014526 (0.064184) | 0.090594 / 0.176557 (-0.085962) | 0.130623 / 0.737135 (-0.606512) | 0.092637 / 0.296338 (-0.203701) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299427 / 0.215209 (0.084218) | 2.929463 / 2.077655 (0.851808) | 1.608905 / 1.504120 (0.104785) | 1.490863 / 1.541195 (-0.050331) | 1.484286 / 1.468490 (0.015796) | 0.568208 / 4.584777 (-4.016569) | 2.447081 / 3.745712 (-1.298632) | 2.801287 / 5.269862 (-2.468574) | 1.744449 / 4.565676 (-2.821227) | 0.064222 / 0.424275 (-0.360053) | 0.004959 / 0.007607 (-0.002648) | 0.350207 / 0.226044 (0.124162) | 3.471944 / 2.268929 (1.203016) | 1.951715 / 55.444624 (-53.492909) | 1.668764 / 6.876477 (-5.207713) | 1.675322 / 2.142072 (-0.466751) | 0.642217 / 4.805227 (-4.163011) | 0.116776 / 6.500664 (-6.383888) | 0.040812 / 0.075469 (-0.034658) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996478 / 1.841788 (-0.845310) | 12.090647 / 8.074308 (4.016339) | 10.723688 / 10.191392 (0.532296) | 0.141770 / 0.680424 (-0.538653) | 0.015578 / 0.534201 (-0.518623) | 0.288236 / 0.579283 (-0.291047) | 0.278542 / 0.434364 (-0.155822) | 0.327411 / 0.540337 (-0.212927) | 0.450309 / 1.386936 (-0.936627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dd4698f483d37afe243db0ffae774cbd34a4af4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004967 / 0.011353 (-0.006385) | 0.003382 / 0.011008 (-0.007627) | 0.063436 / 0.038508 (0.024928) | 0.050769 / 0.023109 (0.027659) | 0.254214 / 0.275898 (-0.021684) | 0.272076 / 0.323480 (-0.051404) | 0.003815 / 0.007986 (-0.004170) | 0.002618 / 0.004328 (-0.001711) | 0.049021 / 0.004250 (0.044771) | 0.037329 / 0.037052 (0.000277) | 0.261112 / 0.258489 (0.002623) | 0.284133 / 0.293841 (-0.009708) | 0.026828 / 0.128546 (-0.101719) | 0.010757 / 0.075646 (-0.064889) | 0.208047 / 0.419271 (-0.211225) | 0.035061 / 0.043533 (-0.008472) | 0.250896 / 0.255139 (-0.004243) | 0.273038 / 0.283200 (-0.010162) | 0.016559 / 0.141683 (-0.125124) | 1.128899 / 1.452155 (-0.323255) | 1.188857 / 1.492716 (-0.303860) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100121 / 0.018006 (0.082114) | 0.298427 / 0.000490 (0.297937) | 0.000218 / 0.000200 (0.000018) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018369 / 0.037411 (-0.019042) | 0.060425 / 0.014526 (0.045899) | 0.073501 / 0.176557 (-0.103055) | 0.120254 / 0.737135 (-0.616881) | 0.074889 / 0.296338 (-0.221450) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287153 / 0.215209 (0.071944) | 2.797036 / 2.077655 (0.719382) | 1.446216 / 1.504120 (-0.057904) | 1.336015 / 1.541195 (-0.205179) | 1.369841 / 1.468490 (-0.098650) | 0.559424 / 4.584777 (-4.025353) | 2.361344 / 3.745712 (-1.384368) | 2.766619 / 5.269862 (-2.503243) | 1.747235 / 4.565676 (-2.818441) | 0.066243 / 0.424275 (-0.358032) | 0.004974 / 0.007607 (-0.002633) | 0.333565 / 0.226044 (0.107520) | 3.319877 / 2.268929 (1.050948) | 1.798024 / 55.444624 (-53.646601) | 1.495896 / 6.876477 (-5.380580) | 1.529243 / 2.142072 (-0.612830) | 0.636609 / 4.805227 (-4.168618) | 0.116151 / 6.500664 (-6.384514) | 0.041779 / 0.075469 (-0.033690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952176 / 1.841788 (-0.889611) | 11.559160 / 8.074308 (3.484852) | 10.556771 / 10.191392 (0.365379) | 0.127118 / 0.680424 (-0.553306) | 0.014142 / 0.534201 (-0.520059) | 0.286585 / 0.579283 (-0.292698) | 0.260233 / 0.434364 (-0.174131) | 0.324012 / 0.540337 (-0.216326) | 0.435131 / 1.386936 (-0.951805) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005171 / 0.011353 (-0.006182) | 0.003402 / 0.011008 (-0.007607) | 0.048826 / 0.038508 (0.010318) | 0.050455 / 0.023109 (0.027346) | 0.272120 / 0.275898 (-0.003778) | 0.290404 / 0.323480 (-0.033076) | 0.003986 / 0.007986 (-0.003999) | 0.002569 / 0.004328 (-0.001760) | 0.047845 / 0.004250 (0.043595) | 0.040203 / 0.037052 (0.003150) | 0.278263 / 0.258489 (0.019774) | 0.299255 / 0.293841 (0.005414) | 0.028643 / 0.128546 (-0.099903) | 0.010584 / 0.075646 (-0.065062) | 0.056921 / 0.419271 (-0.362351) | 0.032362 / 0.043533 (-0.011171) | 0.274010 / 0.255139 (0.018871) | 0.288601 / 0.283200 (0.005401) | 0.017856 / 0.141683 (-0.123827) | 1.154112 / 1.452155 (-0.298043) | 1.216288 / 1.492716 (-0.276428) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091399 / 0.018006 (0.073392) | 0.299966 / 0.000490 (0.299477) | 0.000218 / 0.000200 (0.000018) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021728 / 0.037411 (-0.015683) | 0.068285 / 0.014526 (0.053759) | 0.081767 / 0.176557 (-0.094789) | 0.120000 / 0.737135 (-0.617135) | 0.082149 / 0.296338 (-0.214189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289625 / 0.215209 (0.074416) | 2.835114 / 2.077655 (0.757460) | 1.583207 / 1.504120 (0.079087) | 1.465251 / 1.541195 (-0.075944) | 1.480691 / 1.468490 (0.012200) | 0.569103 / 4.584777 (-4.015674) | 2.416981 / 3.745712 (-1.328731) | 2.761746 / 5.269862 (-2.508115) | 1.720055 / 4.565676 (-2.845621) | 0.063349 / 0.424275 (-0.360926) | 0.004931 / 0.007607 (-0.002676) | 0.343658 / 0.226044 (0.117614) | 3.362996 / 2.268929 (1.094068) | 1.948088 / 55.444624 (-53.496536) | 1.659504 / 6.876477 (-5.216973) | 1.660359 / 2.142072 (-0.481713) | 0.647871 / 4.805227 (-4.157356) | 0.117395 / 6.500664 (-6.383269) | 0.041049 / 0.075469 (-0.034420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953971 / 1.841788 (-0.887817) | 12.076998 / 8.074308 (4.002690) | 10.549021 / 10.191392 (0.357629) | 0.130026 / 0.680424 (-0.550398) | 0.015697 / 0.534201 (-0.518504) | 0.287125 / 0.579283 (-0.292158) | 0.298402 / 0.434364 (-0.135962) | 0.326005 / 0.540337 (-0.214332) | 0.444065 / 1.386936 (-0.942871) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf86d48792f585bf802bb2ff70e0d9c3a4de4bcf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005053 / 0.011353 (-0.006300) | 0.003537 / 0.011008 (-0.007472) | 0.062923 / 0.038508 (0.024415) | 0.053796 / 0.023109 (0.030687) | 0.242523 / 0.275898 (-0.033375) | 0.264014 / 0.323480 (-0.059466) | 0.002879 / 0.007986 (-0.005106) | 0.003273 / 0.004328 (-0.001055) | 0.048735 / 0.004250 (0.044484) | 0.037541 / 0.037052 (0.000488) | 0.248587 / 0.258489 (-0.009902) | 0.275531 / 0.293841 (-0.018310) | 0.027215 / 0.128546 (-0.101331) | 0.010466 / 0.075646 (-0.065180) | 0.206508 / 0.419271 (-0.212763) | 0.035606 / 0.043533 (-0.007927) | 0.251044 / 0.255139 (-0.004095) | 0.267183 / 0.283200 (-0.016016) | 0.018357 / 0.141683 (-0.123326) | 1.083513 / 1.452155 (-0.368642) | 1.152988 / 1.492716 (-0.339728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091749 / 0.018006 (0.073742) | 0.299946 / 0.000490 (0.299456) | 0.000212 / 0.000200 (0.000013) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018300 / 0.037411 (-0.019111) | 0.060691 / 0.014526 (0.046166) | 0.072998 / 0.176557 (-0.103559) | 0.120581 / 0.737135 (-0.616554) | 0.073912 / 0.296338 (-0.222427) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277602 / 0.215209 (0.062393) | 2.719181 / 2.077655 (0.641526) | 1.450894 / 1.504120 (-0.053226) | 1.314344 / 1.541195 (-0.226851) | 1.351996 / 1.468490 (-0.116494) | 0.586231 / 4.584777 (-3.998546) | 2.349746 / 3.745712 (-1.395967) | 2.810060 / 5.269862 (-2.459802) | 1.761362 / 4.565676 (-2.804314) | 0.062535 / 0.424275 (-0.361740) | 0.004918 / 0.007607 (-0.002689) | 0.336091 / 0.226044 (0.110047) | 3.238139 / 2.268929 (0.969211) | 1.769734 / 55.444624 (-53.674890) | 1.505332 / 6.876477 (-5.371145) | 1.527875 / 2.142072 (-0.614198) | 0.640194 / 4.805227 (-4.165033) | 0.116567 / 6.500664 (-6.384097) | 0.042464 / 0.075469 (-0.033005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930919 / 1.841788 (-0.910869) | 11.462498 / 8.074308 (3.388190) | 10.575359 / 10.191392 (0.383967) | 0.130567 / 0.680424 (-0.549857) | 0.014203 / 0.534201 (-0.519998) | 0.286944 / 0.579283 (-0.292339) | 0.264706 / 0.434364 (-0.169658) | 0.324820 / 0.540337 (-0.215517) | 0.434579 / 1.386936 (-0.952357) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005164 / 0.011353 (-0.006189) | 0.003442 / 0.011008 (-0.007567) | 0.050146 / 0.038508 (0.011638) | 0.050800 / 0.023109 (0.027691) | 0.263405 / 0.275898 (-0.012493) | 0.284876 / 0.323480 (-0.038604) | 0.004011 / 0.007986 (-0.003975) | 0.002602 / 0.004328 (-0.001726) | 0.046742 / 0.004250 (0.042491) | 0.040393 / 0.037052 (0.003341) | 0.265052 / 0.258489 (0.006563) | 0.294217 / 0.293841 (0.000377) | 0.028429 / 0.128546 (-0.100118) | 0.010418 / 0.075646 (-0.065228) | 0.057285 / 0.419271 (-0.361987) | 0.032137 / 0.043533 (-0.011396) | 0.265867 / 0.255139 (0.010728) | 0.284764 / 0.283200 (0.001564) | 0.017448 / 0.141683 (-0.124235) | 1.172830 / 1.452155 (-0.279325) | 1.223982 / 1.492716 (-0.268735) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091859 / 0.018006 (0.073853) | 0.285421 / 0.000490 (0.284931) | 0.000220 / 0.000200 (0.000020) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021620 / 0.037411 (-0.015792) | 0.069058 / 0.014526 (0.054532) | 0.082560 / 0.176557 (-0.093997) | 0.119511 / 0.737135 (-0.617624) | 0.082318 / 0.296338 (-0.214021) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291499 / 0.215209 (0.076290) | 2.863352 / 2.077655 (0.785698) | 1.557242 / 1.504120 (0.053122) | 1.430170 / 1.541195 (-0.111024) | 1.432850 / 1.468490 (-0.035640) | 0.559716 / 4.584777 (-4.025061) | 2.385405 / 3.745712 (-1.360307) | 2.748938 / 5.269862 (-2.520924) | 1.740802 / 4.565676 (-2.824874) | 0.061811 / 0.424275 (-0.362465) | 0.005174 / 0.007607 (-0.002433) | 0.348687 / 0.226044 (0.122642) | 3.420120 / 2.268929 (1.151191) | 1.918278 / 55.444624 (-53.526346) | 1.631559 / 6.876477 (-5.244918) | 1.635850 / 2.142072 (-0.506222) | 0.644144 / 4.805227 (-4.161083) | 0.115823 / 6.500664 (-6.384841) | 0.041255 / 0.075469 (-0.034214) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960066 / 1.841788 (-0.881722) | 12.011372 / 8.074308 (3.937064) | 10.580532 / 10.191392 (0.389140) | 0.134763 / 0.680424 (-0.545661) | 0.017027 / 0.534201 (-0.517174) | 0.290484 / 0.579283 (-0.288799) | 0.285171 / 0.434364 (-0.149193) | 0.322453 / 0.540337 (-0.217884) | 0.438088 / 1.386936 (-0.948848) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3fc42882a2d84d7482c27063f1e19539e99b9d3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005212 / 0.011353 (-0.006141) | 0.003440 / 0.011008 (-0.007568) | 0.063612 / 0.038508 (0.025104) | 0.049070 / 0.023109 (0.025961) | 0.269748 / 0.275898 (-0.006150) | 0.283270 / 0.323480 (-0.040210) | 0.002892 / 0.007986 (-0.005094) | 0.002693 / 0.004328 (-0.001635) | 0.049710 / 0.004250 (0.045459) | 0.036707 / 0.037052 (-0.000345) | 0.299035 / 0.258489 (0.040546) | 0.296443 / 0.293841 (0.002602) | 0.028095 / 0.128546 (-0.100451) | 0.010682 / 0.075646 (-0.064964) | 0.213914 / 0.419271 (-0.205358) | 0.036210 / 0.043533 (-0.007323) | 0.235720 / 0.255139 (-0.019419) | 0.252687 / 0.283200 (-0.030512) | 0.016985 / 0.141683 (-0.124698) | 1.099024 / 1.452155 (-0.353130) | 1.162970 / 1.492716 (-0.329746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093114 / 0.018006 (0.075108) | 0.305168 / 0.000490 (0.304678) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018370 / 0.037411 (-0.019041) | 0.060534 / 0.014526 (0.046008) | 0.073960 / 0.176557 (-0.102596) | 0.120325 / 0.737135 (-0.616810) | 0.073754 / 0.296338 (-0.222585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284244 / 0.215209 (0.069035) | 2.756854 / 2.077655 (0.679199) | 1.477304 / 1.504120 (-0.026816) | 1.374635 / 1.541195 (-0.166560) | 1.383284 / 1.468490 (-0.085206) | 0.564656 / 4.584777 (-4.020121) | 2.361719 / 3.745712 (-1.383993) | 2.794822 / 5.269862 (-2.475039) | 1.742981 / 4.565676 (-2.822696) | 0.063443 / 0.424275 (-0.360832) | 0.004952 / 0.007607 (-0.002655) | 0.342058 / 0.226044 (0.116014) | 3.351093 / 2.268929 (1.082164) | 1.857375 / 55.444624 (-53.587250) | 1.541680 / 6.876477 (-5.334797) | 1.580147 / 2.142072 (-0.561926) | 0.645216 / 4.805227 (-4.160012) | 0.118768 / 6.500664 (-6.381896) | 0.042115 / 0.075469 (-0.033354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.925845 / 1.841788 (-0.915943) | 11.444147 / 8.074308 (3.369839) | 10.291297 / 10.191392 (0.099905) | 0.128129 / 0.680424 (-0.552295) | 0.013774 / 0.534201 (-0.520427) | 0.289278 / 0.579283 (-0.290005) | 0.262353 / 0.434364 (-0.172011) | 0.328517 / 0.540337 (-0.211820) | 0.436050 / 1.386936 (-0.950886) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005666 / 0.011353 (-0.005687) | 0.003691 / 0.011008 (-0.007318) | 0.049361 / 0.038508 (0.010853) | 0.054245 / 0.023109 (0.031136) | 0.274433 / 0.275898 (-0.001465) | 0.285648 / 0.323480 (-0.037832) | 0.004080 / 0.007986 (-0.003906) | 0.002666 / 0.004328 (-0.001663) | 0.047539 / 0.004250 (0.043288) | 0.041001 / 0.037052 (0.003948) | 0.296018 / 0.258489 (0.037529) | 0.294542 / 0.293841 (0.000701) | 0.030546 / 0.128546 (-0.098001) | 0.010556 / 0.075646 (-0.065090) | 0.058146 / 0.419271 (-0.361126) | 0.033407 / 0.043533 (-0.010126) | 0.263977 / 0.255139 (0.008838) | 0.286228 / 0.283200 (0.003028) | 0.018088 / 0.141683 (-0.123595) | 1.121295 / 1.452155 (-0.330860) | 1.182183 / 1.492716 (-0.310533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.104540 / 0.018006 (0.086534) | 0.303494 / 0.000490 (0.303004) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021274 / 0.037411 (-0.016137) | 0.070146 / 0.014526 (0.055621) | 0.080343 / 0.176557 (-0.096213) | 0.120017 / 0.737135 (-0.617119) | 0.081303 / 0.296338 (-0.215036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294390 / 0.215209 (0.079181) | 2.883366 / 2.077655 (0.805711) | 1.564629 / 1.504120 (0.060509) | 1.432633 / 1.541195 (-0.108562) | 1.438786 / 1.468490 (-0.029704) | 0.569663 / 4.584777 (-4.015114) | 2.448691 / 3.745712 (-1.297021) | 2.817010 / 5.269862 (-2.452851) | 1.757274 / 4.565676 (-2.808402) | 0.064147 / 0.424275 (-0.360129) | 0.004910 / 0.007607 (-0.002697) | 0.344062 / 0.226044 (0.118018) | 3.394223 / 2.268929 (1.125294) | 1.927139 / 55.444624 (-53.517485) | 1.624983 / 6.876477 (-5.251494) | 1.629076 / 2.142072 (-0.512996) | 0.654239 / 4.805227 (-4.150988) | 0.117309 / 6.500664 (-6.383355) | 0.041067 / 0.075469 (-0.034402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993184 / 1.841788 (-0.848604) | 11.969985 / 8.074308 (3.895677) | 10.363356 / 10.191392 (0.171964) | 0.130708 / 0.680424 (-0.549716) | 0.015577 / 0.534201 (-0.518624) | 0.289579 / 0.579283 (-0.289704) | 0.274875 / 0.434364 (-0.159488) | 0.326736 / 0.540337 (-0.213601) | 0.442770 / 1.386936 (-0.944166) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#796a47e388a5c5711a95bd649648608c18219ac5 \"CML watermark\")\n", "Getting the same windows error as in my other PR. I couldn't reproduce on my windows machine though 🧐 ", "`DataFilesList` is a list so we expect to be able to get its length with zero cost, which wouldn't be the case if we make it lazy no ? ", "But we don't call `len` on it, do we? And I couldn't find an instance of `DataFilesList` being used in GitHub's public repos.", "`DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns", "Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)", "> `DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns\r\n\r\nWould be interesting to know how often these scripts call `len` or do random access on `DataFilesList`.\r\n\r\nStill, I think we should opt for a solution that makes more sense for us. To avoid the breaking change, we can define a `BuilderConfig.data_files` property that resolves this iterable. \r\n\r\n> Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)\r\n\r\nThe `BuilderConfig.data_files` property suggested above should address this, no? \r\n\r\nI think we should be more careful not to make our API needlessly complex because of the YAML README feature. And if this can't be avoided, we should probably refactor the builder API.", "> The BuilderConfig.data_files property suggested above should address this, no?\r\n\r\nThat works indeed ! let me try something", "Implementing lazy DataFilesList and .data_files brings more complexity (less readable, more bad side effects) so I think the current solution is the best one", "I opened https://github.com/huggingface/datasets/pull/6493 to continue this and fix conflicts with https://github.com/huggingface/datasets/pull/6459" ]
2023-11-29T13:18:44
2024-02-08T14:41:35
2024-02-08T14:41:35
MEMBER
null
false
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Related to discussion at https://github.com/huggingface/datasets/pull/6255 this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
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`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'
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[ "Updating `fsspec>=2023.10.0` did solve the issue.", "May be it should be pinned somewhere?", "> Maybe this should go in datasets directly... anyways you can easily fix this error by updating datasets>=2.15.1.dev0.\r\n\r\n@lhoestq @mariosasko for what I understand this is a bug fixed in `datasets` already, right? No need to do anything in `huggingface_hub`?", "I've opened a PR with a fix in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/1875", "Thanks! PR is merged and will be shipped in next release of `huggingface_hub`." ]
2023-11-29T01:57:36
2023-11-29T15:39:03
2023-11-29T02:02:38
NONE
null
null
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### Describe the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Steps to reproduce the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Expected behavior Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Environment info Please see https://github.com/huggingface/huggingface_hub/issues/1872
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Don't require trust_remote_code in inspect_dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005705 / 0.011353 (-0.005648) | 0.003536 / 0.011008 (-0.007473) | 0.062852 / 0.038508 (0.024343) | 0.053902 / 0.023109 (0.030793) | 0.239465 / 0.275898 (-0.036433) | 0.270829 / 0.323480 (-0.052651) | 0.004052 / 0.007986 (-0.003934) | 0.002775 / 0.004328 (-0.001554) | 0.048475 / 0.004250 (0.044225) | 0.039430 / 0.037052 (0.002377) | 0.244318 / 0.258489 (-0.014171) | 0.277539 / 0.293841 (-0.016302) | 0.027637 / 0.128546 (-0.100909) | 0.010875 / 0.075646 (-0.064771) | 0.208839 / 0.419271 (-0.210432) | 0.036984 / 0.043533 (-0.006549) | 0.246355 / 0.255139 (-0.008784) | 0.271200 / 0.283200 (-0.011999) | 0.020636 / 0.141683 (-0.121047) | 1.078472 / 1.452155 (-0.373683) | 1.155701 / 1.492716 (-0.337015) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100971 / 0.018006 (0.082965) | 0.310996 / 0.000490 (0.310507) | 0.000218 / 0.000200 (0.000018) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019300 / 0.037411 (-0.018111) | 0.060625 / 0.014526 (0.046099) | 0.073778 / 0.176557 (-0.102778) | 0.120280 / 0.737135 (-0.616855) | 0.075288 / 0.296338 (-0.221051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289838 / 0.215209 (0.074629) | 2.859492 / 2.077655 (0.781837) | 1.528478 / 1.504120 (0.024358) | 1.417911 / 1.541195 (-0.123283) | 1.444227 / 1.468490 (-0.024263) | 0.566799 / 4.584777 (-4.017978) | 2.402526 / 3.745712 (-1.343186) | 2.805241 / 5.269862 (-2.464620) | 1.798572 / 4.565676 (-2.767104) | 0.062920 / 0.424275 (-0.361355) | 0.004995 / 0.007607 (-0.002612) | 0.340688 / 0.226044 (0.114644) | 3.347967 / 2.268929 (1.079039) | 1.898464 / 55.444624 (-53.546160) | 1.604784 / 6.876477 (-5.271693) | 1.648864 / 2.142072 (-0.493209) | 0.642242 / 4.805227 (-4.162985) | 0.117567 / 6.500664 (-6.383097) | 0.041911 / 0.075469 (-0.033558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949099 / 1.841788 (-0.892689) | 12.367323 / 8.074308 (4.293015) | 10.694238 / 10.191392 (0.502846) | 0.143424 / 0.680424 (-0.537000) | 0.014569 / 0.534201 (-0.519632) | 0.289127 / 0.579283 (-0.290156) | 0.270490 / 0.434364 (-0.163874) | 0.326470 / 0.540337 (-0.213867) | 0.432223 / 1.386936 (-0.954713) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005380 / 0.011353 (-0.005973) | 0.003582 / 0.011008 (-0.007426) | 0.049341 / 0.038508 (0.010833) | 0.053274 / 0.023109 (0.030165) | 0.284319 / 0.275898 (0.008421) | 0.334248 / 0.323480 (0.010768) | 0.004032 / 0.007986 (-0.003953) | 0.002682 / 0.004328 (-0.001646) | 0.048317 / 0.004250 (0.044067) | 0.040157 / 0.037052 (0.003105) | 0.284594 / 0.258489 (0.026105) | 0.341567 / 0.293841 (0.047726) | 0.029639 / 0.128546 (-0.098908) | 0.010780 / 0.075646 (-0.064867) | 0.057990 / 0.419271 (-0.361282) | 0.032730 / 0.043533 (-0.010803) | 0.290328 / 0.255139 (0.035189) | 0.298563 / 0.283200 (0.015363) | 0.018546 / 0.141683 (-0.123137) | 1.143157 / 1.452155 (-0.308998) | 1.191391 / 1.492716 (-0.301326) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093802 / 0.018006 (0.075796) | 0.312771 / 0.000490 (0.312282) | 0.000221 / 0.000200 (0.000021) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021867 / 0.037411 (-0.015544) | 0.069064 / 0.014526 (0.054538) | 0.082270 / 0.176557 (-0.094287) | 0.120222 / 0.737135 (-0.616913) | 0.084628 / 0.296338 (-0.211710) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295505 / 0.215209 (0.080296) | 2.891105 / 2.077655 (0.813450) | 1.619480 / 1.504120 (0.115360) | 1.498290 / 1.541195 (-0.042905) | 1.547896 / 1.468490 (0.079406) | 0.575188 / 4.584777 (-4.009589) | 2.434426 / 3.745712 (-1.311286) | 2.899286 / 5.269862 (-2.370576) | 1.806085 / 4.565676 (-2.759591) | 0.063660 / 0.424275 (-0.360616) | 0.004933 / 0.007607 (-0.002674) | 0.348274 / 0.226044 (0.122229) | 3.447900 / 2.268929 (1.178971) | 1.956237 / 55.444624 (-53.488387) | 1.680416 / 6.876477 (-5.196061) | 1.732307 / 2.142072 (-0.409766) | 0.668428 / 4.805227 (-4.136799) | 0.119161 / 6.500664 (-6.381503) | 0.041694 / 0.075469 (-0.033775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973730 / 1.841788 (-0.868058) | 12.082452 / 8.074308 (4.008144) | 10.624836 / 10.191392 (0.433444) | 0.144027 / 0.680424 (-0.536397) | 0.014830 / 0.534201 (-0.519370) | 0.289946 / 0.579283 (-0.289337) | 0.281939 / 0.434364 (-0.152424) | 0.325639 / 0.540337 (-0.214699) | 0.551690 / 1.386936 (-0.835246) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e1cf8526c9216b08b5431695d9f8e0eec64cc5f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005279 / 0.011353 (-0.006074) | 0.003506 / 0.011008 (-0.007502) | 0.062579 / 0.038508 (0.024071) | 0.052809 / 0.023109 (0.029700) | 0.274693 / 0.275898 (-0.001205) | 0.283917 / 0.323480 (-0.039563) | 0.003950 / 0.007986 (-0.004036) | 0.002772 / 0.004328 (-0.001557) | 0.048127 / 0.004250 (0.043877) | 0.037771 / 0.037052 (0.000719) | 0.280595 / 0.258489 (0.022106) | 0.292310 / 0.293841 (-0.001531) | 0.027890 / 0.128546 (-0.100656) | 0.010771 / 0.075646 (-0.064875) | 0.207285 / 0.419271 (-0.211987) | 0.036179 / 0.043533 (-0.007354) | 0.253617 / 0.255139 (-0.001522) | 0.276107 / 0.283200 (-0.007093) | 0.018253 / 0.141683 (-0.123430) | 1.112219 / 1.452155 (-0.339936) | 1.166756 / 1.492716 (-0.325960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095159 / 0.018006 (0.077152) | 0.306097 / 0.000490 (0.305608) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019056 / 0.037411 (-0.018355) | 0.060445 / 0.014526 (0.045919) | 0.073553 / 0.176557 (-0.103004) | 0.120306 / 0.737135 (-0.616829) | 0.075613 / 0.296338 (-0.220725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277839 / 0.215209 (0.062630) | 2.761037 / 2.077655 (0.683382) | 1.508524 / 1.504120 (0.004404) | 1.368994 / 1.541195 (-0.172201) | 1.415961 / 1.468490 (-0.052529) | 0.570490 / 4.584777 (-4.014287) | 2.356355 / 3.745712 (-1.389357) | 2.806626 / 5.269862 (-2.463235) | 1.757849 / 4.565676 (-2.807827) | 0.063504 / 0.424275 (-0.360771) | 0.005021 / 0.007607 (-0.002586) | 0.338880 / 0.226044 (0.112836) | 3.290947 / 2.268929 (1.022018) | 1.818238 / 55.444624 (-53.626386) | 1.529970 / 6.876477 (-5.346507) | 1.557085 / 2.142072 (-0.584987) | 0.645352 / 4.805227 (-4.159876) | 0.123066 / 6.500664 (-6.377598) | 0.043387 / 0.075469 (-0.032082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974512 / 1.841788 (-0.867276) | 11.976411 / 8.074308 (3.902103) | 10.361084 / 10.191392 (0.169692) | 0.127171 / 0.680424 (-0.553253) | 0.014091 / 0.534201 (-0.520110) | 0.288608 / 0.579283 (-0.290675) | 0.261886 / 0.434364 (-0.172478) | 0.331632 / 0.540337 (-0.208705) | 0.437002 / 1.386936 (-0.949934) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005129 / 0.011353 (-0.006224) | 0.003490 / 0.011008 (-0.007518) | 0.049005 / 0.038508 (0.010497) | 0.054077 / 0.023109 (0.030968) | 0.276653 / 0.275898 (0.000755) | 0.298752 / 0.323480 (-0.024728) | 0.003979 / 0.007986 (-0.004007) | 0.002625 / 0.004328 (-0.001703) | 0.047951 / 0.004250 (0.043701) | 0.040969 / 0.037052 (0.003916) | 0.279879 / 0.258489 (0.021390) | 0.306244 / 0.293841 (0.012403) | 0.029025 / 0.128546 (-0.099522) | 0.010450 / 0.075646 (-0.065197) | 0.056846 / 0.419271 (-0.362426) | 0.033476 / 0.043533 (-0.010057) | 0.273340 / 0.255139 (0.018201) | 0.294783 / 0.283200 (0.011584) | 0.019105 / 0.141683 (-0.122578) | 1.126389 / 1.452155 (-0.325766) | 1.183369 / 1.492716 (-0.309348) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094995 / 0.018006 (0.076989) | 0.306984 / 0.000490 (0.306495) | 0.000224 / 0.000200 (0.000024) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021880 / 0.037411 (-0.015532) | 0.069674 / 0.014526 (0.055148) | 0.082191 / 0.176557 (-0.094366) | 0.120956 / 0.737135 (-0.616179) | 0.083843 / 0.296338 (-0.212495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295139 / 0.215209 (0.079929) | 2.860520 / 2.077655 (0.782865) | 1.578892 / 1.504120 (0.074772) | 1.451003 / 1.541195 (-0.090192) | 1.483099 / 1.468490 (0.014609) | 0.550491 / 4.584777 (-4.034286) | 2.430352 / 3.745712 (-1.315360) | 2.874468 / 5.269862 (-2.395393) | 1.741474 / 4.565676 (-2.824202) | 0.062563 / 0.424275 (-0.361712) | 0.004962 / 0.007607 (-0.002645) | 0.343747 / 0.226044 (0.117703) | 3.419046 / 2.268929 (1.150118) | 1.943774 / 55.444624 (-53.500851) | 1.650989 / 6.876477 (-5.225488) | 1.704083 / 2.142072 (-0.437990) | 0.645447 / 4.805227 (-4.159780) | 0.125105 / 6.500664 (-6.375559) | 0.041319 / 0.075469 (-0.034150) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959708 / 1.841788 (-0.882079) | 12.235906 / 8.074308 (4.161598) | 10.575402 / 10.191392 (0.384010) | 0.143619 / 0.680424 (-0.536805) | 0.015517 / 0.534201 (-0.518684) | 0.285231 / 0.579283 (-0.294052) | 0.281549 / 0.434364 (-0.152815) | 0.326649 / 0.540337 (-0.213689) | 0.565706 / 1.386936 (-0.821230) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb6985bc33277a3ece7f28c74ca742ba84655b0c \"CML watermark\")\n" ]
2023-11-28T19:47:07
2023-11-30T10:40:23
2023-11-30T10:34:12
MEMBER
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false
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don't require `trust_remote_code` in (deprecated) `inspect_dataset` (it defeats its purpose) (not super important but we might as well keep it until the next major release) this is needed to fix the tests in https://github.com/huggingface/datasets/pull/6448
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PR_kwDODunzps5gej3H
6,454
Refactor `dill` logic
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005490 / 0.011353 (-0.005863) | 0.003554 / 0.011008 (-0.007454) | 0.062183 / 0.038508 (0.023675) | 0.053093 / 0.023109 (0.029984) | 0.245370 / 0.275898 (-0.030528) | 0.271637 / 0.323480 (-0.051842) | 0.002997 / 0.007986 (-0.004989) | 0.002811 / 0.004328 (-0.001517) | 0.047874 / 0.004250 (0.043623) | 0.039673 / 0.037052 (0.002620) | 0.253219 / 0.258489 (-0.005271) | 0.280438 / 0.293841 (-0.013403) | 0.028393 / 0.128546 (-0.100153) | 0.010914 / 0.075646 (-0.064732) | 0.207491 / 0.419271 (-0.211781) | 0.037565 / 0.043533 (-0.005968) | 0.252382 / 0.255139 (-0.002757) | 0.272204 / 0.283200 (-0.010995) | 0.019007 / 0.141683 (-0.122676) | 1.099767 / 1.452155 (-0.352388) | 1.173220 / 1.492716 (-0.319496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098777 / 0.018006 (0.080771) | 0.325912 / 0.000490 (0.325422) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018815 / 0.037411 (-0.018596) | 0.070031 / 0.014526 (0.055506) | 0.075395 / 0.176557 (-0.101162) | 0.122633 / 0.737135 (-0.614502) | 0.077621 / 0.296338 (-0.218718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290830 / 0.215209 (0.075621) | 2.869214 / 2.077655 (0.791559) | 1.507337 / 1.504120 (0.003217) | 1.351391 / 1.541195 (-0.189804) | 1.386642 / 1.468490 (-0.081848) | 0.570318 / 4.584777 (-4.014459) | 2.423442 / 3.745712 (-1.322270) | 2.897812 / 5.269862 (-2.372050) | 1.796458 / 4.565676 (-2.769219) | 0.063649 / 0.424275 (-0.360626) | 0.005038 / 0.007607 (-0.002570) | 0.357819 / 0.226044 (0.131774) | 3.535478 / 2.268929 (1.266549) | 1.831764 / 55.444624 (-53.612861) | 1.545035 / 6.876477 (-5.331442) | 1.585919 / 2.142072 (-0.556154) | 0.643333 / 4.805227 (-4.161894) | 0.120319 / 6.500664 (-6.380345) | 0.043031 / 0.075469 (-0.032438) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981155 / 1.841788 (-0.860633) | 12.136069 / 8.074308 (4.061760) | 10.579923 / 10.191392 (0.388531) | 0.152963 / 0.680424 (-0.527461) | 0.014783 / 0.534201 (-0.519418) | 0.289177 / 0.579283 (-0.290106) | 0.271784 / 0.434364 (-0.162580) | 0.322381 / 0.540337 (-0.217956) | 0.420034 / 1.386936 (-0.966902) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005315 / 0.011353 (-0.006038) | 0.003584 / 0.011008 (-0.007424) | 0.048596 / 0.038508 (0.010088) | 0.055940 / 0.023109 (0.032830) | 0.277687 / 0.275898 (0.001789) | 0.301545 / 0.323480 (-0.021935) | 0.004150 / 0.007986 (-0.003836) | 0.002699 / 0.004328 (-0.001629) | 0.047661 / 0.004250 (0.043410) | 0.040618 / 0.037052 (0.003565) | 0.279173 / 0.258489 (0.020684) | 0.306105 / 0.293841 (0.012264) | 0.030099 / 0.128546 (-0.098447) | 0.010784 / 0.075646 (-0.064862) | 0.057418 / 0.419271 (-0.361853) | 0.032632 / 0.043533 (-0.010901) | 0.276064 / 0.255139 (0.020925) | 0.307194 / 0.283200 (0.023995) | 0.017416 / 0.141683 (-0.124267) | 1.107749 / 1.452155 (-0.344406) | 1.161104 / 1.492716 (-0.331612) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102395 / 0.018006 (0.084389) | 0.316933 / 0.000490 (0.316443) | 0.000246 / 0.000200 (0.000046) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022833 / 0.037411 (-0.014579) | 0.069372 / 0.014526 (0.054846) | 0.082139 / 0.176557 (-0.094418) | 0.121666 / 0.737135 (-0.615469) | 0.084039 / 0.296338 (-0.212300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298775 / 0.215209 (0.083566) | 2.973898 / 2.077655 (0.896244) | 1.614436 / 1.504120 (0.110316) | 1.476112 / 1.541195 (-0.065083) | 1.502031 / 1.468490 (0.033541) | 0.580626 / 4.584777 (-4.004151) | 2.493428 / 3.745712 (-1.252285) | 2.931050 / 5.269862 (-2.338811) | 1.823603 / 4.565676 (-2.742073) | 0.064736 / 0.424275 (-0.359539) | 0.004963 / 0.007607 (-0.002644) | 0.355096 / 0.226044 (0.129052) | 3.522801 / 2.268929 (1.253872) | 1.968690 / 55.444624 (-53.475935) | 1.698624 / 6.876477 (-5.177853) | 1.714166 / 2.142072 (-0.427906) | 0.681734 / 4.805227 (-4.123493) | 0.118940 / 6.500664 (-6.381724) | 0.041960 / 0.075469 (-0.033509) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985311 / 1.841788 (-0.856476) | 12.785393 / 8.074308 (4.711085) | 11.289459 / 10.191392 (1.098067) | 0.145297 / 0.680424 (-0.535127) | 0.016125 / 0.534201 (-0.518076) | 0.289445 / 0.579283 (-0.289838) | 0.278974 / 0.434364 (-0.155390) | 0.322456 / 0.540337 (-0.217881) | 0.418218 / 1.386936 (-0.968718) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#66cef090c55d3561412468d94cb545b47fb000fb \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005142 / 0.011353 (-0.006211) | 0.004180 / 0.011008 (-0.006829) | 0.062647 / 0.038508 (0.024139) | 0.055072 / 0.023109 (0.031962) | 0.254681 / 0.275898 (-0.021217) | 0.282650 / 0.323480 (-0.040830) | 0.003950 / 0.007986 (-0.004035) | 0.002862 / 0.004328 (-0.001466) | 0.048420 / 0.004250 (0.044170) | 0.038447 / 0.037052 (0.001394) | 0.258160 / 0.258489 (-0.000329) | 0.288596 / 0.293841 (-0.005245) | 0.027898 / 0.128546 (-0.100648) | 0.011165 / 0.075646 (-0.064482) | 0.206844 / 0.419271 (-0.212427) | 0.036312 / 0.043533 (-0.007221) | 0.257957 / 0.255139 (0.002819) | 0.277387 / 0.283200 (-0.005812) | 0.018205 / 0.141683 (-0.123478) | 1.109870 / 1.452155 (-0.342284) | 1.175005 / 1.492716 (-0.317712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096692 / 0.018006 (0.078686) | 0.307463 / 0.000490 (0.306973) | 0.000218 / 0.000200 (0.000018) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018602 / 0.037411 (-0.018809) | 0.061489 / 0.014526 (0.046964) | 0.072936 / 0.176557 (-0.103620) | 0.119863 / 0.737135 (-0.617272) | 0.073983 / 0.296338 (-0.222355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291444 / 0.215209 (0.076235) | 2.849024 / 2.077655 (0.771369) | 1.533121 / 1.504120 (0.029001) | 1.402148 / 1.541195 (-0.139046) | 1.406397 / 1.468490 (-0.062094) | 0.564241 / 4.584777 (-4.020536) | 2.402052 / 3.745712 (-1.343660) | 2.772639 / 5.269862 (-2.497223) | 1.732342 / 4.565676 (-2.833334) | 0.062361 / 0.424275 (-0.361914) | 0.004945 / 0.007607 (-0.002662) | 0.355841 / 0.226044 (0.129797) | 3.426931 / 2.268929 (1.158003) | 1.865412 / 55.444624 (-53.579212) | 1.592628 / 6.876477 (-5.283849) | 1.662364 / 2.142072 (-0.479708) | 0.653278 / 4.805227 (-4.151949) | 0.118626 / 6.500664 (-6.382038) | 0.042961 / 0.075469 (-0.032508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956279 / 1.841788 (-0.885509) | 11.635540 / 8.074308 (3.561232) | 10.719590 / 10.191392 (0.528198) | 0.130015 / 0.680424 (-0.550409) | 0.014424 / 0.534201 (-0.519777) | 0.288135 / 0.579283 (-0.291148) | 0.270819 / 0.434364 (-0.163545) | 0.320238 / 0.540337 (-0.220099) | 0.421044 / 1.386936 (-0.965892) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005201 / 0.011353 (-0.006152) | 0.003467 / 0.011008 (-0.007541) | 0.048939 / 0.038508 (0.010431) | 0.051841 / 0.023109 (0.028732) | 0.273708 / 0.275898 (-0.002190) | 0.293491 / 0.323480 (-0.029988) | 0.004830 / 0.007986 (-0.003156) | 0.002696 / 0.004328 (-0.001632) | 0.047727 / 0.004250 (0.043476) | 0.041319 / 0.037052 (0.004266) | 0.273837 / 0.258489 (0.015348) | 0.309860 / 0.293841 (0.016019) | 0.029054 / 0.128546 (-0.099492) | 0.010410 / 0.075646 (-0.065237) | 0.058139 / 0.419271 (-0.361133) | 0.032682 / 0.043533 (-0.010850) | 0.273244 / 0.255139 (0.018105) | 0.291579 / 0.283200 (0.008380) | 0.018262 / 0.141683 (-0.123421) | 1.144590 / 1.452155 (-0.307565) | 1.202474 / 1.492716 (-0.290243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097110 / 0.018006 (0.079104) | 0.307344 / 0.000490 (0.306854) | 0.000229 / 0.000200 (0.000029) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022263 / 0.037411 (-0.015148) | 0.070140 / 0.014526 (0.055614) | 0.081251 / 0.176557 (-0.095306) | 0.120839 / 0.737135 (-0.616297) | 0.083312 / 0.296338 (-0.213026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297381 / 0.215209 (0.082172) | 2.895530 / 2.077655 (0.817875) | 1.608442 / 1.504120 (0.104322) | 1.476237 / 1.541195 (-0.064958) | 1.491306 / 1.468490 (0.022816) | 0.567272 / 4.584777 (-4.017505) | 2.463543 / 3.745712 (-1.282170) | 2.814764 / 5.269862 (-2.455098) | 1.725845 / 4.565676 (-2.839831) | 0.064149 / 0.424275 (-0.360126) | 0.004953 / 0.007607 (-0.002654) | 0.359629 / 0.226044 (0.133585) | 3.482414 / 2.268929 (1.213486) | 1.949897 / 55.444624 (-53.494727) | 1.677383 / 6.876477 (-5.199094) | 1.683655 / 2.142072 (-0.458418) | 0.645671 / 4.805227 (-4.159557) | 0.115612 / 6.500664 (-6.385053) | 0.041013 / 0.075469 (-0.034456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967843 / 1.841788 (-0.873945) | 12.376877 / 8.074308 (4.302569) | 10.988174 / 10.191392 (0.796782) | 0.134660 / 0.680424 (-0.545764) | 0.015801 / 0.534201 (-0.518400) | 0.288699 / 0.579283 (-0.290584) | 0.284887 / 0.434364 (-0.149477) | 0.322000 / 0.540337 (-0.218337) | 0.412360 / 1.386936 (-0.974576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#148454d48b7c36507a283217c7c0e3bcc0539f75 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005407 / 0.011353 (-0.005946) | 0.003496 / 0.011008 (-0.007512) | 0.062730 / 0.038508 (0.024222) | 0.051882 / 0.023109 (0.028773) | 0.244766 / 0.275898 (-0.031132) | 0.257963 / 0.323480 (-0.065516) | 0.002894 / 0.007986 (-0.005092) | 0.002567 / 0.004328 (-0.001761) | 0.048756 / 0.004250 (0.044506) | 0.039024 / 0.037052 (0.001971) | 0.247303 / 0.258489 (-0.011186) | 0.278341 / 0.293841 (-0.015500) | 0.026725 / 0.128546 (-0.101821) | 0.010577 / 0.075646 (-0.065069) | 0.210483 / 0.419271 (-0.208789) | 0.035230 / 0.043533 (-0.008303) | 0.246125 / 0.255139 (-0.009014) | 0.264039 / 0.283200 (-0.019160) | 0.019881 / 0.141683 (-0.121802) | 1.113475 / 1.452155 (-0.338679) | 1.149606 / 1.492716 (-0.343110) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092946 / 0.018006 (0.074940) | 0.299985 / 0.000490 (0.299495) | 0.000215 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018421 / 0.037411 (-0.018991) | 0.060531 / 0.014526 (0.046005) | 0.074459 / 0.176557 (-0.102098) | 0.120369 / 0.737135 (-0.616766) | 0.075505 / 0.296338 (-0.220833) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289497 / 0.215209 (0.074288) | 2.783139 / 2.077655 (0.705485) | 1.482533 / 1.504120 (-0.021587) | 1.371013 / 1.541195 (-0.170182) | 1.379114 / 1.468490 (-0.089376) | 0.563953 / 4.584777 (-4.020824) | 2.389996 / 3.745712 (-1.355716) | 2.788067 / 5.269862 (-2.481795) | 1.751772 / 4.565676 (-2.813904) | 0.062680 / 0.424275 (-0.361595) | 0.004901 / 0.007607 (-0.002706) | 0.365193 / 0.226044 (0.139149) | 3.389181 / 2.268929 (1.120252) | 1.861659 / 55.444624 (-53.582965) | 1.558899 / 6.876477 (-5.317577) | 1.591079 / 2.142072 (-0.550993) | 0.648300 / 4.805227 (-4.156927) | 0.117486 / 6.500664 (-6.383178) | 0.041961 / 0.075469 (-0.033508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944391 / 1.841788 (-0.897396) | 11.500823 / 8.074308 (3.426515) | 10.580430 / 10.191392 (0.389038) | 0.142845 / 0.680424 (-0.537579) | 0.014305 / 0.534201 (-0.519896) | 0.290723 / 0.579283 (-0.288560) | 0.266206 / 0.434364 (-0.168158) | 0.325482 / 0.540337 (-0.214856) | 0.416224 / 1.386936 (-0.970712) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005363 / 0.011353 (-0.005990) | 0.003548 / 0.011008 (-0.007460) | 0.048704 / 0.038508 (0.010196) | 0.051025 / 0.023109 (0.027916) | 0.273037 / 0.275898 (-0.002861) | 0.297148 / 0.323480 (-0.026332) | 0.003985 / 0.007986 (-0.004001) | 0.002739 / 0.004328 (-0.001590) | 0.048108 / 0.004250 (0.043857) | 0.040244 / 0.037052 (0.003191) | 0.277825 / 0.258489 (0.019336) | 0.303704 / 0.293841 (0.009863) | 0.029460 / 0.128546 (-0.099086) | 0.010428 / 0.075646 (-0.065218) | 0.057022 / 0.419271 (-0.362249) | 0.032711 / 0.043533 (-0.010822) | 0.274462 / 0.255139 (0.019323) | 0.293499 / 0.283200 (0.010299) | 0.018266 / 0.141683 (-0.123417) | 1.158049 / 1.452155 (-0.294106) | 1.170097 / 1.492716 (-0.322620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093412 / 0.018006 (0.075406) | 0.301538 / 0.000490 (0.301049) | 0.000222 / 0.000200 (0.000022) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021698 / 0.037411 (-0.015713) | 0.068735 / 0.014526 (0.054209) | 0.083010 / 0.176557 (-0.093546) | 0.127491 / 0.737135 (-0.609644) | 0.083005 / 0.296338 (-0.213333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298299 / 0.215209 (0.083090) | 2.894209 / 2.077655 (0.816554) | 1.597455 / 1.504120 (0.093335) | 1.472953 / 1.541195 (-0.068241) | 1.491553 / 1.468490 (0.023063) | 0.556566 / 4.584777 (-4.028211) | 2.419429 / 3.745712 (-1.326283) | 2.788706 / 5.269862 (-2.481156) | 1.759888 / 4.565676 (-2.805789) | 0.062535 / 0.424275 (-0.361740) | 0.004959 / 0.007607 (-0.002648) | 0.345226 / 0.226044 (0.119182) | 3.438539 / 2.268929 (1.169611) | 1.943842 / 55.444624 (-53.500782) | 1.661080 / 6.876477 (-5.215397) | 1.687632 / 2.142072 (-0.454440) | 0.639971 / 4.805227 (-4.165256) | 0.116012 / 6.500664 (-6.384652) | 0.041723 / 0.075469 (-0.033746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965143 / 1.841788 (-0.876645) | 12.086547 / 8.074308 (4.012238) | 10.708787 / 10.191392 (0.517395) | 0.129506 / 0.680424 (-0.550918) | 0.015254 / 0.534201 (-0.518947) | 0.288326 / 0.579283 (-0.290957) | 0.271976 / 0.434364 (-0.162388) | 0.328402 / 0.540337 (-0.211936) | 0.418102 / 1.386936 (-0.968834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b6f13ede3dccedf335bb2d8ff04db306dc710a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005375 / 0.011353 (-0.005978) | 0.003530 / 0.011008 (-0.007478) | 0.062521 / 0.038508 (0.024013) | 0.051514 / 0.023109 (0.028405) | 0.241623 / 0.275898 (-0.034275) | 0.269054 / 0.323480 (-0.054426) | 0.002877 / 0.007986 (-0.005109) | 0.002724 / 0.004328 (-0.001605) | 0.049045 / 0.004250 (0.044794) | 0.038560 / 0.037052 (0.001507) | 0.248437 / 0.258489 (-0.010052) | 0.276762 / 0.293841 (-0.017079) | 0.027522 / 0.128546 (-0.101024) | 0.010817 / 0.075646 (-0.064829) | 0.208686 / 0.419271 (-0.210585) | 0.035818 / 0.043533 (-0.007715) | 0.249398 / 0.255139 (-0.005741) | 0.268288 / 0.283200 (-0.014911) | 0.019039 / 0.141683 (-0.122644) | 1.135115 / 1.452155 (-0.317040) | 1.195531 / 1.492716 (-0.297185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093126 / 0.018006 (0.075120) | 0.301028 / 0.000490 (0.300539) | 0.000222 / 0.000200 (0.000023) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018385 / 0.037411 (-0.019027) | 0.060902 / 0.014526 (0.046376) | 0.073168 / 0.176557 (-0.103389) | 0.119216 / 0.737135 (-0.617919) | 0.074225 / 0.296338 (-0.222114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283749 / 0.215209 (0.068540) | 2.741609 / 2.077655 (0.663954) | 1.483439 / 1.504120 (-0.020681) | 1.352896 / 1.541195 (-0.188299) | 1.378824 / 1.468490 (-0.089667) | 0.548731 / 4.584777 (-4.036046) | 2.342717 / 3.745712 (-1.402995) | 2.791592 / 5.269862 (-2.478269) | 1.740605 / 4.565676 (-2.825071) | 0.062059 / 0.424275 (-0.362216) | 0.005028 / 0.007607 (-0.002579) | 0.339205 / 0.226044 (0.113161) | 3.353386 / 2.268929 (1.084458) | 1.785717 / 55.444624 (-53.658907) | 1.523390 / 6.876477 (-5.353086) | 1.556999 / 2.142072 (-0.585073) | 0.636745 / 4.805227 (-4.168483) | 0.115821 / 6.500664 (-6.384843) | 0.042200 / 0.075469 (-0.033269) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948678 / 1.841788 (-0.893110) | 11.588670 / 8.074308 (3.514362) | 10.897130 / 10.191392 (0.705738) | 0.140068 / 0.680424 (-0.540356) | 0.014565 / 0.534201 (-0.519636) | 0.286336 / 0.579283 (-0.292947) | 0.265292 / 0.434364 (-0.169072) | 0.324146 / 0.540337 (-0.216192) | 0.413463 / 1.386936 (-0.973473) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005187 / 0.011353 (-0.006165) | 0.003471 / 0.011008 (-0.007537) | 0.048968 / 0.038508 (0.010460) | 0.051285 / 0.023109 (0.028176) | 0.283286 / 0.275898 (0.007388) | 0.307046 / 0.323480 (-0.016434) | 0.004017 / 0.007986 (-0.003969) | 0.002655 / 0.004328 (-0.001673) | 0.047762 / 0.004250 (0.043512) | 0.039855 / 0.037052 (0.002803) | 0.283101 / 0.258489 (0.024612) | 0.312905 / 0.293841 (0.019064) | 0.028188 / 0.128546 (-0.100358) | 0.010849 / 0.075646 (-0.064797) | 0.058112 / 0.419271 (-0.361159) | 0.032163 / 0.043533 (-0.011369) | 0.280825 / 0.255139 (0.025686) | 0.300946 / 0.283200 (0.017747) | 0.017409 / 0.141683 (-0.124274) | 1.127360 / 1.452155 (-0.324795) | 1.180409 / 1.492716 (-0.312307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093186 / 0.018006 (0.075180) | 0.300827 / 0.000490 (0.300338) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021560 / 0.037411 (-0.015851) | 0.069158 / 0.014526 (0.054632) | 0.080953 / 0.176557 (-0.095603) | 0.119071 / 0.737135 (-0.618064) | 0.082817 / 0.296338 (-0.213521) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.307259 / 0.215209 (0.092050) | 2.996058 / 2.077655 (0.918404) | 1.627406 / 1.504120 (0.123286) | 1.500715 / 1.541195 (-0.040480) | 1.524278 / 1.468490 (0.055788) | 0.569711 / 4.584777 (-4.015066) | 2.436132 / 3.745712 (-1.309580) | 2.796995 / 5.269862 (-2.472866) | 1.760701 / 4.565676 (-2.804975) | 0.063521 / 0.424275 (-0.360754) | 0.004909 / 0.007607 (-0.002698) | 0.359129 / 0.226044 (0.133085) | 3.567278 / 2.268929 (1.298349) | 2.013821 / 55.444624 (-53.430804) | 1.708021 / 6.876477 (-5.168456) | 1.738959 / 2.142072 (-0.403114) | 0.648620 / 4.805227 (-4.156607) | 0.122016 / 6.500664 (-6.378648) | 0.041802 / 0.075469 (-0.033667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985208 / 1.841788 (-0.856579) | 12.307785 / 8.074308 (4.233477) | 10.587262 / 10.191392 (0.395870) | 0.130468 / 0.680424 (-0.549956) | 0.014912 / 0.534201 (-0.519289) | 0.293822 / 0.579283 (-0.285461) | 0.283021 / 0.434364 (-0.151343) | 0.329560 / 0.540337 (-0.210777) | 0.424741 / 1.386936 (-0.962195) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04426d9c8e0aa5c97af2826064287f8cab6bece0 \"CML watermark\")\n" ]
2023-11-27T20:01:25
2023-11-28T16:29:58
2023-11-28T16:29:31
COLLABORATOR
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false
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Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way) It makes the following improvements to the serialization API: * consistent order of a `dict`'s keys * support for hashing `torch.compile`-ed modules and functions * deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do) ~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed... Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005119 / 0.011353 (-0.006234) | 0.003469 / 0.011008 (-0.007540) | 0.061791 / 0.038508 (0.023283) | 0.051655 / 0.023109 (0.028545) | 0.241157 / 0.275898 (-0.034741) | 0.265930 / 0.323480 (-0.057549) | 0.003851 / 0.007986 (-0.004134) | 0.002412 / 0.004328 (-0.001916) | 0.047498 / 0.004250 (0.043247) | 0.037328 / 0.037052 (0.000276) | 0.250418 / 0.258489 (-0.008071) | 0.277842 / 0.293841 (-0.015999) | 0.027626 / 0.128546 (-0.100920) | 0.009947 / 0.075646 (-0.065699) | 0.204549 / 0.419271 (-0.214722) | 0.037546 / 0.043533 (-0.005987) | 0.245383 / 0.255139 (-0.009756) | 0.263486 / 0.283200 (-0.019713) | 0.017792 / 0.141683 (-0.123891) | 1.158900 / 1.452155 (-0.293255) | 1.194060 / 1.492716 (-0.298657) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090607 / 0.018006 (0.072601) | 0.299909 / 0.000490 (0.299419) | 0.000206 / 0.000200 (0.000006) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018814 / 0.037411 (-0.018597) | 0.062068 / 0.014526 (0.047542) | 0.087221 / 0.176557 (-0.089336) | 0.119594 / 0.737135 (-0.617541) | 0.075485 / 0.296338 (-0.220853) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286093 / 0.215209 (0.070884) | 2.767396 / 2.077655 (0.689741) | 1.500472 / 1.504120 (-0.003648) | 1.389514 / 1.541195 (-0.151680) | 1.438933 / 1.468490 (-0.029557) | 0.562545 / 4.584777 (-4.022232) | 2.383330 / 3.745712 (-1.362382) | 2.799215 / 5.269862 (-2.470647) | 1.732618 / 4.565676 (-2.833058) | 0.061282 / 0.424275 (-0.362993) | 0.005007 / 0.007607 (-0.002601) | 0.339769 / 0.226044 (0.113725) | 3.337146 / 2.268929 (1.068218) | 1.890789 / 55.444624 (-53.553836) | 1.593555 / 6.876477 (-5.282922) | 1.660016 / 2.142072 (-0.482057) | 0.632452 / 4.805227 (-4.172775) | 0.115503 / 6.500664 (-6.385161) | 0.041590 / 0.075469 (-0.033880) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941966 / 1.841788 (-0.899822) | 11.470271 / 8.074308 (3.395963) | 10.579454 / 10.191392 (0.388062) | 0.140970 / 0.680424 (-0.539454) | 0.014057 / 0.534201 (-0.520144) | 0.289326 / 0.579283 (-0.289957) | 0.265366 / 0.434364 (-0.168998) | 0.324612 / 0.540337 (-0.215726) | 0.415832 / 1.386936 (-0.971104) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005208 / 0.011353 (-0.006145) | 0.003199 / 0.011008 (-0.007809) | 0.048299 / 0.038508 (0.009791) | 0.050727 / 0.023109 (0.027618) | 0.274897 / 0.275898 (-0.001001) | 0.298328 / 0.323480 (-0.025152) | 0.003989 / 0.007986 (-0.003997) | 0.002439 / 0.004328 (-0.001890) | 0.047308 / 0.004250 (0.043058) | 0.039726 / 0.037052 (0.002673) | 0.276279 / 0.258489 (0.017790) | 0.303679 / 0.293841 (0.009838) | 0.028943 / 0.128546 (-0.099603) | 0.010223 / 0.075646 (-0.065423) | 0.056694 / 0.419271 (-0.362577) | 0.032283 / 0.043533 (-0.011250) | 0.275344 / 0.255139 (0.020205) | 0.296358 / 0.283200 (0.013158) | 0.017481 / 0.141683 (-0.124201) | 1.131063 / 1.452155 (-0.321092) | 1.181146 / 1.492716 (-0.311570) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092259 / 0.018006 (0.074253) | 0.299381 / 0.000490 (0.298891) | 0.000216 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021693 / 0.037411 (-0.015718) | 0.070441 / 0.014526 (0.055916) | 0.080648 / 0.176557 (-0.095908) | 0.119002 / 0.737135 (-0.618133) | 0.081412 / 0.296338 (-0.214926) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296475 / 0.215209 (0.081266) | 2.905098 / 2.077655 (0.827443) | 1.596321 / 1.504120 (0.092201) | 1.472640 / 1.541195 (-0.068555) | 1.484453 / 1.468490 (0.015963) | 0.565229 / 4.584777 (-4.019548) | 2.390631 / 3.745712 (-1.355081) | 2.765125 / 5.269862 (-2.504737) | 1.738993 / 4.565676 (-2.826683) | 0.063034 / 0.424275 (-0.361241) | 0.004891 / 0.007607 (-0.002716) | 0.350678 / 0.226044 (0.124633) | 3.530919 / 2.268929 (1.261990) | 1.943758 / 55.444624 (-53.500867) | 1.665553 / 6.876477 (-5.210924) | 1.656990 / 2.142072 (-0.485083) | 0.647027 / 4.805227 (-4.158201) | 0.116771 / 6.500664 (-6.383893) | 0.041012 / 0.075469 (-0.034457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.034226 / 1.841788 (-0.807561) | 12.036726 / 8.074308 (3.962418) | 10.934239 / 10.191392 (0.742847) | 0.130142 / 0.680424 (-0.550281) | 0.015537 / 0.534201 (-0.518664) | 0.286020 / 0.579283 (-0.293263) | 0.276739 / 0.434364 (-0.157625) | 0.326284 / 0.540337 (-0.214054) | 0.413392 / 1.386936 (-0.973544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4787c0022c8b59c15256021478b444a6c51fa984 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005400 / 0.011353 (-0.005953) | 0.003415 / 0.011008 (-0.007593) | 0.062416 / 0.038508 (0.023908) | 0.055962 / 0.023109 (0.032853) | 0.234725 / 0.275898 (-0.041173) | 0.261775 / 0.323480 (-0.061705) | 0.002868 / 0.007986 (-0.005118) | 0.002426 / 0.004328 (-0.001902) | 0.047989 / 0.004250 (0.043738) | 0.039214 / 0.037052 (0.002162) | 0.246068 / 0.258489 (-0.012421) | 0.270245 / 0.293841 (-0.023596) | 0.027558 / 0.128546 (-0.100988) | 0.010256 / 0.075646 (-0.065390) | 0.210988 / 0.419271 (-0.208283) | 0.035684 / 0.043533 (-0.007849) | 0.245254 / 0.255139 (-0.009885) | 0.255476 / 0.283200 (-0.027724) | 0.018495 / 0.141683 (-0.123188) | 1.115458 / 1.452155 (-0.336697) | 1.166149 / 1.492716 (-0.326567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092736 / 0.018006 (0.074730) | 0.301040 / 0.000490 (0.300550) | 0.000213 / 0.000200 (0.000013) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018607 / 0.037411 (-0.018805) | 0.062189 / 0.014526 (0.047664) | 0.073782 / 0.176557 (-0.102775) | 0.119895 / 0.737135 (-0.617240) | 0.074907 / 0.296338 (-0.221431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283986 / 0.215209 (0.068777) | 2.824498 / 2.077655 (0.746844) | 1.505848 / 1.504120 (0.001728) | 1.358879 / 1.541195 (-0.182316) | 1.357087 / 1.468490 (-0.111403) | 0.574307 / 4.584777 (-4.010470) | 2.416478 / 3.745712 (-1.329234) | 2.772909 / 5.269862 (-2.496953) | 1.750395 / 4.565676 (-2.815282) | 0.062465 / 0.424275 (-0.361810) | 0.004983 / 0.007607 (-0.002624) | 0.344490 / 0.226044 (0.118445) | 3.405062 / 2.268929 (1.136134) | 1.854972 / 55.444624 (-53.589653) | 1.572789 / 6.876477 (-5.303687) | 1.586109 / 2.142072 (-0.555963) | 0.647431 / 4.805227 (-4.157797) | 0.123079 / 6.500664 (-6.377585) | 0.042766 / 0.075469 (-0.032703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950493 / 1.841788 (-0.891295) | 11.814821 / 8.074308 (3.740513) | 10.494768 / 10.191392 (0.303376) | 0.131322 / 0.680424 (-0.549102) | 0.015253 / 0.534201 (-0.518948) | 0.287405 / 0.579283 (-0.291878) | 0.269664 / 0.434364 (-0.164699) | 0.322700 / 0.540337 (-0.217637) | 0.424103 / 1.386936 (-0.962833) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005264 / 0.011353 (-0.006088) | 0.003304 / 0.011008 (-0.007704) | 0.048531 / 0.038508 (0.010023) | 0.052752 / 0.023109 (0.029643) | 0.274435 / 0.275898 (-0.001463) | 0.297500 / 0.323480 (-0.025980) | 0.003977 / 0.007986 (-0.004009) | 0.002444 / 0.004328 (-0.001884) | 0.048464 / 0.004250 (0.044214) | 0.040192 / 0.037052 (0.003139) | 0.278256 / 0.258489 (0.019767) | 0.303627 / 0.293841 (0.009786) | 0.028709 / 0.128546 (-0.099837) | 0.010530 / 0.075646 (-0.065117) | 0.057427 / 0.419271 (-0.361844) | 0.032539 / 0.043533 (-0.010994) | 0.272237 / 0.255139 (0.017098) | 0.295288 / 0.283200 (0.012088) | 0.018820 / 0.141683 (-0.122862) | 1.116100 / 1.452155 (-0.336055) | 1.180124 / 1.492716 (-0.312592) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092651 / 0.018006 (0.074644) | 0.301481 / 0.000490 (0.300991) | 0.000217 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022461 / 0.037411 (-0.014951) | 0.070623 / 0.014526 (0.056097) | 0.082642 / 0.176557 (-0.093915) | 0.120021 / 0.737135 (-0.617114) | 0.083387 / 0.296338 (-0.212952) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291451 / 0.215209 (0.076242) | 2.865602 / 2.077655 (0.787947) | 1.592051 / 1.504120 (0.087931) | 1.463521 / 1.541195 (-0.077673) | 1.498899 / 1.468490 (0.030409) | 0.570854 / 4.584777 (-4.013923) | 2.410002 / 3.745712 (-1.335710) | 2.768028 / 5.269862 (-2.501834) | 1.740463 / 4.565676 (-2.825214) | 0.063801 / 0.424275 (-0.360474) | 0.005019 / 0.007607 (-0.002588) | 0.348353 / 0.226044 (0.122309) | 3.425793 / 2.268929 (1.156864) | 1.957294 / 55.444624 (-53.487331) | 1.696121 / 6.876477 (-5.180355) | 1.691544 / 2.142072 (-0.450528) | 0.645528 / 4.805227 (-4.159700) | 0.118876 / 6.500664 (-6.381788) | 0.041001 / 0.075469 (-0.034469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983805 / 1.841788 (-0.857983) | 12.085909 / 8.074308 (4.011600) | 10.835395 / 10.191392 (0.644003) | 0.141971 / 0.680424 (-0.538453) | 0.015534 / 0.534201 (-0.518667) | 0.289289 / 0.579283 (-0.289994) | 0.276316 / 0.434364 (-0.158048) | 0.354577 / 0.540337 (-0.185761) | 0.421824 / 1.386936 (-0.965112) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#27d1fe52857c6a25a29cac63a296405136b2797c \"CML watermark\")\n" ]
2023-11-27T09:57:20
2023-11-27T10:23:44
2023-11-27T10:17:34
NONE
null
false
{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6453", "html_url": "https://github.com/huggingface/datasets/pull/6453", "diff_url": "https://github.com/huggingface/datasets/pull/6453.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6453.patch", "merged_at": "2023-11-27T10:17:34" }
Follow up to huggingface/huggingface.js#296
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2023-11-27T07:07:50
2023-11-27T09:28:00
2023-11-27T09:28:00
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Unable to read "marsyas/gtzan" data
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[ "Hi! We've merged a [PR](https://huggingface.co/datasets/marsyas/gtzan/discussions/1) that fixes the script's path logic on Windows.", "I have transferred the discussion to the corresponding dataset: https://huggingface.co/datasets/marsyas/gtzan/discussions/2\r\n\r\nLet's continue there.", "@mariosasko @albertvillanova \r\n\r\nThank you both very much for the speedy resolution :)" ]
2023-11-25T15:13:17
2023-12-01T12:53:46
2023-11-27T09:36:25
NONE
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Hi, this is my code and the error: ``` from datasets import load_dataset gtzan = load_dataset("marsyas/gtzan", "all") ``` [error_trace.txt](https://github.com/huggingface/datasets/files/13464397/error_trace.txt) [audio_yml.txt](https://github.com/huggingface/datasets/files/13464410/audio_yml.txt) Python 3.11.5 Jupyter Notebook 6.5.4 Windows 10 I'm able to download and work with other datasets, but not this one. For example, both these below work fine: ``` from datasets import load_dataset dataset = load_dataset("facebook/voxpopuli", "pl", split="train", streaming=True) minds = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` Thanks for your help https://huggingface.co/datasets/marsyas/gtzan/tree/main
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2,009,491,386
I_kwDODunzps53xme6
6,450
Support multiple image/audio columns in ImageFolder/AudioFolder
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[ "A duplicate of https://github.com/huggingface/datasets/issues/5760" ]
2023-11-24T10:34:09
2023-11-28T11:07:17
2023-11-24T17:24:38
COLLABORATOR
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### Feature request Have a metadata.csv file with multiple columns that point to relative image or audio files. ### Motivation Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files. But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column. ### Your contribution no specific contribution
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Fix metadata file resolution when inferred pattern is `**`
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005551 / 0.011353 (-0.005802) | 0.003297 / 0.011008 (-0.007711) | 0.062524 / 0.038508 (0.024016) | 0.058467 / 0.023109 (0.035358) | 0.255703 / 0.275898 (-0.020195) | 0.281420 / 0.323480 (-0.042060) | 0.003857 / 0.007986 (-0.004129) | 0.002460 / 0.004328 (-0.001868) | 0.047762 / 0.004250 (0.043512) | 0.038757 / 0.037052 (0.001705) | 0.259937 / 0.258489 (0.001448) | 0.290050 / 0.293841 (-0.003791) | 0.028433 / 0.128546 (-0.100113) | 0.010422 / 0.075646 (-0.065224) | 0.207135 / 0.419271 (-0.212136) | 0.036004 / 0.043533 (-0.007529) | 0.268137 / 0.255139 (0.012998) | 0.275020 / 0.283200 (-0.008179) | 0.018301 / 0.141683 (-0.123382) | 1.095479 / 1.452155 (-0.356676) | 1.145452 / 1.492716 (-0.347265) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092046 / 0.018006 (0.074040) | 0.299784 / 0.000490 (0.299294) | 0.000214 / 0.000200 (0.000014) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019071 / 0.037411 (-0.018340) | 0.072836 / 0.014526 (0.058310) | 0.073974 / 0.176557 (-0.102583) | 0.120903 / 0.737135 (-0.616232) | 0.075740 / 0.296338 (-0.220599) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276365 / 0.215209 (0.061156) | 2.671217 / 2.077655 (0.593563) | 1.438862 / 1.504120 (-0.065258) | 1.327348 / 1.541195 (-0.213847) | 1.349514 / 1.468490 (-0.118976) | 0.548793 / 4.584777 (-4.035984) | 2.364458 / 3.745712 (-1.381255) | 2.716205 / 5.269862 (-2.553657) | 1.735714 / 4.565676 (-2.829963) | 0.061140 / 0.424275 (-0.363135) | 0.004926 / 0.007607 (-0.002681) | 0.330449 / 0.226044 (0.104404) | 3.255243 / 2.268929 (0.986315) | 1.824254 / 55.444624 (-53.620371) | 1.540262 / 6.876477 (-5.336215) | 1.535632 / 2.142072 (-0.606441) | 0.635224 / 4.805227 (-4.170003) | 0.116230 / 6.500664 (-6.384435) | 0.042706 / 0.075469 (-0.032763) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948796 / 1.841788 (-0.892992) | 11.448403 / 8.074308 (3.374095) | 10.523862 / 10.191392 (0.332470) | 0.129694 / 0.680424 (-0.550730) | 0.014146 / 0.534201 (-0.520055) | 0.285706 / 0.579283 (-0.293577) | 0.262572 / 0.434364 (-0.171792) | 0.321251 / 0.540337 (-0.219087) | 0.417130 / 1.386936 (-0.969806) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005266 / 0.011353 (-0.006086) | 0.003339 / 0.011008 (-0.007670) | 0.048411 / 0.038508 (0.009903) | 0.053951 / 0.023109 (0.030842) | 0.271228 / 0.275898 (-0.004670) | 0.290066 / 0.323480 (-0.033414) | 0.004087 / 0.007986 (-0.003898) | 0.002446 / 0.004328 (-0.001882) | 0.047049 / 0.004250 (0.042798) | 0.040866 / 0.037052 (0.003813) | 0.273711 / 0.258489 (0.015222) | 0.298192 / 0.293841 (0.004351) | 0.029025 / 0.128546 (-0.099521) | 0.010479 / 0.075646 (-0.065167) | 0.056941 / 0.419271 (-0.362330) | 0.032914 / 0.043533 (-0.010619) | 0.270432 / 0.255139 (0.015293) | 0.291274 / 0.283200 (0.008074) | 0.018602 / 0.141683 (-0.123081) | 1.136707 / 1.452155 (-0.315447) | 1.184704 / 1.492716 (-0.308012) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090041 / 0.018006 (0.072035) | 0.300185 / 0.000490 (0.299696) | 0.000221 / 0.000200 (0.000022) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022074 / 0.037411 (-0.015337) | 0.070763 / 0.014526 (0.056237) | 0.082141 / 0.176557 (-0.094415) | 0.120286 / 0.737135 (-0.616850) | 0.082680 / 0.296338 (-0.213659) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292223 / 0.215209 (0.077014) | 2.856711 / 2.077655 (0.779056) | 1.581194 / 1.504120 (0.077075) | 1.496567 / 1.541195 (-0.044628) | 1.485256 / 1.468490 (0.016766) | 0.550633 / 4.584777 (-4.034144) | 2.420281 / 3.745712 (-1.325431) | 2.764373 / 5.269862 (-2.505489) | 1.735958 / 4.565676 (-2.829719) | 0.062562 / 0.424275 (-0.361714) | 0.004918 / 0.007607 (-0.002689) | 0.346038 / 0.226044 (0.119994) | 3.443478 / 2.268929 (1.174550) | 1.949366 / 55.444624 (-53.495259) | 1.686140 / 6.876477 (-5.190337) | 1.683038 / 2.142072 (-0.459034) | 0.629270 / 4.805227 (-4.175958) | 0.114947 / 6.500664 (-6.385717) | 0.040635 / 0.075469 (-0.034834) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969746 / 1.841788 (-0.872041) | 11.922662 / 8.074308 (3.848354) | 10.441432 / 10.191392 (0.250040) | 0.128950 / 0.680424 (-0.551473) | 0.015964 / 0.534201 (-0.518237) | 0.289176 / 0.579283 (-0.290107) | 0.279203 / 0.434364 (-0.155161) | 0.323833 / 0.540337 (-0.216505) | 0.540297 / 1.386936 (-0.846639) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ed759d0f5aea6d166caa0532aa17c209bb3af79 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005288 / 0.011353 (-0.006065) | 0.003383 / 0.011008 (-0.007625) | 0.061926 / 0.038508 (0.023418) | 0.049080 / 0.023109 (0.025971) | 0.244852 / 0.275898 (-0.031046) | 0.263957 / 0.323480 (-0.059523) | 0.002810 / 0.007986 (-0.005175) | 0.002384 / 0.004328 (-0.001945) | 0.047807 / 0.004250 (0.043556) | 0.038374 / 0.037052 (0.001321) | 0.244414 / 0.258489 (-0.014075) | 0.272257 / 0.293841 (-0.021584) | 0.027356 / 0.128546 (-0.101190) | 0.010235 / 0.075646 (-0.065411) | 0.214896 / 0.419271 (-0.204375) | 0.035604 / 0.043533 (-0.007929) | 0.246584 / 0.255139 (-0.008555) | 0.263281 / 0.283200 (-0.019918) | 0.019689 / 0.141683 (-0.121994) | 1.114100 / 1.452155 (-0.338054) | 1.177644 / 1.492716 (-0.315073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088892 / 0.018006 (0.070886) | 0.298128 / 0.000490 (0.297639) | 0.000199 / 0.000200 (-0.000001) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019337 / 0.037411 (-0.018075) | 0.062096 / 0.014526 (0.047570) | 0.073019 / 0.176557 (-0.103537) | 0.118801 / 0.737135 (-0.618334) | 0.074779 / 0.296338 (-0.221559) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289892 / 0.215209 (0.074683) | 2.824131 / 2.077655 (0.746476) | 1.466351 / 1.504120 (-0.037768) | 1.339528 / 1.541195 (-0.201667) | 1.369257 / 1.468490 (-0.099233) | 0.561175 / 4.584777 (-4.023602) | 2.394174 / 3.745712 (-1.351538) | 2.749668 / 5.269862 (-2.520193) | 1.747146 / 4.565676 (-2.818530) | 0.063054 / 0.424275 (-0.361221) | 0.004970 / 0.007607 (-0.002637) | 0.342985 / 0.226044 (0.116941) | 3.334894 / 2.268929 (1.065966) | 1.838459 / 55.444624 (-53.606165) | 1.579755 / 6.876477 (-5.296722) | 1.560200 / 2.142072 (-0.581872) | 0.642643 / 4.805227 (-4.162585) | 0.117741 / 6.500664 (-6.382923) | 0.042440 / 0.075469 (-0.033029) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937476 / 1.841788 (-0.904312) | 11.403556 / 8.074308 (3.329248) | 10.317207 / 10.191392 (0.125815) | 0.145277 / 0.680424 (-0.535147) | 0.015297 / 0.534201 (-0.518904) | 0.287511 / 0.579283 (-0.291772) | 0.263516 / 0.434364 (-0.170848) | 0.320803 / 0.540337 (-0.219534) | 0.415580 / 1.386936 (-0.971356) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005239 / 0.011353 (-0.006114) | 0.003506 / 0.011008 (-0.007502) | 0.048635 / 0.038508 (0.010127) | 0.052067 / 0.023109 (0.028957) | 0.277526 / 0.275898 (0.001628) | 0.300536 / 0.323480 (-0.022944) | 0.003982 / 0.007986 (-0.004004) | 0.002413 / 0.004328 (-0.001915) | 0.046523 / 0.004250 (0.042273) | 0.039383 / 0.037052 (0.002331) | 0.281208 / 0.258489 (0.022719) | 0.306199 / 0.293841 (0.012359) | 0.028646 / 0.128546 (-0.099900) | 0.010664 / 0.075646 (-0.064982) | 0.057393 / 0.419271 (-0.361879) | 0.032171 / 0.043533 (-0.011362) | 0.277576 / 0.255139 (0.022437) | 0.296039 / 0.283200 (0.012840) | 0.017519 / 0.141683 (-0.124164) | 1.153172 / 1.452155 (-0.298982) | 1.180274 / 1.492716 (-0.312442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088287 / 0.018006 (0.070280) | 0.297922 / 0.000490 (0.297433) | 0.000216 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021936 / 0.037411 (-0.015475) | 0.070181 / 0.014526 (0.055655) | 0.082068 / 0.176557 (-0.094488) | 0.119327 / 0.737135 (-0.617808) | 0.083642 / 0.296338 (-0.212697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299449 / 0.215209 (0.084240) | 2.914362 / 2.077655 (0.836707) | 1.611906 / 1.504120 (0.107786) | 1.488805 / 1.541195 (-0.052390) | 1.536010 / 1.468490 (0.067520) | 0.566772 / 4.584777 (-4.018004) | 2.397897 / 3.745712 (-1.347815) | 2.786048 / 5.269862 (-2.483814) | 1.745153 / 4.565676 (-2.820523) | 0.063870 / 0.424275 (-0.360405) | 0.004968 / 0.007607 (-0.002640) | 0.344455 / 0.226044 (0.118410) | 3.465772 / 2.268929 (1.196844) | 1.965761 / 55.444624 (-53.478863) | 1.687960 / 6.876477 (-5.188516) | 1.713987 / 2.142072 (-0.428085) | 0.643760 / 4.805227 (-4.161467) | 0.117623 / 6.500664 (-6.383042) | 0.041086 / 0.075469 (-0.034383) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985129 / 1.841788 (-0.856659) | 11.986676 / 8.074308 (3.912368) | 10.493440 / 10.191392 (0.302048) | 0.130070 / 0.680424 (-0.550353) | 0.015293 / 0.534201 (-0.518908) | 0.285683 / 0.579283 (-0.293600) | 0.275656 / 0.434364 (-0.158708) | 0.328704 / 0.540337 (-0.211633) | 0.537249 / 1.386936 (-0.849687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7ee58f322082d3af5f11863d1f809444910827a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005170 / 0.011353 (-0.006183) | 0.003267 / 0.011008 (-0.007741) | 0.061992 / 0.038508 (0.023484) | 0.053414 / 0.023109 (0.030305) | 0.245678 / 0.275898 (-0.030220) | 0.261320 / 0.323480 (-0.062160) | 0.003887 / 0.007986 (-0.004099) | 0.002543 / 0.004328 (-0.001786) | 0.048496 / 0.004250 (0.044246) | 0.037392 / 0.037052 (0.000340) | 0.243728 / 0.258489 (-0.014761) | 0.272524 / 0.293841 (-0.021317) | 0.027578 / 0.128546 (-0.100968) | 0.010530 / 0.075646 (-0.065116) | 0.206014 / 0.419271 (-0.213257) | 0.035987 / 0.043533 (-0.007546) | 0.243544 / 0.255139 (-0.011595) | 0.263872 / 0.283200 (-0.019327) | 0.017867 / 0.141683 (-0.123816) | 1.105159 / 1.452155 (-0.346996) | 1.186640 / 1.492716 (-0.306076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092888 / 0.018006 (0.074882) | 0.302024 / 0.000490 (0.301534) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019329 / 0.037411 (-0.018083) | 0.062135 / 0.014526 (0.047609) | 0.075125 / 0.176557 (-0.101431) | 0.120743 / 0.737135 (-0.616393) | 0.078687 / 0.296338 (-0.217652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279449 / 0.215209 (0.064240) | 2.727310 / 2.077655 (0.649656) | 1.442710 / 1.504120 (-0.061410) | 1.315271 / 1.541195 (-0.225923) | 1.360435 / 1.468490 (-0.108055) | 0.567720 / 4.584777 (-4.017057) | 2.397049 / 3.745712 (-1.348663) | 2.891180 / 5.269862 (-2.378682) | 1.774179 / 4.565676 (-2.791497) | 0.063155 / 0.424275 (-0.361120) | 0.004963 / 0.007607 (-0.002644) | 0.337526 / 0.226044 (0.111482) | 3.266016 / 2.268929 (0.997088) | 1.808819 / 55.444624 (-53.635806) | 1.525326 / 6.876477 (-5.351151) | 1.566937 / 2.142072 (-0.575135) | 0.654226 / 4.805227 (-4.151001) | 0.118968 / 6.500664 (-6.381696) | 0.042666 / 0.075469 (-0.032803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940792 / 1.841788 (-0.900996) | 11.736380 / 8.074308 (3.662072) | 10.709538 / 10.191392 (0.518146) | 0.141390 / 0.680424 (-0.539034) | 0.014204 / 0.534201 (-0.519996) | 0.284842 / 0.579283 (-0.294441) | 0.266315 / 0.434364 (-0.168049) | 0.331619 / 0.540337 (-0.208718) | 0.416446 / 1.386936 (-0.970491) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005298 / 0.011353 (-0.006055) | 0.003507 / 0.011008 (-0.007501) | 0.048315 / 0.038508 (0.009807) | 0.054855 / 0.023109 (0.031746) | 0.271558 / 0.275898 (-0.004340) | 0.316851 / 0.323480 (-0.006628) | 0.004054 / 0.007986 (-0.003932) | 0.002433 / 0.004328 (-0.001896) | 0.046442 / 0.004250 (0.042191) | 0.040853 / 0.037052 (0.003801) | 0.272537 / 0.258489 (0.014048) | 0.293736 / 0.293841 (-0.000105) | 0.029112 / 0.128546 (-0.099434) | 0.010573 / 0.075646 (-0.065074) | 0.056501 / 0.419271 (-0.362771) | 0.032541 / 0.043533 (-0.010992) | 0.271004 / 0.255139 (0.015865) | 0.289276 / 0.283200 (0.006076) | 0.018618 / 0.141683 (-0.123065) | 1.149435 / 1.452155 (-0.302719) | 1.205113 / 1.492716 (-0.287604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094726 / 0.018006 (0.076720) | 0.304347 / 0.000490 (0.303857) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021374 / 0.037411 (-0.016037) | 0.070574 / 0.014526 (0.056049) | 0.081749 / 0.176557 (-0.094807) | 0.119829 / 0.737135 (-0.617306) | 0.082602 / 0.296338 (-0.213737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293378 / 0.215209 (0.078169) | 2.893607 / 2.077655 (0.815952) | 1.577734 / 1.504120 (0.073614) | 1.453670 / 1.541195 (-0.087525) | 1.467354 / 1.468490 (-0.001136) | 0.563415 / 4.584777 (-4.021362) | 2.438330 / 3.745712 (-1.307382) | 2.761822 / 5.269862 (-2.508040) | 1.730944 / 4.565676 (-2.834732) | 0.062251 / 0.424275 (-0.362024) | 0.004969 / 0.007607 (-0.002638) | 0.371238 / 0.226044 (0.145194) | 3.399831 / 2.268929 (1.130903) | 1.936156 / 55.444624 (-53.508469) | 1.649716 / 6.876477 (-5.226761) | 1.669107 / 2.142072 (-0.472965) | 0.633696 / 4.805227 (-4.171531) | 0.115857 / 6.500664 (-6.384807) | 0.041012 / 0.075469 (-0.034457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964777 / 1.841788 (-0.877010) | 12.037613 / 8.074308 (3.963305) | 10.579241 / 10.191392 (0.387849) | 0.130932 / 0.680424 (-0.549492) | 0.015621 / 0.534201 (-0.518580) | 0.286898 / 0.579283 (-0.292385) | 0.281139 / 0.434364 (-0.153225) | 0.325240 / 0.540337 (-0.215097) | 0.554302 / 1.386936 (-0.832635) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48d2378944a47987f96562ee856167aef1e78522 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005258 / 0.011353 (-0.006095) | 0.003863 / 0.011008 (-0.007145) | 0.064585 / 0.038508 (0.026077) | 0.058013 / 0.023109 (0.034904) | 0.249042 / 0.275898 (-0.026856) | 0.273434 / 0.323480 (-0.050046) | 0.004779 / 0.007986 (-0.003207) | 0.002550 / 0.004328 (-0.001778) | 0.048290 / 0.004250 (0.044040) | 0.038777 / 0.037052 (0.001725) | 0.253039 / 0.258489 (-0.005450) | 0.285365 / 0.293841 (-0.008476) | 0.028053 / 0.128546 (-0.100494) | 0.010521 / 0.075646 (-0.065125) | 0.210954 / 0.419271 (-0.208317) | 0.035720 / 0.043533 (-0.007813) | 0.252540 / 0.255139 (-0.002599) | 0.264786 / 0.283200 (-0.018414) | 0.018692 / 0.141683 (-0.122990) | 1.108971 / 1.452155 (-0.343183) | 1.201004 / 1.492716 (-0.291712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095936 / 0.018006 (0.077930) | 0.302979 / 0.000490 (0.302489) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018859 / 0.037411 (-0.018552) | 0.062559 / 0.014526 (0.048034) | 0.073545 / 0.176557 (-0.103012) | 0.120780 / 0.737135 (-0.616355) | 0.074998 / 0.296338 (-0.221340) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276728 / 0.215209 (0.061519) | 2.715310 / 2.077655 (0.637655) | 1.444927 / 1.504120 (-0.059193) | 1.323867 / 1.541195 (-0.217328) | 1.364962 / 1.468490 (-0.103528) | 0.556792 / 4.584777 (-4.027985) | 2.409151 / 3.745712 (-1.336561) | 2.811836 / 5.269862 (-2.458026) | 1.777369 / 4.565676 (-2.788308) | 0.061398 / 0.424275 (-0.362877) | 0.004924 / 0.007607 (-0.002683) | 0.341228 / 0.226044 (0.115183) | 3.369570 / 2.268929 (1.100641) | 1.858151 / 55.444624 (-53.586474) | 1.587352 / 6.876477 (-5.289125) | 1.625004 / 2.142072 (-0.517068) | 0.635317 / 4.805227 (-4.169910) | 0.117197 / 6.500664 (-6.383467) | 0.042672 / 0.075469 (-0.032797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940419 / 1.841788 (-0.901368) | 12.156882 / 8.074308 (4.082574) | 10.646780 / 10.191392 (0.455388) | 0.129279 / 0.680424 (-0.551144) | 0.013967 / 0.534201 (-0.520234) | 0.287956 / 0.579283 (-0.291327) | 0.265250 / 0.434364 (-0.169114) | 0.323357 / 0.540337 (-0.216980) | 0.412045 / 1.386936 (-0.974891) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005264 / 0.011353 (-0.006089) | 0.003575 / 0.011008 (-0.007433) | 0.049249 / 0.038508 (0.010741) | 0.057069 / 0.023109 (0.033959) | 0.327547 / 0.275898 (0.051649) | 0.299027 / 0.323480 (-0.024453) | 0.004768 / 0.007986 (-0.003217) | 0.002522 / 0.004328 (-0.001807) | 0.048020 / 0.004250 (0.043770) | 0.041328 / 0.037052 (0.004275) | 0.281385 / 0.258489 (0.022895) | 0.304957 / 0.293841 (0.011116) | 0.031371 / 0.128546 (-0.097175) | 0.010523 / 0.075646 (-0.065124) | 0.057073 / 0.419271 (-0.362198) | 0.032913 / 0.043533 (-0.010620) | 0.284963 / 0.255139 (0.029824) | 0.291997 / 0.283200 (0.008798) | 0.018325 / 0.141683 (-0.123357) | 1.126681 / 1.452155 (-0.325473) | 1.183011 / 1.492716 (-0.309705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092544 / 0.018006 (0.074538) | 0.299841 / 0.000490 (0.299351) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022279 / 0.037411 (-0.015133) | 0.072515 / 0.014526 (0.057989) | 0.083068 / 0.176557 (-0.093488) | 0.120600 / 0.737135 (-0.616536) | 0.083574 / 0.296338 (-0.212765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293393 / 0.215209 (0.078184) | 2.865420 / 2.077655 (0.787765) | 1.562419 / 1.504120 (0.058299) | 1.440846 / 1.541195 (-0.100349) | 1.471993 / 1.468490 (0.003503) | 0.572510 / 4.584777 (-4.012267) | 2.427417 / 3.745712 (-1.318295) | 2.895347 / 5.269862 (-2.374515) | 1.790578 / 4.565676 (-2.775098) | 0.064489 / 0.424275 (-0.359786) | 0.005044 / 0.007607 (-0.002564) | 0.340774 / 0.226044 (0.114730) | 3.391414 / 2.268929 (1.122486) | 1.939980 / 55.444624 (-53.504644) | 1.658514 / 6.876477 (-5.217963) | 1.741406 / 2.142072 (-0.400667) | 0.649033 / 4.805227 (-4.156194) | 0.117587 / 6.500664 (-6.383077) | 0.042042 / 0.075469 (-0.033427) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980490 / 1.841788 (-0.861298) | 12.664045 / 8.074308 (4.589737) | 10.944437 / 10.191392 (0.753045) | 0.142059 / 0.680424 (-0.538365) | 0.015914 / 0.534201 (-0.518287) | 0.288826 / 0.579283 (-0.290457) | 0.282351 / 0.434364 (-0.152013) | 0.325302 / 0.540337 (-0.215035) | 0.416900 / 1.386936 (-0.970036) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005591 / 0.011353 (-0.005762) | 0.003445 / 0.011008 (-0.007563) | 0.064290 / 0.038508 (0.025782) | 0.053046 / 0.023109 (0.029936) | 0.229101 / 0.275898 (-0.046797) | 0.255515 / 0.323480 (-0.067964) | 0.002912 / 0.007986 (-0.005073) | 0.002466 / 0.004328 (-0.001863) | 0.049348 / 0.004250 (0.045098) | 0.039492 / 0.037052 (0.002440) | 0.236301 / 0.258489 (-0.022188) | 0.270109 / 0.293841 (-0.023732) | 0.027506 / 0.128546 (-0.101040) | 0.010381 / 0.075646 (-0.065265) | 0.209999 / 0.419271 (-0.209273) | 0.035827 / 0.043533 (-0.007705) | 0.237231 / 0.255139 (-0.017908) | 0.254345 / 0.283200 (-0.028854) | 0.019689 / 0.141683 (-0.121994) | 1.096103 / 1.452155 (-0.356052) | 1.172393 / 1.492716 (-0.320323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101749 / 0.018006 (0.083743) | 0.310913 / 0.000490 (0.310424) | 0.000217 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018743 / 0.037411 (-0.018669) | 0.064190 / 0.014526 (0.049664) | 0.074575 / 0.176557 (-0.101982) | 0.124143 / 0.737135 (-0.612993) | 0.077415 / 0.296338 (-0.218924) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286175 / 0.215209 (0.070965) | 2.781169 / 2.077655 (0.703515) | 1.495130 / 1.504120 (-0.008990) | 1.379136 / 1.541195 (-0.162059) | 1.397548 / 1.468490 (-0.070942) | 0.564467 / 4.584777 (-4.020310) | 2.408896 / 3.745712 (-1.336816) | 2.857771 / 5.269862 (-2.412091) | 1.776531 / 4.565676 (-2.789145) | 0.062700 / 0.424275 (-0.361575) | 0.004965 / 0.007607 (-0.002642) | 0.344026 / 0.226044 (0.117982) | 3.390829 / 2.268929 (1.121900) | 1.875258 / 55.444624 (-53.569366) | 1.602435 / 6.876477 (-5.274042) | 1.613619 / 2.142072 (-0.528454) | 0.639421 / 4.805227 (-4.165806) | 0.117697 / 6.500664 (-6.382967) | 0.042878 / 0.075469 (-0.032591) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957694 / 1.841788 (-0.884094) | 11.888917 / 8.074308 (3.814609) | 10.643389 / 10.191392 (0.451997) | 0.143358 / 0.680424 (-0.537066) | 0.014382 / 0.534201 (-0.519819) | 0.288731 / 0.579283 (-0.290552) | 0.270040 / 0.434364 (-0.164324) | 0.323586 / 0.540337 (-0.216751) | 0.415743 / 1.386936 (-0.971193) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005228 / 0.011353 (-0.006125) | 0.003445 / 0.011008 (-0.007563) | 0.051072 / 0.038508 (0.012563) | 0.053087 / 0.023109 (0.029978) | 0.273116 / 0.275898 (-0.002782) | 0.298633 / 0.323480 (-0.024847) | 0.004067 / 0.007986 (-0.003919) | 0.002537 / 0.004328 (-0.001791) | 0.049326 / 0.004250 (0.045075) | 0.041011 / 0.037052 (0.003959) | 0.277748 / 0.258489 (0.019258) | 0.304152 / 0.293841 (0.010311) | 0.029012 / 0.128546 (-0.099534) | 0.010589 / 0.075646 (-0.065057) | 0.057564 / 0.419271 (-0.361707) | 0.032785 / 0.043533 (-0.010747) | 0.272508 / 0.255139 (0.017369) | 0.294127 / 0.283200 (0.010927) | 0.018466 / 0.141683 (-0.123217) | 1.129341 / 1.452155 (-0.322814) | 1.194631 / 1.492716 (-0.298086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098558 / 0.018006 (0.080552) | 0.312353 / 0.000490 (0.311863) | 0.000269 / 0.000200 (0.000069) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022148 / 0.037411 (-0.015263) | 0.070601 / 0.014526 (0.056075) | 0.081780 / 0.176557 (-0.094777) | 0.121993 / 0.737135 (-0.615142) | 0.084263 / 0.296338 (-0.212076) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300501 / 0.215209 (0.085292) | 2.927534 / 2.077655 (0.849879) | 1.595527 / 1.504120 (0.091407) | 1.475607 / 1.541195 (-0.065587) | 1.496707 / 1.468490 (0.028217) | 0.559051 / 4.584777 (-4.025726) | 2.427126 / 3.745712 (-1.318586) | 2.820908 / 5.269862 (-2.448953) | 1.757492 / 4.565676 (-2.808185) | 0.062391 / 0.424275 (-0.361884) | 0.004950 / 0.007607 (-0.002657) | 0.351204 / 0.226044 (0.125160) | 3.485068 / 2.268929 (1.216139) | 1.976418 / 55.444624 (-53.468207) | 1.682715 / 6.876477 (-5.193761) | 1.703457 / 2.142072 (-0.438616) | 0.643476 / 4.805227 (-4.161751) | 0.116321 / 6.500664 (-6.384343) | 0.040776 / 0.075469 (-0.034694) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974152 / 1.841788 (-0.867635) | 12.390170 / 8.074308 (4.315862) | 10.866283 / 10.191392 (0.674891) | 0.145049 / 0.680424 (-0.535375) | 0.016404 / 0.534201 (-0.517797) | 0.288799 / 0.579283 (-0.290484) | 0.285917 / 0.434364 (-0.148447) | 0.328455 / 0.540337 (-0.211883) | 0.417286 / 1.386936 (-0.969650) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n" ]
2023-11-23T17:35:02
2023-11-27T10:02:56
2023-11-24T17:13:02
COLLABORATOR
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Refetch metadata files in case they were dropped by `filter_extensions` in the previous step. Fix #6442
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2,008,614,985
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6,448
Use parquet export if possible
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005177 / 0.011353 (-0.006176) | 0.003002 / 0.011008 (-0.008006) | 0.061915 / 0.038508 (0.023407) | 0.052065 / 0.023109 (0.028956) | 0.246114 / 0.275898 (-0.029784) | 0.273974 / 0.323480 (-0.049506) | 0.002983 / 0.007986 (-0.005003) | 0.002444 / 0.004328 (-0.001885) | 0.048424 / 0.004250 (0.044174) | 0.039609 / 0.037052 (0.002557) | 0.257771 / 0.258489 (-0.000718) | 0.286228 / 0.293841 (-0.007613) | 0.023925 / 0.128546 (-0.104621) | 0.007248 / 0.075646 (-0.068398) | 0.202205 / 0.419271 (-0.217067) | 0.037124 / 0.043533 (-0.006409) | 0.254872 / 0.255139 (-0.000267) | 0.275252 / 0.283200 (-0.007947) | 0.019251 / 0.141683 (-0.122432) | 1.074921 / 1.452155 (-0.377234) | 1.146515 / 1.492716 (-0.346202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091998 / 0.018006 (0.073992) | 0.299146 / 0.000490 (0.298656) | 0.000240 / 0.000200 (0.000040) | 0.000054 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019266 / 0.037411 (-0.018145) | 0.062560 / 0.014526 (0.048034) | 0.075012 / 0.176557 (-0.101544) | 0.120077 / 0.737135 (-0.617058) | 0.077851 / 0.296338 (-0.218488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290629 / 0.215209 (0.075420) | 2.823847 / 2.077655 (0.746192) | 1.516966 / 1.504120 (0.012846) | 1.393383 / 1.541195 (-0.147812) | 1.427688 / 1.468490 (-0.040802) | 0.407456 / 4.584777 (-4.177321) | 2.378280 / 3.745712 (-1.367433) | 2.689800 / 5.269862 (-2.580061) | 1.588037 / 4.565676 (-2.977640) | 0.045837 / 0.424275 (-0.378438) | 0.004884 / 0.007607 (-0.002724) | 0.340464 / 0.226044 (0.114420) | 3.377158 / 2.268929 (1.108230) | 1.897854 / 55.444624 (-53.546771) | 1.588285 / 6.876477 (-5.288191) | 1.651708 / 2.142072 (-0.490364) | 0.482018 / 4.805227 (-4.323209) | 0.101583 / 6.500664 (-6.399081) | 0.042306 / 0.075469 (-0.033163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948659 / 1.841788 (-0.893128) | 11.809778 / 8.074308 (3.735470) | 10.481896 / 10.191392 (0.290504) | 0.143538 / 0.680424 (-0.536885) | 0.014105 / 0.534201 (-0.520096) | 0.272278 / 0.579283 (-0.307005) | 0.264241 / 0.434364 (-0.170123) | 0.307187 / 0.540337 (-0.233150) | 0.401270 / 1.386936 (-0.985666) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004831 / 0.011353 (-0.006521) | 0.002896 / 0.011008 (-0.008112) | 0.047479 / 0.038508 (0.008971) | 0.050665 / 0.023109 (0.027555) | 0.275243 / 0.275898 (-0.000655) | 0.296547 / 0.323480 (-0.026933) | 0.004022 / 0.007986 (-0.003963) | 0.002425 / 0.004328 (-0.001904) | 0.047086 / 0.004250 (0.042836) | 0.039611 / 0.037052 (0.002558) | 0.275272 / 0.258489 (0.016783) | 0.302429 / 0.293841 (0.008588) | 0.024308 / 0.128546 (-0.104238) | 0.007167 / 0.075646 (-0.068479) | 0.052825 / 0.419271 (-0.366446) | 0.032319 / 0.043533 (-0.011213) | 0.273334 / 0.255139 (0.018195) | 0.291161 / 0.283200 (0.007961) | 0.017918 / 0.141683 (-0.123764) | 1.110005 / 1.452155 (-0.342150) | 1.176616 / 1.492716 (-0.316100) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092478 / 0.018006 (0.074471) | 0.311431 / 0.000490 (0.310942) | 0.000237 / 0.000200 (0.000037) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021979 / 0.037411 (-0.015432) | 0.080617 / 0.014526 (0.066091) | 0.081534 / 0.176557 (-0.095023) | 0.121073 / 0.737135 (-0.616062) | 0.083235 / 0.296338 (-0.213104) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289527 / 0.215209 (0.074318) | 2.839668 / 2.077655 (0.762013) | 1.601737 / 1.504120 (0.097617) | 1.496028 / 1.541195 (-0.045167) | 1.511933 / 1.468490 (0.043443) | 0.399819 / 4.584777 (-4.184958) | 2.394147 / 3.745712 (-1.351565) | 2.520767 / 5.269862 (-2.749095) | 1.589496 / 4.565676 (-2.976180) | 0.046673 / 0.424275 (-0.377602) | 0.004858 / 0.007607 (-0.002749) | 0.357986 / 0.226044 (0.131941) | 3.376217 / 2.268929 (1.107289) | 1.981853 / 55.444624 (-53.462771) | 1.682240 / 6.876477 (-5.194236) | 1.830643 / 2.142072 (-0.311429) | 0.478286 / 4.805227 (-4.326941) | 0.099589 / 6.500664 (-6.401075) | 0.041173 / 0.075469 (-0.034296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985160 / 1.841788 (-0.856628) | 12.312963 / 8.074308 (4.238655) | 10.577225 / 10.191392 (0.385833) | 0.130167 / 0.680424 (-0.550257) | 0.016657 / 0.534201 (-0.517544) | 0.271330 / 0.579283 (-0.307953) | 0.276979 / 0.434364 (-0.157385) | 0.304904 / 0.540337 (-0.235434) | 0.412090 / 1.386936 (-0.974846) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1adc80151e892122ecb60f4e0b4572b136b2dd47 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6448). All of your documentation changes will be reflected on that endpoint.", "hooray! very excited about this", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005039 / 0.011353 (-0.006314) | 0.003577 / 0.011008 (-0.007431) | 0.062892 / 0.038508 (0.024384) | 0.056334 / 0.023109 (0.033225) | 0.252281 / 0.275898 (-0.023617) | 0.274945 / 0.323480 (-0.048535) | 0.003906 / 0.007986 (-0.004080) | 0.002483 / 0.004328 (-0.001845) | 0.049006 / 0.004250 (0.044756) | 0.038375 / 0.037052 (0.001323) | 0.257376 / 0.258489 (-0.001113) | 0.292512 / 0.293841 (-0.001328) | 0.027134 / 0.128546 (-0.101412) | 0.010579 / 0.075646 (-0.065068) | 0.212021 / 0.419271 (-0.207250) | 0.035851 / 0.043533 (-0.007682) | 0.258076 / 0.255139 (0.002937) | 0.271758 / 0.283200 (-0.011442) | 0.018222 / 0.141683 (-0.123461) | 1.120481 / 1.452155 (-0.331674) | 1.187007 / 1.492716 (-0.305710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094986 / 0.018006 (0.076980) | 0.302121 / 0.000490 (0.301631) | 0.000211 / 0.000200 (0.000011) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019260 / 0.037411 (-0.018152) | 0.062909 / 0.014526 (0.048383) | 0.075644 / 0.176557 (-0.100912) | 0.120966 / 0.737135 (-0.616170) | 0.076678 / 0.296338 (-0.219661) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286754 / 0.215209 (0.071545) | 2.797467 / 2.077655 (0.719812) | 1.436798 / 1.504120 (-0.067322) | 1.315032 / 1.541195 (-0.226163) | 1.367841 / 1.468490 (-0.100649) | 0.578917 / 4.584777 (-4.005860) | 2.439773 / 3.745712 (-1.305939) | 2.932779 / 5.269862 (-2.337082) | 1.843895 / 4.565676 (-2.721782) | 0.063351 / 0.424275 (-0.360925) | 0.004998 / 0.007607 (-0.002610) | 0.347385 / 0.226044 (0.121340) | 3.449969 / 2.268929 (1.181040) | 1.857734 / 55.444624 (-53.586890) | 1.541341 / 6.876477 (-5.335136) | 1.574915 / 2.142072 (-0.567158) | 0.660178 / 4.805227 (-4.145049) | 0.117686 / 6.500664 (-6.382978) | 0.042602 / 0.075469 (-0.032867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937735 / 1.841788 (-0.904052) | 11.962091 / 8.074308 (3.887783) | 10.401715 / 10.191392 (0.210323) | 0.142200 / 0.680424 (-0.538224) | 0.014137 / 0.534201 (-0.520064) | 0.289853 / 0.579283 (-0.289430) | 0.267100 / 0.434364 (-0.167264) | 0.323401 / 0.540337 (-0.216936) | 0.418665 / 1.386936 (-0.968271) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005480 / 0.011353 (-0.005873) | 0.003401 / 0.011008 (-0.007607) | 0.049304 / 0.038508 (0.010796) | 0.062043 / 0.023109 (0.038934) | 0.270571 / 0.275898 (-0.005327) | 0.295226 / 0.323480 (-0.028254) | 0.004152 / 0.007986 (-0.003834) | 0.002511 / 0.004328 (-0.001817) | 0.048480 / 0.004250 (0.044229) | 0.043964 / 0.037052 (0.006912) | 0.273545 / 0.258489 (0.015056) | 0.295152 / 0.293841 (0.001311) | 0.029224 / 0.128546 (-0.099322) | 0.010629 / 0.075646 (-0.065018) | 0.057433 / 0.419271 (-0.361839) | 0.033115 / 0.043533 (-0.010418) | 0.269893 / 0.255139 (0.014754) | 0.288658 / 0.283200 (0.005459) | 0.018216 / 0.141683 (-0.123467) | 1.123039 / 1.452155 (-0.329116) | 1.182892 / 1.492716 (-0.309825) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095948 / 0.018006 (0.077942) | 0.305811 / 0.000490 (0.305321) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022996 / 0.037411 (-0.014415) | 0.073836 / 0.014526 (0.059310) | 0.082658 / 0.176557 (-0.093899) | 0.121970 / 0.737135 (-0.615166) | 0.086096 / 0.296338 (-0.210242) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291032 / 0.215209 (0.075823) | 2.864613 / 2.077655 (0.786958) | 1.567530 / 1.504120 (0.063410) | 1.460291 / 1.541195 (-0.080903) | 1.527066 / 1.468490 (0.058576) | 0.571160 / 4.584777 (-4.013617) | 2.465261 / 3.745712 (-1.280451) | 2.915547 / 5.269862 (-2.354314) | 1.835822 / 4.565676 (-2.729855) | 0.064328 / 0.424275 (-0.359947) | 0.005061 / 0.007607 (-0.002546) | 0.357105 / 0.226044 (0.131061) | 3.491363 / 2.268929 (1.222435) | 1.943213 / 55.444624 (-53.501412) | 1.675778 / 6.876477 (-5.200699) | 1.719016 / 2.142072 (-0.423057) | 0.658993 / 4.805227 (-4.146235) | 0.122320 / 6.500664 (-6.378344) | 0.049030 / 0.075469 (-0.026439) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964762 / 1.841788 (-0.877025) | 12.367251 / 8.074308 (4.292943) | 10.886213 / 10.191392 (0.694821) | 0.141533 / 0.680424 (-0.538891) | 0.015646 / 0.534201 (-0.518555) | 0.288583 / 0.579283 (-0.290700) | 0.280353 / 0.434364 (-0.154010) | 0.329095 / 0.540337 (-0.211242) | 0.565118 / 1.386936 (-0.821818) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#493bf695dc3ee6cc81bfd0aae6a38f70547bb752 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006475 / 0.011353 (-0.004878) | 0.004080 / 0.011008 (-0.006928) | 0.066479 / 0.038508 (0.027971) | 0.073270 / 0.023109 (0.050161) | 0.244412 / 0.275898 (-0.031486) | 0.273778 / 0.323480 (-0.049702) | 0.003186 / 0.007986 (-0.004800) | 0.003419 / 0.004328 (-0.000910) | 0.049743 / 0.004250 (0.045492) | 0.043581 / 0.037052 (0.006529) | 0.248215 / 0.258489 (-0.010274) | 0.280873 / 0.293841 (-0.012967) | 0.029282 / 0.128546 (-0.099264) | 0.011241 / 0.075646 (-0.064405) | 0.215031 / 0.419271 (-0.204241) | 0.038764 / 0.043533 (-0.004769) | 0.259363 / 0.255139 (0.004224) | 0.279253 / 0.283200 (-0.003946) | 0.019524 / 0.141683 (-0.122159) | 1.104735 / 1.452155 (-0.347420) | 1.159823 / 1.492716 (-0.332894) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108383 / 0.018006 (0.090377) | 0.332904 / 0.000490 (0.332415) | 0.000222 / 0.000200 (0.000022) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020693 / 0.037411 (-0.016719) | 0.071764 / 0.014526 (0.057238) | 0.077073 / 0.176557 (-0.099484) | 0.124604 / 0.737135 (-0.612532) | 0.078057 / 0.296338 (-0.218282) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291014 / 0.215209 (0.075805) | 2.865885 / 2.077655 (0.788231) | 1.506141 / 1.504120 (0.002021) | 1.435924 / 1.541195 (-0.105271) | 1.461994 / 1.468490 (-0.006497) | 0.571779 / 4.584777 (-4.012998) | 2.461950 / 3.745712 (-1.283762) | 3.079771 / 5.269862 (-2.190091) | 1.933337 / 4.565676 (-2.632339) | 0.063405 / 0.424275 (-0.360870) | 0.005203 / 0.007607 (-0.002404) | 0.345077 / 0.226044 (0.119032) | 3.487189 / 2.268929 (1.218261) | 1.903733 / 55.444624 (-53.540891) | 1.705596 / 6.876477 (-5.170880) | 1.718849 / 2.142072 (-0.423223) | 0.658745 / 4.805227 (-4.146482) | 0.120847 / 6.500664 (-6.379817) | 0.045670 / 0.075469 (-0.029799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965969 / 1.841788 (-0.875819) | 13.520489 / 8.074308 (5.446181) | 12.322363 / 10.191392 (2.130971) | 0.146605 / 0.680424 (-0.533819) | 0.015061 / 0.534201 (-0.519140) | 0.298125 / 0.579283 (-0.281159) | 0.276864 / 0.434364 (-0.157500) | 0.326787 / 0.540337 (-0.213550) | 0.436897 / 1.386936 (-0.950039) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005862 / 0.011353 (-0.005491) | 0.003716 / 0.011008 (-0.007292) | 0.052849 / 0.038508 (0.014341) | 0.072114 / 0.023109 (0.049005) | 0.277800 / 0.275898 (0.001902) | 0.325321 / 0.323480 (0.001841) | 0.004428 / 0.007986 (-0.003557) | 0.002527 / 0.004328 (-0.001801) | 0.048847 / 0.004250 (0.044596) | 0.047355 / 0.037052 (0.010303) | 0.279331 / 0.258489 (0.020842) | 0.310477 / 0.293841 (0.016636) | 0.029661 / 0.128546 (-0.098886) | 0.010812 / 0.075646 (-0.064834) | 0.059803 / 0.419271 (-0.359469) | 0.033554 / 0.043533 (-0.009978) | 0.276890 / 0.255139 (0.021751) | 0.308911 / 0.283200 (0.025712) | 0.020752 / 0.141683 (-0.120931) | 1.120896 / 1.452155 (-0.331259) | 1.186428 / 1.492716 (-0.306288) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106551 / 0.018006 (0.088545) | 0.354455 / 0.000490 (0.353966) | 0.000353 / 0.000200 (0.000153) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023488 / 0.037411 (-0.013923) | 0.080548 / 0.014526 (0.066022) | 0.084431 / 0.176557 (-0.092126) | 0.140698 / 0.737135 (-0.596438) | 0.085692 / 0.296338 (-0.210647) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.314253 / 0.215209 (0.099044) | 2.993236 / 2.077655 (0.915582) | 1.639013 / 1.504120 (0.134893) | 1.543966 / 1.541195 (0.002771) | 1.567732 / 1.468490 (0.099242) | 0.565857 / 4.584777 (-4.018920) | 2.545339 / 3.745712 (-1.200373) | 3.134546 / 5.269862 (-2.135316) | 1.940350 / 4.565676 (-2.625326) | 0.063847 / 0.424275 (-0.360429) | 0.005079 / 0.007607 (-0.002528) | 0.365762 / 0.226044 (0.139718) | 3.610921 / 2.268929 (1.341993) | 2.035151 / 55.444624 (-53.409473) | 1.773409 / 6.876477 (-5.103068) | 1.790332 / 2.142072 (-0.351741) | 0.683019 / 4.805227 (-4.122209) | 0.119566 / 6.500664 (-6.381099) | 0.043578 / 0.075469 (-0.031891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996568 / 1.841788 (-0.845219) | 14.094366 / 8.074308 (6.020058) | 12.433600 / 10.191392 (2.242208) | 0.139835 / 0.680424 (-0.540589) | 0.016454 / 0.534201 (-0.517747) | 0.294073 / 0.579283 (-0.285210) | 0.309032 / 0.434364 (-0.125332) | 0.330699 / 0.540337 (-0.209638) | 0.619392 / 1.386936 (-0.767544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#026fbce1c93a30188b6d0646bb975da8f56e2a2f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005389 / 0.011353 (-0.005964) | 0.003209 / 0.011008 (-0.007799) | 0.061610 / 0.038508 (0.023102) | 0.049781 / 0.023109 (0.026672) | 0.240208 / 0.275898 (-0.035690) | 0.263307 / 0.323480 (-0.060173) | 0.002908 / 0.007986 (-0.005078) | 0.002375 / 0.004328 (-0.001953) | 0.047462 / 0.004250 (0.043212) | 0.038643 / 0.037052 (0.001591) | 0.246287 / 0.258489 (-0.012202) | 0.278715 / 0.293841 (-0.015126) | 0.027507 / 0.128546 (-0.101039) | 0.010168 / 0.075646 (-0.065479) | 0.204131 / 0.419271 (-0.215140) | 0.035452 / 0.043533 (-0.008081) | 0.251721 / 0.255139 (-0.003418) | 0.266642 / 0.283200 (-0.016558) | 0.017741 / 0.141683 (-0.123942) | 1.094672 / 1.452155 (-0.357482) | 1.162715 / 1.492716 (-0.330002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092154 / 0.018006 (0.074148) | 0.301376 / 0.000490 (0.300886) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018534 / 0.037411 (-0.018877) | 0.061995 / 0.014526 (0.047469) | 0.072654 / 0.176557 (-0.103903) | 0.119501 / 0.737135 (-0.617635) | 0.073756 / 0.296338 (-0.222583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280066 / 0.215209 (0.064857) | 2.744207 / 2.077655 (0.666553) | 1.483367 / 1.504120 (-0.020753) | 1.386173 / 1.541195 (-0.155022) | 1.381833 / 1.468490 (-0.086657) | 0.552780 / 4.584777 (-4.031997) | 2.395541 / 3.745712 (-1.350171) | 2.747507 / 5.269862 (-2.522355) | 1.735074 / 4.565676 (-2.830602) | 0.062096 / 0.424275 (-0.362179) | 0.004905 / 0.007607 (-0.002702) | 0.338327 / 0.226044 (0.112283) | 3.365391 / 2.268929 (1.096462) | 1.839663 / 55.444624 (-53.604961) | 1.577535 / 6.876477 (-5.298942) | 1.558054 / 2.142072 (-0.584018) | 0.636520 / 4.805227 (-4.168708) | 0.116182 / 6.500664 (-6.384482) | 0.042078 / 0.075469 (-0.033391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938512 / 1.841788 (-0.903276) | 11.455749 / 8.074308 (3.381441) | 10.510985 / 10.191392 (0.319593) | 0.140865 / 0.680424 (-0.539559) | 0.014073 / 0.534201 (-0.520128) | 0.294747 / 0.579283 (-0.284536) | 0.266147 / 0.434364 (-0.168217) | 0.325354 / 0.540337 (-0.214984) | 0.422182 / 1.386936 (-0.964754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005231 / 0.011353 (-0.006122) | 0.003032 / 0.011008 (-0.007977) | 0.049608 / 0.038508 (0.011099) | 0.051441 / 0.023109 (0.028332) | 0.273812 / 0.275898 (-0.002086) | 0.294318 / 0.323480 (-0.029162) | 0.003958 / 0.007986 (-0.004028) | 0.002384 / 0.004328 (-0.001944) | 0.047942 / 0.004250 (0.043691) | 0.039179 / 0.037052 (0.002127) | 0.277504 / 0.258489 (0.019014) | 0.299713 / 0.293841 (0.005872) | 0.028989 / 0.128546 (-0.099557) | 0.010267 / 0.075646 (-0.065379) | 0.058318 / 0.419271 (-0.360954) | 0.032214 / 0.043533 (-0.011318) | 0.277964 / 0.255139 (0.022825) | 0.293055 / 0.283200 (0.009856) | 0.018532 / 0.141683 (-0.123151) | 1.128620 / 1.452155 (-0.323535) | 1.187365 / 1.492716 (-0.305351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092137 / 0.018006 (0.074130) | 0.299726 / 0.000490 (0.299236) | 0.000222 / 0.000200 (0.000022) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021342 / 0.037411 (-0.016070) | 0.069943 / 0.014526 (0.055417) | 0.079862 / 0.176557 (-0.096694) | 0.118917 / 0.737135 (-0.618218) | 0.081861 / 0.296338 (-0.214477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295883 / 0.215209 (0.080674) | 2.881640 / 2.077655 (0.803986) | 1.597705 / 1.504120 (0.093585) | 1.473220 / 1.541195 (-0.067975) | 1.501006 / 1.468490 (0.032516) | 0.559409 / 4.584777 (-4.025368) | 2.442709 / 3.745712 (-1.303003) | 2.742139 / 5.269862 (-2.527723) | 1.726002 / 4.565676 (-2.839674) | 0.062436 / 0.424275 (-0.361840) | 0.004896 / 0.007607 (-0.002711) | 0.349203 / 0.226044 (0.123159) | 3.435175 / 2.268929 (1.166247) | 1.954888 / 55.444624 (-53.489737) | 1.666233 / 6.876477 (-5.210243) | 1.680852 / 2.142072 (-0.461221) | 0.644271 / 4.805227 (-4.160956) | 0.115160 / 6.500664 (-6.385504) | 0.040681 / 0.075469 (-0.034788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963810 / 1.841788 (-0.877977) | 11.860860 / 8.074308 (3.786552) | 10.541703 / 10.191392 (0.350311) | 0.131532 / 0.680424 (-0.548892) | 0.016790 / 0.534201 (-0.517411) | 0.286695 / 0.579283 (-0.292588) | 0.279628 / 0.434364 (-0.154735) | 0.324622 / 0.540337 (-0.215715) | 0.535507 / 1.386936 (-0.851429) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11217347e4bcfe1aaf794d164a5dd9f085b2f682 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005672 / 0.011353 (-0.005681) | 0.003411 / 0.011008 (-0.007597) | 0.062528 / 0.038508 (0.024020) | 0.055209 / 0.023109 (0.032100) | 0.248366 / 0.275898 (-0.027532) | 0.279522 / 0.323480 (-0.043957) | 0.002907 / 0.007986 (-0.005079) | 0.002369 / 0.004328 (-0.001959) | 0.047982 / 0.004250 (0.043731) | 0.039009 / 0.037052 (0.001956) | 0.256422 / 0.258489 (-0.002067) | 0.288530 / 0.293841 (-0.005311) | 0.028164 / 0.128546 (-0.100382) | 0.010448 / 0.075646 (-0.065198) | 0.208863 / 0.419271 (-0.210408) | 0.036291 / 0.043533 (-0.007242) | 0.251642 / 0.255139 (-0.003497) | 0.275589 / 0.283200 (-0.007610) | 0.019839 / 0.141683 (-0.121844) | 1.092800 / 1.452155 (-0.359355) | 1.147950 / 1.492716 (-0.344766) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094920 / 0.018006 (0.076914) | 0.303049 / 0.000490 (0.302559) | 0.000199 / 0.000200 (-0.000001) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018820 / 0.037411 (-0.018591) | 0.063319 / 0.014526 (0.048793) | 0.073644 / 0.176557 (-0.102912) | 0.120045 / 0.737135 (-0.617091) | 0.076219 / 0.296338 (-0.220119) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283897 / 0.215209 (0.068688) | 2.822836 / 2.077655 (0.745182) | 1.490505 / 1.504120 (-0.013615) | 1.359777 / 1.541195 (-0.181418) | 1.420536 / 1.468490 (-0.047954) | 0.562308 / 4.584777 (-4.022469) | 2.419249 / 3.745712 (-1.326463) | 2.827620 / 5.269862 (-2.442241) | 1.783171 / 4.565676 (-2.782505) | 0.063206 / 0.424275 (-0.361069) | 0.004966 / 0.007607 (-0.002641) | 0.339647 / 0.226044 (0.113602) | 3.378157 / 2.268929 (1.109229) | 1.873221 / 55.444624 (-53.571403) | 1.606367 / 6.876477 (-5.270109) | 1.624976 / 2.142072 (-0.517096) | 0.652653 / 4.805227 (-4.152574) | 0.117997 / 6.500664 (-6.382667) | 0.041955 / 0.075469 (-0.033514) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961420 / 1.841788 (-0.880368) | 11.807624 / 8.074308 (3.733316) | 10.668249 / 10.191392 (0.476857) | 0.141855 / 0.680424 (-0.538569) | 0.014451 / 0.534201 (-0.519750) | 0.289706 / 0.579283 (-0.289577) | 0.268392 / 0.434364 (-0.165972) | 0.323435 / 0.540337 (-0.216903) | 0.420667 / 1.386936 (-0.966269) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005382 / 0.011353 (-0.005971) | 0.003361 / 0.011008 (-0.007647) | 0.048420 / 0.038508 (0.009912) | 0.053702 / 0.023109 (0.030593) | 0.286976 / 0.275898 (0.011078) | 0.296708 / 0.323480 (-0.026772) | 0.004013 / 0.007986 (-0.003972) | 0.002444 / 0.004328 (-0.001884) | 0.047797 / 0.004250 (0.043547) | 0.042361 / 0.037052 (0.005309) | 0.277543 / 0.258489 (0.019054) | 0.300736 / 0.293841 (0.006896) | 0.029894 / 0.128546 (-0.098653) | 0.014119 / 0.075646 (-0.061527) | 0.057636 / 0.419271 (-0.361636) | 0.032533 / 0.043533 (-0.010999) | 0.280963 / 0.255139 (0.025824) | 0.291305 / 0.283200 (0.008106) | 0.018391 / 0.141683 (-0.123292) | 1.140042 / 1.452155 (-0.312113) | 1.179485 / 1.492716 (-0.313231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094668 / 0.018006 (0.076661) | 0.301677 / 0.000490 (0.301187) | 0.000245 / 0.000200 (0.000045) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021376 / 0.037411 (-0.016036) | 0.070628 / 0.014526 (0.056102) | 0.082249 / 0.176557 (-0.094308) | 0.120423 / 0.737135 (-0.616712) | 0.083792 / 0.296338 (-0.212546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298884 / 0.215209 (0.083675) | 2.931849 / 2.077655 (0.854194) | 1.591888 / 1.504120 (0.087768) | 1.455781 / 1.541195 (-0.085414) | 1.500312 / 1.468490 (0.031822) | 0.558466 / 4.584777 (-4.026311) | 2.450449 / 3.745712 (-1.295263) | 2.842768 / 5.269862 (-2.427094) | 1.755614 / 4.565676 (-2.810062) | 0.063200 / 0.424275 (-0.361075) | 0.005022 / 0.007607 (-0.002585) | 0.358282 / 0.226044 (0.132238) | 3.575392 / 2.268929 (1.306464) | 1.960258 / 55.444624 (-53.484366) | 1.675518 / 6.876477 (-5.200959) | 1.696630 / 2.142072 (-0.445442) | 0.647185 / 4.805227 (-4.158042) | 0.117038 / 6.500664 (-6.383626) | 0.041622 / 0.075469 (-0.033848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962503 / 1.841788 (-0.879285) | 12.194950 / 8.074308 (4.120642) | 10.662233 / 10.191392 (0.470841) | 0.131618 / 0.680424 (-0.548806) | 0.016000 / 0.534201 (-0.518201) | 0.291546 / 0.579283 (-0.287737) | 0.279537 / 0.434364 (-0.154827) | 0.328716 / 0.540337 (-0.211622) | 0.547565 / 1.386936 (-0.839371) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de8f5f09f60613d47b5d7eb901752321c7b6a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005209 / 0.011353 (-0.006144) | 0.003017 / 0.011008 (-0.007991) | 0.062017 / 0.038508 (0.023509) | 0.048268 / 0.023109 (0.025158) | 0.246384 / 0.275898 (-0.029514) | 0.270441 / 0.323480 (-0.053039) | 0.002763 / 0.007986 (-0.005222) | 0.003140 / 0.004328 (-0.001188) | 0.048720 / 0.004250 (0.044470) | 0.038175 / 0.037052 (0.001123) | 0.254184 / 0.258489 (-0.004306) | 0.275515 / 0.293841 (-0.018326) | 0.027309 / 0.128546 (-0.101238) | 0.010507 / 0.075646 (-0.065140) | 0.210315 / 0.419271 (-0.208956) | 0.035203 / 0.043533 (-0.008329) | 0.253015 / 0.255139 (-0.002124) | 0.271465 / 0.283200 (-0.011734) | 0.019543 / 0.141683 (-0.122140) | 1.119242 / 1.452155 (-0.332913) | 1.149359 / 1.492716 (-0.343357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088935 / 0.018006 (0.070928) | 0.293922 / 0.000490 (0.293432) | 0.000202 / 0.000200 (0.000002) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018174 / 0.037411 (-0.019237) | 0.060215 / 0.014526 (0.045689) | 0.072868 / 0.176557 (-0.103689) | 0.117998 / 0.737135 (-0.619137) | 0.074159 / 0.296338 (-0.222179) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289229 / 0.215209 (0.074020) | 2.840414 / 2.077655 (0.762759) | 1.468357 / 1.504120 (-0.035763) | 1.347714 / 1.541195 (-0.193481) | 1.363704 / 1.468490 (-0.104786) | 0.572059 / 4.584777 (-4.012718) | 2.400631 / 3.745712 (-1.345081) | 2.755779 / 5.269862 (-2.514083) | 1.740937 / 4.565676 (-2.824739) | 0.063473 / 0.424275 (-0.360802) | 0.005012 / 0.007607 (-0.002595) | 0.336057 / 0.226044 (0.110012) | 3.382126 / 2.268929 (1.113197) | 1.807838 / 55.444624 (-53.636786) | 1.534594 / 6.876477 (-5.341883) | 1.529951 / 2.142072 (-0.612121) | 0.636661 / 4.805227 (-4.168566) | 0.117090 / 6.500664 (-6.383574) | 0.042310 / 0.075469 (-0.033160) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.924440 / 1.841788 (-0.917347) | 11.120517 / 8.074308 (3.046209) | 10.177210 / 10.191392 (-0.014182) | 0.139060 / 0.680424 (-0.541364) | 0.013818 / 0.534201 (-0.520383) | 0.285634 / 0.579283 (-0.293649) | 0.268657 / 0.434364 (-0.165706) | 0.325842 / 0.540337 (-0.214496) | 0.439902 / 1.386936 (-0.947034) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005202 / 0.011353 (-0.006150) | 0.003002 / 0.011008 (-0.008006) | 0.048729 / 0.038508 (0.010221) | 0.048178 / 0.023109 (0.025069) | 0.288573 / 0.275898 (0.012675) | 0.311122 / 0.323480 (-0.012358) | 0.003953 / 0.007986 (-0.004033) | 0.002544 / 0.004328 (-0.001785) | 0.047762 / 0.004250 (0.043511) | 0.039711 / 0.037052 (0.002658) | 0.308389 / 0.258489 (0.049900) | 0.321913 / 0.293841 (0.028072) | 0.029166 / 0.128546 (-0.099380) | 0.010697 / 0.075646 (-0.064950) | 0.057758 / 0.419271 (-0.361514) | 0.032743 / 0.043533 (-0.010789) | 0.290933 / 0.255139 (0.035794) | 0.309404 / 0.283200 (0.026205) | 0.017691 / 0.141683 (-0.123992) | 1.157713 / 1.452155 (-0.294442) | 1.210485 / 1.492716 (-0.282231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088959 / 0.018006 (0.070953) | 0.298531 / 0.000490 (0.298041) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021129 / 0.037411 (-0.016283) | 0.068419 / 0.014526 (0.053893) | 0.079328 / 0.176557 (-0.097228) | 0.118603 / 0.737135 (-0.618532) | 0.080489 / 0.296338 (-0.215850) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292464 / 0.215209 (0.077254) | 2.898221 / 2.077655 (0.820566) | 1.600868 / 1.504120 (0.096748) | 1.485128 / 1.541195 (-0.056067) | 1.493091 / 1.468490 (0.024600) | 0.576117 / 4.584777 (-4.008660) | 2.450440 / 3.745712 (-1.295273) | 2.746026 / 5.269862 (-2.523836) | 1.722555 / 4.565676 (-2.843122) | 0.062869 / 0.424275 (-0.361406) | 0.004918 / 0.007607 (-0.002689) | 0.348470 / 0.226044 (0.122425) | 3.420267 / 2.268929 (1.151339) | 1.942973 / 55.444624 (-53.501651) | 1.667684 / 6.876477 (-5.208793) | 1.669618 / 2.142072 (-0.472454) | 0.630275 / 4.805227 (-4.174952) | 0.115072 / 6.500664 (-6.385592) | 0.040430 / 0.075469 (-0.035039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989827 / 1.841788 (-0.851961) | 11.578068 / 8.074308 (3.503760) | 10.636060 / 10.191392 (0.444668) | 0.131943 / 0.680424 (-0.548481) | 0.015915 / 0.534201 (-0.518286) | 0.287277 / 0.579283 (-0.292006) | 0.279451 / 0.434364 (-0.154913) | 0.325485 / 0.540337 (-0.214852) | 0.544635 / 1.386936 (-0.842301) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22579be6c73867ac1a3c03e925abaf4872f8437 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005144 / 0.011353 (-0.006209) | 0.003686 / 0.011008 (-0.007322) | 0.064003 / 0.038508 (0.025495) | 0.058962 / 0.023109 (0.035853) | 0.233753 / 0.275898 (-0.042145) | 0.255802 / 0.323480 (-0.067677) | 0.003871 / 0.007986 (-0.004115) | 0.002609 / 0.004328 (-0.001719) | 0.048675 / 0.004250 (0.044425) | 0.037550 / 0.037052 (0.000498) | 0.240658 / 0.258489 (-0.017831) | 0.272303 / 0.293841 (-0.021538) | 0.027455 / 0.128546 (-0.101091) | 0.010706 / 0.075646 (-0.064941) | 0.210878 / 0.419271 (-0.208393) | 0.035763 / 0.043533 (-0.007770) | 0.239937 / 0.255139 (-0.015202) | 0.262520 / 0.283200 (-0.020680) | 0.017676 / 0.141683 (-0.124006) | 1.095036 / 1.452155 (-0.357118) | 1.178318 / 1.492716 (-0.314399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095310 / 0.018006 (0.077304) | 0.307485 / 0.000490 (0.306995) | 0.000212 / 0.000200 (0.000013) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018630 / 0.037411 (-0.018781) | 0.060461 / 0.014526 (0.045936) | 0.073117 / 0.176557 (-0.103440) | 0.119737 / 0.737135 (-0.617399) | 0.073909 / 0.296338 (-0.222430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280938 / 0.215209 (0.065729) | 2.755333 / 2.077655 (0.677679) | 1.468153 / 1.504120 (-0.035967) | 1.350247 / 1.541195 (-0.190948) | 1.379834 / 1.468490 (-0.088656) | 0.564027 / 4.584777 (-4.020750) | 2.387794 / 3.745712 (-1.357918) | 2.768529 / 5.269862 (-2.501333) | 1.761994 / 4.565676 (-2.803682) | 0.062079 / 0.424275 (-0.362196) | 0.005018 / 0.007607 (-0.002589) | 0.337576 / 0.226044 (0.111532) | 3.345347 / 2.268929 (1.076418) | 1.821950 / 55.444624 (-53.622674) | 1.545471 / 6.876477 (-5.331006) | 1.534941 / 2.142072 (-0.607131) | 0.626560 / 4.805227 (-4.178668) | 0.116227 / 6.500664 (-6.384437) | 0.041722 / 0.075469 (-0.033747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950480 / 1.841788 (-0.891307) | 11.616355 / 8.074308 (3.542047) | 10.426687 / 10.191392 (0.235295) | 0.129967 / 0.680424 (-0.550457) | 0.013977 / 0.534201 (-0.520224) | 0.287150 / 0.579283 (-0.292133) | 0.264028 / 0.434364 (-0.170336) | 0.325061 / 0.540337 (-0.215277) | 0.441281 / 1.386936 (-0.945655) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005436 / 0.011353 (-0.005917) | 0.003567 / 0.011008 (-0.007441) | 0.055275 / 0.038508 (0.016767) | 0.053216 / 0.023109 (0.030107) | 0.272826 / 0.275898 (-0.003072) | 0.298399 / 0.323480 (-0.025081) | 0.004803 / 0.007986 (-0.003183) | 0.002681 / 0.004328 (-0.001648) | 0.048704 / 0.004250 (0.044453) | 0.040048 / 0.037052 (0.002996) | 0.278200 / 0.258489 (0.019711) | 0.331167 / 0.293841 (0.037326) | 0.029282 / 0.128546 (-0.099265) | 0.010766 / 0.075646 (-0.064881) | 0.057370 / 0.419271 (-0.361902) | 0.032674 / 0.043533 (-0.010859) | 0.269430 / 0.255139 (0.014291) | 0.288256 / 0.283200 (0.005056) | 0.019340 / 0.141683 (-0.122343) | 1.118058 / 1.452155 (-0.334097) | 1.157811 / 1.492716 (-0.334906) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094091 / 0.018006 (0.076085) | 0.301833 / 0.000490 (0.301343) | 0.000216 / 0.000200 (0.000016) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021327 / 0.037411 (-0.016085) | 0.068636 / 0.014526 (0.054110) | 0.080246 / 0.176557 (-0.096311) | 0.120524 / 0.737135 (-0.616611) | 0.082226 / 0.296338 (-0.214113) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293579 / 0.215209 (0.078370) | 2.880281 / 2.077655 (0.802626) | 1.594647 / 1.504120 (0.090528) | 1.477152 / 1.541195 (-0.064043) | 1.498122 / 1.468490 (0.029632) | 0.555073 / 4.584777 (-4.029704) | 2.446743 / 3.745712 (-1.298970) | 2.794971 / 5.269862 (-2.474890) | 1.749730 / 4.565676 (-2.815947) | 0.062537 / 0.424275 (-0.361738) | 0.004908 / 0.007607 (-0.002699) | 0.350772 / 0.226044 (0.124727) | 3.486535 / 2.268929 (1.217607) | 1.957414 / 55.444624 (-53.487210) | 1.669169 / 6.876477 (-5.207308) | 1.682396 / 2.142072 (-0.459676) | 0.627379 / 4.805227 (-4.177848) | 0.117218 / 6.500664 (-6.383446) | 0.041000 / 0.075469 (-0.034469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958248 / 1.841788 (-0.883539) | 12.022677 / 8.074308 (3.948369) | 10.331661 / 10.191392 (0.140269) | 0.129765 / 0.680424 (-0.550659) | 0.015073 / 0.534201 (-0.519128) | 0.287212 / 0.579283 (-0.292071) | 0.278310 / 0.434364 (-0.156054) | 0.328155 / 0.540337 (-0.212183) | 0.564990 / 1.386936 (-0.821946) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c16e56371e50adae771288945e3389cb81a31fd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005576 / 0.011353 (-0.005777) | 0.003430 / 0.011008 (-0.007578) | 0.062714 / 0.038508 (0.024206) | 0.051240 / 0.023109 (0.028131) | 0.236637 / 0.275898 (-0.039261) | 0.262660 / 0.323480 (-0.060820) | 0.002924 / 0.007986 (-0.005061) | 0.002712 / 0.004328 (-0.001616) | 0.048680 / 0.004250 (0.044430) | 0.038997 / 0.037052 (0.001945) | 0.241426 / 0.258489 (-0.017063) | 0.270652 / 0.293841 (-0.023189) | 0.027355 / 0.128546 (-0.101192) | 0.010640 / 0.075646 (-0.065006) | 0.207754 / 0.419271 (-0.211517) | 0.035921 / 0.043533 (-0.007612) | 0.247645 / 0.255139 (-0.007494) | 0.262933 / 0.283200 (-0.020266) | 0.019658 / 0.141683 (-0.122025) | 1.112576 / 1.452155 (-0.339578) | 1.177362 / 1.492716 (-0.315354) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098100 / 0.018006 (0.080093) | 0.310170 / 0.000490 (0.309680) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019626 / 0.037411 (-0.017785) | 0.065468 / 0.014526 (0.050942) | 0.074767 / 0.176557 (-0.101789) | 0.123619 / 0.737135 (-0.613516) | 0.077159 / 0.296338 (-0.219179) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288585 / 0.215209 (0.073376) | 2.771254 / 2.077655 (0.693599) | 1.457091 / 1.504120 (-0.047029) | 1.324341 / 1.541195 (-0.216854) | 1.361960 / 1.468490 (-0.106530) | 0.574197 / 4.584777 (-4.010580) | 2.391440 / 3.745712 (-1.354273) | 2.935060 / 5.269862 (-2.334802) | 1.802792 / 4.565676 (-2.762884) | 0.063530 / 0.424275 (-0.360745) | 0.005129 / 0.007607 (-0.002478) | 0.345977 / 0.226044 (0.119933) | 3.368042 / 2.268929 (1.099113) | 1.789575 / 55.444624 (-53.655050) | 1.509165 / 6.876477 (-5.367312) | 1.579792 / 2.142072 (-0.562280) | 0.652136 / 4.805227 (-4.153091) | 0.117014 / 6.500664 (-6.383650) | 0.042385 / 0.075469 (-0.033084) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963967 / 1.841788 (-0.877821) | 11.847856 / 8.074308 (3.773548) | 10.584088 / 10.191392 (0.392696) | 0.143953 / 0.680424 (-0.536471) | 0.014355 / 0.534201 (-0.519846) | 0.286936 / 0.579283 (-0.292347) | 0.269039 / 0.434364 (-0.165325) | 0.324531 / 0.540337 (-0.215807) | 0.443187 / 1.386936 (-0.943749) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005448 / 0.011353 (-0.005905) | 0.003742 / 0.011008 (-0.007266) | 0.048808 / 0.038508 (0.010300) | 0.055409 / 0.023109 (0.032300) | 0.271574 / 0.275898 (-0.004324) | 0.295599 / 0.323480 (-0.027881) | 0.004208 / 0.007986 (-0.003778) | 0.002683 / 0.004328 (-0.001645) | 0.048813 / 0.004250 (0.044562) | 0.043672 / 0.037052 (0.006620) | 0.282173 / 0.258489 (0.023684) | 0.295447 / 0.293841 (0.001606) | 0.030461 / 0.128546 (-0.098086) | 0.010988 / 0.075646 (-0.064658) | 0.057050 / 0.419271 (-0.362221) | 0.033329 / 0.043533 (-0.010203) | 0.269700 / 0.255139 (0.014561) | 0.287099 / 0.283200 (0.003899) | 0.018203 / 0.141683 (-0.123480) | 1.142584 / 1.452155 (-0.309571) | 1.181848 / 1.492716 (-0.310869) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096958 / 0.018006 (0.078952) | 0.310563 / 0.000490 (0.310074) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022213 / 0.037411 (-0.015199) | 0.072054 / 0.014526 (0.057528) | 0.086393 / 0.176557 (-0.090163) | 0.122431 / 0.737135 (-0.614704) | 0.085298 / 0.296338 (-0.211041) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290823 / 0.215209 (0.075614) | 2.838026 / 2.077655 (0.760371) | 1.541425 / 1.504120 (0.037305) | 1.431903 / 1.541195 (-0.109292) | 1.476567 / 1.468490 (0.008077) | 0.557856 / 4.584777 (-4.026920) | 2.449101 / 3.745712 (-1.296611) | 2.924633 / 5.269862 (-2.345229) | 1.824420 / 4.565676 (-2.741256) | 0.063735 / 0.424275 (-0.360540) | 0.005025 / 0.007607 (-0.002582) | 0.349458 / 0.226044 (0.123413) | 3.468627 / 2.268929 (1.199699) | 1.925173 / 55.444624 (-53.519451) | 1.655038 / 6.876477 (-5.221439) | 1.698612 / 2.142072 (-0.443460) | 0.643623 / 4.805227 (-4.161604) | 0.116128 / 6.500664 (-6.384536) | 0.042283 / 0.075469 (-0.033186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963029 / 1.841788 (-0.878758) | 13.273985 / 8.074308 (5.199677) | 11.400884 / 10.191392 (1.209492) | 0.152635 / 0.680424 (-0.527788) | 0.016442 / 0.534201 (-0.517759) | 0.289272 / 0.579283 (-0.290012) | 0.285286 / 0.434364 (-0.149078) | 0.330028 / 0.540337 (-0.210310) | 0.596500 / 1.386936 (-0.790436) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c427c4b1dcf84c898ae62dc521bf446bb35e0e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005124 / 0.011353 (-0.006229) | 0.003832 / 0.011008 (-0.007176) | 0.062806 / 0.038508 (0.024298) | 0.053137 / 0.023109 (0.030028) | 0.241155 / 0.275898 (-0.034743) | 0.260521 / 0.323480 (-0.062959) | 0.004005 / 0.007986 (-0.003981) | 0.002754 / 0.004328 (-0.001575) | 0.048934 / 0.004250 (0.044684) | 0.039438 / 0.037052 (0.002385) | 0.242534 / 0.258489 (-0.015955) | 0.275498 / 0.293841 (-0.018343) | 0.027338 / 0.128546 (-0.101208) | 0.010809 / 0.075646 (-0.064837) | 0.206986 / 0.419271 (-0.212285) | 0.035614 / 0.043533 (-0.007919) | 0.245780 / 0.255139 (-0.009359) | 0.259793 / 0.283200 (-0.023407) | 0.018108 / 0.141683 (-0.123575) | 1.103412 / 1.452155 (-0.348742) | 1.162940 / 1.492716 (-0.329776) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092463 / 0.018006 (0.074457) | 0.299516 / 0.000490 (0.299026) | 0.000210 / 0.000200 (0.000010) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018261 / 0.037411 (-0.019150) | 0.060178 / 0.014526 (0.045652) | 0.073043 / 0.176557 (-0.103513) | 0.120541 / 0.737135 (-0.616594) | 0.074972 / 0.296338 (-0.221367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287288 / 0.215209 (0.072078) | 2.814915 / 2.077655 (0.737260) | 1.520221 / 1.504120 (0.016101) | 1.396045 / 1.541195 (-0.145149) | 1.419662 / 1.468490 (-0.048828) | 0.589247 / 4.584777 (-3.995530) | 2.411101 / 3.745712 (-1.334611) | 2.777709 / 5.269862 (-2.492153) | 1.750386 / 4.565676 (-2.815291) | 0.063734 / 0.424275 (-0.360541) | 0.005021 / 0.007607 (-0.002586) | 0.338817 / 0.226044 (0.112773) | 3.371218 / 2.268929 (1.102289) | 1.892691 / 55.444624 (-53.551934) | 1.599039 / 6.876477 (-5.277438) | 1.574726 / 2.142072 (-0.567346) | 0.665623 / 4.805227 (-4.139604) | 0.118628 / 6.500664 (-6.382036) | 0.041803 / 0.075469 (-0.033666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948696 / 1.841788 (-0.893092) | 11.502916 / 8.074308 (3.428608) | 10.301174 / 10.191392 (0.109782) | 0.141752 / 0.680424 (-0.538672) | 0.014064 / 0.534201 (-0.520137) | 0.286701 / 0.579283 (-0.292583) | 0.265805 / 0.434364 (-0.168559) | 0.328420 / 0.540337 (-0.211917) | 0.433619 / 1.386936 (-0.953317) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005262 / 0.011353 (-0.006091) | 0.003361 / 0.011008 (-0.007648) | 0.049525 / 0.038508 (0.011016) | 0.048950 / 0.023109 (0.025841) | 0.273617 / 0.275898 (-0.002281) | 0.296614 / 0.323480 (-0.026866) | 0.004014 / 0.007986 (-0.003971) | 0.002630 / 0.004328 (-0.001698) | 0.048203 / 0.004250 (0.043952) | 0.040912 / 0.037052 (0.003860) | 0.279736 / 0.258489 (0.021247) | 0.301671 / 0.293841 (0.007830) | 0.028546 / 0.128546 (-0.100000) | 0.010440 / 0.075646 (-0.065206) | 0.057869 / 0.419271 (-0.361402) | 0.032876 / 0.043533 (-0.010657) | 0.277649 / 0.255139 (0.022510) | 0.296565 / 0.283200 (0.013365) | 0.017558 / 0.141683 (-0.124125) | 1.155005 / 1.452155 (-0.297149) | 1.204827 / 1.492716 (-0.287889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093248 / 0.018006 (0.075242) | 0.302721 / 0.000490 (0.302231) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021882 / 0.037411 (-0.015530) | 0.068259 / 0.014526 (0.053733) | 0.080982 / 0.176557 (-0.095574) | 0.119386 / 0.737135 (-0.617750) | 0.081745 / 0.296338 (-0.214593) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297812 / 0.215209 (0.082603) | 2.909938 / 2.077655 (0.832283) | 1.603736 / 1.504120 (0.099616) | 1.482989 / 1.541195 (-0.058206) | 1.495107 / 1.468490 (0.026617) | 0.562275 / 4.584777 (-4.022502) | 2.424812 / 3.745712 (-1.320901) | 2.759127 / 5.269862 (-2.510735) | 1.733283 / 4.565676 (-2.832394) | 0.063144 / 0.424275 (-0.361131) | 0.004949 / 0.007607 (-0.002658) | 0.352756 / 0.226044 (0.126711) | 3.496028 / 2.268929 (1.227100) | 1.982804 / 55.444624 (-53.461820) | 1.689787 / 6.876477 (-5.186690) | 1.672699 / 2.142072 (-0.469373) | 0.660169 / 4.805227 (-4.145059) | 0.116535 / 6.500664 (-6.384129) | 0.040616 / 0.075469 (-0.034853) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975055 / 1.841788 (-0.866733) | 11.919295 / 8.074308 (3.844986) | 10.779188 / 10.191392 (0.587796) | 0.143106 / 0.680424 (-0.537318) | 0.015159 / 0.534201 (-0.519041) | 0.289734 / 0.579283 (-0.289549) | 0.278637 / 0.434364 (-0.155727) | 0.328159 / 0.540337 (-0.212178) | 0.570560 / 1.386936 (-0.816376) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#241500208da5fef64ad6ddc1cc5ab2be18f2f76d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005155 / 0.011353 (-0.006198) | 0.003589 / 0.011008 (-0.007419) | 0.064440 / 0.038508 (0.025932) | 0.051020 / 0.023109 (0.027911) | 0.246099 / 0.275898 (-0.029799) | 0.273383 / 0.323480 (-0.050097) | 0.003984 / 0.007986 (-0.004002) | 0.002791 / 0.004328 (-0.001537) | 0.049076 / 0.004250 (0.044826) | 0.037975 / 0.037052 (0.000922) | 0.253709 / 0.258489 (-0.004780) | 0.281730 / 0.293841 (-0.012111) | 0.028060 / 0.128546 (-0.100486) | 0.010808 / 0.075646 (-0.064838) | 0.206663 / 0.419271 (-0.212609) | 0.035989 / 0.043533 (-0.007544) | 0.252635 / 0.255139 (-0.002504) | 0.280042 / 0.283200 (-0.003158) | 0.016982 / 0.141683 (-0.124700) | 1.098679 / 1.452155 (-0.353475) | 1.157051 / 1.492716 (-0.335666) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098238 / 0.018006 (0.080232) | 0.311990 / 0.000490 (0.311501) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018270 / 0.037411 (-0.019141) | 0.062711 / 0.014526 (0.048186) | 0.074381 / 0.176557 (-0.102175) | 0.119946 / 0.737135 (-0.617189) | 0.075013 / 0.296338 (-0.221325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282106 / 0.215209 (0.066897) | 2.752653 / 2.077655 (0.674999) | 1.488771 / 1.504120 (-0.015349) | 1.372552 / 1.541195 (-0.168643) | 1.390270 / 1.468490 (-0.078220) | 0.558928 / 4.584777 (-4.025849) | 2.411821 / 3.745712 (-1.333891) | 2.771441 / 5.269862 (-2.498421) | 1.747507 / 4.565676 (-2.818169) | 0.061360 / 0.424275 (-0.362915) | 0.004956 / 0.007607 (-0.002652) | 0.332330 / 0.226044 (0.106286) | 3.301405 / 2.268929 (1.032476) | 1.786726 / 55.444624 (-53.657899) | 1.529974 / 6.876477 (-5.346502) | 1.538412 / 2.142072 (-0.603660) | 0.637590 / 4.805227 (-4.167637) | 0.117215 / 6.500664 (-6.383449) | 0.042186 / 0.075469 (-0.033283) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945574 / 1.841788 (-0.896213) | 11.616152 / 8.074308 (3.541844) | 10.365114 / 10.191392 (0.173722) | 0.130358 / 0.680424 (-0.550066) | 0.013587 / 0.534201 (-0.520614) | 0.306024 / 0.579283 (-0.273259) | 0.270577 / 0.434364 (-0.163787) | 0.340768 / 0.540337 (-0.199569) | 0.460841 / 1.386936 (-0.926095) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005254 / 0.011353 (-0.006099) | 0.003137 / 0.011008 (-0.007871) | 0.048302 / 0.038508 (0.009794) | 0.051952 / 0.023109 (0.028843) | 0.269078 / 0.275898 (-0.006820) | 0.292044 / 0.323480 (-0.031436) | 0.003985 / 0.007986 (-0.004000) | 0.002597 / 0.004328 (-0.001732) | 0.049998 / 0.004250 (0.045747) | 0.040227 / 0.037052 (0.003174) | 0.274714 / 0.258489 (0.016225) | 0.298160 / 0.293841 (0.004319) | 0.028857 / 0.128546 (-0.099690) | 0.010545 / 0.075646 (-0.065101) | 0.057234 / 0.419271 (-0.362038) | 0.032515 / 0.043533 (-0.011018) | 0.271526 / 0.255139 (0.016387) | 0.288556 / 0.283200 (0.005356) | 0.018155 / 0.141683 (-0.123527) | 1.201906 / 1.452155 (-0.250248) | 1.220068 / 1.492716 (-0.272648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100098 / 0.018006 (0.082092) | 0.311081 / 0.000490 (0.310591) | 0.000231 / 0.000200 (0.000032) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022349 / 0.037411 (-0.015062) | 0.069698 / 0.014526 (0.055172) | 0.081334 / 0.176557 (-0.095222) | 0.120847 / 0.737135 (-0.616289) | 0.082091 / 0.296338 (-0.214248) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293810 / 0.215209 (0.078601) | 2.844191 / 2.077655 (0.766536) | 1.594494 / 1.504120 (0.090374) | 1.486531 / 1.541195 (-0.054664) | 1.506307 / 1.468490 (0.037817) | 0.560247 / 4.584777 (-4.024530) | 2.478309 / 3.745712 (-1.267403) | 2.759024 / 5.269862 (-2.510837) | 1.733063 / 4.565676 (-2.832613) | 0.061838 / 0.424275 (-0.362438) | 0.004869 / 0.007607 (-0.002738) | 0.347267 / 0.226044 (0.121222) | 3.407737 / 2.268929 (1.138808) | 1.944420 / 55.444624 (-53.500204) | 1.660060 / 6.876477 (-5.216417) | 1.704219 / 2.142072 (-0.437854) | 0.646969 / 4.805227 (-4.158258) | 0.115750 / 6.500664 (-6.384914) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972537 / 1.841788 (-0.869251) | 12.013530 / 8.074308 (3.939222) | 10.650215 / 10.191392 (0.458823) | 0.132877 / 0.680424 (-0.547547) | 0.016828 / 0.534201 (-0.517372) | 0.288321 / 0.579283 (-0.290962) | 0.284203 / 0.434364 (-0.150161) | 0.324016 / 0.540337 (-0.216321) | 0.575403 / 1.386936 (-0.811533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17ec1a7a610adba3db44f316a930b979872d4ef7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005925 / 0.011353 (-0.005427) | 0.005138 / 0.011008 (-0.005870) | 0.069865 / 0.038508 (0.031356) | 0.067181 / 0.023109 (0.044072) | 0.309642 / 0.275898 (0.033743) | 0.302919 / 0.323480 (-0.020561) | 0.003365 / 0.007986 (-0.004620) | 0.003148 / 0.004328 (-0.001180) | 0.054102 / 0.004250 (0.049852) | 0.044196 / 0.037052 (0.007143) | 0.306882 / 0.258489 (0.048393) | 0.315153 / 0.293841 (0.021313) | 0.030458 / 0.128546 (-0.098089) | 0.011773 / 0.075646 (-0.063874) | 0.235075 / 0.419271 (-0.184196) | 0.040840 / 0.043533 (-0.002693) | 0.279897 / 0.255139 (0.024758) | 0.316334 / 0.283200 (0.033135) | 0.020128 / 0.141683 (-0.121555) | 1.237327 / 1.452155 (-0.214828) | 1.290386 / 1.492716 (-0.202331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.118540 / 0.018006 (0.100534) | 0.363282 / 0.000490 (0.362792) | 0.000266 / 0.000200 (0.000066) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021435 / 0.037411 (-0.015977) | 0.068124 / 0.014526 (0.053598) | 0.082747 / 0.176557 (-0.093809) | 0.137179 / 0.737135 (-0.599956) | 0.084815 / 0.296338 (-0.211523) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.307836 / 0.215209 (0.092626) | 2.983444 / 2.077655 (0.905790) | 1.616430 / 1.504120 (0.112310) | 1.466843 / 1.541195 (-0.074351) | 1.512440 / 1.468490 (0.043950) | 0.652311 / 4.584777 (-3.932466) | 2.676420 / 3.745712 (-1.069292) | 3.265747 / 5.269862 (-2.004115) | 2.028586 / 4.565676 (-2.537090) | 0.071997 / 0.424275 (-0.352278) | 0.007068 / 0.007607 (-0.000539) | 0.367199 / 0.226044 (0.141155) | 3.617970 / 2.268929 (1.349042) | 1.991345 / 55.444624 (-53.453280) | 1.670015 / 6.876477 (-5.206462) | 1.720515 / 2.142072 (-0.421557) | 0.724649 / 4.805227 (-4.080579) | 0.134888 / 6.500664 (-6.365776) | 0.048325 / 0.075469 (-0.027144) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.051058 / 1.841788 (-0.790730) | 13.772809 / 8.074308 (5.698501) | 11.813879 / 10.191392 (1.622487) | 0.160065 / 0.680424 (-0.520359) | 0.016256 / 0.534201 (-0.517945) | 0.320393 / 0.579283 (-0.258890) | 0.314462 / 0.434364 (-0.119901) | 0.371911 / 0.540337 (-0.168427) | 0.506864 / 1.386936 (-0.880072) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005857 / 0.011353 (-0.005496) | 0.004077 / 0.011008 (-0.006931) | 0.056033 / 0.038508 (0.017525) | 0.067622 / 0.023109 (0.044513) | 0.298956 / 0.275898 (0.023058) | 0.323484 / 0.323480 (0.000004) | 0.004825 / 0.007986 (-0.003160) | 0.003120 / 0.004328 (-0.001208) | 0.055227 / 0.004250 (0.050976) | 0.048439 / 0.037052 (0.011387) | 0.303207 / 0.258489 (0.044718) | 0.329478 / 0.293841 (0.035637) | 0.032516 / 0.128546 (-0.096031) | 0.012260 / 0.075646 (-0.063386) | 0.065037 / 0.419271 (-0.354234) | 0.038799 / 0.043533 (-0.004734) | 0.299102 / 0.255139 (0.043963) | 0.318248 / 0.283200 (0.035048) | 0.020190 / 0.141683 (-0.121493) | 1.263479 / 1.452155 (-0.188676) | 1.329788 / 1.492716 (-0.162928) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.119801 / 0.018006 (0.101794) | 0.359618 / 0.000490 (0.359129) | 0.000260 / 0.000200 (0.000060) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026876 / 0.037411 (-0.010535) | 0.080637 / 0.014526 (0.066111) | 0.092260 / 0.176557 (-0.084297) | 0.137260 / 0.737135 (-0.599875) | 0.093309 / 0.296338 (-0.203029) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.329327 / 0.215209 (0.114118) | 3.193014 / 2.077655 (1.115359) | 1.755838 / 1.504120 (0.251718) | 1.612279 / 1.541195 (0.071084) | 1.631958 / 1.468490 (0.163468) | 0.630886 / 4.584777 (-3.953891) | 2.739731 / 3.745712 (-1.005981) | 3.186745 / 5.269862 (-2.083117) | 1.987125 / 4.565676 (-2.578552) | 0.070694 / 0.424275 (-0.353581) | 0.006461 / 0.007607 (-0.001146) | 0.386367 / 0.226044 (0.160323) | 3.815837 / 2.268929 (1.546908) | 2.155904 / 55.444624 (-53.288720) | 1.832575 / 6.876477 (-5.043902) | 1.842097 / 2.142072 (-0.299975) | 0.716394 / 4.805227 (-4.088833) | 0.130796 / 6.500664 (-6.369869) | 0.045674 / 0.075469 (-0.029795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.109117 / 1.841788 (-0.732671) | 14.116582 / 8.074308 (6.042274) | 11.926356 / 10.191392 (1.734964) | 0.150543 / 0.680424 (-0.529881) | 0.017426 / 0.534201 (-0.516775) | 0.323058 / 0.579283 (-0.256225) | 0.330228 / 0.434364 (-0.104136) | 0.372533 / 0.540337 (-0.167804) | 0.661348 / 1.386936 (-0.725588) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04ffd22a30ecc7545234559edd9d23c85c6d84d9 \"CML watermark\")\n", "Thanks for the review, I took your comments into account !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005477 / 0.011353 (-0.005876) | 0.003509 / 0.011008 (-0.007499) | 0.062884 / 0.038508 (0.024376) | 0.051042 / 0.023109 (0.027933) | 0.285180 / 0.275898 (0.009282) | 0.315353 / 0.323480 (-0.008127) | 0.002943 / 0.007986 (-0.005043) | 0.003286 / 0.004328 (-0.001042) | 0.048885 / 0.004250 (0.044635) | 0.038591 / 0.037052 (0.001539) | 0.288527 / 0.258489 (0.030038) | 0.316102 / 0.293841 (0.022261) | 0.028252 / 0.128546 (-0.100295) | 0.010622 / 0.075646 (-0.065024) | 0.205573 / 0.419271 (-0.213699) | 0.035764 / 0.043533 (-0.007769) | 0.285729 / 0.255139 (0.030590) | 0.304578 / 0.283200 (0.021378) | 0.019862 / 0.141683 (-0.121821) | 1.102866 / 1.452155 (-0.349288) | 1.175161 / 1.492716 (-0.317555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095253 / 0.018006 (0.077246) | 0.302290 / 0.000490 (0.301800) | 0.000243 / 0.000200 (0.000043) | 0.000061 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018680 / 0.037411 (-0.018731) | 0.060375 / 0.014526 (0.045849) | 0.074033 / 0.176557 (-0.102524) | 0.120290 / 0.737135 (-0.616845) | 0.075350 / 0.296338 (-0.220989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277617 / 0.215209 (0.062408) | 2.718201 / 2.077655 (0.640546) | 1.462952 / 1.504120 (-0.041168) | 1.339199 / 1.541195 (-0.201996) | 1.375805 / 1.468490 (-0.092685) | 0.559956 / 4.584777 (-4.024821) | 2.373865 / 3.745712 (-1.371847) | 2.795732 / 5.269862 (-2.474129) | 1.755490 / 4.565676 (-2.810186) | 0.062002 / 0.424275 (-0.362273) | 0.004935 / 0.007607 (-0.002672) | 0.334786 / 0.226044 (0.108741) | 3.237499 / 2.268929 (0.968571) | 1.787561 / 55.444624 (-53.657064) | 1.513300 / 6.876477 (-5.363176) | 1.549797 / 2.142072 (-0.592275) | 0.643587 / 4.805227 (-4.161640) | 0.117275 / 6.500664 (-6.383389) | 0.042184 / 0.075469 (-0.033285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933366 / 1.841788 (-0.908421) | 11.792282 / 8.074308 (3.717973) | 10.466608 / 10.191392 (0.275216) | 0.142148 / 0.680424 (-0.538275) | 0.014084 / 0.534201 (-0.520117) | 0.287233 / 0.579283 (-0.292050) | 0.266022 / 0.434364 (-0.168342) | 0.326854 / 0.540337 (-0.213483) | 0.451348 / 1.386936 (-0.935588) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005384 / 0.011353 (-0.005969) | 0.003562 / 0.011008 (-0.007446) | 0.049014 / 0.038508 (0.010506) | 0.057480 / 0.023109 (0.034371) | 0.274456 / 0.275898 (-0.001442) | 0.298387 / 0.323480 (-0.025093) | 0.003909 / 0.007986 (-0.004076) | 0.002646 / 0.004328 (-0.001683) | 0.048374 / 0.004250 (0.044124) | 0.040907 / 0.037052 (0.003854) | 0.278267 / 0.258489 (0.019778) | 0.299862 / 0.293841 (0.006021) | 0.029108 / 0.128546 (-0.099439) | 0.010752 / 0.075646 (-0.064894) | 0.057523 / 0.419271 (-0.361749) | 0.032692 / 0.043533 (-0.010841) | 0.276288 / 0.255139 (0.021149) | 0.291572 / 0.283200 (0.008372) | 0.017818 / 0.141683 (-0.123865) | 1.129517 / 1.452155 (-0.322638) | 1.186630 / 1.492716 (-0.306086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093405 / 0.018006 (0.075399) | 0.301254 / 0.000490 (0.300764) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021793 / 0.037411 (-0.015618) | 0.069033 / 0.014526 (0.054508) | 0.083502 / 0.176557 (-0.093055) | 0.122149 / 0.737135 (-0.614986) | 0.083801 / 0.296338 (-0.212537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299149 / 0.215209 (0.083940) | 2.936550 / 2.077655 (0.858895) | 1.595766 / 1.504120 (0.091647) | 1.487117 / 1.541195 (-0.054078) | 1.494606 / 1.468490 (0.026116) | 0.569346 / 4.584777 (-4.015431) | 2.445642 / 3.745712 (-1.300070) | 2.805696 / 5.269862 (-2.464165) | 1.743796 / 4.565676 (-2.821881) | 0.062695 / 0.424275 (-0.361580) | 0.004885 / 0.007607 (-0.002723) | 0.354186 / 0.226044 (0.128142) | 3.487926 / 2.268929 (1.218997) | 1.965703 / 55.444624 (-53.478922) | 1.682284 / 6.876477 (-5.194193) | 1.705586 / 2.142072 (-0.436487) | 0.655099 / 4.805227 (-4.150128) | 0.116441 / 6.500664 (-6.384223) | 0.040851 / 0.075469 (-0.034618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967361 / 1.841788 (-0.874427) | 12.037718 / 8.074308 (3.963409) | 10.599761 / 10.191392 (0.408369) | 0.143127 / 0.680424 (-0.537297) | 0.015063 / 0.534201 (-0.519138) | 0.286894 / 0.579283 (-0.292389) | 0.301505 / 0.434364 (-0.132859) | 0.324339 / 0.540337 (-0.215999) | 0.591782 / 1.386936 (-0.795154) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96ff08d4aa6dbafc8a10a9d03dfabe236378bcd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005337 / 0.011353 (-0.006015) | 0.004074 / 0.011008 (-0.006934) | 0.062653 / 0.038508 (0.024145) | 0.054295 / 0.023109 (0.031186) | 0.248284 / 0.275898 (-0.027614) | 0.271604 / 0.323480 (-0.051876) | 0.003931 / 0.007986 (-0.004055) | 0.002907 / 0.004328 (-0.001422) | 0.047991 / 0.004250 (0.043740) | 0.042842 / 0.037052 (0.005790) | 0.253648 / 0.258489 (-0.004841) | 0.282546 / 0.293841 (-0.011295) | 0.028005 / 0.128546 (-0.100541) | 0.010734 / 0.075646 (-0.064912) | 0.210023 / 0.419271 (-0.209248) | 0.035940 / 0.043533 (-0.007592) | 0.250766 / 0.255139 (-0.004373) | 0.267644 / 0.283200 (-0.015556) | 0.020451 / 0.141683 (-0.121232) | 1.114972 / 1.452155 (-0.337183) | 1.159823 / 1.492716 (-0.332893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095527 / 0.018006 (0.077521) | 0.303321 / 0.000490 (0.302831) | 0.000216 / 0.000200 (0.000016) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018725 / 0.037411 (-0.018686) | 0.062537 / 0.014526 (0.048011) | 0.073091 / 0.176557 (-0.103466) | 0.119570 / 0.737135 (-0.617565) | 0.074863 / 0.296338 (-0.221476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284936 / 0.215209 (0.069727) | 2.802498 / 2.077655 (0.724843) | 1.493316 / 1.504120 (-0.010804) | 1.372319 / 1.541195 (-0.168875) | 1.403657 / 1.468490 (-0.064833) | 0.569303 / 4.584777 (-4.015474) | 2.402498 / 3.745712 (-1.343214) | 2.834778 / 5.269862 (-2.435084) | 1.791312 / 4.565676 (-2.774365) | 0.062526 / 0.424275 (-0.361749) | 0.004947 / 0.007607 (-0.002660) | 0.345141 / 0.226044 (0.119097) | 3.371863 / 2.268929 (1.102934) | 1.846023 / 55.444624 (-53.598602) | 1.596368 / 6.876477 (-5.280109) | 1.615902 / 2.142072 (-0.526170) | 0.644333 / 4.805227 (-4.160894) | 0.119460 / 6.500664 (-6.381204) | 0.049122 / 0.075469 (-0.026347) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951839 / 1.841788 (-0.889948) | 11.677074 / 8.074308 (3.602766) | 10.562586 / 10.191392 (0.371194) | 0.143633 / 0.680424 (-0.536791) | 0.014157 / 0.534201 (-0.520044) | 0.289141 / 0.579283 (-0.290142) | 0.264719 / 0.434364 (-0.169645) | 0.327862 / 0.540337 (-0.212476) | 0.451215 / 1.386936 (-0.935721) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005343 / 0.011353 (-0.006010) | 0.003522 / 0.011008 (-0.007486) | 0.049354 / 0.038508 (0.010846) | 0.051441 / 0.023109 (0.028332) | 0.259350 / 0.275898 (-0.016548) | 0.288946 / 0.323480 (-0.034534) | 0.004052 / 0.007986 (-0.003934) | 0.002690 / 0.004328 (-0.001639) | 0.049996 / 0.004250 (0.045746) | 0.040224 / 0.037052 (0.003171) | 0.264588 / 0.258489 (0.006099) | 0.296474 / 0.293841 (0.002633) | 0.028868 / 0.128546 (-0.099679) | 0.010917 / 0.075646 (-0.064730) | 0.057866 / 0.419271 (-0.361405) | 0.032610 / 0.043533 (-0.010923) | 0.260657 / 0.255139 (0.005518) | 0.276947 / 0.283200 (-0.006253) | 0.018877 / 0.141683 (-0.122806) | 1.126205 / 1.452155 (-0.325949) | 1.206173 / 1.492716 (-0.286543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094464 / 0.018006 (0.076458) | 0.304473 / 0.000490 (0.303984) | 0.000231 / 0.000200 (0.000031) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021472 / 0.037411 (-0.015939) | 0.070864 / 0.014526 (0.056338) | 0.086607 / 0.176557 (-0.089950) | 0.120679 / 0.737135 (-0.616456) | 0.084271 / 0.296338 (-0.212068) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296448 / 0.215209 (0.081239) | 2.893996 / 2.077655 (0.816341) | 1.573409 / 1.504120 (0.069289) | 1.438799 / 1.541195 (-0.102396) | 1.461241 / 1.468490 (-0.007249) | 0.566737 / 4.584777 (-4.018040) | 2.425709 / 3.745712 (-1.320003) | 2.826764 / 5.269862 (-2.443098) | 1.785330 / 4.565676 (-2.780347) | 0.063721 / 0.424275 (-0.360554) | 0.005158 / 0.007607 (-0.002449) | 0.354961 / 0.226044 (0.128916) | 3.457499 / 2.268929 (1.188570) | 1.931374 / 55.444624 (-53.513251) | 1.646515 / 6.876477 (-5.229962) | 1.629891 / 2.142072 (-0.512182) | 0.648922 / 4.805227 (-4.156305) | 0.114953 / 6.500664 (-6.385711) | 0.040997 / 0.075469 (-0.034472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951049 / 1.841788 (-0.890739) | 12.258298 / 8.074308 (4.183990) | 10.663309 / 10.191392 (0.471917) | 0.142933 / 0.680424 (-0.537491) | 0.015927 / 0.534201 (-0.518273) | 0.286914 / 0.579283 (-0.292369) | 0.286600 / 0.434364 (-0.147764) | 0.324464 / 0.540337 (-0.215874) | 0.575075 / 1.386936 (-0.811861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed47b9d5e9c6aa03a0aa07d8abfd3fa8241da353 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005298 / 0.011353 (-0.006055) | 0.003645 / 0.011008 (-0.007363) | 0.061629 / 0.038508 (0.023121) | 0.052322 / 0.023109 (0.029212) | 0.242579 / 0.275898 (-0.033319) | 0.263525 / 0.323480 (-0.059955) | 0.002794 / 0.007986 (-0.005192) | 0.002152 / 0.004328 (-0.002177) | 0.048301 / 0.004250 (0.044050) | 0.038177 / 0.037052 (0.001125) | 0.247724 / 0.258489 (-0.010765) | 0.274455 / 0.293841 (-0.019386) | 0.026992 / 0.128546 (-0.101555) | 0.010110 / 0.075646 (-0.065536) | 0.205662 / 0.419271 (-0.213609) | 0.034901 / 0.043533 (-0.008632) | 0.241920 / 0.255139 (-0.013219) | 0.262048 / 0.283200 (-0.021152) | 0.019111 / 0.141683 (-0.122572) | 1.127600 / 1.452155 (-0.324555) | 1.193931 / 1.492716 (-0.298786) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090321 / 0.018006 (0.072315) | 0.299046 / 0.000490 (0.298556) | 0.000197 / 0.000200 (-0.000003) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018278 / 0.037411 (-0.019133) | 0.060114 / 0.014526 (0.045588) | 0.073602 / 0.176557 (-0.102954) | 0.119676 / 0.737135 (-0.617459) | 0.074786 / 0.296338 (-0.221552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280385 / 0.215209 (0.065176) | 2.764259 / 2.077655 (0.686604) | 1.501027 / 1.504120 (-0.003093) | 1.376900 / 1.541195 (-0.164295) | 1.390587 / 1.468490 (-0.077903) | 0.555180 / 4.584777 (-4.029597) | 2.354307 / 3.745712 (-1.391405) | 2.755862 / 5.269862 (-2.514000) | 1.714771 / 4.565676 (-2.850906) | 0.062507 / 0.424275 (-0.361768) | 0.004974 / 0.007607 (-0.002633) | 0.333900 / 0.226044 (0.107856) | 3.266922 / 2.268929 (0.997994) | 1.805401 / 55.444624 (-53.639223) | 1.526970 / 6.876477 (-5.349507) | 1.539425 / 2.142072 (-0.602647) | 0.629364 / 4.805227 (-4.175863) | 0.114929 / 6.500664 (-6.385735) | 0.041258 / 0.075469 (-0.034211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968601 / 1.841788 (-0.873187) | 11.260937 / 8.074308 (3.186629) | 10.393839 / 10.191392 (0.202447) | 0.127988 / 0.680424 (-0.552436) | 0.014564 / 0.534201 (-0.519637) | 0.286560 / 0.579283 (-0.292723) | 0.260493 / 0.434364 (-0.173871) | 0.330949 / 0.540337 (-0.209388) | 0.435798 / 1.386936 (-0.951138) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005232 / 0.011353 (-0.006121) | 0.003030 / 0.011008 (-0.007978) | 0.048513 / 0.038508 (0.010005) | 0.049501 / 0.023109 (0.026392) | 0.270545 / 0.275898 (-0.005353) | 0.289128 / 0.323480 (-0.034352) | 0.003925 / 0.007986 (-0.004061) | 0.002568 / 0.004328 (-0.001761) | 0.047692 / 0.004250 (0.043442) | 0.039854 / 0.037052 (0.002802) | 0.272654 / 0.258489 (0.014165) | 0.296275 / 0.293841 (0.002434) | 0.029027 / 0.128546 (-0.099519) | 0.010335 / 0.075646 (-0.065311) | 0.056726 / 0.419271 (-0.362546) | 0.033257 / 0.043533 (-0.010275) | 0.272672 / 0.255139 (0.017533) | 0.286298 / 0.283200 (0.003098) | 0.017877 / 0.141683 (-0.123806) | 1.150322 / 1.452155 (-0.301833) | 1.221031 / 1.492716 (-0.271685) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102838 / 0.018006 (0.084832) | 0.298810 / 0.000490 (0.298320) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021232 / 0.037411 (-0.016180) | 0.067949 / 0.014526 (0.053423) | 0.116487 / 0.176557 (-0.060070) | 0.124035 / 0.737135 (-0.613100) | 0.081075 / 0.296338 (-0.215263) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289098 / 0.215209 (0.073889) | 2.844476 / 2.077655 (0.766821) | 1.609576 / 1.504120 (0.105456) | 1.480453 / 1.541195 (-0.060742) | 1.489672 / 1.468490 (0.021182) | 0.589661 / 4.584777 (-3.995116) | 2.453804 / 3.745712 (-1.291908) | 2.722381 / 5.269862 (-2.547480) | 1.720251 / 4.565676 (-2.845425) | 0.066085 / 0.424275 (-0.358190) | 0.004943 / 0.007607 (-0.002664) | 0.355149 / 0.226044 (0.129104) | 3.444323 / 2.268929 (1.175395) | 1.971157 / 55.444624 (-53.473467) | 1.683029 / 6.876477 (-5.193448) | 1.672798 / 2.142072 (-0.469274) | 0.644812 / 4.805227 (-4.160416) | 0.115098 / 6.500664 (-6.385566) | 0.039883 / 0.075469 (-0.035586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960454 / 1.841788 (-0.881334) | 11.604732 / 8.074308 (3.530424) | 10.405481 / 10.191392 (0.214089) | 0.129146 / 0.680424 (-0.551278) | 0.014945 / 0.534201 (-0.519256) | 0.286239 / 0.579283 (-0.293044) | 0.281041 / 0.434364 (-0.153323) | 0.320448 / 0.540337 (-0.219890) | 0.554304 / 1.386936 (-0.832632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2cfb7859b029654829c4dfee230812ddab1f104 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005510 / 0.011353 (-0.005843) | 0.003575 / 0.011008 (-0.007433) | 0.062232 / 0.038508 (0.023724) | 0.051115 / 0.023109 (0.028006) | 0.250709 / 0.275898 (-0.025189) | 0.274837 / 0.323480 (-0.048642) | 0.002972 / 0.007986 (-0.005014) | 0.002708 / 0.004328 (-0.001621) | 0.048088 / 0.004250 (0.043838) | 0.038588 / 0.037052 (0.001535) | 0.252550 / 0.258489 (-0.005939) | 0.285238 / 0.293841 (-0.008603) | 0.027867 / 0.128546 (-0.100679) | 0.011000 / 0.075646 (-0.064646) | 0.206918 / 0.419271 (-0.212354) | 0.035711 / 0.043533 (-0.007822) | 0.255306 / 0.255139 (0.000167) | 0.298636 / 0.283200 (0.015436) | 0.018222 / 0.141683 (-0.123461) | 1.122276 / 1.452155 (-0.329879) | 1.196471 / 1.492716 (-0.296245) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092072 / 0.018006 (0.074066) | 0.301469 / 0.000490 (0.300979) | 0.000225 / 0.000200 (0.000025) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018672 / 0.037411 (-0.018739) | 0.060235 / 0.014526 (0.045709) | 0.074036 / 0.176557 (-0.102521) | 0.119578 / 0.737135 (-0.617557) | 0.073605 / 0.296338 (-0.222734) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286474 / 0.215209 (0.071264) | 2.779427 / 2.077655 (0.701772) | 1.478746 / 1.504120 (-0.025373) | 1.362692 / 1.541195 (-0.178503) | 1.388194 / 1.468490 (-0.080296) | 0.560707 / 4.584777 (-4.024070) | 2.352846 / 3.745712 (-1.392866) | 2.784400 / 5.269862 (-2.485461) | 1.775642 / 4.565676 (-2.790035) | 0.062324 / 0.424275 (-0.361951) | 0.004938 / 0.007607 (-0.002669) | 0.334149 / 0.226044 (0.108105) | 3.319446 / 2.268929 (1.050517) | 1.810369 / 55.444624 (-53.634255) | 1.559462 / 6.876477 (-5.317014) | 1.611199 / 2.142072 (-0.530873) | 0.655984 / 4.805227 (-4.149244) | 0.118508 / 6.500664 (-6.382156) | 0.043661 / 0.075469 (-0.031808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935046 / 1.841788 (-0.906742) | 11.413501 / 8.074308 (3.339192) | 10.392314 / 10.191392 (0.200922) | 0.131507 / 0.680424 (-0.548917) | 0.014827 / 0.534201 (-0.519374) | 0.289069 / 0.579283 (-0.290214) | 0.268288 / 0.434364 (-0.166076) | 0.326843 / 0.540337 (-0.213495) | 0.441283 / 1.386936 (-0.945653) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005375 / 0.011353 (-0.005978) | 0.003549 / 0.011008 (-0.007459) | 0.048996 / 0.038508 (0.010488) | 0.051408 / 0.023109 (0.028298) | 0.272265 / 0.275898 (-0.003633) | 0.293228 / 0.323480 (-0.030252) | 0.004147 / 0.007986 (-0.003839) | 0.002673 / 0.004328 (-0.001655) | 0.048116 / 0.004250 (0.043865) | 0.039926 / 0.037052 (0.002874) | 0.276987 / 0.258489 (0.018498) | 0.302955 / 0.293841 (0.009115) | 0.029488 / 0.128546 (-0.099058) | 0.010797 / 0.075646 (-0.064849) | 0.057552 / 0.419271 (-0.361720) | 0.032827 / 0.043533 (-0.010706) | 0.270888 / 0.255139 (0.015749) | 0.289136 / 0.283200 (0.005937) | 0.018815 / 0.141683 (-0.122868) | 1.148624 / 1.452155 (-0.303530) | 1.191184 / 1.492716 (-0.301532) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091712 / 0.018006 (0.073706) | 0.311198 / 0.000490 (0.310708) | 0.000226 / 0.000200 (0.000026) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022097 / 0.037411 (-0.015314) | 0.070641 / 0.014526 (0.056116) | 0.080084 / 0.176557 (-0.096472) | 0.118998 / 0.737135 (-0.618137) | 0.081827 / 0.296338 (-0.214512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298599 / 0.215209 (0.083390) | 2.884759 / 2.077655 (0.807105) | 1.630794 / 1.504120 (0.126674) | 1.454309 / 1.541195 (-0.086886) | 1.466795 / 1.468490 (-0.001695) | 0.565405 / 4.584777 (-4.019372) | 2.460883 / 3.745712 (-1.284829) | 2.764193 / 5.269862 (-2.505668) | 1.734270 / 4.565676 (-2.831407) | 0.063408 / 0.424275 (-0.360867) | 0.004887 / 0.007607 (-0.002720) | 0.347762 / 0.226044 (0.121717) | 3.458385 / 2.268929 (1.189457) | 1.965434 / 55.444624 (-53.479190) | 1.671047 / 6.876477 (-5.205430) | 1.665642 / 2.142072 (-0.476430) | 0.640665 / 4.805227 (-4.164562) | 0.116025 / 6.500664 (-6.384639) | 0.040147 / 0.075469 (-0.035322) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982194 / 1.841788 (-0.859593) | 11.983487 / 8.074308 (3.909179) | 10.660605 / 10.191392 (0.469213) | 0.140647 / 0.680424 (-0.539777) | 0.015870 / 0.534201 (-0.518331) | 0.287032 / 0.579283 (-0.292251) | 0.276629 / 0.434364 (-0.157735) | 0.331171 / 0.540337 (-0.209166) | 0.575346 / 1.386936 (-0.811590) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56433c2f6a42d5fcc5acb46c6275911c29afc371 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005014 / 0.011353 (-0.006339) | 0.003434 / 0.011008 (-0.007574) | 0.063283 / 0.038508 (0.024775) | 0.048068 / 0.023109 (0.024959) | 0.239521 / 0.275898 (-0.036377) | 0.265294 / 0.323480 (-0.058186) | 0.003790 / 0.007986 (-0.004196) | 0.002577 / 0.004328 (-0.001751) | 0.048618 / 0.004250 (0.044368) | 0.037427 / 0.037052 (0.000375) | 0.245263 / 0.258489 (-0.013226) | 0.276618 / 0.293841 (-0.017223) | 0.026615 / 0.128546 (-0.101931) | 0.010378 / 0.075646 (-0.065268) | 0.205670 / 0.419271 (-0.213601) | 0.035076 / 0.043533 (-0.008457) | 0.245062 / 0.255139 (-0.010077) | 0.264584 / 0.283200 (-0.018616) | 0.017760 / 0.141683 (-0.123922) | 1.148061 / 1.452155 (-0.304094) | 1.192762 / 1.492716 (-0.299955) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090870 / 0.018006 (0.072864) | 0.305458 / 0.000490 (0.304968) | 0.000207 / 0.000200 (0.000007) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018597 / 0.037411 (-0.018814) | 0.060349 / 0.014526 (0.045823) | 0.074854 / 0.176557 (-0.101702) | 0.123243 / 0.737135 (-0.613892) | 0.075843 / 0.296338 (-0.220496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275855 / 0.215209 (0.060645) | 2.723965 / 2.077655 (0.646311) | 1.436010 / 1.504120 (-0.068110) | 1.323495 / 1.541195 (-0.217700) | 1.356234 / 1.468490 (-0.112256) | 0.564388 / 4.584777 (-4.020389) | 2.390180 / 3.745712 (-1.355532) | 2.782863 / 5.269862 (-2.486998) | 1.765048 / 4.565676 (-2.800628) | 0.062680 / 0.424275 (-0.361595) | 0.004929 / 0.007607 (-0.002678) | 0.337578 / 0.226044 (0.111533) | 3.316780 / 2.268929 (1.047851) | 1.803829 / 55.444624 (-53.640795) | 1.524585 / 6.876477 (-5.351891) | 1.549695 / 2.142072 (-0.592377) | 0.638053 / 4.805227 (-4.167174) | 0.116983 / 6.500664 (-6.383681) | 0.042251 / 0.075469 (-0.033218) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946978 / 1.841788 (-0.894810) | 11.809483 / 8.074308 (3.735175) | 10.459974 / 10.191392 (0.268582) | 0.130015 / 0.680424 (-0.550409) | 0.013843 / 0.534201 (-0.520358) | 0.286972 / 0.579283 (-0.292311) | 0.268904 / 0.434364 (-0.165460) | 0.325591 / 0.540337 (-0.214746) | 0.439233 / 1.386936 (-0.947703) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005804 / 0.011353 (-0.005549) | 0.003431 / 0.011008 (-0.007577) | 0.049041 / 0.038508 (0.010533) | 0.054758 / 0.023109 (0.031649) | 0.262330 / 0.275898 (-0.013568) | 0.288872 / 0.323480 (-0.034608) | 0.004016 / 0.007986 (-0.003970) | 0.002606 / 0.004328 (-0.001722) | 0.047878 / 0.004250 (0.043628) | 0.045066 / 0.037052 (0.008013) | 0.266310 / 0.258489 (0.007820) | 0.290072 / 0.293841 (-0.003768) | 0.028738 / 0.128546 (-0.099809) | 0.010667 / 0.075646 (-0.064979) | 0.057300 / 0.419271 (-0.361972) | 0.032715 / 0.043533 (-0.010818) | 0.264043 / 0.255139 (0.008904) | 0.278652 / 0.283200 (-0.004547) | 0.017873 / 0.141683 (-0.123810) | 1.125981 / 1.452155 (-0.326174) | 1.168548 / 1.492716 (-0.324168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090997 / 0.018006 (0.072991) | 0.300807 / 0.000490 (0.300317) | 0.000223 / 0.000200 (0.000023) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021510 / 0.037411 (-0.015901) | 0.068251 / 0.014526 (0.053725) | 0.082073 / 0.176557 (-0.094484) | 0.120071 / 0.737135 (-0.617064) | 0.082245 / 0.296338 (-0.214093) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290601 / 0.215209 (0.075392) | 2.871855 / 2.077655 (0.794200) | 1.558239 / 1.504120 (0.054119) | 1.447767 / 1.541195 (-0.093427) | 1.446851 / 1.468490 (-0.021639) | 0.573990 / 4.584777 (-4.010787) | 2.439859 / 3.745712 (-1.305853) | 2.795899 / 5.269862 (-2.473963) | 1.746751 / 4.565676 (-2.818926) | 0.062100 / 0.424275 (-0.362175) | 0.004948 / 0.007607 (-0.002659) | 0.344281 / 0.226044 (0.118236) | 3.427499 / 2.268929 (1.158570) | 1.940348 / 55.444624 (-53.504276) | 1.660926 / 6.876477 (-5.215551) | 1.669485 / 2.142072 (-0.472588) | 0.634034 / 4.805227 (-4.171193) | 0.114748 / 6.500664 (-6.385916) | 0.041617 / 0.075469 (-0.033852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966411 / 1.841788 (-0.875376) | 12.040753 / 8.074308 (3.966445) | 10.506542 / 10.191392 (0.315150) | 0.129659 / 0.680424 (-0.550764) | 0.015691 / 0.534201 (-0.518510) | 0.286911 / 0.579283 (-0.292372) | 0.273588 / 0.434364 (-0.160776) | 0.333642 / 0.540337 (-0.206695) | 0.568550 / 1.386936 (-0.818386) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b38ed4705263df92ae06d89baab0932ae10065e0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005023 / 0.011353 (-0.006330) | 0.003492 / 0.011008 (-0.007516) | 0.062808 / 0.038508 (0.024300) | 0.051649 / 0.023109 (0.028540) | 0.246871 / 0.275898 (-0.029027) | 0.273430 / 0.323480 (-0.050050) | 0.003851 / 0.007986 (-0.004135) | 0.002643 / 0.004328 (-0.001686) | 0.048499 / 0.004250 (0.044248) | 0.037713 / 0.037052 (0.000661) | 0.256431 / 0.258489 (-0.002058) | 0.306956 / 0.293841 (0.013116) | 0.027116 / 0.128546 (-0.101430) | 0.010769 / 0.075646 (-0.064877) | 0.206218 / 0.419271 (-0.213053) | 0.035592 / 0.043533 (-0.007941) | 0.249629 / 0.255139 (-0.005510) | 0.268438 / 0.283200 (-0.014761) | 0.018557 / 0.141683 (-0.123125) | 1.123988 / 1.452155 (-0.328167) | 1.158196 / 1.492716 (-0.334520) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090221 / 0.018006 (0.072215) | 0.300892 / 0.000490 (0.300402) | 0.000209 / 0.000200 (0.000009) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018305 / 0.037411 (-0.019106) | 0.060294 / 0.014526 (0.045769) | 0.073330 / 0.176557 (-0.103227) | 0.119620 / 0.737135 (-0.617515) | 0.074611 / 0.296338 (-0.221727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285347 / 0.215209 (0.070138) | 2.795144 / 2.077655 (0.717490) | 1.468321 / 1.504120 (-0.035799) | 1.343848 / 1.541195 (-0.197347) | 1.388998 / 1.468490 (-0.079492) | 0.559609 / 4.584777 (-4.025168) | 2.355056 / 3.745712 (-1.390656) | 2.798763 / 5.269862 (-2.471099) | 1.764371 / 4.565676 (-2.801305) | 0.062563 / 0.424275 (-0.361712) | 0.005101 / 0.007607 (-0.002506) | 0.339205 / 0.226044 (0.113161) | 3.336729 / 2.268929 (1.067800) | 1.801987 / 55.444624 (-53.642637) | 1.526720 / 6.876477 (-5.349757) | 1.539324 / 2.142072 (-0.602749) | 0.635805 / 4.805227 (-4.169422) | 0.138762 / 6.500664 (-6.361902) | 0.042092 / 0.075469 (-0.033377) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.928755 / 1.841788 (-0.913032) | 11.468224 / 8.074308 (3.393916) | 10.784568 / 10.191392 (0.593176) | 0.130332 / 0.680424 (-0.550092) | 0.014203 / 0.534201 (-0.519998) | 0.287125 / 0.579283 (-0.292158) | 0.263921 / 0.434364 (-0.170443) | 0.327824 / 0.540337 (-0.212513) | 0.434679 / 1.386936 (-0.952257) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003411 / 0.011008 (-0.007598) | 0.050122 / 0.038508 (0.011614) | 0.049378 / 0.023109 (0.026269) | 0.272980 / 0.275898 (-0.002918) | 0.298047 / 0.323480 (-0.025433) | 0.003945 / 0.007986 (-0.004041) | 0.002633 / 0.004328 (-0.001696) | 0.048935 / 0.004250 (0.044685) | 0.040157 / 0.037052 (0.003104) | 0.277056 / 0.258489 (0.018567) | 0.299824 / 0.293841 (0.005983) | 0.028997 / 0.128546 (-0.099550) | 0.010868 / 0.075646 (-0.064779) | 0.057895 / 0.419271 (-0.361377) | 0.033522 / 0.043533 (-0.010010) | 0.274912 / 0.255139 (0.019773) | 0.288902 / 0.283200 (0.005702) | 0.018016 / 0.141683 (-0.123667) | 1.116669 / 1.452155 (-0.335485) | 1.175007 / 1.492716 (-0.317710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090169 / 0.018006 (0.072163) | 0.310577 / 0.000490 (0.310087) | 0.000215 / 0.000200 (0.000015) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020448 / 0.037411 (-0.016963) | 0.068216 / 0.014526 (0.053690) | 0.081798 / 0.176557 (-0.094759) | 0.119151 / 0.737135 (-0.617985) | 0.085197 / 0.296338 (-0.211142) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294957 / 0.215209 (0.079748) | 2.874065 / 2.077655 (0.796410) | 1.590963 / 1.504120 (0.086843) | 1.459596 / 1.541195 (-0.081599) | 1.467931 / 1.468490 (-0.000559) | 0.562832 / 4.584777 (-4.021944) | 2.426384 / 3.745712 (-1.319328) | 2.767749 / 5.269862 (-2.502112) | 1.746702 / 4.565676 (-2.818975) | 0.063353 / 0.424275 (-0.360922) | 0.005073 / 0.007607 (-0.002534) | 0.348258 / 0.226044 (0.122213) | 3.390351 / 2.268929 (1.121423) | 1.950092 / 55.444624 (-53.494532) | 1.671227 / 6.876477 (-5.205250) | 1.683349 / 2.142072 (-0.458723) | 0.637613 / 4.805227 (-4.167614) | 0.115172 / 6.500664 (-6.385492) | 0.040202 / 0.075469 (-0.035267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963085 / 1.841788 (-0.878702) | 11.895384 / 8.074308 (3.821076) | 10.609906 / 10.191392 (0.418513) | 0.130865 / 0.680424 (-0.549559) | 0.016020 / 0.534201 (-0.518181) | 0.287540 / 0.579283 (-0.291743) | 0.278204 / 0.434364 (-0.156160) | 0.326007 / 0.540337 (-0.214330) | 0.590881 / 1.386936 (-0.796055) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c291e330a7d460ff09d867377de1d4c53fd5394c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005266 / 0.011353 (-0.006087) | 0.003751 / 0.011008 (-0.007257) | 0.063835 / 0.038508 (0.025327) | 0.052688 / 0.023109 (0.029579) | 0.261957 / 0.275898 (-0.013941) | 0.284264 / 0.323480 (-0.039216) | 0.003958 / 0.007986 (-0.004027) | 0.002696 / 0.004328 (-0.001633) | 0.052791 / 0.004250 (0.048540) | 0.038294 / 0.037052 (0.001242) | 0.259488 / 0.258489 (0.000999) | 0.298368 / 0.293841 (0.004528) | 0.028309 / 0.128546 (-0.100237) | 0.010819 / 0.075646 (-0.064827) | 0.208221 / 0.419271 (-0.211050) | 0.036373 / 0.043533 (-0.007160) | 0.257000 / 0.255139 (0.001861) | 0.273108 / 0.283200 (-0.010092) | 0.019674 / 0.141683 (-0.122009) | 1.119196 / 1.452155 (-0.332958) | 1.161613 / 1.492716 (-0.331104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093408 / 0.018006 (0.075401) | 0.302278 / 0.000490 (0.301788) | 0.000212 / 0.000200 (0.000012) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019417 / 0.037411 (-0.017995) | 0.060847 / 0.014526 (0.046321) | 0.075399 / 0.176557 (-0.101158) | 0.121233 / 0.737135 (-0.615902) | 0.076916 / 0.296338 (-0.219422) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281265 / 0.215209 (0.066056) | 2.651726 / 2.077655 (0.574072) | 1.457726 / 1.504120 (-0.046394) | 1.339250 / 1.541195 (-0.201945) | 1.398529 / 1.468490 (-0.069961) | 0.566574 / 4.584777 (-4.018203) | 2.431576 / 3.745712 (-1.314136) | 2.845884 / 5.269862 (-2.423977) | 1.798051 / 4.565676 (-2.767626) | 0.063619 / 0.424275 (-0.360656) | 0.005286 / 0.007607 (-0.002321) | 0.332834 / 0.226044 (0.106789) | 3.293222 / 2.268929 (1.024293) | 1.837810 / 55.444624 (-53.606815) | 1.568511 / 6.876477 (-5.307966) | 1.627518 / 2.142072 (-0.514555) | 0.643520 / 4.805227 (-4.161708) | 0.118482 / 6.500664 (-6.382182) | 0.049563 / 0.075469 (-0.025906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947767 / 1.841788 (-0.894021) | 11.994999 / 8.074308 (3.920691) | 10.662651 / 10.191392 (0.471259) | 0.142070 / 0.680424 (-0.538354) | 0.014276 / 0.534201 (-0.519925) | 0.288455 / 0.579283 (-0.290828) | 0.266335 / 0.434364 (-0.168029) | 0.328455 / 0.540337 (-0.211883) | 0.440740 / 1.386936 (-0.946196) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005636 / 0.011353 (-0.005717) | 0.003664 / 0.011008 (-0.007344) | 0.050340 / 0.038508 (0.011832) | 0.062795 / 0.023109 (0.039685) | 0.280874 / 0.275898 (0.004976) | 0.314056 / 0.323480 (-0.009424) | 0.004089 / 0.007986 (-0.003897) | 0.002780 / 0.004328 (-0.001548) | 0.048468 / 0.004250 (0.044218) | 0.042924 / 0.037052 (0.005871) | 0.281381 / 0.258489 (0.022892) | 0.308232 / 0.293841 (0.014391) | 0.030294 / 0.128546 (-0.098252) | 0.011098 / 0.075646 (-0.064548) | 0.057535 / 0.419271 (-0.361736) | 0.034217 / 0.043533 (-0.009316) | 0.283022 / 0.255139 (0.027883) | 0.298425 / 0.283200 (0.015225) | 0.019285 / 0.141683 (-0.122398) | 1.117722 / 1.452155 (-0.334433) | 1.185878 / 1.492716 (-0.306839) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094915 / 0.018006 (0.076909) | 0.311782 / 0.000490 (0.311293) | 0.000217 / 0.000200 (0.000017) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022652 / 0.037411 (-0.014759) | 0.069766 / 0.014526 (0.055240) | 0.084495 / 0.176557 (-0.092061) | 0.121295 / 0.737135 (-0.615841) | 0.082447 / 0.296338 (-0.213891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294286 / 0.215209 (0.079077) | 2.863694 / 2.077655 (0.786039) | 1.578338 / 1.504120 (0.074219) | 1.478737 / 1.541195 (-0.062458) | 1.528569 / 1.468490 (0.060079) | 0.576944 / 4.584777 (-4.007833) | 2.438730 / 3.745712 (-1.306982) | 2.956138 / 5.269862 (-2.313723) | 1.844484 / 4.565676 (-2.721192) | 0.065980 / 0.424275 (-0.358295) | 0.004998 / 0.007607 (-0.002609) | 0.352063 / 0.226044 (0.126019) | 3.456355 / 2.268929 (1.187426) | 1.971582 / 55.444624 (-53.473042) | 1.684536 / 6.876477 (-5.191940) | 1.726823 / 2.142072 (-0.415250) | 0.660235 / 4.805227 (-4.144992) | 0.119029 / 6.500664 (-6.381635) | 0.042497 / 0.075469 (-0.032972) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971817 / 1.841788 (-0.869970) | 12.900324 / 8.074308 (4.826015) | 10.957495 / 10.191392 (0.766103) | 0.133705 / 0.680424 (-0.546718) | 0.015669 / 0.534201 (-0.518532) | 0.287340 / 0.579283 (-0.291943) | 0.280380 / 0.434364 (-0.153984) | 0.330369 / 0.540337 (-0.209969) | 0.581793 / 1.386936 (-0.805143) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2af5efae1985499d6a0a1b6ab4120337eebf776 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005038 / 0.011353 (-0.006315) | 0.003737 / 0.011008 (-0.007272) | 0.063118 / 0.038508 (0.024610) | 0.050120 / 0.023109 (0.027011) | 0.240722 / 0.275898 (-0.035176) | 0.263128 / 0.323480 (-0.060352) | 0.003839 / 0.007986 (-0.004147) | 0.002718 / 0.004328 (-0.001610) | 0.047869 / 0.004250 (0.043618) | 0.038092 / 0.037052 (0.001040) | 0.245759 / 0.258489 (-0.012730) | 0.277728 / 0.293841 (-0.016113) | 0.027466 / 0.128546 (-0.101081) | 0.011767 / 0.075646 (-0.063879) | 0.205505 / 0.419271 (-0.213766) | 0.035429 / 0.043533 (-0.008104) | 0.241665 / 0.255139 (-0.013474) | 0.260908 / 0.283200 (-0.022292) | 0.017133 / 0.141683 (-0.124550) | 1.107725 / 1.452155 (-0.344429) | 1.169707 / 1.492716 (-0.323009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094112 / 0.018006 (0.076106) | 0.302596 / 0.000490 (0.302106) | 0.000237 / 0.000200 (0.000037) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017923 / 0.037411 (-0.019488) | 0.060356 / 0.014526 (0.045830) | 0.073708 / 0.176557 (-0.102849) | 0.119952 / 0.737135 (-0.617183) | 0.075350 / 0.296338 (-0.220989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289253 / 0.215209 (0.074044) | 2.800772 / 2.077655 (0.723117) | 1.538368 / 1.504120 (0.034248) | 1.401037 / 1.541195 (-0.140158) | 1.427170 / 1.468490 (-0.041320) | 0.560497 / 4.584777 (-4.024280) | 2.417844 / 3.745712 (-1.327868) | 2.798377 / 5.269862 (-2.471484) | 1.756517 / 4.565676 (-2.809160) | 0.063897 / 0.424275 (-0.360378) | 0.005323 / 0.007607 (-0.002284) | 0.339881 / 0.226044 (0.113836) | 3.354858 / 2.268929 (1.085929) | 1.877233 / 55.444624 (-53.567391) | 1.578713 / 6.876477 (-5.297764) | 1.631898 / 2.142072 (-0.510175) | 0.640303 / 4.805227 (-4.164924) | 0.116731 / 6.500664 (-6.383933) | 0.041978 / 0.075469 (-0.033491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963259 / 1.841788 (-0.878529) | 11.983646 / 8.074308 (3.909338) | 10.561596 / 10.191392 (0.370204) | 0.135863 / 0.680424 (-0.544561) | 0.015607 / 0.534201 (-0.518594) | 0.295164 / 0.579283 (-0.284119) | 0.283366 / 0.434364 (-0.150998) | 0.341848 / 0.540337 (-0.198489) | 0.448359 / 1.386936 (-0.938577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005366 / 0.011353 (-0.005987) | 0.003621 / 0.011008 (-0.007387) | 0.048615 / 0.038508 (0.010107) | 0.053950 / 0.023109 (0.030841) | 0.273112 / 0.275898 (-0.002786) | 0.295655 / 0.323480 (-0.027825) | 0.004066 / 0.007986 (-0.003920) | 0.002700 / 0.004328 (-0.001628) | 0.047899 / 0.004250 (0.043648) | 0.041633 / 0.037052 (0.004581) | 0.277760 / 0.258489 (0.019271) | 0.302068 / 0.293841 (0.008227) | 0.028879 / 0.128546 (-0.099668) | 0.010756 / 0.075646 (-0.064891) | 0.057190 / 0.419271 (-0.362082) | 0.032555 / 0.043533 (-0.010978) | 0.272045 / 0.255139 (0.016906) | 0.289330 / 0.283200 (0.006130) | 0.018466 / 0.141683 (-0.123216) | 1.180435 / 1.452155 (-0.271720) | 1.192228 / 1.492716 (-0.300488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094871 / 0.018006 (0.076864) | 0.302552 / 0.000490 (0.302062) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022008 / 0.037411 (-0.015403) | 0.068528 / 0.014526 (0.054002) | 0.081735 / 0.176557 (-0.094821) | 0.120990 / 0.737135 (-0.616145) | 0.083155 / 0.296338 (-0.213184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305030 / 0.215209 (0.089821) | 3.009812 / 2.077655 (0.932158) | 1.677773 / 1.504120 (0.173654) | 1.552280 / 1.541195 (0.011085) | 1.606248 / 1.468490 (0.137758) | 0.557093 / 4.584777 (-4.027684) | 2.418292 / 3.745712 (-1.327420) | 2.813049 / 5.269862 (-2.456813) | 1.764507 / 4.565676 (-2.801169) | 0.065089 / 0.424275 (-0.359186) | 0.004944 / 0.007607 (-0.002663) | 0.360672 / 0.226044 (0.134628) | 3.525850 / 2.268929 (1.256921) | 2.030091 / 55.444624 (-53.414533) | 1.754669 / 6.876477 (-5.121807) | 1.772673 / 2.142072 (-0.369399) | 0.642904 / 4.805227 (-4.162324) | 0.116018 / 6.500664 (-6.384646) | 0.041308 / 0.075469 (-0.034161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986386 / 1.841788 (-0.855401) | 12.291623 / 8.074308 (4.217315) | 10.655932 / 10.191392 (0.464540) | 0.141736 / 0.680424 (-0.538688) | 0.016669 / 0.534201 (-0.517532) | 0.286875 / 0.579283 (-0.292408) | 0.281898 / 0.434364 (-0.152466) | 0.325206 / 0.540337 (-0.215132) | 0.577607 / 1.386936 (-0.809329) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cf33502493fb9760ea8cc8e51622bf94d0c9e31 \"CML watermark\")\n", "Alright tests are passing (except one on temp dir cleanup windows but I don't think it's related to this PR ?)\r\n\r\n```\r\nFAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmpqy3f2ft_\\\\hf-internal-testing___dataset_with_script\\\\default\\\\0.0.0\\\\c240e2be3370bdbd\\\\dataset_with_script-train.arrow'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005072 / 0.011353 (-0.006281) | 0.003449 / 0.011008 (-0.007559) | 0.062630 / 0.038508 (0.024122) | 0.054276 / 0.023109 (0.031167) | 0.253345 / 0.275898 (-0.022553) | 0.273460 / 0.323480 (-0.050020) | 0.003859 / 0.007986 (-0.004127) | 0.002646 / 0.004328 (-0.001683) | 0.048289 / 0.004250 (0.044038) | 0.037943 / 0.037052 (0.000891) | 0.256569 / 0.258489 (-0.001920) | 0.287809 / 0.293841 (-0.006032) | 0.027675 / 0.128546 (-0.100872) | 0.010554 / 0.075646 (-0.065092) | 0.205157 / 0.419271 (-0.214115) | 0.035464 / 0.043533 (-0.008069) | 0.254300 / 0.255139 (-0.000839) | 0.272907 / 0.283200 (-0.010292) | 0.018146 / 0.141683 (-0.123537) | 1.110528 / 1.452155 (-0.341626) | 1.170156 / 1.492716 (-0.322560) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093151 / 0.018006 (0.075144) | 0.302087 / 0.000490 (0.301598) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018744 / 0.037411 (-0.018667) | 0.059843 / 0.014526 (0.045317) | 0.073165 / 0.176557 (-0.103391) | 0.120464 / 0.737135 (-0.616671) | 0.074992 / 0.296338 (-0.221347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285103 / 0.215209 (0.069894) | 2.820254 / 2.077655 (0.742600) | 1.505336 / 1.504120 (0.001216) | 1.368631 / 1.541195 (-0.172564) | 1.404140 / 1.468490 (-0.064350) | 0.563906 / 4.584777 (-4.020871) | 2.411871 / 3.745712 (-1.333841) | 2.788390 / 5.269862 (-2.481471) | 1.749788 / 4.565676 (-2.815888) | 0.062171 / 0.424275 (-0.362104) | 0.004918 / 0.007607 (-0.002689) | 0.339615 / 0.226044 (0.113571) | 3.337789 / 2.268929 (1.068861) | 1.808445 / 55.444624 (-53.636180) | 1.541015 / 6.876477 (-5.335462) | 1.572389 / 2.142072 (-0.569683) | 0.641739 / 4.805227 (-4.163488) | 0.115844 / 6.500664 (-6.384820) | 0.042504 / 0.075469 (-0.032965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942463 / 1.841788 (-0.899325) | 11.602364 / 8.074308 (3.528056) | 10.628921 / 10.191392 (0.437529) | 0.136154 / 0.680424 (-0.544270) | 0.013842 / 0.534201 (-0.520359) | 0.287085 / 0.579283 (-0.292198) | 0.269860 / 0.434364 (-0.164503) | 0.329525 / 0.540337 (-0.210812) | 0.441287 / 1.386936 (-0.945649) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005215 / 0.011353 (-0.006138) | 0.003549 / 0.011008 (-0.007460) | 0.049199 / 0.038508 (0.010691) | 0.051655 / 0.023109 (0.028545) | 0.272150 / 0.275898 (-0.003748) | 0.291978 / 0.323480 (-0.031502) | 0.003985 / 0.007986 (-0.004001) | 0.002668 / 0.004328 (-0.001661) | 0.048524 / 0.004250 (0.044274) | 0.039824 / 0.037052 (0.002772) | 0.275566 / 0.258489 (0.017077) | 0.298076 / 0.293841 (0.004235) | 0.029508 / 0.128546 (-0.099038) | 0.010673 / 0.075646 (-0.064973) | 0.057327 / 0.419271 (-0.361944) | 0.032590 / 0.043533 (-0.010943) | 0.273295 / 0.255139 (0.018156) | 0.289127 / 0.283200 (0.005928) | 0.017694 / 0.141683 (-0.123989) | 1.134502 / 1.452155 (-0.317653) | 1.185603 / 1.492716 (-0.307114) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098403 / 0.018006 (0.080396) | 0.302735 / 0.000490 (0.302245) | 0.000228 / 0.000200 (0.000028) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025192 / 0.037411 (-0.012219) | 0.068149 / 0.014526 (0.053623) | 0.082220 / 0.176557 (-0.094336) | 0.119491 / 0.737135 (-0.617645) | 0.082484 / 0.296338 (-0.213855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295339 / 0.215209 (0.080130) | 2.868411 / 2.077655 (0.790757) | 1.590665 / 1.504120 (0.086545) | 1.465995 / 1.541195 (-0.075200) | 1.489205 / 1.468490 (0.020715) | 0.562503 / 4.584777 (-4.022274) | 2.480100 / 3.745712 (-1.265613) | 2.774216 / 5.269862 (-2.495646) | 1.733129 / 4.565676 (-2.832548) | 0.062698 / 0.424275 (-0.361577) | 0.004910 / 0.007607 (-0.002697) | 0.354766 / 0.226044 (0.128722) | 3.435541 / 2.268929 (1.166613) | 1.953357 / 55.444624 (-53.491267) | 1.673584 / 6.876477 (-5.202893) | 1.677749 / 2.142072 (-0.464323) | 0.632601 / 4.805227 (-4.172626) | 0.114875 / 6.500664 (-6.385789) | 0.040577 / 0.075469 (-0.034892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967003 / 1.841788 (-0.874785) | 11.964490 / 8.074308 (3.890181) | 10.493812 / 10.191392 (0.302420) | 0.132177 / 0.680424 (-0.548247) | 0.015149 / 0.534201 (-0.519052) | 0.289011 / 0.579283 (-0.290272) | 0.285479 / 0.434364 (-0.148885) | 0.327090 / 0.540337 (-0.213248) | 0.571747 / 1.386936 (-0.815189) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c9b4cb7ee4720415261216d72051e2a3320fe41 \"CML watermark\")\n" ]
2023-11-23T17:31:57
2023-12-01T17:57:17
2023-12-01T17:50:59
MEMBER
null
false
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The idea is to make this code work for datasets with scripts if they have a Parquet export ```python ds = load_dataset("squad", trust_remote_code=False) ``` And more generally, it means we use the Parquet export whenever it's possible (it's safer and faster than dataset scripts). I also added a `config.USE_PARQUET_EXPORT` variable to use in the datasets-server parquet conversion job - [x] Needs https://github.com/huggingface/datasets/pull/6429 to be merged first cc @severo I use the /parquet and /info endpoints from datasets-server
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I_kwDODunzps53sqDi
6,447
Support one dataset loader per config when using YAML
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2023-11-23T13:03:07
2023-11-23T13:03:07
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COLLABORATOR
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### Feature request See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1 I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc. ### Motivation It would be more flexible for the users ### Your contribution No specific contribution
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Speech Commands v2 dataset doesn't match AST-v2 config
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[ "You can use `.align_labels_with_mapping` on the dataset to align the labels with the model config.\r\n\r\nRegarding the number of labels, only the special `_silence_` label corresponding to noise is missing, which is consistent with the model paper (reports training on 35 labels). You can run a `.filter` to drop it.\r\n\r\nPS: You should create a discussion on a model/dataset repo (on the Hub) for these kinds of questions", "Thanks, will keep that in mind. But I tried running `dataset_aligned = dataset.align_labels_with_mapping(model.config.id2label, 'label')`, and received this error: \r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in align_labels_with_mapping\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in <dictcomp>\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\nAttributeError: 'int' object has no attribute 'lower'\r\n```\r\nMy guess is that the dataset `label` column is purely an int ID, and I'm not sure there's a way to identify which class label the ID belongs to in the dataset easily.", "Replacing `model.config.id2label` with `model.config.label2id` should fix the issue.\r\n\r\nSo, the full code to align the labels with the model config is as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoFeatureExtractor, AutoModelForAudioClassification\r\n\r\n# extractor = AutoFeatureExtractor.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\nmodel = AutoModelForAudioClassification.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\n\r\nds = load_dataset(\"speech_commands\", \"v0.02\")\r\nds = ds.filter(lambda label: label != ds[\"train\"].features[\"label\"].str2int(\"_silence_\"), input_columns=\"label\")\r\nds = ds.align_labels_with_mapping(model.config.label2id, \"label\")\r\n```" ]
2023-11-22T20:46:36
2023-11-28T14:46:08
2023-11-28T14:46:08
NONE
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### Describe the bug [According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`. ### Steps to reproduce the bug ``` >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2") >>> model.config.id2label {0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'} >>> dataset = load_dataset("speech_commands", "v0.02", split="test") >>> torch.unique(torch.Tensor(dataset['label'])) tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.]) ``` If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`. ### Expected behavior The labels should match completely and there should be the same number of label classes between the model config and the dataset itself. ### Environment info datasets = 2.14.6, transformers = 4.33.3
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Use `filelock` package for file locking
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005431 / 0.011353 (-0.005922) | 0.003255 / 0.011008 (-0.007753) | 0.062867 / 0.038508 (0.024359) | 0.051917 / 0.023109 (0.028808) | 0.254229 / 0.275898 (-0.021669) | 0.276949 / 0.323480 (-0.046531) | 0.002868 / 0.007986 (-0.005117) | 0.002539 / 0.004328 (-0.001789) | 0.048366 / 0.004250 (0.044115) | 0.038497 / 0.037052 (0.001445) | 0.252158 / 0.258489 (-0.006332) | 0.288868 / 0.293841 (-0.004973) | 0.027956 / 0.128546 (-0.100591) | 0.010500 / 0.075646 (-0.065147) | 0.209263 / 0.419271 (-0.210008) | 0.035415 / 0.043533 (-0.008118) | 0.253104 / 0.255139 (-0.002035) | 0.274646 / 0.283200 (-0.008554) | 0.019923 / 0.141683 (-0.121760) | 1.081870 / 1.452155 (-0.370285) | 1.157159 / 1.492716 (-0.335557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097420 / 0.018006 (0.079414) | 0.315021 / 0.000490 (0.314531) | 0.000218 / 0.000200 (0.000018) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018826 / 0.037411 (-0.018585) | 0.061921 / 0.014526 (0.047395) | 0.086825 / 0.176557 (-0.089731) | 0.120606 / 0.737135 (-0.616529) | 0.074344 / 0.296338 (-0.221994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283238 / 0.215209 (0.068028) | 2.771817 / 2.077655 (0.694162) | 1.500194 / 1.504120 (-0.003926) | 1.379286 / 1.541195 (-0.161908) | 1.447747 / 1.468490 (-0.020743) | 0.587176 / 4.584777 (-3.997601) | 2.411260 / 3.745712 (-1.334452) | 2.897682 / 5.269862 (-2.372180) | 1.821720 / 4.565676 (-2.743957) | 0.063299 / 0.424275 (-0.360976) | 0.004969 / 0.007607 (-0.002639) | 0.346417 / 0.226044 (0.120373) | 3.432936 / 2.268929 (1.164007) | 1.898662 / 55.444624 (-53.545963) | 1.624339 / 6.876477 (-5.252138) | 1.641653 / 2.142072 (-0.500419) | 0.655773 / 4.805227 (-4.149454) | 0.118588 / 6.500664 (-6.382076) | 0.043919 / 0.075469 (-0.031551) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949466 / 1.841788 (-0.892322) | 12.378025 / 8.074308 (4.303717) | 10.750942 / 10.191392 (0.559550) | 0.146575 / 0.680424 (-0.533849) | 0.015453 / 0.534201 (-0.518748) | 0.290608 / 0.579283 (-0.288676) | 0.273000 / 0.434364 (-0.161364) | 0.328019 / 0.540337 (-0.212318) | 0.417396 / 1.386936 (-0.969540) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005363 / 0.011353 (-0.005990) | 0.003421 / 0.011008 (-0.007587) | 0.049429 / 0.038508 (0.010920) | 0.052774 / 0.023109 (0.029664) | 0.274058 / 0.275898 (-0.001840) | 0.297307 / 0.323480 (-0.026173) | 0.004000 / 0.007986 (-0.003986) | 0.002463 / 0.004328 (-0.001866) | 0.048824 / 0.004250 (0.044574) | 0.041064 / 0.037052 (0.004012) | 0.279066 / 0.258489 (0.020577) | 0.302420 / 0.293841 (0.008579) | 0.029665 / 0.128546 (-0.098881) | 0.010628 / 0.075646 (-0.065018) | 0.057678 / 0.419271 (-0.361594) | 0.032731 / 0.043533 (-0.010802) | 0.274662 / 0.255139 (0.019523) | 0.291878 / 0.283200 (0.008678) | 0.018820 / 0.141683 (-0.122863) | 1.124042 / 1.452155 (-0.328112) | 1.175020 / 1.492716 (-0.317697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099419 / 0.018006 (0.081413) | 0.311511 / 0.000490 (0.311022) | 0.000228 / 0.000200 (0.000028) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022478 / 0.037411 (-0.014933) | 0.071955 / 0.014526 (0.057429) | 0.081423 / 0.176557 (-0.095134) | 0.119574 / 0.737135 (-0.617561) | 0.084724 / 0.296338 (-0.211615) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295537 / 0.215209 (0.080328) | 2.893855 / 2.077655 (0.816201) | 1.602065 / 1.504120 (0.097945) | 1.478193 / 1.541195 (-0.063002) | 1.508250 / 1.468490 (0.039760) | 0.566140 / 4.584777 (-4.018637) | 2.455474 / 3.745712 (-1.290238) | 2.849525 / 5.269862 (-2.420337) | 1.763830 / 4.565676 (-2.801846) | 0.062375 / 0.424275 (-0.361900) | 0.004992 / 0.007607 (-0.002615) | 0.346068 / 0.226044 (0.120023) | 3.452421 / 2.268929 (1.183492) | 1.970346 / 55.444624 (-53.474278) | 1.690865 / 6.876477 (-5.185612) | 1.705358 / 2.142072 (-0.436714) | 0.644261 / 4.805227 (-4.160967) | 0.120596 / 6.500664 (-6.380068) | 0.042699 / 0.075469 (-0.032770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980506 / 1.841788 (-0.861281) | 12.401901 / 8.074308 (4.327593) | 11.169413 / 10.191392 (0.978021) | 0.142540 / 0.680424 (-0.537884) | 0.015730 / 0.534201 (-0.518471) | 0.288871 / 0.579283 (-0.290412) | 0.287487 / 0.434364 (-0.146877) | 0.325133 / 0.540337 (-0.215204) | 0.417979 / 1.386936 (-0.968957) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#965685891db0d06979490aaebab72d5dc628e42b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005062 / 0.011353 (-0.006291) | 0.003024 / 0.011008 (-0.007984) | 0.061801 / 0.038508 (0.023293) | 0.048934 / 0.023109 (0.025825) | 0.248024 / 0.275898 (-0.027874) | 0.265665 / 0.323480 (-0.057815) | 0.003885 / 0.007986 (-0.004100) | 0.002371 / 0.004328 (-0.001957) | 0.047895 / 0.004250 (0.043644) | 0.039015 / 0.037052 (0.001963) | 0.252320 / 0.258489 (-0.006169) | 0.286533 / 0.293841 (-0.007308) | 0.027694 / 0.128546 (-0.100852) | 0.010254 / 0.075646 (-0.065392) | 0.206586 / 0.419271 (-0.212685) | 0.035681 / 0.043533 (-0.007852) | 0.251645 / 0.255139 (-0.003494) | 0.285462 / 0.283200 (0.002262) | 0.017326 / 0.141683 (-0.124357) | 1.086927 / 1.452155 (-0.365228) | 1.153172 / 1.492716 (-0.339545) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093020 / 0.018006 (0.075014) | 0.300018 / 0.000490 (0.299528) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018828 / 0.037411 (-0.018584) | 0.062569 / 0.014526 (0.048043) | 0.074130 / 0.176557 (-0.102427) | 0.119304 / 0.737135 (-0.617832) | 0.076409 / 0.296338 (-0.219930) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285938 / 0.215209 (0.070729) | 2.780662 / 2.077655 (0.703007) | 1.522401 / 1.504120 (0.018281) | 1.392475 / 1.541195 (-0.148720) | 1.412517 / 1.468490 (-0.055973) | 0.562768 / 4.584777 (-4.022009) | 2.421406 / 3.745712 (-1.324306) | 2.786271 / 5.269862 (-2.483591) | 1.737193 / 4.565676 (-2.828484) | 0.062775 / 0.424275 (-0.361500) | 0.004908 / 0.007607 (-0.002699) | 0.345070 / 0.226044 (0.119026) | 3.383700 / 2.268929 (1.114771) | 1.795974 / 55.444624 (-53.648651) | 1.527656 / 6.876477 (-5.348820) | 1.514035 / 2.142072 (-0.628037) | 0.647652 / 4.805227 (-4.157575) | 0.120121 / 6.500664 (-6.380543) | 0.042259 / 0.075469 (-0.033210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948951 / 1.841788 (-0.892837) | 11.514971 / 8.074308 (3.440663) | 10.722668 / 10.191392 (0.531276) | 0.143034 / 0.680424 (-0.537390) | 0.014800 / 0.534201 (-0.519401) | 0.286189 / 0.579283 (-0.293094) | 0.270735 / 0.434364 (-0.163629) | 0.323907 / 0.540337 (-0.216430) | 0.417569 / 1.386936 (-0.969367) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005670 / 0.011353 (-0.005683) | 0.003238 / 0.011008 (-0.007770) | 0.048520 / 0.038508 (0.010012) | 0.051341 / 0.023109 (0.028232) | 0.273883 / 0.275898 (-0.002015) | 0.295165 / 0.323480 (-0.028315) | 0.004755 / 0.007986 (-0.003231) | 0.002471 / 0.004328 (-0.001857) | 0.047487 / 0.004250 (0.043237) | 0.040225 / 0.037052 (0.003172) | 0.276758 / 0.258489 (0.018269) | 0.301182 / 0.293841 (0.007341) | 0.029749 / 0.128546 (-0.098797) | 0.010340 / 0.075646 (-0.065306) | 0.057193 / 0.419271 (-0.362079) | 0.033067 / 0.043533 (-0.010466) | 0.272716 / 0.255139 (0.017577) | 0.292301 / 0.283200 (0.009101) | 0.019075 / 0.141683 (-0.122608) | 1.101778 / 1.452155 (-0.350376) | 1.173573 / 1.492716 (-0.319143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091008 / 0.018006 (0.073002) | 0.300749 / 0.000490 (0.300259) | 0.000218 / 0.000200 (0.000018) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021760 / 0.037411 (-0.015651) | 0.071407 / 0.014526 (0.056881) | 0.081151 / 0.176557 (-0.095406) | 0.120140 / 0.737135 (-0.616995) | 0.082408 / 0.296338 (-0.213931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294828 / 0.215209 (0.079619) | 2.880701 / 2.077655 (0.803047) | 1.604187 / 1.504120 (0.100068) | 1.479236 / 1.541195 (-0.061959) | 1.498875 / 1.468490 (0.030385) | 0.561950 / 4.584777 (-4.022827) | 2.462531 / 3.745712 (-1.283181) | 2.800905 / 5.269862 (-2.468957) | 1.746535 / 4.565676 (-2.819141) | 0.062732 / 0.424275 (-0.361544) | 0.004932 / 0.007607 (-0.002675) | 0.347125 / 0.226044 (0.121081) | 3.431343 / 2.268929 (1.162415) | 1.964999 / 55.444624 (-53.479625) | 1.669709 / 6.876477 (-5.206768) | 1.675148 / 2.142072 (-0.466924) | 0.635436 / 4.805227 (-4.169792) | 0.116598 / 6.500664 (-6.384066) | 0.041447 / 0.075469 (-0.034022) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975751 / 1.841788 (-0.866037) | 12.060246 / 8.074308 (3.985938) | 10.871641 / 10.191392 (0.680249) | 0.142936 / 0.680424 (-0.537488) | 0.015779 / 0.534201 (-0.518422) | 0.287120 / 0.579283 (-0.292163) | 0.283963 / 0.434364 (-0.150401) | 0.341231 / 0.540337 (-0.199107) | 0.419518 / 1.386936 (-0.967418) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0943ff0072dcef473530d8a494f314048f3a3d51 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005105 / 0.011353 (-0.006248) | 0.002855 / 0.011008 (-0.008153) | 0.062044 / 0.038508 (0.023536) | 0.052948 / 0.023109 (0.029839) | 0.249841 / 0.275898 (-0.026057) | 0.276687 / 0.323480 (-0.046792) | 0.003792 / 0.007986 (-0.004194) | 0.002385 / 0.004328 (-0.001943) | 0.048648 / 0.004250 (0.044398) | 0.038317 / 0.037052 (0.001264) | 0.255235 / 0.258489 (-0.003254) | 0.287870 / 0.293841 (-0.005971) | 0.027429 / 0.128546 (-0.101117) | 0.010182 / 0.075646 (-0.065464) | 0.206980 / 0.419271 (-0.212291) | 0.035444 / 0.043533 (-0.008089) | 0.255073 / 0.255139 (-0.000066) | 0.270636 / 0.283200 (-0.012563) | 0.018003 / 0.141683 (-0.123680) | 1.124691 / 1.452155 (-0.327463) | 1.191872 / 1.492716 (-0.300844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088824 / 0.018006 (0.070818) | 0.302771 / 0.000490 (0.302281) | 0.000210 / 0.000200 (0.000010) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018102 / 0.037411 (-0.019310) | 0.062131 / 0.014526 (0.047605) | 0.073230 / 0.176557 (-0.103327) | 0.119789 / 0.737135 (-0.617346) | 0.074804 / 0.296338 (-0.221534) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293244 / 0.215209 (0.078035) | 2.891401 / 2.077655 (0.813746) | 1.504481 / 1.504120 (0.000361) | 1.381251 / 1.541195 (-0.159944) | 1.387245 / 1.468490 (-0.081245) | 0.552732 / 4.584777 (-4.032045) | 2.386439 / 3.745712 (-1.359273) | 2.718918 / 5.269862 (-2.550944) | 1.725401 / 4.565676 (-2.840275) | 0.061946 / 0.424275 (-0.362329) | 0.004957 / 0.007607 (-0.002650) | 0.342776 / 0.226044 (0.116731) | 3.418911 / 2.268929 (1.149983) | 1.838283 / 55.444624 (-53.606341) | 1.538013 / 6.876477 (-5.338464) | 1.545144 / 2.142072 (-0.596928) | 0.637857 / 4.805227 (-4.167370) | 0.116451 / 6.500664 (-6.384213) | 0.042228 / 0.075469 (-0.033241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943575 / 1.841788 (-0.898212) | 11.492939 / 8.074308 (3.418631) | 10.601605 / 10.191392 (0.410212) | 0.139084 / 0.680424 (-0.541340) | 0.013691 / 0.534201 (-0.520510) | 0.286696 / 0.579283 (-0.292587) | 0.259979 / 0.434364 (-0.174385) | 0.322578 / 0.540337 (-0.217759) | 0.411950 / 1.386936 (-0.974986) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005168 / 0.011353 (-0.006185) | 0.003238 / 0.011008 (-0.007770) | 0.049028 / 0.038508 (0.010520) | 0.052930 / 0.023109 (0.029821) | 0.274750 / 0.275898 (-0.001148) | 0.294023 / 0.323480 (-0.029457) | 0.003829 / 0.007986 (-0.004157) | 0.002372 / 0.004328 (-0.001956) | 0.048689 / 0.004250 (0.044439) | 0.040056 / 0.037052 (0.003003) | 0.280147 / 0.258489 (0.021658) | 0.304871 / 0.293841 (0.011030) | 0.028734 / 0.128546 (-0.099812) | 0.010624 / 0.075646 (-0.065022) | 0.058705 / 0.419271 (-0.360566) | 0.032140 / 0.043533 (-0.011393) | 0.276702 / 0.255139 (0.021563) | 0.293186 / 0.283200 (0.009987) | 0.018124 / 0.141683 (-0.123559) | 1.139398 / 1.452155 (-0.312757) | 1.174862 / 1.492716 (-0.317855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087627 / 0.018006 (0.069620) | 0.298376 / 0.000490 (0.297886) | 0.000238 / 0.000200 (0.000038) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021344 / 0.037411 (-0.016067) | 0.070208 / 0.014526 (0.055682) | 0.081177 / 0.176557 (-0.095380) | 0.120170 / 0.737135 (-0.616965) | 0.082472 / 0.296338 (-0.213866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293227 / 0.215209 (0.078018) | 2.844619 / 2.077655 (0.766964) | 1.586922 / 1.504120 (0.082803) | 1.460256 / 1.541195 (-0.080938) | 1.475955 / 1.468490 (0.007465) | 0.553226 / 4.584777 (-4.031551) | 2.418869 / 3.745712 (-1.326843) | 2.709256 / 5.269862 (-2.560606) | 1.705935 / 4.565676 (-2.859741) | 0.062391 / 0.424275 (-0.361884) | 0.004929 / 0.007607 (-0.002678) | 0.350358 / 0.226044 (0.124313) | 3.448824 / 2.268929 (1.179896) | 1.929451 / 55.444624 (-53.515174) | 1.669438 / 6.876477 (-5.207038) | 1.660923 / 2.142072 (-0.481150) | 0.633107 / 4.805227 (-4.172120) | 0.114657 / 6.500664 (-6.386007) | 0.041256 / 0.075469 (-0.034214) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968408 / 1.841788 (-0.873380) | 11.749754 / 8.074308 (3.675446) | 10.796670 / 10.191392 (0.605278) | 0.128881 / 0.680424 (-0.551543) | 0.015326 / 0.534201 (-0.518875) | 0.286407 / 0.579283 (-0.292876) | 0.276324 / 0.434364 (-0.158040) | 0.326201 / 0.540337 (-0.214136) | 0.419854 / 1.386936 (-0.967082) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1731d5a8cd103533ef6b438b4429ab51d3a6a0ce \"CML watermark\")\n" ]
2023-11-22T19:04:45
2023-11-23T18:47:30
2023-11-23T18:41:23
COLLABORATOR
null
false
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Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂. (Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)
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2,006,842,179
PR_kwDODunzps5gKG_e
6,444
Remove `Table.__getstate__` and `Table.__setstate__`
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[ "Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides.", "@mariosasko \r\nCool!\r\nI removed these overrides, and it worked.\r\n\r\nAll modifications are committed. Ready for review!", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005251 / 0.011353 (-0.006102) | 0.003804 / 0.011008 (-0.007204) | 0.063143 / 0.038508 (0.024635) | 0.059409 / 0.023109 (0.036300) | 0.255319 / 0.275898 (-0.020579) | 0.279194 / 0.323480 (-0.044285) | 0.004643 / 0.007986 (-0.003343) | 0.002560 / 0.004328 (-0.001768) | 0.047490 / 0.004250 (0.043240) | 0.039034 / 0.037052 (0.001982) | 0.257352 / 0.258489 (-0.001137) | 0.293029 / 0.293841 (-0.000812) | 0.027548 / 0.128546 (-0.100998) | 0.011307 / 0.075646 (-0.064339) | 0.210325 / 0.419271 (-0.208946) | 0.035161 / 0.043533 (-0.008372) | 0.253491 / 0.255139 (-0.001648) | 0.272085 / 0.283200 (-0.011115) | 0.018924 / 0.141683 (-0.122759) | 1.111148 / 1.452155 (-0.341007) | 1.178076 / 1.492716 (-0.314641) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092447 / 0.018006 (0.074441) | 0.303680 / 0.000490 (0.303190) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019087 / 0.037411 (-0.018325) | 0.062663 / 0.014526 (0.048137) | 0.074651 / 0.176557 (-0.101905) | 0.121334 / 0.737135 (-0.615802) | 0.076703 / 0.296338 (-0.219636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286505 / 0.215209 (0.071295) | 2.804942 / 2.077655 (0.727287) | 1.481930 / 1.504120 (-0.022190) | 1.369485 / 1.541195 (-0.171710) | 1.424467 / 1.468490 (-0.044023) | 0.556810 / 4.584777 (-4.027967) | 2.416338 / 3.745712 (-1.329374) | 2.901869 / 5.269862 (-2.367992) | 1.827007 / 4.565676 (-2.738669) | 0.062252 / 0.424275 (-0.362024) | 0.005076 / 0.007607 (-0.002531) | 0.343850 / 0.226044 (0.117805) | 3.377611 / 2.268929 (1.108683) | 1.860214 / 55.444624 (-53.584410) | 1.595146 / 6.876477 (-5.281331) | 1.627234 / 2.142072 (-0.514838) | 0.651027 / 4.805227 (-4.154200) | 0.119214 / 6.500664 (-6.381450) | 0.043342 / 0.075469 (-0.032127) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942863 / 1.841788 (-0.898924) | 12.484633 / 8.074308 (4.410324) | 10.560668 / 10.191392 (0.369276) | 0.144647 / 0.680424 (-0.535777) | 0.014734 / 0.534201 (-0.519466) | 0.286575 / 0.579283 (-0.292708) | 0.270913 / 0.434364 (-0.163451) | 0.323792 / 0.540337 (-0.216545) | 0.419186 / 1.386936 (-0.967750) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005315 / 0.011353 (-0.006038) | 0.003548 / 0.011008 (-0.007460) | 0.049271 / 0.038508 (0.010763) | 0.055198 / 0.023109 (0.032089) | 0.275940 / 0.275898 (0.000042) | 0.307637 / 0.323480 (-0.015843) | 0.003997 / 0.007986 (-0.003988) | 0.002544 / 0.004328 (-0.001785) | 0.050381 / 0.004250 (0.046130) | 0.041158 / 0.037052 (0.004105) | 0.281519 / 0.258489 (0.023030) | 0.308085 / 0.293841 (0.014244) | 0.030464 / 0.128546 (-0.098083) | 0.010690 / 0.075646 (-0.064957) | 0.057458 / 0.419271 (-0.361814) | 0.032814 / 0.043533 (-0.010719) | 0.282435 / 0.255139 (0.027296) | 0.301342 / 0.283200 (0.018142) | 0.017556 / 0.141683 (-0.124127) | 1.159423 / 1.452155 (-0.292732) | 1.177344 / 1.492716 (-0.315372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091086 / 0.018006 (0.073079) | 0.305316 / 0.000490 (0.304826) | 0.000218 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021832 / 0.037411 (-0.015579) | 0.071055 / 0.014526 (0.056529) | 0.082982 / 0.176557 (-0.093574) | 0.119966 / 0.737135 (-0.617169) | 0.083539 / 0.296338 (-0.212800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302501 / 0.215209 (0.087292) | 2.936347 / 2.077655 (0.858692) | 1.601658 / 1.504120 (0.097538) | 1.467267 / 1.541195 (-0.073928) | 1.514656 / 1.468490 (0.046166) | 0.563934 / 4.584777 (-4.020843) | 2.513715 / 3.745712 (-1.231997) | 2.813014 / 5.269862 (-2.456847) | 1.773243 / 4.565676 (-2.792433) | 0.063208 / 0.424275 (-0.361067) | 0.004979 / 0.007607 (-0.002628) | 0.360694 / 0.226044 (0.134650) | 3.520578 / 2.268929 (1.251650) | 1.975369 / 55.444624 (-53.469255) | 1.691257 / 6.876477 (-5.185220) | 1.730872 / 2.142072 (-0.411200) | 0.655366 / 4.805227 (-4.149861) | 0.146043 / 6.500664 (-6.354621) | 0.041386 / 0.075469 (-0.034083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979840 / 1.841788 (-0.861948) | 12.456924 / 8.074308 (4.382616) | 10.938595 / 10.191392 (0.747203) | 0.133853 / 0.680424 (-0.546571) | 0.015744 / 0.534201 (-0.518457) | 0.289585 / 0.579283 (-0.289698) | 0.291143 / 0.434364 (-0.143221) | 0.328109 / 0.540337 (-0.212228) | 0.561897 / 1.386936 (-0.825039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f \"CML watermark\")\n" ]
2023-11-22T17:55:10
2023-11-23T15:19:43
2023-11-23T15:13:28
CONTRIBUTOR
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false
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When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'` ```python from torch import distributed as dist if dist.get_rank() == 0: dataset = process_dataset(*args, **kwargs) objects = [dataset] else: objects = [None] dist.broadcast_object_list(objects, src=0) dataset = objects[0] ```
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2,006,568,368
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6,443
Trouble loading files defined in YAML explicitly
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[ "There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ", "wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?" ]
2023-11-22T15:18:10
2023-11-23T09:06:20
null
COLLABORATOR
null
null
null
Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```
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2,006,086,907
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6,442
Trouble loading image folder with additional features - metadata file ignored
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[ "I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)" ]
2023-11-22T11:01:35
2023-11-24T17:13:03
2023-11-24T17:13:03
NONE
null
null
null
### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0
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6,441
Trouble Loading a Gated Dataset For User with Granted Permission
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[ "> Also when they try to click the url link for the dataset they get a 404 error.\r\n\r\nThis seems to be a Hub error then (cc @SBrandeis)", "Could you report this to https://discuss.huggingface.co/c/hub/23, providing the URL of the dataset, or at least if the dataset is public or private?", "Thanks for the reply! I've created an issue on the hub's board here: https://discuss.huggingface.co/t/trouble-loading-a-gated-dataset-for-user-with-granted-permission/65565" ]
2023-11-21T19:24:36
2023-12-13T08:27:16
2023-12-13T08:27:16
NONE
null
null
null
### Describe the bug I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get `FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error. ### Steps to reproduce the bug 1. Grant access to gated dataset for specific users 2. Users accept invitation 3. Users login to hugging face hub using cli login 4. Users run load_dataset ### Expected behavior Dataset is loaded normally for users who were granted access to the gated dataset. ### Environment info datasets==2.15.0
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2,004,509,301
I_kwDODunzps53emJ1
6,440
`.map` not hashing under python 3.9
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[ "Tried to upgrade Python to 3.11 - still get this message. A partial solution is to NOT use `num_proc` at all. It will be considerably longer to finish the job.", "Hi! The `model = torch.compile(model)` line is problematic for our hashing logic. We would have to merge https://github.com/huggingface/datasets/pull/5867 to support hashing `torch.compile`-ed models/functions. \r\n\r\nI've started refactoring the hashing logic and plan to incorporate a fix for `torch.compile` as part of it, so this should be addressed soon (probably this or next week). " ]
2023-11-21T15:14:54
2023-11-28T16:29:33
2023-11-28T16:29:33
NONE
null
null
null
### Describe the bug The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message: `Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ### Steps to reproduce the bug ```python def map_to_pred(batch): """ Perform inference on an audio batch Parameters: batch (dict): A dictionary containing audio data and other related information. Returns: dict: The input batch dictionary with added prediction and transcription fields. """ audio = batch['audio'] input_features = processor( audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features input_features = input_features.to('cuda') with torch.no_grad(): predicted_ids = model.generate(input_features) preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] batch['prediction'] = processor.tokenizer._normalize(preds) batch["transcription"] = processor.tokenizer._normalize(batch['transcription']) return batch MODEL_CARD = "openai/whisper-small" MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1] model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD) processor = AutoProcessor.from_pretrained( MODEL_CARD, language="english", task="transcribe") model = torch.compile(model) dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test") dt = dt.cast_column("audio", Audio(sampling_rate=16000)) result = coraal_dt.map(map_to_pred, num_proc=16) ``` ### Expected behavior Hashed and cached dataset starts inferencing ### Environment info - `transformers` version: 4.35.0 - Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - Safetensors version: 0.4.0 - Accelerate version: 0.24.1 - Accelerate config: not found - PyTorch version (GPU?): 2.1.0 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no
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6,439
Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding
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2023-11-20T20:07:23
2023-11-20T20:07:37
null
NONE
null
null
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### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1
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2,002,032,804
I_kwDODunzps53VJik
6,438
Support GeoParquet
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[ "Thank you, @severo ! I would be more than happy to help in any way I can. I am not familiar with this repo's codebase, but I would be eager to contribute. :)\r\n\r\nFor the preview in Datasets Hub, I think it makes sense to just display the geospatial column as text. If there were a dataset loader, though, I think it should be able to support the geospatial components. Geopandas is probably the most user-friendly interface for that. I'm not sure if it's currently relevant in the context of geoparquet, but I think the pyogrio driver is faster than fiona.\r\n\r\nBut the whole gdal dependency thing can be a real pain. If anything, it would need to be an optional dependency. Maybe it would be best if the loader tries importing relevant geospatial libraries, and in the event of an ImportError, falls back to text for the geometry column.\r\n\r\nPlease let me know if I can be of assistance, and thanks again for creating this Issue. :)", "Just hitting into this same issue too showing GeoParquet files in Datasets Viewer. I tried to implement a custom reader for GeoParquet in https://huggingface.co/datasets/weiji14/clay_vector_embeddings/discussions/1, but it seems like HuggingFace has disabled datasets with custom loading scripts from using the dataset viewer according to https://discuss.huggingface.co/t/dataset-repo-requires-arbitrary-python-code-execution/59346 :frowning_face: \r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/2f84d8ce-91c2-48cb-b72c-547ea8583892)\r\n\r\nI'm thinking now if there's a way to simply map files with GeoParquet extensions (*.gpq, *.geoparquet, etc) to use the Parquet reader. Maybe we could allowlist these geoparquet file extensions at https://github.com/huggingface/datasets/blame/0caf91285116ec910f409e82cc6e1f4cff7496e3/src/datasets/packaged_modules/__init__.py#L30-L51? Having the table columns show up would be a quick win.\r\n\r\nLonger term though, it would certainly be nice if the WKB geometry columns could be displayed in a nicer form. Geopandas' [read_parquet](https://geopandas.org/en/v0.14.1/docs/reference/api/geopandas.read_parquet.html) function is supposedly faster than `pyogrio.read_dataframe` according to https://github.com/geopandas/geopandas/discussions/2724#discussioncomment-4606048, but there's also [`pyogrio.raw.read_arrow`](https://pyogrio.readthedocs.io/en/latest/api.html#pyogrio.raw.read_arrow) now that can read into a `pyarrow.Table` directly.", "Update: It looks like renaming the GeoParquet file to have a file extension of `*.parquet` works (see https://huggingface.co/datasets/weiji14/clay_vector_embeddings). HuggingFace's default parquet reader is able to read the GeoParquet file, though the geometry column is of an unknown type:\r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/9060c300-d595-4409-9ccb-5e0207396883)\r\n\r\nI've opened a quick PR at #6508 to allow files with a `*.geoparquet` or `*.gpq` extension to be read using the default Parquet reader. Let's see how that goes :smile:", "@joshuasundance-swca, @weiji14, If I'm understanding this correctly, the code below wouldn't be recommended to due to dependency headaches? If that's the case, what solution would there be to see the geometry features for .gpq files in huggingfaceHub? \r\n\r\ncode for dataset_loader.py\r\n```\r\nimport geopandas as gpd\r\n# ... (other imports remain the same)\r\n\r\nclass ClayVectorEmbeddings(datasets.ArrowBasedBuilder):\r\n # ... (other parts of the class remain the same)\r\n\r\n def _info(self):\r\n # Read the GeoParquet file to get the schema for the 'geometry' feature\r\n gdf = gpd.read_file(\"path/to/your/geoparquet/file.gpq\") # Replace with your file path\r\n geometry_schema = str(gdf.geometry.dtype)\r\n\r\n return datasets.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=\"Clay Vector Embeddings in GeoParquet format.\",\r\n # This defines the different columns of the dataset and their types\r\n features=datasets.Features(\r\n {\r\n \"source_url\": datasets.Value(dtype=\"string\"),\r\n \"date\": datasets.Value(dtype=\"date32\"),\r\n \"embeddings\": datasets.Value(\"string\"),\r\n \"geometry\": datasets.Value(dtype=geometry_schema), # Use the schema read by GeoPandas\r\n # ... (other features)\r\n }\r\n ),\r\n )\r\n\r\n# ... (rest of the script remains the same)\r\n\r\n```", "Hi @mehrdad-es, I'm not sure if HuggingFace would be keen to add `geopandas` to HuggingFace Hub (maybe a question for @severo?). Having a geometry viewer would be an even bigger task, and if you're thinking of a map-viewer, it might involve some redesign of the website UI. Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.", "I've created https://github.com/huggingface/datasets-server/issues/2416 to discuss the possibility of supporting (vectorial) geospatial columns in the dataset viewer, or in the converted parquet files.\r\n\r\nAt the same time, it would be super interesting to see what is already possible to do with a Hugging Face dataset that hosts geospatial data. \r\n\r\n> Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.\r\n\r\nIt would be awesome to show this inside a [Space](https://huggingface.co/docs/hub/spaces)." ]
2023-11-20T11:54:58
2024-02-07T08:36:51
null
COLLABORATOR
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### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)
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2,001,272,606
I_kwDODunzps53SP8e
6,437
Problem in training iterable dataset
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[ "Has anyone ever encountered this problem before?", "`split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n\r\nHowever if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.", "> `split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n> \r\n> However if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.\r\n\r\nThanks for your answer! I finally solve it by using the torch.distributed.algorithms.join.Join. I think maybe some rookie like me would face the same question the day after tomorrow hh.", "Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?", "> Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?\r\n\r\nI think it's none. I have tried barrier() to solve the problem but I failed. Maybe it's a tool for other situation." ]
2023-11-20T03:04:02
2024-05-22T03:14:13
null
NONE
null
null
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### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, **train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, **test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```
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TypeError: <lambda>() takes 0 positional arguments but 1 was given
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[ "This looks like a problem with your environment rather than `datasets`.", "I meet the same problem,\r\nand originally use\r\n```python\r\nlocale.getpreferredencoding = lambda : \"UTF-8\"\r\n```\r\nand change to\r\n```\r\nlocale.getpreferredencoding = lambda x: \"UTF-8\"\r\n```\r\nand it works." ]
2023-11-19T13:10:20
2024-06-25T06:00:31
2023-11-29T16:28:34
NONE
null
null
null
### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`
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I_kwDODunzps53QB1R
6,435
Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
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[ "[This doc section](https://huggingface.co/docs/datasets/main/en/process#multiprocessing) explains how to modify the script to avoid this error.", "@mariosasko thank you very much, i'll check it", "@mariosasko no it does not\r\n\r\n`Dataset.filter() got an unexpected keyword argument 'with_rank'`" ]
2023-11-19T04:21:16
2024-01-27T17:14:20
2023-12-04T16:57:43
NONE
null
null
null
### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux
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Use `ruff` for formatting
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004293 / 0.011353 (-0.007060) | 0.002953 / 0.011008 (-0.008055) | 0.063712 / 0.038508 (0.025204) | 0.029963 / 0.023109 (0.006854) | 0.248574 / 0.275898 (-0.027324) | 0.272757 / 0.323480 (-0.050723) | 0.003878 / 0.007986 (-0.004108) | 0.002456 / 0.004328 (-0.001872) | 0.047959 / 0.004250 (0.043709) | 0.043277 / 0.037052 (0.006224) | 0.255071 / 0.258489 (-0.003418) | 0.283934 / 0.293841 (-0.009907) | 0.022870 / 0.128546 (-0.105676) | 0.007224 / 0.075646 (-0.068422) | 0.221595 / 0.419271 (-0.197677) | 0.053468 / 0.043533 (0.009935) | 0.249906 / 0.255139 (-0.005233) | 0.274894 / 0.283200 (-0.008305) | 0.017246 / 0.141683 (-0.124437) | 1.112440 / 1.452155 (-0.339714) | 1.167293 / 1.492716 (-0.325424) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092684 / 0.018006 (0.074677) | 0.301721 / 0.000490 (0.301231) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018289 / 0.037411 (-0.019122) | 0.061898 / 0.014526 (0.047372) | 0.072904 / 0.176557 (-0.103653) | 0.118515 / 0.737135 (-0.618621) | 0.074000 / 0.296338 (-0.222338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287044 / 0.215209 (0.071835) | 2.818091 / 2.077655 (0.740436) | 1.502401 / 1.504120 (-0.001719) | 1.374688 / 1.541195 (-0.166506) | 1.410254 / 1.468490 (-0.058236) | 0.407519 / 4.584777 (-4.177258) | 2.379199 / 3.745712 (-1.366513) | 2.585745 / 5.269862 (-2.684117) | 1.562336 / 4.565676 (-3.003341) | 0.045977 / 0.424275 (-0.378299) | 0.004809 / 0.007607 (-0.002798) | 0.347942 / 0.226044 (0.121897) | 3.383318 / 2.268929 (1.114390) | 1.844784 / 55.444624 (-53.599841) | 1.561949 / 6.876477 (-5.314528) | 1.571082 / 2.142072 (-0.570990) | 0.482469 / 4.805227 (-4.322758) | 0.099357 / 6.500664 (-6.401307) | 0.041039 / 0.075469 (-0.034430) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944236 / 1.841788 (-0.897551) | 11.519623 / 8.074308 (3.445315) | 10.353829 / 10.191392 (0.162437) | 0.137530 / 0.680424 (-0.542894) | 0.014454 / 0.534201 (-0.519747) | 0.268657 / 0.579283 (-0.310626) | 0.265165 / 0.434364 (-0.169199) | 0.302626 / 0.540337 (-0.237712) | 0.426923 / 1.386936 (-0.960013) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004711 / 0.011353 (-0.006641) | 0.002504 / 0.011008 (-0.008504) | 0.047671 / 0.038508 (0.009163) | 0.051147 / 0.023109 (0.028037) | 0.272848 / 0.275898 (-0.003050) | 0.291705 / 0.323480 (-0.031775) | 0.004002 / 0.007986 (-0.003984) | 0.002382 / 0.004328 (-0.001947) | 0.047583 / 0.004250 (0.043332) | 0.038203 / 0.037052 (0.001150) | 0.278536 / 0.258489 (0.020047) | 0.305872 / 0.293841 (0.012031) | 0.023890 / 0.128546 (-0.104657) | 0.006954 / 0.075646 (-0.068693) | 0.053716 / 0.419271 (-0.365556) | 0.032158 / 0.043533 (-0.011375) | 0.273939 / 0.255139 (0.018800) | 0.290722 / 0.283200 (0.007522) | 0.016946 / 0.141683 (-0.124737) | 1.102726 / 1.452155 (-0.349429) | 1.169356 / 1.492716 (-0.323360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092520 / 0.018006 (0.074514) | 0.301949 / 0.000490 (0.301459) | 0.000248 / 0.000200 (0.000048) | 0.000061 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021013 / 0.037411 (-0.016399) | 0.069965 / 0.014526 (0.055439) | 0.080105 / 0.176557 (-0.096451) | 0.119802 / 0.737135 (-0.617334) | 0.081615 / 0.296338 (-0.214724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301170 / 0.215209 (0.085960) | 2.884817 / 2.077655 (0.807162) | 1.596376 / 1.504120 (0.092256) | 1.471205 / 1.541195 (-0.069990) | 1.499061 / 1.468490 (0.030571) | 0.407729 / 4.584777 (-4.177048) | 2.432824 / 3.745712 (-1.312888) | 2.561905 / 5.269862 (-2.707957) | 1.535364 / 4.565676 (-3.030313) | 0.046592 / 0.424275 (-0.377683) | 0.004773 / 0.007607 (-0.002834) | 0.350872 / 0.226044 (0.124828) | 3.474874 / 2.268929 (1.205945) | 1.963114 / 55.444624 (-53.481510) | 1.688213 / 6.876477 (-5.188263) | 1.686325 / 2.142072 (-0.455748) | 0.487151 / 4.805227 (-4.318076) | 0.104253 / 6.500664 (-6.396411) | 0.043499 / 0.075469 (-0.031970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980395 / 1.841788 (-0.861393) | 11.907393 / 8.074308 (3.833085) | 10.983688 / 10.191392 (0.792296) | 0.142875 / 0.680424 (-0.537549) | 0.015375 / 0.534201 (-0.518826) | 0.270043 / 0.579283 (-0.309240) | 0.295092 / 0.434364 (-0.139272) | 0.309466 / 0.540337 (-0.230871) | 0.409812 / 1.386936 (-0.977124) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17f97ca8ec66f6664d3e9b7ceb84fe3ca49a9c18 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004703 / 0.011353 (-0.006650) | 0.002767 / 0.011008 (-0.008241) | 0.063162 / 0.038508 (0.024654) | 0.052241 / 0.023109 (0.029132) | 0.237138 / 0.275898 (-0.038760) | 0.262793 / 0.323480 (-0.060687) | 0.003873 / 0.007986 (-0.004113) | 0.002433 / 0.004328 (-0.001896) | 0.048647 / 0.004250 (0.044397) | 0.037887 / 0.037052 (0.000834) | 0.244939 / 0.258489 (-0.013551) | 0.304015 / 0.293841 (0.010174) | 0.022859 / 0.128546 (-0.105688) | 0.006763 / 0.075646 (-0.068883) | 0.202728 / 0.419271 (-0.216544) | 0.035369 / 0.043533 (-0.008164) | 0.240785 / 0.255139 (-0.014354) | 0.255109 / 0.283200 (-0.028091) | 0.017951 / 0.141683 (-0.123732) | 1.096103 / 1.452155 (-0.356052) | 1.167662 / 1.492716 (-0.325054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092285 / 0.018006 (0.074279) | 0.300201 / 0.000490 (0.299711) | 0.000222 / 0.000200 (0.000022) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018271 / 0.037411 (-0.019140) | 0.062306 / 0.014526 (0.047780) | 0.072615 / 0.176557 (-0.103942) | 0.119357 / 0.737135 (-0.617779) | 0.073365 / 0.296338 (-0.222974) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278763 / 0.215209 (0.063554) | 2.714943 / 2.077655 (0.637288) | 1.426318 / 1.504120 (-0.077802) | 1.313296 / 1.541195 (-0.227898) | 1.330920 / 1.468490 (-0.137570) | 0.391466 / 4.584777 (-4.193311) | 2.380521 / 3.745712 (-1.365191) | 2.545042 / 5.269862 (-2.724819) | 1.549696 / 4.565676 (-3.015980) | 0.044661 / 0.424275 (-0.379614) | 0.005269 / 0.007607 (-0.002338) | 0.331112 / 0.226044 (0.105068) | 3.241120 / 2.268929 (0.972192) | 1.783771 / 55.444624 (-53.660853) | 1.506205 / 6.876477 (-5.370272) | 1.521062 / 2.142072 (-0.621010) | 0.462339 / 4.805227 (-4.342888) | 0.097646 / 6.500664 (-6.403018) | 0.041365 / 0.075469 (-0.034104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939653 / 1.841788 (-0.902135) | 11.415472 / 8.074308 (3.341164) | 10.338961 / 10.191392 (0.147569) | 0.128543 / 0.680424 (-0.551881) | 0.013997 / 0.534201 (-0.520204) | 0.270034 / 0.579283 (-0.309249) | 0.266766 / 0.434364 (-0.167598) | 0.305290 / 0.540337 (-0.235047) | 0.395969 / 1.386936 (-0.990967) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004869 / 0.011353 (-0.006484) | 0.002445 / 0.011008 (-0.008563) | 0.051256 / 0.038508 (0.012748) | 0.050871 / 0.023109 (0.027761) | 0.271044 / 0.275898 (-0.004854) | 0.294138 / 0.323480 (-0.029342) | 0.003974 / 0.007986 (-0.004012) | 0.002423 / 0.004328 (-0.001906) | 0.048277 / 0.004250 (0.044027) | 0.039685 / 0.037052 (0.002632) | 0.277092 / 0.258489 (0.018603) | 0.302097 / 0.293841 (0.008256) | 0.024515 / 0.128546 (-0.104031) | 0.006892 / 0.075646 (-0.068754) | 0.053528 / 0.419271 (-0.365744) | 0.032243 / 0.043533 (-0.011290) | 0.272098 / 0.255139 (0.016959) | 0.291678 / 0.283200 (0.008479) | 0.018368 / 0.141683 (-0.123315) | 1.160151 / 1.452155 (-0.292004) | 1.193643 / 1.492716 (-0.299073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096669 / 0.018006 (0.078663) | 0.299043 / 0.000490 (0.298553) | 0.000227 / 0.000200 (0.000027) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021557 / 0.037411 (-0.015855) | 0.069875 / 0.014526 (0.055349) | 0.080952 / 0.176557 (-0.095605) | 0.119509 / 0.737135 (-0.617626) | 0.082030 / 0.296338 (-0.214308) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303062 / 0.215209 (0.087853) | 2.943823 / 2.077655 (0.866169) | 1.607816 / 1.504120 (0.103696) | 1.479773 / 1.541195 (-0.061422) | 1.482663 / 1.468490 (0.014173) | 0.411923 / 4.584777 (-4.172854) | 2.450138 / 3.745712 (-1.295574) | 2.466111 / 5.269862 (-2.803751) | 1.543852 / 4.565676 (-3.021825) | 0.046256 / 0.424275 (-0.378019) | 0.004787 / 0.007607 (-0.002820) | 0.353673 / 0.226044 (0.127628) | 3.528218 / 2.268929 (1.259289) | 1.984663 / 55.444624 (-53.459962) | 1.675785 / 6.876477 (-5.200691) | 1.775646 / 2.142072 (-0.366426) | 0.483277 / 4.805227 (-4.321950) | 0.097781 / 6.500664 (-6.402883) | 0.040291 / 0.075469 (-0.035178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975458 / 1.841788 (-0.866330) | 11.961966 / 8.074308 (3.887658) | 10.558559 / 10.191392 (0.367167) | 0.131372 / 0.680424 (-0.549052) | 0.016156 / 0.534201 (-0.518045) | 0.269254 / 0.579283 (-0.310029) | 0.274896 / 0.434364 (-0.159468) | 0.304672 / 0.540337 (-0.235665) | 0.517652 / 1.386936 (-0.869284) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1a1e7416892dcb71097b47120bc9b26b3d90f06a \"CML watermark\")\n" ]
2023-11-17T16:53:22
2023-11-21T14:19:21
2023-11-21T14:13:13
COLLABORATOR
null
false
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Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).
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1,999,419,105
PR_kwDODunzps5fxDoG
6,433
Better `tqdm` wrapper
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005070 / 0.011353 (-0.006283) | 0.003251 / 0.011008 (-0.007757) | 0.061528 / 0.038508 (0.023020) | 0.055386 / 0.023109 (0.032276) | 0.248536 / 0.275898 (-0.027362) | 0.272346 / 0.323480 (-0.051134) | 0.003875 / 0.007986 (-0.004111) | 0.002396 / 0.004328 (-0.001933) | 0.047659 / 0.004250 (0.043409) | 0.037448 / 0.037052 (0.000396) | 0.251101 / 0.258489 (-0.007388) | 0.282353 / 0.293841 (-0.011488) | 0.027784 / 0.128546 (-0.100762) | 0.010534 / 0.075646 (-0.065113) | 0.206025 / 0.419271 (-0.213246) | 0.035410 / 0.043533 (-0.008123) | 0.250626 / 0.255139 (-0.004513) | 0.266801 / 0.283200 (-0.016399) | 0.017704 / 0.141683 (-0.123979) | 1.089970 / 1.452155 (-0.362185) | 1.171683 / 1.492716 (-0.321033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092700 / 0.018006 (0.074694) | 0.301314 / 0.000490 (0.300824) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018385 / 0.037411 (-0.019026) | 0.062364 / 0.014526 (0.047838) | 0.075887 / 0.176557 (-0.100670) | 0.119484 / 0.737135 (-0.617651) | 0.074490 / 0.296338 (-0.221849) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283893 / 0.215209 (0.068684) | 2.746772 / 2.077655 (0.669118) | 1.486568 / 1.504120 (-0.017552) | 1.376451 / 1.541195 (-0.164744) | 1.377928 / 1.468490 (-0.090562) | 0.572393 / 4.584777 (-4.012384) | 2.383282 / 3.745712 (-1.362430) | 2.791614 / 5.269862 (-2.478248) | 1.753373 / 4.565676 (-2.812303) | 0.063539 / 0.424275 (-0.360736) | 0.005014 / 0.007607 (-0.002593) | 0.341300 / 0.226044 (0.115256) | 3.376960 / 2.268929 (1.108032) | 1.914162 / 55.444624 (-53.530462) | 1.590188 / 6.876477 (-5.286289) | 1.618420 / 2.142072 (-0.523652) | 0.648723 / 4.805227 (-4.156504) | 0.117745 / 6.500664 (-6.382919) | 0.048858 / 0.075469 (-0.026611) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944422 / 1.841788 (-0.897366) | 11.603590 / 8.074308 (3.529282) | 10.707000 / 10.191392 (0.515608) | 0.130779 / 0.680424 (-0.549645) | 0.015126 / 0.534201 (-0.519075) | 0.284869 / 0.579283 (-0.294414) | 0.266778 / 0.434364 (-0.167585) | 0.320646 / 0.540337 (-0.219691) | 0.417167 / 1.386936 (-0.969769) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005384 / 0.011353 (-0.005969) | 0.003311 / 0.011008 (-0.007698) | 0.049933 / 0.038508 (0.011425) | 0.052791 / 0.023109 (0.029681) | 0.277061 / 0.275898 (0.001162) | 0.302149 / 0.323480 (-0.021331) | 0.004006 / 0.007986 (-0.003979) | 0.002394 / 0.004328 (-0.001934) | 0.049020 / 0.004250 (0.044770) | 0.040168 / 0.037052 (0.003116) | 0.278625 / 0.258489 (0.020136) | 0.308641 / 0.293841 (0.014800) | 0.029808 / 0.128546 (-0.098738) | 0.010873 / 0.075646 (-0.064774) | 0.058040 / 0.419271 (-0.361231) | 0.032706 / 0.043533 (-0.010827) | 0.277254 / 0.255139 (0.022115) | 0.295208 / 0.283200 (0.012008) | 0.017769 / 0.141683 (-0.123914) | 1.126416 / 1.452155 (-0.325739) | 1.169046 / 1.492716 (-0.323670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094776 / 0.018006 (0.076770) | 0.306262 / 0.000490 (0.305772) | 0.000223 / 0.000200 (0.000023) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022279 / 0.037411 (-0.015132) | 0.086784 / 0.014526 (0.072258) | 0.082268 / 0.176557 (-0.094289) | 0.120131 / 0.737135 (-0.617004) | 0.082862 / 0.296338 (-0.213476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300565 / 0.215209 (0.085356) | 2.923424 / 2.077655 (0.845769) | 1.594836 / 1.504120 (0.090716) | 1.504323 / 1.541195 (-0.036872) | 1.498495 / 1.468490 (0.030005) | 0.570969 / 4.584777 (-4.013808) | 2.476966 / 3.745712 (-1.268746) | 2.785190 / 5.269862 (-2.484672) | 1.749839 / 4.565676 (-2.815837) | 0.062809 / 0.424275 (-0.361466) | 0.004908 / 0.007607 (-0.002699) | 0.361513 / 0.226044 (0.135469) | 3.587135 / 2.268929 (1.318207) | 1.952030 / 55.444624 (-53.492595) | 1.661552 / 6.876477 (-5.214925) | 1.678673 / 2.142072 (-0.463399) | 0.645083 / 4.805227 (-4.160144) | 0.117098 / 6.500664 (-6.383566) | 0.041630 / 0.075469 (-0.033839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987883 / 1.841788 (-0.853904) | 12.300764 / 8.074308 (4.226456) | 10.962068 / 10.191392 (0.770675) | 0.143200 / 0.680424 (-0.537224) | 0.015743 / 0.534201 (-0.518458) | 0.289733 / 0.579283 (-0.289550) | 0.276384 / 0.434364 (-0.157979) | 0.328542 / 0.540337 (-0.211795) | 0.552175 / 1.386936 (-0.834761) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81a65a57cf9fd0abdf85b664a144c9a06cb2896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005110 / 0.011353 (-0.006243) | 0.003311 / 0.011008 (-0.007697) | 0.061962 / 0.038508 (0.023454) | 0.050250 / 0.023109 (0.027140) | 0.245313 / 0.275898 (-0.030585) | 0.268748 / 0.323480 (-0.054732) | 0.004693 / 0.007986 (-0.003293) | 0.002465 / 0.004328 (-0.001863) | 0.047698 / 0.004250 (0.043447) | 0.037314 / 0.037052 (0.000262) | 0.250370 / 0.258489 (-0.008119) | 0.286023 / 0.293841 (-0.007818) | 0.027891 / 0.128546 (-0.100655) | 0.010574 / 0.075646 (-0.065072) | 0.204895 / 0.419271 (-0.214376) | 0.036014 / 0.043533 (-0.007519) | 0.250959 / 0.255139 (-0.004180) | 0.266710 / 0.283200 (-0.016489) | 0.018492 / 0.141683 (-0.123191) | 1.115340 / 1.452155 (-0.336815) | 1.176488 / 1.492716 (-0.316229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099409 / 0.018006 (0.081402) | 0.310151 / 0.000490 (0.309661) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018132 / 0.037411 (-0.019279) | 0.061820 / 0.014526 (0.047294) | 0.074960 / 0.176557 (-0.101596) | 0.119793 / 0.737135 (-0.617342) | 0.074132 / 0.296338 (-0.222206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286388 / 0.215209 (0.071179) | 2.830791 / 2.077655 (0.753137) | 1.514588 / 1.504120 (0.010468) | 1.376514 / 1.541195 (-0.164681) | 1.405080 / 1.468490 (-0.063410) | 0.555297 / 4.584777 (-4.029480) | 2.364838 / 3.745712 (-1.380874) | 2.806050 / 5.269862 (-2.463812) | 1.756114 / 4.565676 (-2.809562) | 0.062254 / 0.424275 (-0.362022) | 0.005020 / 0.007607 (-0.002588) | 0.346272 / 0.226044 (0.120227) | 3.453195 / 2.268929 (1.184266) | 1.837810 / 55.444624 (-53.606814) | 1.577984 / 6.876477 (-5.298493) | 1.560821 / 2.142072 (-0.581251) | 0.633930 / 4.805227 (-4.171297) | 0.116414 / 6.500664 (-6.384250) | 0.042007 / 0.075469 (-0.033462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941322 / 1.841788 (-0.900466) | 11.740927 / 8.074308 (3.666618) | 10.450543 / 10.191392 (0.259151) | 0.128820 / 0.680424 (-0.551604) | 0.014856 / 0.534201 (-0.519345) | 0.285636 / 0.579283 (-0.293647) | 0.270051 / 0.434364 (-0.164313) | 0.321244 / 0.540337 (-0.219093) | 0.415486 / 1.386936 (-0.971450) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005333 / 0.011353 (-0.006020) | 0.003370 / 0.011008 (-0.007638) | 0.049046 / 0.038508 (0.010538) | 0.055767 / 0.023109 (0.032658) | 0.273463 / 0.275898 (-0.002435) | 0.292909 / 0.323480 (-0.030571) | 0.004102 / 0.007986 (-0.003883) | 0.002460 / 0.004328 (-0.001868) | 0.048025 / 0.004250 (0.043775) | 0.040342 / 0.037052 (0.003290) | 0.275114 / 0.258489 (0.016625) | 0.295988 / 0.293841 (0.002147) | 0.029461 / 0.128546 (-0.099085) | 0.010654 / 0.075646 (-0.064992) | 0.057196 / 0.419271 (-0.362076) | 0.033238 / 0.043533 (-0.010295) | 0.275885 / 0.255139 (0.020746) | 0.288566 / 0.283200 (0.005366) | 0.018058 / 0.141683 (-0.123625) | 1.130513 / 1.452155 (-0.321642) | 1.173608 / 1.492716 (-0.319108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097751 / 0.018006 (0.079745) | 0.312106 / 0.000490 (0.311616) | 0.000232 / 0.000200 (0.000032) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021201 / 0.037411 (-0.016211) | 0.070150 / 0.014526 (0.055624) | 0.081073 / 0.176557 (-0.095484) | 0.119520 / 0.737135 (-0.617615) | 0.084479 / 0.296338 (-0.211859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292322 / 0.215209 (0.077113) | 2.844070 / 2.077655 (0.766415) | 1.581838 / 1.504120 (0.077718) | 1.462665 / 1.541195 (-0.078530) | 1.483013 / 1.468490 (0.014523) | 0.558705 / 4.584777 (-4.026072) | 2.422368 / 3.745712 (-1.323344) | 2.772274 / 5.269862 (-2.497587) | 1.725901 / 4.565676 (-2.839775) | 0.062993 / 0.424275 (-0.361282) | 0.004982 / 0.007607 (-0.002625) | 0.344336 / 0.226044 (0.118292) | 3.425230 / 2.268929 (1.156302) | 1.947199 / 55.444624 (-53.497425) | 1.670362 / 6.876477 (-5.206115) | 1.674112 / 2.142072 (-0.467961) | 0.633857 / 4.805227 (-4.171370) | 0.114837 / 6.500664 (-6.385827) | 0.042558 / 0.075469 (-0.032911) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979474 / 1.841788 (-0.862314) | 12.110856 / 8.074308 (4.036548) | 10.605998 / 10.191392 (0.414606) | 0.130769 / 0.680424 (-0.549654) | 0.016057 / 0.534201 (-0.518144) | 0.296448 / 0.579283 (-0.282835) | 0.278078 / 0.434364 (-0.156286) | 0.320809 / 0.540337 (-0.219528) | 0.570756 / 1.386936 (-0.816180) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeb9727cc680a8f8172a012920bf84f285fec5a0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005181 / 0.011353 (-0.006172) | 0.003434 / 0.011008 (-0.007574) | 0.062333 / 0.038508 (0.023825) | 0.058544 / 0.023109 (0.035435) | 0.233794 / 0.275898 (-0.042104) | 0.258774 / 0.323480 (-0.064706) | 0.003869 / 0.007986 (-0.004117) | 0.002478 / 0.004328 (-0.001850) | 0.047871 / 0.004250 (0.043620) | 0.037997 / 0.037052 (0.000945) | 0.241269 / 0.258489 (-0.017220) | 0.270103 / 0.293841 (-0.023738) | 0.027710 / 0.128546 (-0.100836) | 0.010683 / 0.075646 (-0.064963) | 0.213204 / 0.419271 (-0.206067) | 0.036156 / 0.043533 (-0.007377) | 0.240061 / 0.255139 (-0.015078) | 0.253627 / 0.283200 (-0.029573) | 0.017880 / 0.141683 (-0.123803) | 1.102965 / 1.452155 (-0.349189) | 1.176919 / 1.492716 (-0.315797) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093206 / 0.018006 (0.075200) | 0.300960 / 0.000490 (0.300470) | 0.000214 / 0.000200 (0.000014) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019417 / 0.037411 (-0.017994) | 0.061948 / 0.014526 (0.047422) | 0.073560 / 0.176557 (-0.102997) | 0.120682 / 0.737135 (-0.616453) | 0.074925 / 0.296338 (-0.221413) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280157 / 0.215209 (0.064948) | 2.760648 / 2.077655 (0.682994) | 1.482129 / 1.504120 (-0.021991) | 1.364091 / 1.541195 (-0.177104) | 1.415680 / 1.468490 (-0.052810) | 0.564697 / 4.584777 (-4.020080) | 2.364080 / 3.745712 (-1.381633) | 2.794018 / 5.269862 (-2.475844) | 1.752157 / 4.565676 (-2.813520) | 0.062234 / 0.424275 (-0.362041) | 0.004927 / 0.007607 (-0.002680) | 0.337835 / 0.226044 (0.111790) | 3.313819 / 2.268929 (1.044891) | 1.834095 / 55.444624 (-53.610530) | 1.559964 / 6.876477 (-5.316513) | 1.598489 / 2.142072 (-0.543584) | 0.636829 / 4.805227 (-4.168399) | 0.116436 / 6.500664 (-6.384228) | 0.042506 / 0.075469 (-0.032963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951508 / 1.841788 (-0.890280) | 11.599532 / 8.074308 (3.525224) | 10.492355 / 10.191392 (0.300963) | 0.151582 / 0.680424 (-0.528842) | 0.014356 / 0.534201 (-0.519845) | 0.288448 / 0.579283 (-0.290835) | 0.265607 / 0.434364 (-0.168757) | 0.324455 / 0.540337 (-0.215883) | 0.416718 / 1.386936 (-0.970218) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005489 / 0.011353 (-0.005864) | 0.003481 / 0.011008 (-0.007527) | 0.048952 / 0.038508 (0.010444) | 0.054650 / 0.023109 (0.031540) | 0.280853 / 0.275898 (0.004955) | 0.298089 / 0.323480 (-0.025391) | 0.004762 / 0.007986 (-0.003224) | 0.002500 / 0.004328 (-0.001828) | 0.048503 / 0.004250 (0.044253) | 0.042048 / 0.037052 (0.004995) | 0.281729 / 0.258489 (0.023240) | 0.303625 / 0.293841 (0.009785) | 0.028887 / 0.128546 (-0.099659) | 0.010687 / 0.075646 (-0.064960) | 0.058093 / 0.419271 (-0.361178) | 0.032366 / 0.043533 (-0.011167) | 0.281987 / 0.255139 (0.026848) | 0.295554 / 0.283200 (0.012355) | 0.019242 / 0.141683 (-0.122441) | 1.127760 / 1.452155 (-0.324395) | 1.166868 / 1.492716 (-0.325848) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092367 / 0.018006 (0.074361) | 0.300195 / 0.000490 (0.299706) | 0.000222 / 0.000200 (0.000022) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022062 / 0.037411 (-0.015349) | 0.069955 / 0.014526 (0.055429) | 0.081224 / 0.176557 (-0.095333) | 0.120183 / 0.737135 (-0.616953) | 0.082846 / 0.296338 (-0.213492) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295880 / 0.215209 (0.080671) | 2.902508 / 2.077655 (0.824853) | 1.616311 / 1.504120 (0.112191) | 1.491320 / 1.541195 (-0.049875) | 1.517333 / 1.468490 (0.048843) | 0.566824 / 4.584777 (-4.017953) | 2.428397 / 3.745712 (-1.317315) | 2.807241 / 5.269862 (-2.462620) | 1.786364 / 4.565676 (-2.779312) | 0.065253 / 0.424275 (-0.359022) | 0.004971 / 0.007607 (-0.002636) | 0.350095 / 0.226044 (0.124051) | 3.422226 / 2.268929 (1.153297) | 1.972955 / 55.444624 (-53.471670) | 1.686142 / 6.876477 (-5.190335) | 1.694539 / 2.142072 (-0.447533) | 0.645709 / 4.805227 (-4.159518) | 0.117774 / 6.500664 (-6.382890) | 0.041679 / 0.075469 (-0.033790) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976835 / 1.841788 (-0.864952) | 12.358039 / 8.074308 (4.283730) | 10.774304 / 10.191392 (0.582912) | 0.130442 / 0.680424 (-0.549982) | 0.016071 / 0.534201 (-0.518130) | 0.289911 / 0.579283 (-0.289372) | 0.280693 / 0.434364 (-0.153671) | 0.325598 / 0.540337 (-0.214739) | 0.549618 / 1.386936 (-0.837318) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1570235228b67a15dce1ed535e905edd7442117f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005176 / 0.011353 (-0.006177) | 0.003297 / 0.011008 (-0.007711) | 0.061673 / 0.038508 (0.023165) | 0.052174 / 0.023109 (0.029065) | 0.245897 / 0.275898 (-0.030001) | 0.273102 / 0.323480 (-0.050377) | 0.003870 / 0.007986 (-0.004115) | 0.002385 / 0.004328 (-0.001943) | 0.047675 / 0.004250 (0.043424) | 0.037722 / 0.037052 (0.000670) | 0.250780 / 0.258489 (-0.007709) | 0.279464 / 0.293841 (-0.014376) | 0.028107 / 0.128546 (-0.100439) | 0.010460 / 0.075646 (-0.065187) | 0.205522 / 0.419271 (-0.213750) | 0.035781 / 0.043533 (-0.007752) | 0.246526 / 0.255139 (-0.008613) | 0.263919 / 0.283200 (-0.019281) | 0.018634 / 0.141683 (-0.123049) | 1.103845 / 1.452155 (-0.348310) | 1.175536 / 1.492716 (-0.317181) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091696 / 0.018006 (0.073690) | 0.301284 / 0.000490 (0.300794) | 0.000213 / 0.000200 (0.000013) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019153 / 0.037411 (-0.018258) | 0.063846 / 0.014526 (0.049320) | 0.073635 / 0.176557 (-0.102922) | 0.119625 / 0.737135 (-0.617511) | 0.075161 / 0.296338 (-0.221177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285637 / 0.215209 (0.070428) | 2.751787 / 2.077655 (0.674132) | 1.465098 / 1.504120 (-0.039022) | 1.341676 / 1.541195 (-0.199519) | 1.390636 / 1.468490 (-0.077854) | 0.567663 / 4.584777 (-4.017114) | 2.378183 / 3.745712 (-1.367529) | 2.801830 / 5.269862 (-2.468032) | 1.750125 / 4.565676 (-2.815551) | 0.063705 / 0.424275 (-0.360570) | 0.004967 / 0.007607 (-0.002640) | 0.373302 / 0.226044 (0.147258) | 3.301847 / 2.268929 (1.032918) | 1.830117 / 55.444624 (-53.614508) | 1.564360 / 6.876477 (-5.312117) | 1.551766 / 2.142072 (-0.590306) | 0.654424 / 4.805227 (-4.150803) | 0.120656 / 6.500664 (-6.380008) | 0.042383 / 0.075469 (-0.033086) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.931815 / 1.841788 (-0.909973) | 11.755904 / 8.074308 (3.681596) | 10.571707 / 10.191392 (0.380315) | 0.131118 / 0.680424 (-0.549306) | 0.015241 / 0.534201 (-0.518960) | 0.287137 / 0.579283 (-0.292146) | 0.265651 / 0.434364 (-0.168713) | 0.329083 / 0.540337 (-0.211254) | 0.417501 / 1.386936 (-0.969435) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005355 / 0.011353 (-0.005998) | 0.003305 / 0.011008 (-0.007703) | 0.048289 / 0.038508 (0.009781) | 0.059223 / 0.023109 (0.036114) | 0.267213 / 0.275898 (-0.008685) | 0.290151 / 0.323480 (-0.033329) | 0.004683 / 0.007986 (-0.003303) | 0.002413 / 0.004328 (-0.001916) | 0.047982 / 0.004250 (0.043732) | 0.040943 / 0.037052 (0.003891) | 0.270967 / 0.258489 (0.012478) | 0.297644 / 0.293841 (0.003803) | 0.029309 / 0.128546 (-0.099237) | 0.010624 / 0.075646 (-0.065023) | 0.057359 / 0.419271 (-0.361913) | 0.032716 / 0.043533 (-0.010816) | 0.268602 / 0.255139 (0.013463) | 0.286016 / 0.283200 (0.002817) | 0.018578 / 0.141683 (-0.123105) | 1.120275 / 1.452155 (-0.331880) | 1.195514 / 1.492716 (-0.297202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092590 / 0.018006 (0.074584) | 0.302589 / 0.000490 (0.302099) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022439 / 0.037411 (-0.014972) | 0.070914 / 0.014526 (0.056388) | 0.084927 / 0.176557 (-0.091629) | 0.123154 / 0.737135 (-0.613981) | 0.085527 / 0.296338 (-0.210812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292652 / 0.215209 (0.077443) | 2.843736 / 2.077655 (0.766081) | 1.561289 / 1.504120 (0.057169) | 1.439500 / 1.541195 (-0.101695) | 1.485074 / 1.468490 (0.016584) | 0.570520 / 4.584777 (-4.014257) | 2.436611 / 3.745712 (-1.309102) | 2.925600 / 5.269862 (-2.344261) | 1.796518 / 4.565676 (-2.769159) | 0.065075 / 0.424275 (-0.359200) | 0.004995 / 0.007607 (-0.002612) | 0.349976 / 0.226044 (0.123932) | 3.442535 / 2.268929 (1.173607) | 1.919002 / 55.444624 (-53.525622) | 1.659222 / 6.876477 (-5.217255) | 1.648370 / 2.142072 (-0.493703) | 0.643119 / 4.805227 (-4.162108) | 0.118015 / 6.500664 (-6.382649) | 0.041229 / 0.075469 (-0.034240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986226 / 1.841788 (-0.855562) | 12.302487 / 8.074308 (4.228179) | 10.528848 / 10.191392 (0.337456) | 0.143911 / 0.680424 (-0.536513) | 0.015265 / 0.534201 (-0.518936) | 0.287692 / 0.579283 (-0.291591) | 0.277011 / 0.434364 (-0.157353) | 0.327650 / 0.540337 (-0.212688) | 0.552951 / 1.386936 (-0.833985) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0af18e68664db94e863f0dcde4b0f3a7adcc80e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005234 / 0.011353 (-0.006119) | 0.003324 / 0.011008 (-0.007684) | 0.062429 / 0.038508 (0.023921) | 0.051619 / 0.023109 (0.028510) | 0.256850 / 0.275898 (-0.019048) | 0.260566 / 0.323480 (-0.062914) | 0.002914 / 0.007986 (-0.005071) | 0.003093 / 0.004328 (-0.001235) | 0.047947 / 0.004250 (0.043696) | 0.038753 / 0.037052 (0.001701) | 0.246810 / 0.258489 (-0.011679) | 0.275128 / 0.293841 (-0.018713) | 0.027171 / 0.128546 (-0.101375) | 0.010290 / 0.075646 (-0.065356) | 0.206069 / 0.419271 (-0.213203) | 0.035514 / 0.043533 (-0.008019) | 0.240645 / 0.255139 (-0.014494) | 0.259693 / 0.283200 (-0.023507) | 0.019722 / 0.141683 (-0.121961) | 1.128534 / 1.452155 (-0.323620) | 1.139602 / 1.492716 (-0.353115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095837 / 0.018006 (0.077830) | 0.304754 / 0.000490 (0.304264) | 0.000204 / 0.000200 (0.000004) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018349 / 0.037411 (-0.019063) | 0.062763 / 0.014526 (0.048237) | 0.074443 / 0.176557 (-0.102113) | 0.120607 / 0.737135 (-0.616528) | 0.077721 / 0.296338 (-0.218617) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281852 / 0.215209 (0.066643) | 2.770806 / 2.077655 (0.693151) | 1.466255 / 1.504120 (-0.037864) | 1.349611 / 1.541195 (-0.191584) | 1.385463 / 1.468490 (-0.083027) | 0.566489 / 4.584777 (-4.018288) | 2.420932 / 3.745712 (-1.324780) | 2.809397 / 5.269862 (-2.460464) | 1.749734 / 4.565676 (-2.815942) | 0.063407 / 0.424275 (-0.360868) | 0.005038 / 0.007607 (-0.002569) | 0.379121 / 0.226044 (0.153077) | 3.500938 / 2.268929 (1.232010) | 1.852207 / 55.444624 (-53.592417) | 1.570474 / 6.876477 (-5.306002) | 1.555222 / 2.142072 (-0.586850) | 0.657198 / 4.805227 (-4.148030) | 0.119835 / 6.500664 (-6.380829) | 0.042453 / 0.075469 (-0.033016) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949953 / 1.841788 (-0.891835) | 11.736811 / 8.074308 (3.662503) | 10.558049 / 10.191392 (0.366657) | 0.146230 / 0.680424 (-0.534194) | 0.014922 / 0.534201 (-0.519279) | 0.289100 / 0.579283 (-0.290183) | 0.267130 / 0.434364 (-0.167234) | 0.320055 / 0.540337 (-0.220282) | 0.417244 / 1.386936 (-0.969692) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003329 / 0.011008 (-0.007679) | 0.048576 / 0.038508 (0.010068) | 0.055219 / 0.023109 (0.032110) | 0.271522 / 0.275898 (-0.004376) | 0.294435 / 0.323480 (-0.029045) | 0.004018 / 0.007986 (-0.003968) | 0.002456 / 0.004328 (-0.001873) | 0.047939 / 0.004250 (0.043689) | 0.041195 / 0.037052 (0.004143) | 0.274819 / 0.258489 (0.016330) | 0.299407 / 0.293841 (0.005566) | 0.029145 / 0.128546 (-0.099401) | 0.010680 / 0.075646 (-0.064966) | 0.057238 / 0.419271 (-0.362034) | 0.032722 / 0.043533 (-0.010810) | 0.272066 / 0.255139 (0.016927) | 0.289223 / 0.283200 (0.006023) | 0.017826 / 0.141683 (-0.123857) | 1.119079 / 1.452155 (-0.333076) | 1.179109 / 1.492716 (-0.313608) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095662 / 0.018006 (0.077656) | 0.307652 / 0.000490 (0.307162) | 0.000213 / 0.000200 (0.000013) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022263 / 0.037411 (-0.015149) | 0.070224 / 0.014526 (0.055698) | 0.081477 / 0.176557 (-0.095079) | 0.120763 / 0.737135 (-0.616372) | 0.083152 / 0.296338 (-0.213187) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295780 / 0.215209 (0.080571) | 2.926623 / 2.077655 (0.848968) | 1.605901 / 1.504120 (0.101781) | 1.482874 / 1.541195 (-0.058321) | 1.501467 / 1.468490 (0.032977) | 0.569566 / 4.584777 (-4.015211) | 2.474948 / 3.745712 (-1.270764) | 2.831877 / 5.269862 (-2.437985) | 1.761229 / 4.565676 (-2.804448) | 0.064129 / 0.424275 (-0.360147) | 0.004964 / 0.007607 (-0.002643) | 0.350081 / 0.226044 (0.124037) | 3.446766 / 2.268929 (1.177837) | 1.974998 / 55.444624 (-53.469627) | 1.683381 / 6.876477 (-5.193095) | 1.711543 / 2.142072 (-0.430530) | 0.648695 / 4.805227 (-4.156532) | 0.118224 / 6.500664 (-6.382440) | 0.040895 / 0.075469 (-0.034574) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960208 / 1.841788 (-0.881580) | 12.164941 / 8.074308 (4.090633) | 10.860573 / 10.191392 (0.669181) | 0.133525 / 0.680424 (-0.546899) | 0.015643 / 0.534201 (-0.518558) | 0.290898 / 0.579283 (-0.288386) | 0.289612 / 0.434364 (-0.144752) | 0.325836 / 0.540337 (-0.214501) | 0.565592 / 1.386936 (-0.821344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d19a315920c6d4293f8226273d99bf3de5c1d4e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006097 / 0.011353 (-0.005256) | 0.004386 / 0.011008 (-0.006622) | 0.064481 / 0.038508 (0.025973) | 0.059983 / 0.023109 (0.036873) | 0.268177 / 0.275898 (-0.007721) | 0.296207 / 0.323480 (-0.027273) | 0.002986 / 0.007986 (-0.005000) | 0.002923 / 0.004328 (-0.001406) | 0.048798 / 0.004250 (0.044547) | 0.039945 / 0.037052 (0.002893) | 0.271234 / 0.258489 (0.012745) | 0.295461 / 0.293841 (0.001620) | 0.028771 / 0.128546 (-0.099775) | 0.011104 / 0.075646 (-0.064542) | 0.207471 / 0.419271 (-0.211800) | 0.036955 / 0.043533 (-0.006578) | 0.254761 / 0.255139 (-0.000378) | 0.275933 / 0.283200 (-0.007267) | 0.021232 / 0.141683 (-0.120451) | 1.170771 / 1.452155 (-0.281384) | 1.188900 / 1.492716 (-0.303816) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092328 / 0.018006 (0.074322) | 0.302591 / 0.000490 (0.302102) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019207 / 0.037411 (-0.018204) | 0.070247 / 0.014526 (0.055721) | 0.074963 / 0.176557 (-0.101593) | 0.124301 / 0.737135 (-0.612834) | 0.077356 / 0.296338 (-0.218982) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283321 / 0.215209 (0.068112) | 2.800448 / 2.077655 (0.722793) | 1.510278 / 1.504120 (0.006158) | 1.390353 / 1.541195 (-0.150842) | 1.387881 / 1.468490 (-0.080609) | 0.563927 / 4.584777 (-4.020850) | 2.387753 / 3.745712 (-1.357959) | 2.776655 / 5.269862 (-2.493207) | 1.767383 / 4.565676 (-2.798293) | 0.064864 / 0.424275 (-0.359411) | 0.004999 / 0.007607 (-0.002608) | 0.351173 / 0.226044 (0.125129) | 3.459446 / 2.268929 (1.190517) | 1.873078 / 55.444624 (-53.571547) | 1.602831 / 6.876477 (-5.273646) | 1.595612 / 2.142072 (-0.546460) | 0.648786 / 4.805227 (-4.156441) | 0.118720 / 6.500664 (-6.381944) | 0.042821 / 0.075469 (-0.032649) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970738 / 1.841788 (-0.871049) | 12.273548 / 8.074308 (4.199240) | 11.191375 / 10.191392 (0.999983) | 0.131903 / 0.680424 (-0.548521) | 0.014512 / 0.534201 (-0.519689) | 0.289382 / 0.579283 (-0.289901) | 0.269449 / 0.434364 (-0.164915) | 0.327557 / 0.540337 (-0.212781) | 0.427052 / 1.386936 (-0.959884) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005472 / 0.011353 (-0.005881) | 0.003380 / 0.011008 (-0.007628) | 0.050677 / 0.038508 (0.012169) | 0.059606 / 0.023109 (0.036497) | 0.275798 / 0.275898 (-0.000100) | 0.303733 / 0.323480 (-0.019747) | 0.004187 / 0.007986 (-0.003799) | 0.002657 / 0.004328 (-0.001672) | 0.048713 / 0.004250 (0.044463) | 0.043501 / 0.037052 (0.006449) | 0.278845 / 0.258489 (0.020356) | 0.305322 / 0.293841 (0.011481) | 0.030665 / 0.128546 (-0.097881) | 0.010600 / 0.075646 (-0.065047) | 0.058923 / 0.419271 (-0.360349) | 0.032936 / 0.043533 (-0.010596) | 0.272835 / 0.255139 (0.017696) | 0.293975 / 0.283200 (0.010775) | 0.018193 / 0.141683 (-0.123490) | 1.144903 / 1.452155 (-0.307251) | 1.192220 / 1.492716 (-0.300497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094519 / 0.018006 (0.076513) | 0.305591 / 0.000490 (0.305101) | 0.000221 / 0.000200 (0.000021) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022108 / 0.037411 (-0.015303) | 0.070184 / 0.014526 (0.055658) | 0.081640 / 0.176557 (-0.094916) | 0.124661 / 0.737135 (-0.612474) | 0.082229 / 0.296338 (-0.214110) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303710 / 0.215209 (0.088501) | 2.966478 / 2.077655 (0.888824) | 1.646066 / 1.504120 (0.141946) | 1.551454 / 1.541195 (0.010259) | 1.557995 / 1.468490 (0.089505) | 0.577723 / 4.584777 (-4.007054) | 2.510321 / 3.745712 (-1.235391) | 2.951343 / 5.269862 (-2.318519) | 1.857550 / 4.565676 (-2.708127) | 0.064079 / 0.424275 (-0.360196) | 0.004971 / 0.007607 (-0.002636) | 0.359022 / 0.226044 (0.132978) | 3.628716 / 2.268929 (1.359788) | 2.011380 / 55.444624 (-53.433245) | 1.710407 / 6.876477 (-5.166070) | 1.756235 / 2.142072 (-0.385838) | 0.659185 / 4.805227 (-4.146042) | 0.120245 / 6.500664 (-6.380419) | 0.042751 / 0.075469 (-0.032718) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026794 / 1.841788 (-0.814993) | 12.695125 / 8.074308 (4.620816) | 10.864908 / 10.191392 (0.673516) | 0.136128 / 0.680424 (-0.544295) | 0.016824 / 0.534201 (-0.517377) | 0.289717 / 0.579283 (-0.289567) | 0.282919 / 0.434364 (-0.151445) | 0.323345 / 0.540337 (-0.216992) | 0.556375 / 1.386936 (-0.830561) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52207295162f734235b71428d13e6a42c6fdc370 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005407 / 0.011353 (-0.005946) | 0.003464 / 0.011008 (-0.007544) | 0.062084 / 0.038508 (0.023576) | 0.052582 / 0.023109 (0.029472) | 0.251239 / 0.275898 (-0.024659) | 0.276675 / 0.323480 (-0.046805) | 0.002894 / 0.007986 (-0.005092) | 0.003850 / 0.004328 (-0.000479) | 0.047789 / 0.004250 (0.043538) | 0.038955 / 0.037052 (0.001903) | 0.258333 / 0.258489 (-0.000156) | 0.290103 / 0.293841 (-0.003738) | 0.027291 / 0.128546 (-0.101256) | 0.010575 / 0.075646 (-0.065071) | 0.207208 / 0.419271 (-0.212063) | 0.035848 / 0.043533 (-0.007685) | 0.253918 / 0.255139 (-0.001221) | 0.269870 / 0.283200 (-0.013330) | 0.019830 / 0.141683 (-0.121853) | 1.085332 / 1.452155 (-0.366823) | 1.171385 / 1.492716 (-0.321331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094956 / 0.018006 (0.076950) | 0.301104 / 0.000490 (0.300614) | 0.000204 / 0.000200 (0.000004) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019045 / 0.037411 (-0.018367) | 0.070815 / 0.014526 (0.056289) | 0.073763 / 0.176557 (-0.102794) | 0.120668 / 0.737135 (-0.616467) | 0.075197 / 0.296338 (-0.221141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286072 / 0.215209 (0.070863) | 2.762868 / 2.077655 (0.685213) | 1.504481 / 1.504120 (0.000361) | 1.390301 / 1.541195 (-0.150894) | 1.449571 / 1.468490 (-0.018919) | 0.555598 / 4.584777 (-4.029179) | 2.404975 / 3.745712 (-1.340737) | 2.864359 / 5.269862 (-2.405503) | 1.764913 / 4.565676 (-2.800763) | 0.062956 / 0.424275 (-0.361320) | 0.005116 / 0.007607 (-0.002491) | 0.344027 / 0.226044 (0.117983) | 3.426781 / 2.268929 (1.157852) | 1.891040 / 55.444624 (-53.553584) | 1.599972 / 6.876477 (-5.276505) | 1.603464 / 2.142072 (-0.538608) | 0.638136 / 4.805227 (-4.167091) | 0.117808 / 6.500664 (-6.382857) | 0.043740 / 0.075469 (-0.031730) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.934654 / 1.841788 (-0.907133) | 12.243698 / 8.074308 (4.169390) | 10.566791 / 10.191392 (0.375399) | 0.130440 / 0.680424 (-0.549983) | 0.014019 / 0.534201 (-0.520182) | 0.285453 / 0.579283 (-0.293831) | 0.266121 / 0.434364 (-0.168243) | 0.325962 / 0.540337 (-0.214375) | 0.422181 / 1.386936 (-0.964755) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005151 / 0.011353 (-0.006202) | 0.003704 / 0.011008 (-0.007304) | 0.049483 / 0.038508 (0.010975) | 0.055147 / 0.023109 (0.032038) | 0.277589 / 0.275898 (0.001691) | 0.301274 / 0.323480 (-0.022206) | 0.004031 / 0.007986 (-0.003955) | 0.002568 / 0.004328 (-0.001760) | 0.048830 / 0.004250 (0.044580) | 0.040391 / 0.037052 (0.003339) | 0.281031 / 0.258489 (0.022541) | 0.304263 / 0.293841 (0.010422) | 0.029237 / 0.128546 (-0.099309) | 0.010598 / 0.075646 (-0.065048) | 0.058089 / 0.419271 (-0.361182) | 0.032529 / 0.043533 (-0.011004) | 0.275761 / 0.255139 (0.020622) | 0.294427 / 0.283200 (0.011227) | 0.017227 / 0.141683 (-0.124456) | 1.138036 / 1.452155 (-0.314119) | 1.201946 / 1.492716 (-0.290770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094241 / 0.018006 (0.076234) | 0.301622 / 0.000490 (0.301132) | 0.000229 / 0.000200 (0.000029) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022731 / 0.037411 (-0.014680) | 0.071217 / 0.014526 (0.056691) | 0.082619 / 0.176557 (-0.093937) | 0.123308 / 0.737135 (-0.613827) | 0.083552 / 0.296338 (-0.212787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295770 / 0.215209 (0.080561) | 2.886069 / 2.077655 (0.808414) | 1.597686 / 1.504120 (0.093566) | 1.458612 / 1.541195 (-0.082583) | 1.501171 / 1.468490 (0.032680) | 0.575653 / 4.584777 (-4.009124) | 2.444021 / 3.745712 (-1.301691) | 2.860192 / 5.269862 (-2.409669) | 1.758896 / 4.565676 (-2.806780) | 0.063334 / 0.424275 (-0.360941) | 0.004913 / 0.007607 (-0.002694) | 0.341828 / 0.226044 (0.115783) | 3.420310 / 2.268929 (1.151381) | 1.996099 / 55.444624 (-53.448525) | 1.680112 / 6.876477 (-5.196365) | 1.693418 / 2.142072 (-0.448654) | 0.697321 / 4.805227 (-4.107906) | 0.120474 / 6.500664 (-6.380190) | 0.042192 / 0.075469 (-0.033277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975876 / 1.841788 (-0.865912) | 12.174933 / 8.074308 (4.100625) | 10.400906 / 10.191392 (0.209514) | 0.162244 / 0.680424 (-0.518180) | 0.016443 / 0.534201 (-0.517758) | 0.293430 / 0.579283 (-0.285853) | 0.285664 / 0.434364 (-0.148700) | 0.332322 / 0.540337 (-0.208015) | 0.609815 / 1.386936 (-0.777121) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2c417d087d232b5abf9054ffb10305cc06c5440 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005155 / 0.011353 (-0.006198) | 0.003226 / 0.011008 (-0.007782) | 0.062651 / 0.038508 (0.024143) | 0.051314 / 0.023109 (0.028205) | 0.246075 / 0.275898 (-0.029823) | 0.266859 / 0.323480 (-0.056621) | 0.003895 / 0.007986 (-0.004091) | 0.002462 / 0.004328 (-0.001866) | 0.048097 / 0.004250 (0.043846) | 0.037313 / 0.037052 (0.000261) | 0.253208 / 0.258489 (-0.005281) | 0.280255 / 0.293841 (-0.013585) | 0.027052 / 0.128546 (-0.101494) | 0.010276 / 0.075646 (-0.065370) | 0.205663 / 0.419271 (-0.213608) | 0.035111 / 0.043533 (-0.008422) | 0.253757 / 0.255139 (-0.001382) | 0.265466 / 0.283200 (-0.017733) | 0.017873 / 0.141683 (-0.123810) | 1.118906 / 1.452155 (-0.333249) | 1.176384 / 1.492716 (-0.316332) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094921 / 0.018006 (0.076914) | 0.300459 / 0.000490 (0.299970) | 0.000214 / 0.000200 (0.000014) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018430 / 0.037411 (-0.018981) | 0.062690 / 0.014526 (0.048165) | 0.074215 / 0.176557 (-0.102342) | 0.119969 / 0.737135 (-0.617166) | 0.075846 / 0.296338 (-0.220493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273492 / 0.215209 (0.058283) | 2.667937 / 2.077655 (0.590282) | 1.405912 / 1.504120 (-0.098208) | 1.269041 / 1.541195 (-0.272153) | 1.313461 / 1.468490 (-0.155029) | 0.554633 / 4.584777 (-4.030144) | 2.325552 / 3.745712 (-1.420160) | 2.825580 / 5.269862 (-2.444282) | 1.745432 / 4.565676 (-2.820245) | 0.062497 / 0.424275 (-0.361778) | 0.004935 / 0.007607 (-0.002673) | 0.337045 / 0.226044 (0.111001) | 3.246360 / 2.268929 (0.977432) | 1.775329 / 55.444624 (-53.669296) | 1.491812 / 6.876477 (-5.384665) | 1.499783 / 2.142072 (-0.642290) | 0.636768 / 4.805227 (-4.168459) | 0.116471 / 6.500664 (-6.384193) | 0.041838 / 0.075469 (-0.033631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937388 / 1.841788 (-0.904400) | 11.950930 / 8.074308 (3.876622) | 10.532062 / 10.191392 (0.340670) | 0.129490 / 0.680424 (-0.550934) | 0.013907 / 0.534201 (-0.520294) | 0.287503 / 0.579283 (-0.291780) | 0.270548 / 0.434364 (-0.163816) | 0.324321 / 0.540337 (-0.216016) | 0.427639 / 1.386936 (-0.959297) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005272 / 0.011353 (-0.006081) | 0.003413 / 0.011008 (-0.007595) | 0.049800 / 0.038508 (0.011292) | 0.055978 / 0.023109 (0.032868) | 0.274365 / 0.275898 (-0.001533) | 0.293414 / 0.323480 (-0.030066) | 0.003994 / 0.007986 (-0.003992) | 0.002480 / 0.004328 (-0.001848) | 0.048787 / 0.004250 (0.044537) | 0.040520 / 0.037052 (0.003468) | 0.276198 / 0.258489 (0.017709) | 0.301085 / 0.293841 (0.007244) | 0.028352 / 0.128546 (-0.100194) | 0.010631 / 0.075646 (-0.065015) | 0.057103 / 0.419271 (-0.362168) | 0.032277 / 0.043533 (-0.011256) | 0.274472 / 0.255139 (0.019333) | 0.289953 / 0.283200 (0.006754) | 0.018048 / 0.141683 (-0.123635) | 1.120329 / 1.452155 (-0.331826) | 1.175784 / 1.492716 (-0.316932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102519 / 0.018006 (0.084512) | 0.322030 / 0.000490 (0.321540) | 0.000234 / 0.000200 (0.000034) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023084 / 0.037411 (-0.014327) | 0.069592 / 0.014526 (0.055066) | 0.081293 / 0.176557 (-0.095264) | 0.119546 / 0.737135 (-0.617589) | 0.083249 / 0.296338 (-0.213090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294997 / 0.215209 (0.079788) | 2.925517 / 2.077655 (0.847863) | 1.607824 / 1.504120 (0.103705) | 1.469586 / 1.541195 (-0.071608) | 1.492350 / 1.468490 (0.023860) | 0.561351 / 4.584777 (-4.023426) | 2.446741 / 3.745712 (-1.298972) | 2.842588 / 5.269862 (-2.427273) | 1.789189 / 4.565676 (-2.776487) | 0.064064 / 0.424275 (-0.360211) | 0.005011 / 0.007607 (-0.002597) | 0.351059 / 0.226044 (0.125015) | 3.485277 / 2.268929 (1.216348) | 1.981821 / 55.444624 (-53.462803) | 1.671846 / 6.876477 (-5.204631) | 1.702014 / 2.142072 (-0.440058) | 0.645205 / 4.805227 (-4.160023) | 0.117358 / 6.500664 (-6.383306) | 0.041633 / 0.075469 (-0.033836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963281 / 1.841788 (-0.878506) | 12.141256 / 8.074308 (4.066947) | 10.595207 / 10.191392 (0.403815) | 0.130401 / 0.680424 (-0.550023) | 0.015490 / 0.534201 (-0.518710) | 0.284201 / 0.579283 (-0.295082) | 0.280244 / 0.434364 (-0.154120) | 0.323545 / 0.540337 (-0.216792) | 0.561246 / 1.386936 (-0.825690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3193829cf0dd9888c42bd7640a71d9d656cba2a \"CML watermark\")\n" ]
2023-11-17T15:45:15
2023-11-22T16:48:18
2023-11-22T16:42:08
COLLABORATOR
null
false
{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6433", "html_url": "https://github.com/huggingface/datasets/pull/6433", "diff_url": "https://github.com/huggingface/datasets/pull/6433.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6433.patch", "merged_at": "2023-11-22T16:42:08" }
This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone. Additionally, it improves the doc page about the `datasets`' utilities, and the handling of local `fsspec` paths in `cached_path`. Fix #6409
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6,432
load_dataset does not load all of the data in my input file
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[ "You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves." ]
2023-11-17T14:28:50
2023-11-22T17:34:58
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### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1
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Create DatasetNotFoundError and DataFilesNotFoundError
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004459 / 0.011353 (-0.006894) | 0.002883 / 0.011008 (-0.008125) | 0.062434 / 0.038508 (0.023925) | 0.030353 / 0.023109 (0.007244) | 0.256696 / 0.275898 (-0.019202) | 0.280557 / 0.323480 (-0.042923) | 0.003903 / 0.007986 (-0.004083) | 0.002424 / 0.004328 (-0.001905) | 0.048509 / 0.004250 (0.044259) | 0.043583 / 0.037052 (0.006531) | 0.253900 / 0.258489 (-0.004590) | 0.309146 / 0.293841 (0.015305) | 0.023253 / 0.128546 (-0.105294) | 0.007073 / 0.075646 (-0.068573) | 0.204118 / 0.419271 (-0.215154) | 0.056429 / 0.043533 (0.012897) | 0.247331 / 0.255139 (-0.007808) | 0.271581 / 0.283200 (-0.011619) | 0.017021 / 0.141683 (-0.124662) | 1.115057 / 1.452155 (-0.337098) | 1.209947 / 1.492716 (-0.282770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093141 / 0.018006 (0.075134) | 0.295987 / 0.000490 (0.295497) | 0.000221 / 0.000200 (0.000021) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019182 / 0.037411 (-0.018230) | 0.062049 / 0.014526 (0.047523) | 0.073824 / 0.176557 (-0.102733) | 0.120175 / 0.737135 (-0.616960) | 0.074700 / 0.296338 (-0.221639) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280036 / 0.215209 (0.064827) | 2.731512 / 2.077655 (0.653857) | 1.414606 / 1.504120 (-0.089514) | 1.302433 / 1.541195 (-0.238761) | 1.313012 / 1.468490 (-0.155478) | 0.399722 / 4.584777 (-4.185055) | 2.371249 / 3.745712 (-1.374463) | 2.582520 / 5.269862 (-2.687342) | 1.558505 / 4.565676 (-3.007171) | 0.045765 / 0.424275 (-0.378510) | 0.004748 / 0.007607 (-0.002859) | 0.327623 / 0.226044 (0.101578) | 3.258742 / 2.268929 (0.989814) | 1.756798 / 55.444624 (-53.687826) | 1.494551 / 6.876477 (-5.381925) | 1.518161 / 2.142072 (-0.623911) | 0.468560 / 4.805227 (-4.336667) | 0.101034 / 6.500664 (-6.399630) | 0.048259 / 0.075469 (-0.027210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938146 / 1.841788 (-0.903642) | 11.636387 / 8.074308 (3.562078) | 10.638909 / 10.191392 (0.447517) | 0.128340 / 0.680424 (-0.552084) | 0.015194 / 0.534201 (-0.519007) | 0.275961 / 0.579283 (-0.303322) | 0.264629 / 0.434364 (-0.169735) | 0.308580 / 0.540337 (-0.231758) | 0.433658 / 1.386936 (-0.953278) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004797 / 0.011353 (-0.006556) | 0.002801 / 0.011008 (-0.008208) | 0.048101 / 0.038508 (0.009593) | 0.056406 / 0.023109 (0.033296) | 0.274966 / 0.275898 (-0.000932) | 0.298310 / 0.323480 (-0.025170) | 0.004115 / 0.007986 (-0.003871) | 0.002437 / 0.004328 (-0.001891) | 0.047921 / 0.004250 (0.043671) | 0.038812 / 0.037052 (0.001760) | 0.279594 / 0.258489 (0.021105) | 0.313703 / 0.293841 (0.019862) | 0.024485 / 0.128546 (-0.104061) | 0.007095 / 0.075646 (-0.068551) | 0.053398 / 0.419271 (-0.365874) | 0.032306 / 0.043533 (-0.011227) | 0.278014 / 0.255139 (0.022875) | 0.301156 / 0.283200 (0.017956) | 0.017353 / 0.141683 (-0.124330) | 1.150168 / 1.452155 (-0.301987) | 1.190822 / 1.492716 (-0.301894) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092162 / 0.018006 (0.074156) | 0.301031 / 0.000490 (0.300541) | 0.000244 / 0.000200 (0.000044) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020918 / 0.037411 (-0.016494) | 0.072030 / 0.014526 (0.057504) | 0.081813 / 0.176557 (-0.094743) | 0.120233 / 0.737135 (-0.616903) | 0.082874 / 0.296338 (-0.213465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291659 / 0.215209 (0.076450) | 2.841978 / 2.077655 (0.764323) | 1.594207 / 1.504120 (0.090087) | 1.473941 / 1.541195 (-0.067254) | 1.514393 / 1.468490 (0.045903) | 0.393393 / 4.584777 (-4.191384) | 2.443663 / 3.745712 (-1.302050) | 2.545747 / 5.269862 (-2.724114) | 1.521130 / 4.565676 (-3.044546) | 0.046246 / 0.424275 (-0.378030) | 0.004826 / 0.007607 (-0.002781) | 0.340909 / 0.226044 (0.114865) | 3.319474 / 2.268929 (1.050546) | 1.933110 / 55.444624 (-53.511515) | 1.662463 / 6.876477 (-5.214014) | 1.670331 / 2.142072 (-0.471742) | 0.458062 / 4.805227 (-4.347165) | 0.098397 / 6.500664 (-6.402267) | 0.041339 / 0.075469 (-0.034130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973718 / 1.841788 (-0.868070) | 12.095266 / 8.074308 (4.020957) | 10.761212 / 10.191392 (0.569820) | 0.142352 / 0.680424 (-0.538072) | 0.015423 / 0.534201 (-0.518778) | 0.270912 / 0.579283 (-0.308371) | 0.276618 / 0.434364 (-0.157746) | 0.309120 / 0.540337 (-0.231217) | 0.415330 / 1.386936 (-0.971606) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004676 / 0.011353 (-0.006677) | 0.003101 / 0.011008 (-0.007907) | 0.062260 / 0.038508 (0.023752) | 0.030012 / 0.023109 (0.006903) | 0.253704 / 0.275898 (-0.022194) | 0.276404 / 0.323480 (-0.047075) | 0.004060 / 0.007986 (-0.003926) | 0.002467 / 0.004328 (-0.001861) | 0.047921 / 0.004250 (0.043670) | 0.045760 / 0.037052 (0.008708) | 0.254529 / 0.258489 (-0.003960) | 0.286283 / 0.293841 (-0.007558) | 0.023301 / 0.128546 (-0.105246) | 0.007407 / 0.075646 (-0.068239) | 0.204541 / 0.419271 (-0.214730) | 0.056387 / 0.043533 (0.012854) | 0.252120 / 0.255139 (-0.003019) | 0.275795 / 0.283200 (-0.007404) | 0.018648 / 0.141683 (-0.123034) | 1.113484 / 1.452155 (-0.338671) | 1.168685 / 1.492716 (-0.324031) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098286 / 0.018006 (0.080280) | 0.304619 / 0.000490 (0.304129) | 0.000225 / 0.000200 (0.000025) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019183 / 0.037411 (-0.018229) | 0.062183 / 0.014526 (0.047657) | 0.074288 / 0.176557 (-0.102269) | 0.120576 / 0.737135 (-0.616560) | 0.074833 / 0.296338 (-0.221505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280512 / 0.215209 (0.065303) | 2.770052 / 2.077655 (0.692397) | 1.471234 / 1.504120 (-0.032886) | 1.352080 / 1.541195 (-0.189114) | 1.374518 / 1.468490 (-0.093973) | 0.407108 / 4.584777 (-4.177669) | 2.400581 / 3.745712 (-1.345131) | 2.677507 / 5.269862 (-2.592355) | 1.578042 / 4.565676 (-2.987635) | 0.048539 / 0.424275 (-0.375736) | 0.004905 / 0.007607 (-0.002703) | 0.346676 / 0.226044 (0.120631) | 3.367732 / 2.268929 (1.098803) | 1.844405 / 55.444624 (-53.600220) | 1.576883 / 6.876477 (-5.299594) | 1.666986 / 2.142072 (-0.475086) | 0.495872 / 4.805227 (-4.309355) | 0.103142 / 6.500664 (-6.397522) | 0.044037 / 0.075469 (-0.031432) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980865 / 1.841788 (-0.860923) | 12.268525 / 8.074308 (4.194217) | 10.756554 / 10.191392 (0.565162) | 0.129954 / 0.680424 (-0.550470) | 0.013864 / 0.534201 (-0.520337) | 0.267653 / 0.579283 (-0.311630) | 0.265120 / 0.434364 (-0.169244) | 0.309050 / 0.540337 (-0.231288) | 0.423877 / 1.386936 (-0.963059) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005074 / 0.011353 (-0.006279) | 0.003001 / 0.011008 (-0.008007) | 0.048271 / 0.038508 (0.009763) | 0.061206 / 0.023109 (0.038097) | 0.279268 / 0.275898 (0.003370) | 0.302592 / 0.323480 (-0.020888) | 0.004177 / 0.007986 (-0.003809) | 0.002452 / 0.004328 (-0.001876) | 0.048259 / 0.004250 (0.044009) | 0.040032 / 0.037052 (0.002979) | 0.281398 / 0.258489 (0.022909) | 0.314121 / 0.293841 (0.020280) | 0.025137 / 0.128546 (-0.103409) | 0.007230 / 0.075646 (-0.068416) | 0.054537 / 0.419271 (-0.364735) | 0.033266 / 0.043533 (-0.010267) | 0.277305 / 0.255139 (0.022166) | 0.295993 / 0.283200 (0.012794) | 0.019278 / 0.141683 (-0.122405) | 1.131700 / 1.452155 (-0.320454) | 1.183848 / 1.492716 (-0.308868) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092258 / 0.018006 (0.074251) | 0.310668 / 0.000490 (0.310178) | 0.000219 / 0.000200 (0.000019) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021838 / 0.037411 (-0.015574) | 0.071382 / 0.014526 (0.056857) | 0.081389 / 0.176557 (-0.095168) | 0.120389 / 0.737135 (-0.616746) | 0.084135 / 0.296338 (-0.212203) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291676 / 0.215209 (0.076467) | 2.840623 / 2.077655 (0.762968) | 1.565748 / 1.504120 (0.061628) | 1.452529 / 1.541195 (-0.088666) | 1.490633 / 1.468490 (0.022143) | 0.402878 / 4.584777 (-4.181899) | 2.486192 / 3.745712 (-1.259520) | 2.520563 / 5.269862 (-2.749299) | 1.518550 / 4.565676 (-3.047127) | 0.047423 / 0.424275 (-0.376852) | 0.004823 / 0.007607 (-0.002784) | 0.353122 / 0.226044 (0.127078) | 3.452136 / 2.268929 (1.183208) | 1.973798 / 55.444624 (-53.470827) | 1.669569 / 6.876477 (-5.206907) | 1.654910 / 2.142072 (-0.487163) | 0.486746 / 4.805227 (-4.318481) | 0.097260 / 6.500664 (-6.403404) | 0.040608 / 0.075469 (-0.034861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989705 / 1.841788 (-0.852083) | 12.114386 / 8.074308 (4.040077) | 11.284551 / 10.191392 (1.093159) | 0.141408 / 0.680424 (-0.539016) | 0.015275 / 0.534201 (-0.518926) | 0.267407 / 0.579283 (-0.311877) | 0.281007 / 0.434364 (-0.153357) | 0.309617 / 0.540337 (-0.230720) | 0.414033 / 1.386936 (-0.972903) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004888 / 0.011353 (-0.006465) | 0.002775 / 0.011008 (-0.008233) | 0.062000 / 0.038508 (0.023492) | 0.050694 / 0.023109 (0.027584) | 0.257063 / 0.275898 (-0.018835) | 0.282743 / 0.323480 (-0.040736) | 0.002862 / 0.007986 (-0.005124) | 0.002305 / 0.004328 (-0.002023) | 0.049549 / 0.004250 (0.045299) | 0.038754 / 0.037052 (0.001701) | 0.264047 / 0.258489 (0.005558) | 0.310162 / 0.293841 (0.016321) | 0.022901 / 0.128546 (-0.105645) | 0.006894 / 0.075646 (-0.068752) | 0.202467 / 0.419271 (-0.216805) | 0.035901 / 0.043533 (-0.007631) | 0.262344 / 0.255139 (0.007205) | 0.285563 / 0.283200 (0.002364) | 0.017070 / 0.141683 (-0.124613) | 1.113972 / 1.452155 (-0.338182) | 1.176261 / 1.492716 (-0.316455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092912 / 0.018006 (0.074906) | 0.302610 / 0.000490 (0.302120) | 0.000204 / 0.000200 (0.000005) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018232 / 0.037411 (-0.019179) | 0.062367 / 0.014526 (0.047841) | 0.074570 / 0.176557 (-0.101987) | 0.120468 / 0.737135 (-0.616668) | 0.075187 / 0.296338 (-0.221151) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279760 / 0.215209 (0.064551) | 2.715372 / 2.077655 (0.637717) | 1.461636 / 1.504120 (-0.042484) | 1.324220 / 1.541195 (-0.216975) | 1.350724 / 1.468490 (-0.117766) | 0.395648 / 4.584777 (-4.189129) | 2.376548 / 3.745712 (-1.369164) | 2.594662 / 5.269862 (-2.675200) | 1.553528 / 4.565676 (-3.012148) | 0.047875 / 0.424275 (-0.376400) | 0.005287 / 0.007607 (-0.002321) | 0.334734 / 0.226044 (0.108689) | 3.294753 / 2.268929 (1.025825) | 1.797901 / 55.444624 (-53.646724) | 1.510907 / 6.876477 (-5.365570) | 1.536070 / 2.142072 (-0.606003) | 0.474672 / 4.805227 (-4.330555) | 0.099323 / 6.500664 (-6.401341) | 0.041703 / 0.075469 (-0.033766) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947441 / 1.841788 (-0.894347) | 11.451378 / 8.074308 (3.377070) | 10.283213 / 10.191392 (0.091821) | 0.131032 / 0.680424 (-0.549392) | 0.014423 / 0.534201 (-0.519777) | 0.272568 / 0.579283 (-0.306715) | 0.267127 / 0.434364 (-0.167237) | 0.307361 / 0.540337 (-0.232976) | 0.403858 / 1.386936 (-0.983078) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004836 / 0.011353 (-0.006517) | 0.002544 / 0.011008 (-0.008464) | 0.047979 / 0.038508 (0.009471) | 0.052211 / 0.023109 (0.029102) | 0.273394 / 0.275898 (-0.002504) | 0.291202 / 0.323480 (-0.032277) | 0.004094 / 0.007986 (-0.003891) | 0.002415 / 0.004328 (-0.001914) | 0.048057 / 0.004250 (0.043807) | 0.039756 / 0.037052 (0.002703) | 0.277301 / 0.258489 (0.018812) | 0.297626 / 0.293841 (0.003785) | 0.024641 / 0.128546 (-0.103905) | 0.006957 / 0.075646 (-0.068690) | 0.053574 / 0.419271 (-0.365697) | 0.036532 / 0.043533 (-0.007001) | 0.273753 / 0.255139 (0.018614) | 0.294254 / 0.283200 (0.011054) | 0.022252 / 0.141683 (-0.119431) | 1.128609 / 1.452155 (-0.323546) | 1.217322 / 1.492716 (-0.275394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091050 / 0.018006 (0.073044) | 0.300089 / 0.000490 (0.299600) | 0.000215 / 0.000200 (0.000015) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021423 / 0.037411 (-0.015988) | 0.069892 / 0.014526 (0.055366) | 0.081125 / 0.176557 (-0.095432) | 0.118725 / 0.737135 (-0.618411) | 0.081357 / 0.296338 (-0.214981) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295046 / 0.215209 (0.079837) | 2.868813 / 2.077655 (0.791159) | 1.579613 / 1.504120 (0.075493) | 1.449308 / 1.541195 (-0.091887) | 1.478804 / 1.468490 (0.010314) | 0.416916 / 4.584777 (-4.167861) | 2.461093 / 3.745712 (-1.284619) | 2.449792 / 5.269862 (-2.820070) | 1.573930 / 4.565676 (-2.991746) | 0.046808 / 0.424275 (-0.377467) | 0.004811 / 0.007607 (-0.002796) | 0.352805 / 0.226044 (0.126761) | 3.495034 / 2.268929 (1.226105) | 1.952019 / 55.444624 (-53.492606) | 1.642607 / 6.876477 (-5.233869) | 1.775235 / 2.142072 (-0.366837) | 0.482196 / 4.805227 (-4.323032) | 0.099562 / 6.500664 (-6.401102) | 0.040709 / 0.075469 (-0.034760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972750 / 1.841788 (-0.869038) | 11.905172 / 8.074308 (3.830864) | 10.613847 / 10.191392 (0.422455) | 0.129892 / 0.680424 (-0.550532) | 0.015611 / 0.534201 (-0.518590) | 0.271884 / 0.579283 (-0.307400) | 0.275270 / 0.434364 (-0.159094) | 0.303213 / 0.540337 (-0.237125) | 0.402338 / 1.386936 (-0.984598) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004826 / 0.011353 (-0.006527) | 0.002979 / 0.011008 (-0.008029) | 0.062055 / 0.038508 (0.023547) | 0.056574 / 0.023109 (0.033465) | 0.244342 / 0.275898 (-0.031556) | 0.278040 / 0.323480 (-0.045439) | 0.004020 / 0.007986 (-0.003965) | 0.002474 / 0.004328 (-0.001855) | 0.048451 / 0.004250 (0.044200) | 0.038633 / 0.037052 (0.001580) | 0.251389 / 0.258489 (-0.007100) | 0.282739 / 0.293841 (-0.011102) | 0.023298 / 0.128546 (-0.105248) | 0.007513 / 0.075646 (-0.068134) | 0.203014 / 0.419271 (-0.216257) | 0.036216 / 0.043533 (-0.007317) | 0.250988 / 0.255139 (-0.004151) | 0.281228 / 0.283200 (-0.001972) | 0.018259 / 0.141683 (-0.123424) | 1.121200 / 1.452155 (-0.330955) | 1.184298 / 1.492716 (-0.308419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093730 / 0.018006 (0.075724) | 0.301716 / 0.000490 (0.301226) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019238 / 0.037411 (-0.018173) | 0.064329 / 0.014526 (0.049803) | 0.075657 / 0.176557 (-0.100899) | 0.122616 / 0.737135 (-0.614519) | 0.077459 / 0.296338 (-0.218880) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280153 / 0.215209 (0.064944) | 2.715488 / 2.077655 (0.637833) | 1.449666 / 1.504120 (-0.054454) | 1.331903 / 1.541195 (-0.209292) | 1.396200 / 1.468490 (-0.072290) | 0.398861 / 4.584777 (-4.185916) | 2.402814 / 3.745712 (-1.342898) | 2.664033 / 5.269862 (-2.605829) | 1.619589 / 4.565676 (-2.946088) | 0.044798 / 0.424275 (-0.379477) | 0.004989 / 0.007607 (-0.002618) | 0.336822 / 0.226044 (0.110777) | 3.245604 / 2.268929 (0.976676) | 1.815633 / 55.444624 (-53.628991) | 1.557975 / 6.876477 (-5.318501) | 1.603655 / 2.142072 (-0.538417) | 0.462980 / 4.805227 (-4.342247) | 0.098340 / 6.500664 (-6.402324) | 0.042750 / 0.075469 (-0.032719) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973785 / 1.841788 (-0.868003) | 12.379356 / 8.074308 (4.305048) | 10.540164 / 10.191392 (0.348772) | 0.144803 / 0.680424 (-0.535621) | 0.013875 / 0.534201 (-0.520326) | 0.270192 / 0.579283 (-0.309091) | 0.264614 / 0.434364 (-0.169750) | 0.313454 / 0.540337 (-0.226883) | 0.402310 / 1.386936 (-0.984626) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004987 / 0.011353 (-0.006366) | 0.003017 / 0.011008 (-0.007992) | 0.048592 / 0.038508 (0.010084) | 0.059370 / 0.023109 (0.036261) | 0.277536 / 0.275898 (0.001638) | 0.300592 / 0.323480 (-0.022888) | 0.004870 / 0.007986 (-0.003115) | 0.002452 / 0.004328 (-0.001876) | 0.047972 / 0.004250 (0.043721) | 0.042336 / 0.037052 (0.005283) | 0.277570 / 0.258489 (0.019081) | 0.304739 / 0.293841 (0.010898) | 0.025313 / 0.128546 (-0.103233) | 0.007219 / 0.075646 (-0.068427) | 0.053967 / 0.419271 (-0.365304) | 0.033314 / 0.043533 (-0.010219) | 0.273908 / 0.255139 (0.018769) | 0.291913 / 0.283200 (0.008713) | 0.019440 / 0.141683 (-0.122243) | 1.111047 / 1.452155 (-0.341107) | 1.191276 / 1.492716 (-0.301440) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093985 / 0.018006 (0.075979) | 0.303105 / 0.000490 (0.302615) | 0.000235 / 0.000200 (0.000035) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022226 / 0.037411 (-0.015186) | 0.072151 / 0.014526 (0.057625) | 0.081700 / 0.176557 (-0.094857) | 0.121407 / 0.737135 (-0.615729) | 0.083217 / 0.296338 (-0.213121) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297286 / 0.215209 (0.082077) | 2.913392 / 2.077655 (0.835738) | 1.591758 / 1.504120 (0.087638) | 1.463339 / 1.541195 (-0.077856) | 1.495095 / 1.468490 (0.026605) | 0.414341 / 4.584777 (-4.170436) | 2.412438 / 3.745712 (-1.333275) | 2.611452 / 5.269862 (-2.658410) | 1.658545 / 4.565676 (-2.907132) | 0.047269 / 0.424275 (-0.377007) | 0.004872 / 0.007607 (-0.002735) | 0.350746 / 0.226044 (0.124701) | 3.491482 / 2.268929 (1.222554) | 1.999009 / 55.444624 (-53.445616) | 1.672862 / 6.876477 (-5.203615) | 1.863095 / 2.142072 (-0.278977) | 0.484746 / 4.805227 (-4.320481) | 0.100774 / 6.500664 (-6.399890) | 0.042519 / 0.075469 (-0.032950) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984497 / 1.841788 (-0.857291) | 12.972576 / 8.074308 (4.898268) | 10.886021 / 10.191392 (0.694629) | 0.141639 / 0.680424 (-0.538785) | 0.015726 / 0.534201 (-0.518475) | 0.284160 / 0.579283 (-0.295123) | 0.291437 / 0.434364 (-0.142927) | 0.314121 / 0.540337 (-0.226217) | 0.420439 / 1.386936 (-0.966497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ad7c7767b9cda62113c207f0ff42506a8f27c0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004881 / 0.011353 (-0.006472) | 0.002550 / 0.011008 (-0.008458) | 0.062171 / 0.038508 (0.023663) | 0.055341 / 0.023109 (0.032232) | 0.243132 / 0.275898 (-0.032766) | 0.265174 / 0.323480 (-0.058306) | 0.002934 / 0.007986 (-0.005052) | 0.002233 / 0.004328 (-0.002096) | 0.049302 / 0.004250 (0.045052) | 0.039491 / 0.037052 (0.002439) | 0.252776 / 0.258489 (-0.005713) | 0.280923 / 0.293841 (-0.012918) | 0.022585 / 0.128546 (-0.105962) | 0.006888 / 0.075646 (-0.068759) | 0.202751 / 0.419271 (-0.216521) | 0.035250 / 0.043533 (-0.008283) | 0.251745 / 0.255139 (-0.003394) | 0.267431 / 0.283200 (-0.015768) | 0.019486 / 0.141683 (-0.122197) | 1.161783 / 1.452155 (-0.290372) | 1.194254 / 1.492716 (-0.298463) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097772 / 0.018006 (0.079766) | 0.309137 / 0.000490 (0.308647) | 0.000225 / 0.000200 (0.000025) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018719 / 0.037411 (-0.018693) | 0.062211 / 0.014526 (0.047686) | 0.074291 / 0.176557 (-0.102266) | 0.119436 / 0.737135 (-0.617699) | 0.075519 / 0.296338 (-0.220820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279778 / 0.215209 (0.064569) | 2.730678 / 2.077655 (0.653023) | 1.413922 / 1.504120 (-0.090198) | 1.286747 / 1.541195 (-0.254447) | 1.299835 / 1.468490 (-0.168656) | 0.392516 / 4.584777 (-4.192261) | 2.381816 / 3.745712 (-1.363896) | 2.616944 / 5.269862 (-2.652918) | 1.606152 / 4.565676 (-2.959525) | 0.044867 / 0.424275 (-0.379408) | 0.004915 / 0.007607 (-0.002692) | 0.334078 / 0.226044 (0.108034) | 3.388096 / 2.268929 (1.119167) | 1.756666 / 55.444624 (-53.687958) | 1.497211 / 6.876477 (-5.379266) | 1.496787 / 2.142072 (-0.645285) | 0.469145 / 4.805227 (-4.336082) | 0.097821 / 6.500664 (-6.402843) | 0.041850 / 0.075469 (-0.033619) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956878 / 1.841788 (-0.884910) | 11.520184 / 8.074308 (3.445875) | 10.659216 / 10.191392 (0.467824) | 0.143687 / 0.680424 (-0.536737) | 0.014118 / 0.534201 (-0.520083) | 0.270990 / 0.579283 (-0.308293) | 0.270057 / 0.434364 (-0.164306) | 0.311109 / 0.540337 (-0.229229) | 0.407042 / 1.386936 (-0.979894) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004816 / 0.011353 (-0.006537) | 0.002898 / 0.011008 (-0.008110) | 0.048540 / 0.038508 (0.010032) | 0.055286 / 0.023109 (0.032176) | 0.279086 / 0.275898 (0.003187) | 0.298950 / 0.323480 (-0.024529) | 0.004090 / 0.007986 (-0.003896) | 0.002497 / 0.004328 (-0.001832) | 0.049160 / 0.004250 (0.044910) | 0.040612 / 0.037052 (0.003560) | 0.287832 / 0.258489 (0.029343) | 0.305617 / 0.293841 (0.011776) | 0.023936 / 0.128546 (-0.104610) | 0.007565 / 0.075646 (-0.068081) | 0.054037 / 0.419271 (-0.365235) | 0.032389 / 0.043533 (-0.011144) | 0.283031 / 0.255139 (0.027892) | 0.295411 / 0.283200 (0.012212) | 0.018466 / 0.141683 (-0.123217) | 1.134660 / 1.452155 (-0.317495) | 1.196212 / 1.492716 (-0.296504) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099961 / 0.018006 (0.081955) | 0.310831 / 0.000490 (0.310342) | 0.000238 / 0.000200 (0.000038) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021566 / 0.037411 (-0.015845) | 0.070255 / 0.014526 (0.055729) | 0.081221 / 0.176557 (-0.095336) | 0.119404 / 0.737135 (-0.617732) | 0.083005 / 0.296338 (-0.213333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302788 / 0.215209 (0.087579) | 2.928876 / 2.077655 (0.851221) | 1.601221 / 1.504120 (0.097101) | 1.485147 / 1.541195 (-0.056047) | 1.508698 / 1.468490 (0.040207) | 0.402783 / 4.584777 (-4.181994) | 2.432151 / 3.745712 (-1.313561) | 2.476848 / 5.269862 (-2.793013) | 1.585487 / 4.565676 (-2.980189) | 0.045965 / 0.424275 (-0.378310) | 0.004818 / 0.007607 (-0.002789) | 0.354847 / 0.226044 (0.128803) | 3.500670 / 2.268929 (1.231742) | 1.951904 / 55.444624 (-53.492720) | 1.675152 / 6.876477 (-5.201325) | 1.795971 / 2.142072 (-0.346101) | 0.470625 / 4.805227 (-4.334602) | 0.126080 / 6.500664 (-6.374584) | 0.040506 / 0.075469 (-0.034963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985251 / 1.841788 (-0.856536) | 12.316710 / 8.074308 (4.242402) | 10.674437 / 10.191392 (0.483045) | 0.133622 / 0.680424 (-0.546802) | 0.016756 / 0.534201 (-0.517445) | 0.269318 / 0.579283 (-0.309965) | 0.282258 / 0.434364 (-0.152106) | 0.309941 / 0.540337 (-0.230396) | 0.403189 / 1.386936 (-0.983747) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08ceb927025575c453228cab31291b74043dba1a \"CML watermark\")\n", "I am merging this PR because we need it by `datasets-server`.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004935 / 0.011353 (-0.006418) | 0.002643 / 0.011008 (-0.008365) | 0.064449 / 0.038508 (0.025941) | 0.053110 / 0.023109 (0.030001) | 0.261576 / 0.275898 (-0.014322) | 0.270866 / 0.323480 (-0.052614) | 0.002895 / 0.007986 (-0.005091) | 0.002349 / 0.004328 (-0.001979) | 0.047620 / 0.004250 (0.043370) | 0.038699 / 0.037052 (0.001647) | 0.246663 / 0.258489 (-0.011826) | 0.282021 / 0.293841 (-0.011820) | 0.022807 / 0.128546 (-0.105739) | 0.007242 / 0.075646 (-0.068404) | 0.204236 / 0.419271 (-0.215035) | 0.035429 / 0.043533 (-0.008104) | 0.241684 / 0.255139 (-0.013455) | 0.262343 / 0.283200 (-0.020857) | 0.020036 / 0.141683 (-0.121647) | 1.112687 / 1.452155 (-0.339467) | 1.167086 / 1.492716 (-0.325630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107059 / 0.018006 (0.089053) | 0.301036 / 0.000490 (0.300546) | 0.000224 / 0.000200 (0.000024) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018464 / 0.037411 (-0.018947) | 0.063822 / 0.014526 (0.049296) | 0.073562 / 0.176557 (-0.102994) | 0.120136 / 0.737135 (-0.616999) | 0.074934 / 0.296338 (-0.221405) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275474 / 0.215209 (0.060265) | 2.714239 / 2.077655 (0.636584) | 1.455535 / 1.504120 (-0.048585) | 1.336530 / 1.541195 (-0.204665) | 1.359607 / 1.468490 (-0.108883) | 0.396303 / 4.584777 (-4.188474) | 2.366076 / 3.745712 (-1.379636) | 2.600755 / 5.269862 (-2.669107) | 1.572382 / 4.565676 (-2.993294) | 0.045795 / 0.424275 (-0.378480) | 0.004932 / 0.007607 (-0.002675) | 0.332175 / 0.226044 (0.106130) | 3.257843 / 2.268929 (0.988915) | 1.799021 / 55.444624 (-53.645603) | 1.532813 / 6.876477 (-5.343663) | 1.552279 / 2.142072 (-0.589794) | 0.471369 / 4.805227 (-4.333858) | 0.098931 / 6.500664 (-6.401733) | 0.042735 / 0.075469 (-0.032734) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960779 / 1.841788 (-0.881009) | 11.741631 / 8.074308 (3.667322) | 10.355721 / 10.191392 (0.164329) | 0.129025 / 0.680424 (-0.551399) | 0.013794 / 0.534201 (-0.520407) | 0.267268 / 0.579283 (-0.312015) | 0.265582 / 0.434364 (-0.168782) | 0.306242 / 0.540337 (-0.234095) | 0.400367 / 1.386936 (-0.986569) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004966 / 0.011353 (-0.006387) | 0.002846 / 0.011008 (-0.008163) | 0.049104 / 0.038508 (0.010596) | 0.055436 / 0.023109 (0.032327) | 0.273892 / 0.275898 (-0.002006) | 0.300207 / 0.323480 (-0.023273) | 0.004017 / 0.007986 (-0.003969) | 0.002465 / 0.004328 (-0.001863) | 0.048088 / 0.004250 (0.043837) | 0.040037 / 0.037052 (0.002984) | 0.279918 / 0.258489 (0.021429) | 0.305378 / 0.293841 (0.011537) | 0.024326 / 0.128546 (-0.104220) | 0.006992 / 0.075646 (-0.068654) | 0.053545 / 0.419271 (-0.365726) | 0.032312 / 0.043533 (-0.011221) | 0.272899 / 0.255139 (0.017760) | 0.289683 / 0.283200 (0.006483) | 0.019121 / 0.141683 (-0.122562) | 1.133296 / 1.452155 (-0.318858) | 1.220989 / 1.492716 (-0.271728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093193 / 0.018006 (0.075187) | 0.307658 / 0.000490 (0.307168) | 0.000224 / 0.000200 (0.000024) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022906 / 0.037411 (-0.014506) | 0.080931 / 0.014526 (0.066405) | 0.081442 / 0.176557 (-0.095115) | 0.121150 / 0.737135 (-0.615986) | 0.083387 / 0.296338 (-0.212952) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294979 / 0.215209 (0.079770) | 2.900090 / 2.077655 (0.822435) | 1.610061 / 1.504120 (0.105941) | 1.455118 / 1.541195 (-0.086077) | 1.456599 / 1.468490 (-0.011891) | 0.397919 / 4.584777 (-4.186858) | 2.421010 / 3.745712 (-1.324702) | 2.486527 / 5.269862 (-2.783334) | 1.573854 / 4.565676 (-2.991822) | 0.046199 / 0.424275 (-0.378076) | 0.004888 / 0.007607 (-0.002719) | 0.342183 / 0.226044 (0.116139) | 3.392068 / 2.268929 (1.123140) | 1.963688 / 55.444624 (-53.480936) | 1.667611 / 6.876477 (-5.208866) | 1.833706 / 2.142072 (-0.308367) | 0.509421 / 4.805227 (-4.295806) | 0.099669 / 6.500664 (-6.400995) | 0.041004 / 0.075469 (-0.034465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956314 / 1.841788 (-0.885474) | 12.190194 / 8.074308 (4.115886) | 10.417839 / 10.191392 (0.226447) | 0.144139 / 0.680424 (-0.536285) | 0.015841 / 0.534201 (-0.518359) | 0.270436 / 0.579283 (-0.308847) | 0.273952 / 0.434364 (-0.160412) | 0.303018 / 0.540337 (-0.237319) | 0.410163 / 1.386936 (-0.976773) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa8558fc7fe1f9f7675c7c5d21a14d1a19598296 \"CML watermark\")\n" ]
2023-11-16T16:02:55
2023-11-22T15:18:51
2023-11-22T15:12:33
MEMBER
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Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo
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1,996,723,698
PR_kwDODunzps5fn1r_
6,429
Add trust_remote_code argument
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004947 / 0.011353 (-0.006405) | 0.002961 / 0.011008 (-0.008047) | 0.063474 / 0.038508 (0.024966) | 0.030162 / 0.023109 (0.007053) | 0.232388 / 0.275898 (-0.043511) | 0.257654 / 0.323480 (-0.065826) | 0.002969 / 0.007986 (-0.005017) | 0.002336 / 0.004328 (-0.001993) | 0.049724 / 0.004250 (0.045473) | 0.045608 / 0.037052 (0.008555) | 0.236079 / 0.258489 (-0.022410) | 0.267809 / 0.293841 (-0.026032) | 0.023805 / 0.128546 (-0.104741) | 0.007177 / 0.075646 (-0.068470) | 0.202167 / 0.419271 (-0.217104) | 0.056181 / 0.043533 (0.012648) | 0.256464 / 0.255139 (0.001325) | 0.271908 / 0.283200 (-0.011292) | 0.020211 / 0.141683 (-0.121472) | 1.114112 / 1.452155 (-0.338042) | 1.174879 / 1.492716 (-0.317837) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093457 / 0.018006 (0.075451) | 0.307643 / 0.000490 (0.307154) | 0.000212 / 0.000200 (0.000012) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018635 / 0.037411 (-0.018777) | 0.062099 / 0.014526 (0.047573) | 0.073619 / 0.176557 (-0.102938) | 0.119986 / 0.737135 (-0.617149) | 0.075439 / 0.296338 (-0.220899) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280142 / 0.215209 (0.064933) | 2.733790 / 2.077655 (0.656136) | 1.457633 / 1.504120 (-0.046487) | 1.336288 / 1.541195 (-0.204907) | 1.363191 / 1.468490 (-0.105299) | 0.399331 / 4.584777 (-4.185446) | 2.343099 / 3.745712 (-1.402614) | 2.617059 / 5.269862 (-2.652802) | 1.575912 / 4.565676 (-2.989765) | 0.045621 / 0.424275 (-0.378655) | 0.004825 / 0.007607 (-0.002782) | 0.346669 / 0.226044 (0.120625) | 3.225982 / 2.268929 (0.957054) | 1.787067 / 55.444624 (-53.657557) | 1.503883 / 6.876477 (-5.372593) | 1.527593 / 2.142072 (-0.614479) | 0.466806 / 4.805227 (-4.338421) | 0.098537 / 6.500664 (-6.402127) | 0.042028 / 0.075469 (-0.033441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945040 / 1.841788 (-0.896748) | 11.970022 / 8.074308 (3.895714) | 10.261176 / 10.191392 (0.069784) | 0.138231 / 0.680424 (-0.542193) | 0.013933 / 0.534201 (-0.520268) | 0.270640 / 0.579283 (-0.308643) | 0.263185 / 0.434364 (-0.171178) | 0.306686 / 0.540337 (-0.233651) | 0.423164 / 1.386936 (-0.963772) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004765 / 0.011353 (-0.006588) | 0.003158 / 0.011008 (-0.007850) | 0.047813 / 0.038508 (0.009305) | 0.053363 / 0.023109 (0.030254) | 0.278570 / 0.275898 (0.002671) | 0.291500 / 0.323480 (-0.031980) | 0.003987 / 0.007986 (-0.003998) | 0.002430 / 0.004328 (-0.001898) | 0.048059 / 0.004250 (0.043809) | 0.038595 / 0.037052 (0.001542) | 0.276383 / 0.258489 (0.017894) | 0.304234 / 0.293841 (0.010393) | 0.024402 / 0.128546 (-0.104144) | 0.007303 / 0.075646 (-0.068343) | 0.055091 / 0.419271 (-0.364180) | 0.032735 / 0.043533 (-0.010797) | 0.270905 / 0.255139 (0.015766) | 0.287181 / 0.283200 (0.003981) | 0.018919 / 0.141683 (-0.122764) | 1.153814 / 1.452155 (-0.298341) | 1.197009 / 1.492716 (-0.295707) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093743 / 0.018006 (0.075737) | 0.302877 / 0.000490 (0.302387) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021279 / 0.037411 (-0.016133) | 0.070886 / 0.014526 (0.056360) | 0.081628 / 0.176557 (-0.094928) | 0.119721 / 0.737135 (-0.617414) | 0.083093 / 0.296338 (-0.213245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297788 / 0.215209 (0.082579) | 2.915235 / 2.077655 (0.837580) | 1.587580 / 1.504120 (0.083460) | 1.461699 / 1.541195 (-0.079495) | 1.520609 / 1.468490 (0.052119) | 0.398363 / 4.584777 (-4.186413) | 2.408415 / 3.745712 (-1.337297) | 2.552776 / 5.269862 (-2.717086) | 1.508219 / 4.565676 (-3.057457) | 0.045884 / 0.424275 (-0.378391) | 0.004842 / 0.007607 (-0.002765) | 0.341376 / 0.226044 (0.115331) | 3.420192 / 2.268929 (1.151264) | 1.974938 / 55.444624 (-53.469686) | 1.678283 / 6.876477 (-5.198194) | 1.702439 / 2.142072 (-0.439633) | 0.467056 / 4.805227 (-4.338172) | 0.098684 / 6.500664 (-6.401980) | 0.041052 / 0.075469 (-0.034417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990145 / 1.841788 (-0.851643) | 12.143198 / 8.074308 (4.068890) | 10.911039 / 10.191392 (0.719647) | 0.130384 / 0.680424 (-0.550040) | 0.015602 / 0.534201 (-0.518599) | 0.270799 / 0.579283 (-0.308484) | 0.279060 / 0.434364 (-0.155304) | 0.315108 / 0.540337 (-0.225230) | 0.413576 / 1.386936 (-0.973360) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d99b8225e28cca88ed9c2d9b1d8e0342762c4ece \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004911 / 0.011353 (-0.006442) | 0.002808 / 0.011008 (-0.008200) | 0.061367 / 0.038508 (0.022859) | 0.050154 / 0.023109 (0.027045) | 0.250403 / 0.275898 (-0.025495) | 0.273831 / 0.323480 (-0.049649) | 0.002914 / 0.007986 (-0.005071) | 0.002493 / 0.004328 (-0.001836) | 0.048288 / 0.004250 (0.044037) | 0.039219 / 0.037052 (0.002167) | 0.260043 / 0.258489 (0.001554) | 0.288177 / 0.293841 (-0.005664) | 0.023123 / 0.128546 (-0.105423) | 0.006981 / 0.075646 (-0.068666) | 0.201306 / 0.419271 (-0.217965) | 0.035670 / 0.043533 (-0.007863) | 0.255237 / 0.255139 (0.000098) | 0.283701 / 0.283200 (0.000502) | 0.019349 / 0.141683 (-0.122334) | 1.100963 / 1.452155 (-0.351192) | 1.152725 / 1.492716 (-0.339992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106350 / 0.018006 (0.088344) | 0.300577 / 0.000490 (0.300087) | 0.000206 / 0.000200 (0.000006) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019028 / 0.037411 (-0.018384) | 0.062643 / 0.014526 (0.048118) | 0.072771 / 0.176557 (-0.103786) | 0.119873 / 0.737135 (-0.617263) | 0.074470 / 0.296338 (-0.221869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287032 / 0.215209 (0.071823) | 2.826134 / 2.077655 (0.748480) | 1.507362 / 1.504120 (0.003242) | 1.382929 / 1.541195 (-0.158266) | 1.385361 / 1.468490 (-0.083129) | 0.412081 / 4.584777 (-4.172696) | 2.384289 / 3.745712 (-1.361423) | 2.551316 / 5.269862 (-2.718546) | 1.562954 / 4.565676 (-3.002722) | 0.046669 / 0.424275 (-0.377606) | 0.004804 / 0.007607 (-0.002803) | 0.337751 / 0.226044 (0.111707) | 3.378894 / 2.268929 (1.109965) | 1.848817 / 55.444624 (-53.595807) | 1.564560 / 6.876477 (-5.311917) | 1.579577 / 2.142072 (-0.562496) | 0.484531 / 4.805227 (-4.320697) | 0.101157 / 6.500664 (-6.399507) | 0.042272 / 0.075469 (-0.033197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948289 / 1.841788 (-0.893498) | 11.490877 / 8.074308 (3.416569) | 10.492787 / 10.191392 (0.301395) | 0.128575 / 0.680424 (-0.551849) | 0.013716 / 0.534201 (-0.520485) | 0.271075 / 0.579283 (-0.308208) | 0.269749 / 0.434364 (-0.164615) | 0.306378 / 0.540337 (-0.233959) | 0.400204 / 1.386936 (-0.986732) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004821 / 0.011353 (-0.006532) | 0.002773 / 0.011008 (-0.008235) | 0.048934 / 0.038508 (0.010426) | 0.049490 / 0.023109 (0.026380) | 0.271107 / 0.275898 (-0.004791) | 0.291472 / 0.323480 (-0.032008) | 0.004734 / 0.007986 (-0.003252) | 0.002437 / 0.004328 (-0.001892) | 0.048840 / 0.004250 (0.044590) | 0.039757 / 0.037052 (0.002704) | 0.276037 / 0.258489 (0.017548) | 0.298220 / 0.293841 (0.004379) | 0.024595 / 0.128546 (-0.103952) | 0.007320 / 0.075646 (-0.068327) | 0.054693 / 0.419271 (-0.364578) | 0.032672 / 0.043533 (-0.010861) | 0.271555 / 0.255139 (0.016416) | 0.287685 / 0.283200 (0.004485) | 0.017159 / 0.141683 (-0.124524) | 1.118496 / 1.452155 (-0.333659) | 1.177389 / 1.492716 (-0.315327) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090469 / 0.018006 (0.072463) | 0.306014 / 0.000490 (0.305525) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021452 / 0.037411 (-0.015960) | 0.070014 / 0.014526 (0.055488) | 0.081917 / 0.176557 (-0.094639) | 0.120615 / 0.737135 (-0.616520) | 0.081745 / 0.296338 (-0.214593) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294049 / 0.215209 (0.078840) | 2.886802 / 2.077655 (0.809147) | 1.607817 / 1.504120 (0.103697) | 1.474172 / 1.541195 (-0.067023) | 1.474744 / 1.468490 (0.006254) | 0.398178 / 4.584777 (-4.186599) | 2.455908 / 3.745712 (-1.289804) | 2.463003 / 5.269862 (-2.806858) | 1.560402 / 4.565676 (-3.005275) | 0.046208 / 0.424275 (-0.378067) | 0.004862 / 0.007607 (-0.002745) | 0.350862 / 0.226044 (0.124817) | 3.463958 / 2.268929 (1.195030) | 1.934696 / 55.444624 (-53.509928) | 1.660090 / 6.876477 (-5.216387) | 1.770920 / 2.142072 (-0.371153) | 0.468409 / 4.805227 (-4.336819) | 0.096812 / 6.500664 (-6.403852) | 0.040580 / 0.075469 (-0.034889) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978102 / 1.841788 (-0.863686) | 11.943265 / 8.074308 (3.868957) | 10.684995 / 10.191392 (0.493603) | 0.131554 / 0.680424 (-0.548870) | 0.015608 / 0.534201 (-0.518593) | 0.271449 / 0.579283 (-0.307834) | 0.282485 / 0.434364 (-0.151879) | 0.302376 / 0.540337 (-0.237962) | 0.524908 / 1.386936 (-0.862028) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bb0b21e37a57257a7d428f8744c862ca92c0c7e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004926 / 0.011353 (-0.006427) | 0.003020 / 0.011008 (-0.007988) | 0.061899 / 0.038508 (0.023391) | 0.063836 / 0.023109 (0.040726) | 0.239252 / 0.275898 (-0.036646) | 0.268320 / 0.323480 (-0.055160) | 0.003939 / 0.007986 (-0.004046) | 0.002557 / 0.004328 (-0.001772) | 0.048469 / 0.004250 (0.044219) | 0.038707 / 0.037052 (0.001655) | 0.247563 / 0.258489 (-0.010926) | 0.281171 / 0.293841 (-0.012670) | 0.023564 / 0.128546 (-0.104983) | 0.007699 / 0.075646 (-0.067948) | 0.207561 / 0.419271 (-0.211710) | 0.036362 / 0.043533 (-0.007171) | 0.248324 / 0.255139 (-0.006814) | 0.269673 / 0.283200 (-0.013527) | 0.018841 / 0.141683 (-0.122842) | 1.123407 / 1.452155 (-0.328748) | 1.170422 / 1.492716 (-0.322295) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096278 / 0.018006 (0.078272) | 0.311477 / 0.000490 (0.310988) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019470 / 0.037411 (-0.017942) | 0.071888 / 0.014526 (0.057362) | 0.074264 / 0.176557 (-0.102292) | 0.124413 / 0.737135 (-0.612723) | 0.075602 / 0.296338 (-0.220737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284755 / 0.215209 (0.069546) | 2.770789 / 2.077655 (0.693135) | 1.478276 / 1.504120 (-0.025843) | 1.375287 / 1.541195 (-0.165907) | 1.398032 / 1.468490 (-0.070458) | 0.420457 / 4.584777 (-4.164320) | 2.445929 / 3.745712 (-1.299783) | 2.819548 / 5.269862 (-2.450313) | 1.628506 / 4.565676 (-2.937171) | 0.047687 / 0.424275 (-0.376588) | 0.004861 / 0.007607 (-0.002746) | 0.340173 / 0.226044 (0.114129) | 3.340703 / 2.268929 (1.071774) | 1.882803 / 55.444624 (-53.561821) | 1.587206 / 6.876477 (-5.289271) | 1.645298 / 2.142072 (-0.496774) | 0.490957 / 4.805227 (-4.314270) | 0.102779 / 6.500664 (-6.397885) | 0.048372 / 0.075469 (-0.027098) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958311 / 1.841788 (-0.883477) | 12.354981 / 8.074308 (4.280673) | 10.864826 / 10.191392 (0.673434) | 0.149053 / 0.680424 (-0.531371) | 0.015078 / 0.534201 (-0.519123) | 0.270117 / 0.579283 (-0.309166) | 0.274495 / 0.434364 (-0.159869) | 0.307584 / 0.540337 (-0.232753) | 0.405603 / 1.386936 (-0.981333) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004996 / 0.011353 (-0.006357) | 0.002995 / 0.011008 (-0.008014) | 0.047897 / 0.038508 (0.009389) | 0.056413 / 0.023109 (0.033303) | 0.277669 / 0.275898 (0.001771) | 0.300679 / 0.323480 (-0.022801) | 0.004094 / 0.007986 (-0.003892) | 0.002519 / 0.004328 (-0.001810) | 0.049536 / 0.004250 (0.045285) | 0.042341 / 0.037052 (0.005288) | 0.281533 / 0.258489 (0.023044) | 0.306771 / 0.293841 (0.012930) | 0.025379 / 0.128546 (-0.103167) | 0.007495 / 0.075646 (-0.068152) | 0.054453 / 0.419271 (-0.364818) | 0.032616 / 0.043533 (-0.010917) | 0.277844 / 0.255139 (0.022705) | 0.296265 / 0.283200 (0.013065) | 0.019462 / 0.141683 (-0.122221) | 1.115841 / 1.452155 (-0.336313) | 1.169662 / 1.492716 (-0.323054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095459 / 0.018006 (0.077453) | 0.301590 / 0.000490 (0.301100) | 0.000230 / 0.000200 (0.000030) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022182 / 0.037411 (-0.015229) | 0.085367 / 0.014526 (0.070842) | 0.084006 / 0.176557 (-0.092550) | 0.121260 / 0.737135 (-0.615876) | 0.084137 / 0.296338 (-0.212202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.310335 / 0.215209 (0.095126) | 3.002531 / 2.077655 (0.924876) | 1.642282 / 1.504120 (0.138162) | 1.573044 / 1.541195 (0.031849) | 1.572076 / 1.468490 (0.103586) | 0.422037 / 4.584777 (-4.162740) | 2.495295 / 3.745712 (-1.250417) | 2.523707 / 5.269862 (-2.746155) | 1.725824 / 4.565676 (-2.839853) | 0.047814 / 0.424275 (-0.376461) | 0.004868 / 0.007607 (-0.002739) | 0.352833 / 0.226044 (0.126789) | 3.477241 / 2.268929 (1.208313) | 1.983888 / 55.444624 (-53.460736) | 1.696883 / 6.876477 (-5.179594) | 1.831665 / 2.142072 (-0.310407) | 0.502976 / 4.805227 (-4.302251) | 0.101264 / 6.500664 (-6.399400) | 0.041779 / 0.075469 (-0.033690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981629 / 1.841788 (-0.860159) | 12.550634 / 8.074308 (4.476326) | 11.113382 / 10.191392 (0.921990) | 0.136565 / 0.680424 (-0.543859) | 0.016742 / 0.534201 (-0.517459) | 0.274316 / 0.579283 (-0.304967) | 0.284687 / 0.434364 (-0.149676) | 0.309966 / 0.540337 (-0.230372) | 0.557990 / 1.386936 (-0.828946) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0c30facb87af83107a645eeffcd18c0775afe11 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004980 / 0.011353 (-0.006373) | 0.002786 / 0.011008 (-0.008222) | 0.062460 / 0.038508 (0.023952) | 0.051811 / 0.023109 (0.028702) | 0.231734 / 0.275898 (-0.044164) | 0.254075 / 0.323480 (-0.069405) | 0.002884 / 0.007986 (-0.005102) | 0.002317 / 0.004328 (-0.002011) | 0.049044 / 0.004250 (0.044793) | 0.038984 / 0.037052 (0.001931) | 0.241193 / 0.258489 (-0.017296) | 0.272091 / 0.293841 (-0.021750) | 0.023098 / 0.128546 (-0.105448) | 0.007190 / 0.075646 (-0.068456) | 0.201409 / 0.419271 (-0.217863) | 0.036100 / 0.043533 (-0.007433) | 0.238185 / 0.255139 (-0.016954) | 0.257127 / 0.283200 (-0.026072) | 0.019542 / 0.141683 (-0.122141) | 1.127925 / 1.452155 (-0.324230) | 1.174354 / 1.492716 (-0.318362) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099608 / 0.018006 (0.081601) | 0.315046 / 0.000490 (0.314556) | 0.000282 / 0.000200 (0.000082) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018710 / 0.037411 (-0.018701) | 0.062557 / 0.014526 (0.048031) | 0.074021 / 0.176557 (-0.102536) | 0.119670 / 0.737135 (-0.617465) | 0.076491 / 0.296338 (-0.219847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282940 / 0.215209 (0.067731) | 2.788542 / 2.077655 (0.710887) | 1.496039 / 1.504120 (-0.008080) | 1.367542 / 1.541195 (-0.173653) | 1.393705 / 1.468490 (-0.074785) | 0.405910 / 4.584777 (-4.178867) | 2.422544 / 3.745712 (-1.323168) | 2.602822 / 5.269862 (-2.667039) | 1.586853 / 4.565676 (-2.978823) | 0.045440 / 0.424275 (-0.378836) | 0.004792 / 0.007607 (-0.002815) | 0.342059 / 0.226044 (0.116015) | 3.366880 / 2.268929 (1.097952) | 1.810566 / 55.444624 (-53.634058) | 1.527112 / 6.876477 (-5.349364) | 1.548906 / 2.142072 (-0.593166) | 0.479491 / 4.805227 (-4.325736) | 0.099807 / 6.500664 (-6.400857) | 0.041951 / 0.075469 (-0.033518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953723 / 1.841788 (-0.888065) | 11.837240 / 8.074308 (3.762932) | 10.562979 / 10.191392 (0.371587) | 0.145064 / 0.680424 (-0.535360) | 0.014285 / 0.534201 (-0.519916) | 0.270605 / 0.579283 (-0.308678) | 0.264086 / 0.434364 (-0.170278) | 0.308000 / 0.540337 (-0.232337) | 0.403916 / 1.386936 (-0.983020) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004796 / 0.011353 (-0.006557) | 0.002997 / 0.011008 (-0.008011) | 0.048702 / 0.038508 (0.010193) | 0.053377 / 0.023109 (0.030267) | 0.271852 / 0.275898 (-0.004046) | 0.293366 / 0.323480 (-0.030114) | 0.004041 / 0.007986 (-0.003945) | 0.002459 / 0.004328 (-0.001869) | 0.048197 / 0.004250 (0.043947) | 0.040094 / 0.037052 (0.003042) | 0.275837 / 0.258489 (0.017348) | 0.301174 / 0.293841 (0.007333) | 0.024433 / 0.128546 (-0.104113) | 0.007203 / 0.075646 (-0.068444) | 0.054080 / 0.419271 (-0.365192) | 0.033237 / 0.043533 (-0.010295) | 0.271177 / 0.255139 (0.016038) | 0.293062 / 0.283200 (0.009862) | 0.018399 / 0.141683 (-0.123284) | 1.149527 / 1.452155 (-0.302628) | 1.202717 / 1.492716 (-0.290000) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093168 / 0.018006 (0.075162) | 0.290536 / 0.000490 (0.290046) | 0.000290 / 0.000200 (0.000090) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021191 / 0.037411 (-0.016221) | 0.069990 / 0.014526 (0.055465) | 0.080636 / 0.176557 (-0.095920) | 0.120151 / 0.737135 (-0.616984) | 0.082944 / 0.296338 (-0.213395) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289673 / 0.215209 (0.074463) | 2.828419 / 2.077655 (0.750764) | 1.590741 / 1.504120 (0.086621) | 1.480969 / 1.541195 (-0.060226) | 1.512761 / 1.468490 (0.044271) | 0.398328 / 4.584777 (-4.186449) | 2.441134 / 3.745712 (-1.304578) | 2.487606 / 5.269862 (-2.782256) | 1.586604 / 4.565676 (-2.979073) | 0.045578 / 0.424275 (-0.378697) | 0.004842 / 0.007607 (-0.002766) | 0.344556 / 0.226044 (0.118512) | 3.395982 / 2.268929 (1.127053) | 1.963354 / 55.444624 (-53.481271) | 1.680496 / 6.876477 (-5.195980) | 1.827916 / 2.142072 (-0.314157) | 0.476203 / 4.805227 (-4.329024) | 0.098016 / 6.500664 (-6.402648) | 0.041234 / 0.075469 (-0.034235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977820 / 1.841788 (-0.863968) | 12.139614 / 8.074308 (4.065306) | 10.643071 / 10.191392 (0.451679) | 0.130928 / 0.680424 (-0.549496) | 0.015341 / 0.534201 (-0.518860) | 0.271304 / 0.579283 (-0.307979) | 0.284671 / 0.434364 (-0.149693) | 0.306210 / 0.540337 (-0.234128) | 0.546498 / 1.386936 (-0.840438) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bf7408a171db4a744d1760a9e32ba21deb8d41d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004748 / 0.011353 (-0.006605) | 0.002942 / 0.011008 (-0.008066) | 0.061298 / 0.038508 (0.022790) | 0.052873 / 0.023109 (0.029764) | 0.250573 / 0.275898 (-0.025325) | 0.270636 / 0.323480 (-0.052844) | 0.002925 / 0.007986 (-0.005061) | 0.003126 / 0.004328 (-0.001203) | 0.047340 / 0.004250 (0.043090) | 0.038662 / 0.037052 (0.001609) | 0.252151 / 0.258489 (-0.006338) | 0.284700 / 0.293841 (-0.009141) | 0.025145 / 0.128546 (-0.103402) | 0.007075 / 0.075646 (-0.068572) | 0.200501 / 0.419271 (-0.218771) | 0.035623 / 0.043533 (-0.007910) | 0.249657 / 0.255139 (-0.005482) | 0.272384 / 0.283200 (-0.010815) | 0.018331 / 0.141683 (-0.123351) | 1.095064 / 1.452155 (-0.357091) | 1.145304 / 1.492716 (-0.347412) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092548 / 0.018006 (0.074542) | 0.299338 / 0.000490 (0.298848) | 0.000212 / 0.000200 (0.000012) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018723 / 0.037411 (-0.018688) | 0.062226 / 0.014526 (0.047700) | 0.072840 / 0.176557 (-0.103717) | 0.120073 / 0.737135 (-0.617063) | 0.074536 / 0.296338 (-0.221802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284862 / 0.215209 (0.069653) | 2.791842 / 2.077655 (0.714188) | 1.506481 / 1.504120 (0.002361) | 1.368952 / 1.541195 (-0.172243) | 1.372555 / 1.468490 (-0.095935) | 0.408292 / 4.584777 (-4.176485) | 2.381155 / 3.745712 (-1.364558) | 2.613617 / 5.269862 (-2.656244) | 1.575892 / 4.565676 (-2.989785) | 0.047526 / 0.424275 (-0.376749) | 0.004792 / 0.007607 (-0.002815) | 0.344818 / 0.226044 (0.118773) | 3.344965 / 2.268929 (1.076036) | 1.883659 / 55.444624 (-53.560965) | 1.596039 / 6.876477 (-5.280437) | 1.584410 / 2.142072 (-0.557662) | 0.486672 / 4.805227 (-4.318555) | 0.101464 / 6.500664 (-6.399200) | 0.041824 / 0.075469 (-0.033645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930491 / 1.841788 (-0.911296) | 11.636526 / 8.074308 (3.562218) | 10.371829 / 10.191392 (0.180437) | 0.138181 / 0.680424 (-0.542243) | 0.014307 / 0.534201 (-0.519894) | 0.268322 / 0.579283 (-0.310961) | 0.264173 / 0.434364 (-0.170191) | 0.303649 / 0.540337 (-0.236688) | 0.399958 / 1.386936 (-0.986978) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004802 / 0.011353 (-0.006551) | 0.002861 / 0.011008 (-0.008147) | 0.048843 / 0.038508 (0.010335) | 0.053887 / 0.023109 (0.030778) | 0.278690 / 0.275898 (0.002792) | 0.302729 / 0.323480 (-0.020751) | 0.003929 / 0.007986 (-0.004057) | 0.002376 / 0.004328 (-0.001953) | 0.048146 / 0.004250 (0.043896) | 0.039842 / 0.037052 (0.002790) | 0.281595 / 0.258489 (0.023106) | 0.305813 / 0.293841 (0.011972) | 0.024214 / 0.128546 (-0.104333) | 0.007201 / 0.075646 (-0.068446) | 0.053604 / 0.419271 (-0.365667) | 0.032841 / 0.043533 (-0.010691) | 0.276168 / 0.255139 (0.021029) | 0.293869 / 0.283200 (0.010669) | 0.017550 / 0.141683 (-0.124132) | 1.121508 / 1.452155 (-0.330647) | 1.177694 / 1.492716 (-0.315022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091805 / 0.018006 (0.073799) | 0.299026 / 0.000490 (0.298536) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021094 / 0.037411 (-0.016318) | 0.069769 / 0.014526 (0.055243) | 0.081191 / 0.176557 (-0.095366) | 0.118884 / 0.737135 (-0.618252) | 0.081955 / 0.296338 (-0.214383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292159 / 0.215209 (0.076950) | 2.874473 / 2.077655 (0.796819) | 1.614695 / 1.504120 (0.110575) | 1.492123 / 1.541195 (-0.049071) | 1.505293 / 1.468490 (0.036803) | 0.394498 / 4.584777 (-4.190279) | 2.455539 / 3.745712 (-1.290173) | 2.458184 / 5.269862 (-2.811677) | 1.569108 / 4.565676 (-2.996569) | 0.046576 / 0.424275 (-0.377699) | 0.005093 / 0.007607 (-0.002514) | 0.346142 / 0.226044 (0.120098) | 3.398171 / 2.268929 (1.129242) | 1.971953 / 55.444624 (-53.472672) | 1.695275 / 6.876477 (-5.181201) | 1.840511 / 2.142072 (-0.301562) | 0.465932 / 4.805227 (-4.339295) | 0.098578 / 6.500664 (-6.402086) | 0.040456 / 0.075469 (-0.035013) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977636 / 1.841788 (-0.864152) | 12.083585 / 8.074308 (4.009277) | 10.509082 / 10.191392 (0.317690) | 0.130717 / 0.680424 (-0.549707) | 0.015958 / 0.534201 (-0.518243) | 0.273504 / 0.579283 (-0.305780) | 0.276498 / 0.434364 (-0.157866) | 0.306139 / 0.540337 (-0.234199) | 0.522521 / 1.386936 (-0.864415) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e17dd8acec9a958ba82a5f753276b842eaadf52 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004859 / 0.011353 (-0.006493) | 0.002423 / 0.011008 (-0.008585) | 0.060969 / 0.038508 (0.022461) | 0.048758 / 0.023109 (0.025649) | 0.245400 / 0.275898 (-0.030498) | 0.263686 / 0.323480 (-0.059794) | 0.002852 / 0.007986 (-0.005134) | 0.002273 / 0.004328 (-0.002055) | 0.047648 / 0.004250 (0.043398) | 0.038310 / 0.037052 (0.001258) | 0.249849 / 0.258489 (-0.008640) | 0.279305 / 0.293841 (-0.014536) | 0.022897 / 0.128546 (-0.105649) | 0.006882 / 0.075646 (-0.068764) | 0.202793 / 0.419271 (-0.216478) | 0.034557 / 0.043533 (-0.008976) | 0.252147 / 0.255139 (-0.002992) | 0.267414 / 0.283200 (-0.015785) | 0.019956 / 0.141683 (-0.121727) | 1.106181 / 1.452155 (-0.345973) | 1.158423 / 1.492716 (-0.334293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.086848 / 0.018006 (0.068842) | 0.295235 / 0.000490 (0.294745) | 0.000211 / 0.000200 (0.000011) | 0.000041 / 0.000054 (-0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018209 / 0.037411 (-0.019203) | 0.061967 / 0.014526 (0.047441) | 0.071551 / 0.176557 (-0.105005) | 0.117525 / 0.737135 (-0.619611) | 0.073401 / 0.296338 (-0.222937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.272388 / 0.215209 (0.057179) | 2.689797 / 2.077655 (0.612143) | 1.440897 / 1.504120 (-0.063223) | 1.334689 / 1.541195 (-0.206505) | 1.356395 / 1.468490 (-0.112095) | 0.387201 / 4.584777 (-4.197576) | 2.342908 / 3.745712 (-1.402804) | 2.480156 / 5.269862 (-2.789706) | 1.512342 / 4.565676 (-3.053335) | 0.042324 / 0.424275 (-0.381951) | 0.004744 / 0.007607 (-0.002863) | 0.323568 / 0.226044 (0.097523) | 3.190021 / 2.268929 (0.921093) | 1.765046 / 55.444624 (-53.679578) | 1.513958 / 6.876477 (-5.362519) | 1.504943 / 2.142072 (-0.637129) | 0.452302 / 4.805227 (-4.352925) | 0.094728 / 6.500664 (-6.405936) | 0.038641 / 0.075469 (-0.036828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939721 / 1.841788 (-0.902067) | 11.174180 / 8.074308 (3.099872) | 10.046717 / 10.191392 (-0.144675) | 0.124877 / 0.680424 (-0.555547) | 0.013687 / 0.534201 (-0.520514) | 0.261002 / 0.579283 (-0.318282) | 0.267349 / 0.434364 (-0.167015) | 0.306545 / 0.540337 (-0.233792) | 0.389322 / 1.386936 (-0.997614) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004702 / 0.011353 (-0.006651) | 0.002431 / 0.011008 (-0.008577) | 0.046138 / 0.038508 (0.007630) | 0.048356 / 0.023109 (0.025246) | 0.272154 / 0.275898 (-0.003744) | 0.292676 / 0.323480 (-0.030804) | 0.003870 / 0.007986 (-0.004115) | 0.002294 / 0.004328 (-0.002035) | 0.048129 / 0.004250 (0.043879) | 0.039026 / 0.037052 (0.001974) | 0.273900 / 0.258489 (0.015411) | 0.295927 / 0.293841 (0.002086) | 0.024044 / 0.128546 (-0.104502) | 0.006906 / 0.075646 (-0.068740) | 0.053268 / 0.419271 (-0.366004) | 0.032360 / 0.043533 (-0.011173) | 0.273470 / 0.255139 (0.018331) | 0.286207 / 0.283200 (0.003007) | 0.017580 / 0.141683 (-0.124103) | 1.091064 / 1.452155 (-0.361091) | 1.159645 / 1.492716 (-0.333071) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087149 / 0.018006 (0.069143) | 0.293489 / 0.000490 (0.293000) | 0.000217 / 0.000200 (0.000017) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021779 / 0.037411 (-0.015632) | 0.066453 / 0.014526 (0.051928) | 0.078517 / 0.176557 (-0.098039) | 0.117317 / 0.737135 (-0.619819) | 0.079828 / 0.296338 (-0.216511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287605 / 0.215209 (0.072396) | 2.811094 / 2.077655 (0.733439) | 1.572474 / 1.504120 (0.068354) | 1.450294 / 1.541195 (-0.090900) | 1.456052 / 1.468490 (-0.012438) | 0.402095 / 4.584777 (-4.182682) | 2.444709 / 3.745712 (-1.301003) | 2.390837 / 5.269862 (-2.879024) | 1.530519 / 4.565676 (-3.035157) | 0.043520 / 0.424275 (-0.380755) | 0.004788 / 0.007607 (-0.002819) | 0.337436 / 0.226044 (0.111391) | 3.326111 / 2.268929 (1.057182) | 1.889273 / 55.444624 (-53.555352) | 1.624423 / 6.876477 (-5.252054) | 1.715766 / 2.142072 (-0.426307) | 0.484570 / 4.805227 (-4.320657) | 0.091691 / 6.500664 (-6.408973) | 0.038278 / 0.075469 (-0.037191) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961708 / 1.841788 (-0.880079) | 11.496471 / 8.074308 (3.422162) | 10.211589 / 10.191392 (0.020197) | 0.127584 / 0.680424 (-0.552840) | 0.015178 / 0.534201 (-0.519023) | 0.267290 / 0.579283 (-0.311993) | 0.259305 / 0.434364 (-0.175059) | 0.303433 / 0.540337 (-0.236905) | 0.508016 / 1.386936 (-0.878920) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72880aa8a3e4b49438db72b13fb9a2541331820b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004558 / 0.011353 (-0.006795) | 0.002563 / 0.011008 (-0.008445) | 0.061314 / 0.038508 (0.022806) | 0.049312 / 0.023109 (0.026203) | 0.240988 / 0.275898 (-0.034910) | 0.260548 / 0.323480 (-0.062932) | 0.002817 / 0.007986 (-0.005169) | 0.002904 / 0.004328 (-0.001425) | 0.048515 / 0.004250 (0.044264) | 0.037511 / 0.037052 (0.000459) | 0.244880 / 0.258489 (-0.013609) | 0.276118 / 0.293841 (-0.017723) | 0.022636 / 0.128546 (-0.105910) | 0.006694 / 0.075646 (-0.068953) | 0.201336 / 0.419271 (-0.217936) | 0.035228 / 0.043533 (-0.008305) | 0.242424 / 0.255139 (-0.012715) | 0.260178 / 0.283200 (-0.023022) | 0.017836 / 0.141683 (-0.123847) | 1.122296 / 1.452155 (-0.329859) | 1.189024 / 1.492716 (-0.303692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090051 / 0.018006 (0.072045) | 0.298562 / 0.000490 (0.298073) | 0.000216 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018228 / 0.037411 (-0.019184) | 0.062379 / 0.014526 (0.047853) | 0.073482 / 0.176557 (-0.103075) | 0.120341 / 0.737135 (-0.616794) | 0.073868 / 0.296338 (-0.222470) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280195 / 0.215209 (0.064986) | 2.743333 / 2.077655 (0.665678) | 1.470078 / 1.504120 (-0.034042) | 1.335874 / 1.541195 (-0.205321) | 1.342961 / 1.468490 (-0.125529) | 0.409203 / 4.584777 (-4.175574) | 2.392217 / 3.745712 (-1.353495) | 2.544161 / 5.269862 (-2.725701) | 1.544016 / 4.565676 (-3.021660) | 0.059485 / 0.424275 (-0.364790) | 0.004833 / 0.007607 (-0.002775) | 0.335114 / 0.226044 (0.109070) | 3.289009 / 2.268929 (1.020080) | 1.854666 / 55.444624 (-53.589959) | 1.566282 / 6.876477 (-5.310195) | 1.561287 / 2.142072 (-0.580786) | 0.484961 / 4.805227 (-4.320267) | 0.099651 / 6.500664 (-6.401013) | 0.041408 / 0.075469 (-0.034061) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941743 / 1.841788 (-0.900044) | 11.165692 / 8.074308 (3.091383) | 10.236693 / 10.191392 (0.045301) | 0.129694 / 0.680424 (-0.550730) | 0.014879 / 0.534201 (-0.519322) | 0.275120 / 0.579283 (-0.304163) | 0.263822 / 0.434364 (-0.170542) | 0.306429 / 0.540337 (-0.233909) | 0.397611 / 1.386936 (-0.989325) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004714 / 0.011353 (-0.006639) | 0.002430 / 0.011008 (-0.008578) | 0.047644 / 0.038508 (0.009136) | 0.049710 / 0.023109 (0.026601) | 0.271950 / 0.275898 (-0.003948) | 0.290996 / 0.323480 (-0.032483) | 0.003888 / 0.007986 (-0.004097) | 0.002367 / 0.004328 (-0.001962) | 0.047623 / 0.004250 (0.043372) | 0.039574 / 0.037052 (0.002522) | 0.274540 / 0.258489 (0.016051) | 0.298065 / 0.293841 (0.004224) | 0.024677 / 0.128546 (-0.103869) | 0.006844 / 0.075646 (-0.068802) | 0.053180 / 0.419271 (-0.366091) | 0.032391 / 0.043533 (-0.011141) | 0.273222 / 0.255139 (0.018083) | 0.290336 / 0.283200 (0.007136) | 0.017911 / 0.141683 (-0.123772) | 1.105879 / 1.452155 (-0.346276) | 1.176979 / 1.492716 (-0.315737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089563 / 0.018006 (0.071557) | 0.296392 / 0.000490 (0.295903) | 0.000214 / 0.000200 (0.000014) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021588 / 0.037411 (-0.015824) | 0.069951 / 0.014526 (0.055425) | 0.080397 / 0.176557 (-0.096160) | 0.118772 / 0.737135 (-0.618363) | 0.080356 / 0.296338 (-0.215983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288492 / 0.215209 (0.073283) | 2.839553 / 2.077655 (0.761898) | 1.597504 / 1.504120 (0.093384) | 1.475001 / 1.541195 (-0.066193) | 1.481561 / 1.468490 (0.013071) | 0.411851 / 4.584777 (-4.172926) | 2.397322 / 3.745712 (-1.348390) | 2.444078 / 5.269862 (-2.825784) | 1.557106 / 4.565676 (-3.008571) | 0.047159 / 0.424275 (-0.377116) | 0.004842 / 0.007607 (-0.002765) | 0.346221 / 0.226044 (0.120177) | 3.387900 / 2.268929 (1.118972) | 1.962167 / 55.444624 (-53.482457) | 1.675017 / 6.876477 (-5.201460) | 1.788745 / 2.142072 (-0.353328) | 0.488063 / 4.805227 (-4.317164) | 0.098878 / 6.500664 (-6.401786) | 0.040369 / 0.075469 (-0.035100) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977999 / 1.841788 (-0.863789) | 11.671558 / 8.074308 (3.597250) | 10.327847 / 10.191392 (0.136455) | 0.129317 / 0.680424 (-0.551107) | 0.015600 / 0.534201 (-0.518601) | 0.267967 / 0.579283 (-0.311316) | 0.273811 / 0.434364 (-0.160553) | 0.301749 / 0.540337 (-0.238588) | 0.515493 / 1.386936 (-0.871443) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5394939b0b3d124674f938e1f1cd9e8de3cbdbf7 \"CML watermark\")\n", "I added tests and docs @mariosasko @albertvillanova let le know what you think !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004867 / 0.011353 (-0.006486) | 0.002952 / 0.011008 (-0.008056) | 0.062008 / 0.038508 (0.023500) | 0.055279 / 0.023109 (0.032170) | 0.248160 / 0.275898 (-0.027738) | 0.276173 / 0.323480 (-0.047307) | 0.003945 / 0.007986 (-0.004041) | 0.002371 / 0.004328 (-0.001958) | 0.048385 / 0.004250 (0.044134) | 0.038997 / 0.037052 (0.001945) | 0.257465 / 0.258489 (-0.001024) | 0.286920 / 0.293841 (-0.006921) | 0.023031 / 0.128546 (-0.105515) | 0.007075 / 0.075646 (-0.068571) | 0.201897 / 0.419271 (-0.217375) | 0.035637 / 0.043533 (-0.007896) | 0.252050 / 0.255139 (-0.003089) | 0.272580 / 0.283200 (-0.010620) | 0.018578 / 0.141683 (-0.123105) | 1.129427 / 1.452155 (-0.322727) | 1.172182 / 1.492716 (-0.320534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091806 / 0.018006 (0.073800) | 0.298632 / 0.000490 (0.298143) | 0.000202 / 0.000200 (0.000002) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019123 / 0.037411 (-0.018288) | 0.062603 / 0.014526 (0.048077) | 0.074352 / 0.176557 (-0.102205) | 0.120431 / 0.737135 (-0.616704) | 0.074622 / 0.296338 (-0.221717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276019 / 0.215209 (0.060810) | 2.701610 / 2.077655 (0.623955) | 1.398388 / 1.504120 (-0.105732) | 1.270902 / 1.541195 (-0.270292) | 1.307992 / 1.468490 (-0.160499) | 0.396350 / 4.584777 (-4.188427) | 2.351064 / 3.745712 (-1.394648) | 2.606229 / 5.269862 (-2.663632) | 1.591075 / 4.565676 (-2.974601) | 0.046429 / 0.424275 (-0.377846) | 0.004832 / 0.007607 (-0.002775) | 0.327887 / 0.226044 (0.101843) | 3.277847 / 2.268929 (1.008918) | 1.767210 / 55.444624 (-53.677414) | 1.483997 / 6.876477 (-5.392479) | 1.515689 / 2.142072 (-0.626383) | 0.471326 / 4.805227 (-4.333902) | 0.098821 / 6.500664 (-6.401843) | 0.041914 / 0.075469 (-0.033555) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956278 / 1.841788 (-0.885510) | 11.924373 / 8.074308 (3.850065) | 10.493926 / 10.191392 (0.302534) | 0.140214 / 0.680424 (-0.540210) | 0.013679 / 0.534201 (-0.520522) | 0.270304 / 0.579283 (-0.308979) | 0.266518 / 0.434364 (-0.167846) | 0.310113 / 0.540337 (-0.230224) | 0.399811 / 1.386936 (-0.987125) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004793 / 0.011353 (-0.006560) | 0.002879 / 0.011008 (-0.008130) | 0.048632 / 0.038508 (0.010124) | 0.051413 / 0.023109 (0.028304) | 0.272704 / 0.275898 (-0.003194) | 0.291541 / 0.323480 (-0.031939) | 0.003913 / 0.007986 (-0.004072) | 0.002387 / 0.004328 (-0.001941) | 0.049045 / 0.004250 (0.044795) | 0.040164 / 0.037052 (0.003112) | 0.273052 / 0.258489 (0.014563) | 0.300139 / 0.293841 (0.006298) | 0.024225 / 0.128546 (-0.104321) | 0.007060 / 0.075646 (-0.068587) | 0.054360 / 0.419271 (-0.364911) | 0.032882 / 0.043533 (-0.010650) | 0.270295 / 0.255139 (0.015157) | 0.312253 / 0.283200 (0.029054) | 0.017413 / 0.141683 (-0.124270) | 1.137306 / 1.452155 (-0.314849) | 1.203705 / 1.492716 (-0.289011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091083 / 0.018006 (0.073077) | 0.301607 / 0.000490 (0.301117) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021753 / 0.037411 (-0.015658) | 0.069693 / 0.014526 (0.055167) | 0.080481 / 0.176557 (-0.096075) | 0.118581 / 0.737135 (-0.618555) | 0.082231 / 0.296338 (-0.214108) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300014 / 0.215209 (0.084805) | 2.885934 / 2.077655 (0.808279) | 1.594120 / 1.504120 (0.090000) | 1.472312 / 1.541195 (-0.068883) | 1.491663 / 1.468490 (0.023173) | 0.412946 / 4.584777 (-4.171831) | 2.494168 / 3.745712 (-1.251544) | 2.527987 / 5.269862 (-2.741875) | 1.589187 / 4.565676 (-2.976490) | 0.046594 / 0.424275 (-0.377681) | 0.004810 / 0.007607 (-0.002797) | 0.345496 / 0.226044 (0.119452) | 3.428850 / 2.268929 (1.159921) | 1.952696 / 55.444624 (-53.491929) | 1.663285 / 6.876477 (-5.213191) | 1.822187 / 2.142072 (-0.319885) | 0.483798 / 4.805227 (-4.321430) | 0.101403 / 6.500664 (-6.399261) | 0.041773 / 0.075469 (-0.033696) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974247 / 1.841788 (-0.867541) | 12.459980 / 8.074308 (4.385672) | 10.354792 / 10.191392 (0.163400) | 0.129083 / 0.680424 (-0.551341) | 0.015225 / 0.534201 (-0.518976) | 0.267673 / 0.579283 (-0.311610) | 0.281011 / 0.434364 (-0.153352) | 0.303054 / 0.540337 (-0.237283) | 0.405719 / 1.386936 (-0.981217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33dc51fc1a8122b842bb7839ff0eda32f173c325 \"CML watermark\")\n", "I switched to using `deepmind/code_contests` in examples in the docs to avoid having to pass trust_remote_code, and remove the DEFAULT naming stuff :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005169 / 0.011353 (-0.006184) | 0.003066 / 0.011008 (-0.007942) | 0.068884 / 0.038508 (0.030376) | 0.060345 / 0.023109 (0.037236) | 0.243050 / 0.275898 (-0.032848) | 0.265523 / 0.323480 (-0.057957) | 0.002918 / 0.007986 (-0.005067) | 0.002495 / 0.004328 (-0.001834) | 0.051538 / 0.004250 (0.047288) | 0.040010 / 0.037052 (0.002957) | 0.249603 / 0.258489 (-0.008886) | 0.287955 / 0.293841 (-0.005886) | 0.024003 / 0.128546 (-0.104543) | 0.007111 / 0.075646 (-0.068535) | 0.205041 / 0.419271 (-0.214231) | 0.036296 / 0.043533 (-0.007237) | 0.246135 / 0.255139 (-0.009004) | 0.268801 / 0.283200 (-0.014399) | 0.018451 / 0.141683 (-0.123232) | 1.130387 / 1.452155 (-0.321767) | 1.162041 / 1.492716 (-0.330675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096370 / 0.018006 (0.078364) | 0.309867 / 0.000490 (0.309377) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018688 / 0.037411 (-0.018723) | 0.062859 / 0.014526 (0.048333) | 0.076383 / 0.176557 (-0.100173) | 0.120385 / 0.737135 (-0.616750) | 0.080192 / 0.296338 (-0.216147) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282994 / 0.215209 (0.067785) | 2.742341 / 2.077655 (0.664686) | 1.432041 / 1.504120 (-0.072079) | 1.303282 / 1.541195 (-0.237913) | 1.347198 / 1.468490 (-0.121292) | 0.399145 / 4.584777 (-4.185632) | 2.359766 / 3.745712 (-1.385947) | 2.753577 / 5.269862 (-2.516285) | 1.639953 / 4.565676 (-2.925724) | 0.047111 / 0.424275 (-0.377164) | 0.004946 / 0.007607 (-0.002661) | 0.338857 / 0.226044 (0.112813) | 3.328709 / 2.268929 (1.059781) | 1.794729 / 55.444624 (-53.649895) | 1.508514 / 6.876477 (-5.367963) | 1.550737 / 2.142072 (-0.591335) | 0.484227 / 4.805227 (-4.321000) | 0.101001 / 6.500664 (-6.399663) | 0.042792 / 0.075469 (-0.032677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956471 / 1.841788 (-0.885317) | 12.031362 / 8.074308 (3.957054) | 10.512914 / 10.191392 (0.321522) | 0.141841 / 0.680424 (-0.538583) | 0.014343 / 0.534201 (-0.519858) | 0.273916 / 0.579283 (-0.305367) | 0.266150 / 0.434364 (-0.168214) | 0.312020 / 0.540337 (-0.228317) | 0.410465 / 1.386936 (-0.976471) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004945 / 0.011353 (-0.006408) | 0.003288 / 0.011008 (-0.007720) | 0.048247 / 0.038508 (0.009739) | 0.057892 / 0.023109 (0.034783) | 0.269741 / 0.275898 (-0.006157) | 0.293728 / 0.323480 (-0.029752) | 0.004789 / 0.007986 (-0.003197) | 0.002477 / 0.004328 (-0.001852) | 0.047825 / 0.004250 (0.043575) | 0.040780 / 0.037052 (0.003727) | 0.273355 / 0.258489 (0.014865) | 0.300057 / 0.293841 (0.006216) | 0.024481 / 0.128546 (-0.104066) | 0.007285 / 0.075646 (-0.068361) | 0.053046 / 0.419271 (-0.366226) | 0.032342 / 0.043533 (-0.011190) | 0.272293 / 0.255139 (0.017154) | 0.290842 / 0.283200 (0.007642) | 0.017546 / 0.141683 (-0.124137) | 1.155816 / 1.452155 (-0.296339) | 1.195839 / 1.492716 (-0.296878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094177 / 0.018006 (0.076170) | 0.305122 / 0.000490 (0.304632) | 0.000237 / 0.000200 (0.000037) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021817 / 0.037411 (-0.015595) | 0.070711 / 0.014526 (0.056185) | 0.084028 / 0.176557 (-0.092528) | 0.120160 / 0.737135 (-0.616975) | 0.083085 / 0.296338 (-0.213254) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289127 / 0.215209 (0.073918) | 2.826365 / 2.077655 (0.748710) | 1.582910 / 1.504120 (0.078790) | 1.472796 / 1.541195 (-0.068399) | 1.497491 / 1.468490 (0.029000) | 0.412276 / 4.584777 (-4.172501) | 2.430692 / 3.745712 (-1.315020) | 2.556444 / 5.269862 (-2.713418) | 1.625782 / 4.565676 (-2.939895) | 0.047921 / 0.424275 (-0.376354) | 0.004809 / 0.007607 (-0.002798) | 0.345569 / 0.226044 (0.119524) | 3.417785 / 2.268929 (1.148856) | 1.959223 / 55.444624 (-53.485401) | 1.672765 / 6.876477 (-5.203712) | 1.852444 / 2.142072 (-0.289628) | 0.489225 / 4.805227 (-4.316002) | 0.100624 / 6.500664 (-6.400040) | 0.041242 / 0.075469 (-0.034227) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971130 / 1.841788 (-0.870658) | 12.652204 / 8.074308 (4.577896) | 10.661821 / 10.191392 (0.470429) | 0.147636 / 0.680424 (-0.532787) | 0.015738 / 0.534201 (-0.518463) | 0.272763 / 0.579283 (-0.306520) | 0.282623 / 0.434364 (-0.151741) | 0.341303 / 0.540337 (-0.199035) | 0.412149 / 1.386936 (-0.974787) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9499908c97ceef1792f69b71e93e36602880a4ae \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004589 / 0.011353 (-0.006764) | 0.002730 / 0.011008 (-0.008279) | 0.061862 / 0.038508 (0.023353) | 0.050945 / 0.023109 (0.027836) | 0.240776 / 0.275898 (-0.035122) | 0.266000 / 0.323480 (-0.057480) | 0.003823 / 0.007986 (-0.004162) | 0.002345 / 0.004328 (-0.001983) | 0.047821 / 0.004250 (0.043571) | 0.037813 / 0.037052 (0.000761) | 0.251075 / 0.258489 (-0.007415) | 0.279430 / 0.293841 (-0.014411) | 0.022957 / 0.128546 (-0.105590) | 0.007294 / 0.075646 (-0.068353) | 0.206092 / 0.419271 (-0.213180) | 0.035308 / 0.043533 (-0.008225) | 0.247197 / 0.255139 (-0.007942) | 0.264988 / 0.283200 (-0.018212) | 0.017588 / 0.141683 (-0.124095) | 1.093291 / 1.452155 (-0.358864) | 1.165477 / 1.492716 (-0.327240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.104057 / 0.018006 (0.086051) | 0.303424 / 0.000490 (0.302934) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019040 / 0.037411 (-0.018371) | 0.063161 / 0.014526 (0.048635) | 0.085333 / 0.176557 (-0.091224) | 0.155973 / 0.737135 (-0.581162) | 0.077528 / 0.296338 (-0.218810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276104 / 0.215209 (0.060895) | 2.738174 / 2.077655 (0.660519) | 1.479484 / 1.504120 (-0.024636) | 1.354094 / 1.541195 (-0.187100) | 1.385312 / 1.468490 (-0.083178) | 0.401398 / 4.584777 (-4.183379) | 2.368503 / 3.745712 (-1.377209) | 2.586405 / 5.269862 (-2.683457) | 1.573978 / 4.565676 (-2.991699) | 0.046969 / 0.424275 (-0.377306) | 0.004874 / 0.007607 (-0.002733) | 0.334028 / 0.226044 (0.107984) | 3.269645 / 2.268929 (1.000717) | 1.834528 / 55.444624 (-53.610096) | 1.559883 / 6.876477 (-5.316594) | 1.581380 / 2.142072 (-0.560693) | 0.479580 / 4.805227 (-4.325647) | 0.099077 / 6.500664 (-6.401587) | 0.041166 / 0.075469 (-0.034303) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.918810 / 1.841788 (-0.922978) | 11.505017 / 8.074308 (3.430709) | 10.331934 / 10.191392 (0.140542) | 0.128079 / 0.680424 (-0.552345) | 0.013716 / 0.534201 (-0.520485) | 0.271567 / 0.579283 (-0.307716) | 0.264846 / 0.434364 (-0.169518) | 0.305245 / 0.540337 (-0.235092) | 0.401391 / 1.386936 (-0.985546) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004860 / 0.011353 (-0.006493) | 0.002854 / 0.011008 (-0.008155) | 0.048327 / 0.038508 (0.009819) | 0.051377 / 0.023109 (0.028268) | 0.264344 / 0.275898 (-0.011554) | 0.286800 / 0.323480 (-0.036680) | 0.003969 / 0.007986 (-0.004016) | 0.002415 / 0.004328 (-0.001914) | 0.048498 / 0.004250 (0.044247) | 0.040399 / 0.037052 (0.003347) | 0.267254 / 0.258489 (0.008765) | 0.292049 / 0.293841 (-0.001792) | 0.024730 / 0.128546 (-0.103817) | 0.007275 / 0.075646 (-0.068371) | 0.053725 / 0.419271 (-0.365546) | 0.033142 / 0.043533 (-0.010391) | 0.265418 / 0.255139 (0.010279) | 0.286242 / 0.283200 (0.003042) | 0.017824 / 0.141683 (-0.123859) | 1.135978 / 1.452155 (-0.316176) | 1.192506 / 1.492716 (-0.300210) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091907 / 0.018006 (0.073900) | 0.307152 / 0.000490 (0.306663) | 0.000223 / 0.000200 (0.000023) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021909 / 0.037411 (-0.015502) | 0.070676 / 0.014526 (0.056150) | 0.081651 / 0.176557 (-0.094906) | 0.120915 / 0.737135 (-0.616220) | 0.085882 / 0.296338 (-0.210456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288008 / 0.215209 (0.072799) | 2.861352 / 2.077655 (0.783697) | 1.539045 / 1.504120 (0.034925) | 1.412175 / 1.541195 (-0.129019) | 1.421236 / 1.468490 (-0.047254) | 0.404921 / 4.584777 (-4.179856) | 2.480211 / 3.745712 (-1.265501) | 2.473083 / 5.269862 (-2.796779) | 1.558894 / 4.565676 (-3.006783) | 0.046692 / 0.424275 (-0.377584) | 0.004802 / 0.007607 (-0.002805) | 0.346046 / 0.226044 (0.120001) | 3.464387 / 2.268929 (1.195459) | 1.937298 / 55.444624 (-53.507326) | 1.593701 / 6.876477 (-5.282776) | 1.730688 / 2.142072 (-0.411385) | 0.481069 / 4.805227 (-4.324158) | 0.098991 / 6.500664 (-6.401673) | 0.040491 / 0.075469 (-0.034978) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967809 / 1.841788 (-0.873979) | 11.952335 / 8.074308 (3.878027) | 10.616711 / 10.191392 (0.425319) | 0.128938 / 0.680424 (-0.551486) | 0.015455 / 0.534201 (-0.518746) | 0.272100 / 0.579283 (-0.307183) | 0.278275 / 0.434364 (-0.156089) | 0.309711 / 0.540337 (-0.230627) | 0.411026 / 1.386936 (-0.975910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#495bc04226a67983f523d12d42b680172f8d4893 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008470 / 0.011353 (-0.002883) | 0.003201 / 0.011008 (-0.007808) | 0.063193 / 0.038508 (0.024685) | 0.064174 / 0.023109 (0.041064) | 0.248316 / 0.275898 (-0.027582) | 0.281598 / 0.323480 (-0.041882) | 0.004076 / 0.007986 (-0.003909) | 0.002397 / 0.004328 (-0.001932) | 0.048834 / 0.004250 (0.044584) | 0.056517 / 0.037052 (0.019465) | 0.254164 / 0.258489 (-0.004326) | 0.289800 / 0.293841 (-0.004041) | 0.031092 / 0.128546 (-0.097454) | 0.010885 / 0.075646 (-0.064762) | 0.219198 / 0.419271 (-0.200073) | 0.040087 / 0.043533 (-0.003446) | 0.250900 / 0.255139 (-0.004239) | 0.267787 / 0.283200 (-0.015413) | 0.019666 / 0.141683 (-0.122017) | 1.114960 / 1.452155 (-0.337194) | 1.266675 / 1.492716 (-0.226041) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091429 / 0.018006 (0.073422) | 0.301804 / 0.000490 (0.301314) | 0.000212 / 0.000200 (0.000012) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021053 / 0.037411 (-0.016358) | 0.062407 / 0.014526 (0.047881) | 0.073166 / 0.176557 (-0.103391) | 0.119642 / 0.737135 (-0.617493) | 0.074771 / 0.296338 (-0.221567) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278582 / 0.215209 (0.063373) | 2.773023 / 2.077655 (0.695368) | 1.459977 / 1.504120 (-0.044143) | 1.330453 / 1.541195 (-0.210742) | 1.372797 / 1.468490 (-0.095693) | 0.628845 / 4.584777 (-3.955932) | 3.428779 / 3.745712 (-0.316933) | 3.138967 / 5.269862 (-2.130895) | 2.126891 / 4.565676 (-2.438785) | 0.062340 / 0.424275 (-0.361935) | 0.004939 / 0.007607 (-0.002668) | 0.336058 / 0.226044 (0.110014) | 3.463741 / 2.268929 (1.194813) | 1.847504 / 55.444624 (-53.597120) | 1.984173 / 6.876477 (-4.892304) | 1.602962 / 2.142072 (-0.539110) | 0.637683 / 4.805227 (-4.167545) | 0.117898 / 6.500664 (-6.382766) | 0.043308 / 0.075469 (-0.032161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.087773 / 1.841788 (-0.754014) | 14.959526 / 8.074308 (6.885218) | 10.886003 / 10.191392 (0.694611) | 0.163385 / 0.680424 (-0.517039) | 0.016679 / 0.534201 (-0.517522) | 0.351913 / 0.579283 (-0.227370) | 0.359007 / 0.434364 (-0.075357) | 0.323824 / 0.540337 (-0.216513) | 0.549268 / 1.386936 (-0.837668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005265 / 0.011353 (-0.006088) | 0.003367 / 0.011008 (-0.007641) | 0.062741 / 0.038508 (0.024233) | 0.068463 / 0.023109 (0.045354) | 0.258497 / 0.275898 (-0.017401) | 0.355360 / 0.323480 (0.031880) | 0.003910 / 0.007986 (-0.004075) | 0.002399 / 0.004328 (-0.001929) | 0.055564 / 0.004250 (0.051313) | 0.039644 / 0.037052 (0.002591) | 0.258313 / 0.258489 (-0.000176) | 0.328927 / 0.293841 (0.035086) | 0.035634 / 0.128546 (-0.092912) | 0.010378 / 0.075646 (-0.065268) | 0.073109 / 0.419271 (-0.346163) | 0.039752 / 0.043533 (-0.003781) | 0.258237 / 0.255139 (0.003098) | 0.330329 / 0.283200 (0.047129) | 0.023924 / 0.141683 (-0.117759) | 1.198639 / 1.452155 (-0.253515) | 1.202307 / 1.492716 (-0.290409) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091297 / 0.018006 (0.073290) | 0.298729 / 0.000490 (0.298240) | 0.000210 / 0.000200 (0.000010) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022381 / 0.037411 (-0.015030) | 0.070226 / 0.014526 (0.055700) | 0.080549 / 0.176557 (-0.096007) | 0.119677 / 0.737135 (-0.617458) | 0.082612 / 0.296338 (-0.213727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289270 / 0.215209 (0.074061) | 2.853830 / 2.077655 (0.776175) | 1.528938 / 1.504120 (0.024818) | 1.398429 / 1.541195 (-0.142766) | 1.472465 / 1.468490 (0.003975) | 0.779015 / 4.584777 (-3.805762) | 3.287724 / 3.745712 (-0.457988) | 3.020908 / 5.269862 (-2.248953) | 1.926094 / 4.565676 (-2.639583) | 0.063163 / 0.424275 (-0.361112) | 0.005175 / 0.007607 (-0.002432) | 0.342884 / 0.226044 (0.116840) | 3.476837 / 2.268929 (1.207908) | 1.880683 / 55.444624 (-53.563942) | 1.613845 / 6.876477 (-5.262632) | 1.624734 / 2.142072 (-0.517338) | 0.626220 / 4.805227 (-4.179007) | 0.114976 / 6.500664 (-6.385689) | 0.040670 / 0.075469 (-0.034799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.116815 / 1.841788 (-0.724973) | 15.388426 / 8.074308 (7.314118) | 10.825276 / 10.191392 (0.633884) | 0.172659 / 0.680424 (-0.507765) | 0.015468 / 0.534201 (-0.518733) | 0.285552 / 0.579283 (-0.293731) | 0.346886 / 0.434364 (-0.087478) | 0.348696 / 0.540337 (-0.191641) | 0.729335 / 1.386936 (-0.657601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7bbf346dc268b8084dee406b2a6e2b96d44bc3b \"CML watermark\")\n" ]
2023-11-16T12:12:54
2023-11-28T16:10:39
2023-11-28T16:03:43
MEMBER
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Draft about adding `trust_remote_code` to `load_dataset`. ```python ds = load_dataset(..., trust_remote_code=True) # run remote code (current default) ``` It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course). ```python # in the future ds = load_dataset(...) # prompt the user to check the code before running it (future default) ds = load_dataset(..., trust_remote_code=True) # run remote code ds = load_dataset(..., trust_remote_code=False) # disallow remote code ``` Related to https://github.com/huggingface/datasets/issues/6400 Will do a separate PR to use the parquet export when possible
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6428). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004839 / 0.011353 (-0.006514) | 0.002928 / 0.011008 (-0.008080) | 0.061730 / 0.038508 (0.023221) | 0.030523 / 0.023109 (0.007414) | 0.252679 / 0.275898 (-0.023219) | 0.281597 / 0.323480 (-0.041883) | 0.003025 / 0.007986 (-0.004961) | 0.002374 / 0.004328 (-0.001955) | 0.048134 / 0.004250 (0.043884) | 0.045843 / 0.037052 (0.008791) | 0.256274 / 0.258489 (-0.002215) | 0.288704 / 0.293841 (-0.005137) | 0.023486 / 0.128546 (-0.105060) | 0.007186 / 0.075646 (-0.068461) | 0.202519 / 0.419271 (-0.216753) | 0.058192 / 0.043533 (0.014659) | 0.256448 / 0.255139 (0.001309) | 0.279417 / 0.283200 (-0.003783) | 0.019942 / 0.141683 (-0.121740) | 1.100954 / 1.452155 (-0.351201) | 1.168183 / 1.492716 (-0.324533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091314 / 0.018006 (0.073308) | 0.298614 / 0.000490 (0.298124) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018071 / 0.037411 (-0.019340) | 0.062265 / 0.014526 (0.047740) | 0.073228 / 0.176557 (-0.103328) | 0.119163 / 0.737135 (-0.617972) | 0.074717 / 0.296338 (-0.221622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273906 / 0.215209 (0.058697) | 2.683995 / 2.077655 (0.606340) | 1.418773 / 1.504120 (-0.085347) | 1.310473 / 1.541195 (-0.230722) | 1.303152 / 1.468490 (-0.165339) | 0.390846 / 4.584777 (-4.193931) | 2.346407 / 3.745712 (-1.399305) | 2.582945 / 5.269862 (-2.686916) | 1.569549 / 4.565676 (-2.996128) | 0.044893 / 0.424275 (-0.379383) | 0.004754 / 0.007607 (-0.002853) | 0.323491 / 0.226044 (0.097447) | 3.229736 / 2.268929 (0.960808) | 1.783551 / 55.444624 (-53.661074) | 1.499685 / 6.876477 (-5.376792) | 1.515826 / 2.142072 (-0.626246) | 0.475768 / 4.805227 (-4.329460) | 0.099579 / 6.500664 (-6.401085) | 0.042709 / 0.075469 (-0.032760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.926120 / 1.841788 (-0.915667) | 11.597189 / 8.074308 (3.522881) | 10.327055 / 10.191392 (0.135663) | 0.127479 / 0.680424 (-0.552945) | 0.014844 / 0.534201 (-0.519357) | 0.261181 / 0.579283 (-0.318102) | 0.258407 / 0.434364 (-0.175957) | 0.303192 / 0.540337 (-0.237146) | 0.416665 / 1.386936 (-0.970271) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004759 / 0.011353 (-0.006594) | 0.002780 / 0.011008 (-0.008228) | 0.047991 / 0.038508 (0.009483) | 0.052263 / 0.023109 (0.029153) | 0.261228 / 0.275898 (-0.014670) | 0.287779 / 0.323480 (-0.035701) | 0.003961 / 0.007986 (-0.004024) | 0.002357 / 0.004328 (-0.001971) | 0.047755 / 0.004250 (0.043505) | 0.038066 / 0.037052 (0.001014) | 0.269502 / 0.258489 (0.011013) | 0.298348 / 0.293841 (0.004507) | 0.024398 / 0.128546 (-0.104149) | 0.007189 / 0.075646 (-0.068457) | 0.053356 / 0.419271 (-0.365915) | 0.032459 / 0.043533 (-0.011074) | 0.266389 / 0.255139 (0.011250) | 0.305367 / 0.283200 (0.022168) | 0.017629 / 0.141683 (-0.124054) | 1.145789 / 1.452155 (-0.306366) | 1.204778 / 1.492716 (-0.287938) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091347 / 0.018006 (0.073341) | 0.298671 / 0.000490 (0.298181) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021374 / 0.037411 (-0.016037) | 0.068869 / 0.014526 (0.054344) | 0.080443 / 0.176557 (-0.096113) | 0.118759 / 0.737135 (-0.618376) | 0.081646 / 0.296338 (-0.214692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295274 / 0.215209 (0.080065) | 2.889349 / 2.077655 (0.811695) | 1.561020 / 1.504120 (0.056900) | 1.425025 / 1.541195 (-0.116170) | 1.495446 / 1.468490 (0.026956) | 0.403825 / 4.584777 (-4.180952) | 2.404905 / 3.745712 (-1.340807) | 2.590104 / 5.269862 (-2.679758) | 1.570559 / 4.565676 (-2.995118) | 0.046342 / 0.424275 (-0.377933) | 0.004799 / 0.007607 (-0.002809) | 0.349981 / 0.226044 (0.123937) | 3.437341 / 2.268929 (1.168412) | 1.948155 / 55.444624 (-53.496469) | 1.637765 / 6.876477 (-5.238711) | 1.671521 / 2.142072 (-0.470551) | 0.479500 / 4.805227 (-4.325727) | 0.098305 / 6.500664 (-6.402359) | 0.040864 / 0.075469 (-0.034605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979986 / 1.841788 (-0.861801) | 12.169722 / 8.074308 (4.095413) | 11.297345 / 10.191392 (1.105953) | 0.129123 / 0.680424 (-0.551301) | 0.015389 / 0.534201 (-0.518812) | 0.270964 / 0.579283 (-0.308319) | 0.269590 / 0.434364 (-0.164774) | 0.310662 / 0.540337 (-0.229675) | 0.406272 / 1.386936 (-0.980664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31873f1e9acbe013e6d396d1ed5492db8cd59dd3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004620 / 0.011353 (-0.006733) | 0.002971 / 0.011008 (-0.008038) | 0.062864 / 0.038508 (0.024355) | 0.028743 / 0.023109 (0.005634) | 0.246729 / 0.275898 (-0.029169) | 0.271165 / 0.323480 (-0.052315) | 0.003930 / 0.007986 (-0.004056) | 0.002422 / 0.004328 (-0.001906) | 0.047430 / 0.004250 (0.043180) | 0.044895 / 0.037052 (0.007843) | 0.249128 / 0.258489 (-0.009361) | 0.283384 / 0.293841 (-0.010457) | 0.023288 / 0.128546 (-0.105259) | 0.007241 / 0.075646 (-0.068405) | 0.207551 / 0.419271 (-0.211720) | 0.055008 / 0.043533 (0.011475) | 0.252781 / 0.255139 (-0.002358) | 0.296924 / 0.283200 (0.013724) | 0.017860 / 0.141683 (-0.123822) | 1.094597 / 1.452155 (-0.357558) | 1.162314 / 1.492716 (-0.330402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091423 / 0.018006 (0.073417) | 0.302833 / 0.000490 (0.302343) | 0.000242 / 0.000200 (0.000042) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018143 / 0.037411 (-0.019268) | 0.066371 / 0.014526 (0.051845) | 0.072774 / 0.176557 (-0.103783) | 0.119062 / 0.737135 (-0.618073) | 0.102836 / 0.296338 (-0.193502) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280117 / 0.215209 (0.064908) | 2.757955 / 2.077655 (0.680301) | 1.494994 / 1.504120 (-0.009126) | 1.375325 / 1.541195 (-0.165870) | 1.384179 / 1.468490 (-0.084311) | 0.399824 / 4.584777 (-4.184953) | 2.368575 / 3.745712 (-1.377137) | 2.574035 / 5.269862 (-2.695827) | 1.548738 / 4.565676 (-3.016939) | 0.045841 / 0.424275 (-0.378434) | 0.004799 / 0.007607 (-0.002808) | 0.331522 / 0.226044 (0.105478) | 3.324471 / 2.268929 (1.055543) | 1.838637 / 55.444624 (-53.605987) | 1.562854 / 6.876477 (-5.313623) | 1.581736 / 2.142072 (-0.560336) | 0.468832 / 4.805227 (-4.336396) | 0.099309 / 6.500664 (-6.401355) | 0.042078 / 0.075469 (-0.033391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.928468 / 1.841788 (-0.913320) | 11.331143 / 8.074308 (3.256835) | 10.296213 / 10.191392 (0.104821) | 0.138912 / 0.680424 (-0.541511) | 0.014044 / 0.534201 (-0.520157) | 0.267293 / 0.579283 (-0.311991) | 0.267267 / 0.434364 (-0.167097) | 0.306560 / 0.540337 (-0.233778) | 0.423926 / 1.386936 (-0.963010) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004842 / 0.011353 (-0.006511) | 0.002917 / 0.011008 (-0.008091) | 0.048263 / 0.038508 (0.009755) | 0.051453 / 0.023109 (0.028344) | 0.278330 / 0.275898 (0.002432) | 0.298569 / 0.323480 (-0.024911) | 0.003936 / 0.007986 (-0.004049) | 0.002479 / 0.004328 (-0.001850) | 0.048281 / 0.004250 (0.044031) | 0.038925 / 0.037052 (0.001872) | 0.285258 / 0.258489 (0.026769) | 0.313701 / 0.293841 (0.019860) | 0.024916 / 0.128546 (-0.103630) | 0.007142 / 0.075646 (-0.068504) | 0.053634 / 0.419271 (-0.365638) | 0.032842 / 0.043533 (-0.010690) | 0.279373 / 0.255139 (0.024234) | 0.295844 / 0.283200 (0.012644) | 0.018142 / 0.141683 (-0.123541) | 1.136960 / 1.452155 (-0.315195) | 1.184438 / 1.492716 (-0.308278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090271 / 0.018006 (0.072264) | 0.299940 / 0.000490 (0.299450) | 0.000234 / 0.000200 (0.000034) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021175 / 0.037411 (-0.016237) | 0.070924 / 0.014526 (0.056398) | 0.080584 / 0.176557 (-0.095972) | 0.119278 / 0.737135 (-0.617857) | 0.082361 / 0.296338 (-0.213977) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298312 / 0.215209 (0.083103) | 2.895361 / 2.077655 (0.817706) | 1.616120 / 1.504120 (0.112001) | 1.484444 / 1.541195 (-0.056750) | 1.541893 / 1.468490 (0.073403) | 0.409968 / 4.584777 (-4.174809) | 2.423639 / 3.745712 (-1.322073) | 2.585122 / 5.269862 (-2.684740) | 1.540343 / 4.565676 (-3.025333) | 0.046604 / 0.424275 (-0.377671) | 0.004742 / 0.007607 (-0.002865) | 0.341659 / 0.226044 (0.115614) | 3.409259 / 2.268929 (1.140330) | 2.007068 / 55.444624 (-53.437556) | 1.681348 / 6.876477 (-5.195129) | 1.719253 / 2.142072 (-0.422819) | 0.482301 / 4.805227 (-4.322926) | 0.099619 / 6.500664 (-6.401045) | 0.041247 / 0.075469 (-0.034222) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971783 / 1.841788 (-0.870004) | 12.208000 / 8.074308 (4.133692) | 10.948230 / 10.191392 (0.756838) | 0.131824 / 0.680424 (-0.548599) | 0.015696 / 0.534201 (-0.518505) | 0.272265 / 0.579283 (-0.307018) | 0.276093 / 0.434364 (-0.158270) | 0.305897 / 0.540337 (-0.234441) | 0.411632 / 1.386936 (-0.975304) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bf75fe522c6fedd16d00b4a928f613dee11f73c \"CML watermark\")\n" ]
2023-11-16T08:12:55
2023-11-16T08:19:39
2023-11-16T08:13:28
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004331 / 0.011353 (-0.007022) | 0.002573 / 0.011008 (-0.008435) | 0.061002 / 0.038508 (0.022494) | 0.029259 / 0.023109 (0.006149) | 0.242983 / 0.275898 (-0.032915) | 0.267629 / 0.323480 (-0.055851) | 0.003906 / 0.007986 (-0.004080) | 0.002383 / 0.004328 (-0.001946) | 0.047574 / 0.004250 (0.043323) | 0.042153 / 0.037052 (0.005101) | 0.245821 / 0.258489 (-0.012668) | 0.276479 / 0.293841 (-0.017362) | 0.022498 / 0.128546 (-0.106049) | 0.006775 / 0.075646 (-0.068871) | 0.201795 / 0.419271 (-0.217477) | 0.052443 / 0.043533 (0.008910) | 0.248320 / 0.255139 (-0.006819) | 0.261964 / 0.283200 (-0.021235) | 0.016764 / 0.141683 (-0.124919) | 1.118702 / 1.452155 (-0.333453) | 1.203079 / 1.492716 (-0.289638) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088808 / 0.018006 (0.070801) | 0.296526 / 0.000490 (0.296037) | 0.000203 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018816 / 0.037411 (-0.018595) | 0.062295 / 0.014526 (0.047769) | 0.075228 / 0.176557 (-0.101329) | 0.119916 / 0.737135 (-0.617219) | 0.077206 / 0.296338 (-0.219132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276723 / 0.215209 (0.061514) | 2.711431 / 2.077655 (0.633776) | 1.425590 / 1.504120 (-0.078530) | 1.301383 / 1.541195 (-0.239812) | 1.316314 / 1.468490 (-0.152176) | 0.402709 / 4.584777 (-4.182068) | 2.347229 / 3.745712 (-1.398483) | 2.596937 / 5.269862 (-2.672925) | 1.560658 / 4.565676 (-3.005018) | 0.046162 / 0.424275 (-0.378113) | 0.004760 / 0.007607 (-0.002848) | 0.330522 / 0.226044 (0.104478) | 3.244072 / 2.268929 (0.975143) | 1.747603 / 55.444624 (-53.697021) | 1.475534 / 6.876477 (-5.400943) | 1.485135 / 2.142072 (-0.656938) | 0.476794 / 4.805227 (-4.328433) | 0.098496 / 6.500664 (-6.402168) | 0.040740 / 0.075469 (-0.034729) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939020 / 1.841788 (-0.902768) | 11.235187 / 8.074308 (3.160878) | 10.194975 / 10.191392 (0.003583) | 0.126241 / 0.680424 (-0.554182) | 0.013990 / 0.534201 (-0.520211) | 0.269149 / 0.579283 (-0.310134) | 0.256950 / 0.434364 (-0.177414) | 0.301282 / 0.540337 (-0.239056) | 0.421490 / 1.386936 (-0.965446) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004956 / 0.011353 (-0.006397) | 0.002478 / 0.011008 (-0.008530) | 0.047773 / 0.038508 (0.009265) | 0.050076 / 0.023109 (0.026967) | 0.261915 / 0.275898 (-0.013983) | 0.282553 / 0.323480 (-0.040927) | 0.003881 / 0.007986 (-0.004105) | 0.002329 / 0.004328 (-0.001999) | 0.048091 / 0.004250 (0.043841) | 0.038188 / 0.037052 (0.001135) | 0.265502 / 0.258489 (0.007013) | 0.292568 / 0.293841 (-0.001273) | 0.024172 / 0.128546 (-0.104374) | 0.006865 / 0.075646 (-0.068781) | 0.053199 / 0.419271 (-0.366072) | 0.032201 / 0.043533 (-0.011332) | 0.265774 / 0.255139 (0.010635) | 0.277954 / 0.283200 (-0.005245) | 0.017798 / 0.141683 (-0.123885) | 1.121503 / 1.452155 (-0.330652) | 1.176319 / 1.492716 (-0.316398) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087027 / 0.018006 (0.069020) | 0.296182 / 0.000490 (0.295693) | 0.000216 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020990 / 0.037411 (-0.016421) | 0.069693 / 0.014526 (0.055168) | 0.081098 / 0.176557 (-0.095459) | 0.117760 / 0.737135 (-0.619375) | 0.081493 / 0.296338 (-0.214845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295078 / 0.215209 (0.079869) | 2.876602 / 2.077655 (0.798947) | 1.558011 / 1.504120 (0.053891) | 1.426715 / 1.541195 (-0.114480) | 1.443785 / 1.468490 (-0.024705) | 0.400826 / 4.584777 (-4.183951) | 2.378903 / 3.745712 (-1.366810) | 2.473128 / 5.269862 (-2.796734) | 1.500785 / 4.565676 (-3.064891) | 0.045438 / 0.424275 (-0.378837) | 0.004953 / 0.007607 (-0.002654) | 0.348182 / 0.226044 (0.122137) | 3.427751 / 2.268929 (1.158822) | 1.925173 / 55.444624 (-53.519451) | 1.633354 / 6.876477 (-5.243123) | 1.651573 / 2.142072 (-0.490499) | 0.473260 / 4.805227 (-4.331968) | 0.097613 / 6.500664 (-6.403051) | 0.040196 / 0.075469 (-0.035273) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951780 / 1.841788 (-0.890008) | 11.709342 / 8.074308 (3.635034) | 10.571831 / 10.191392 (0.380439) | 0.134344 / 0.680424 (-0.546079) | 0.022116 / 0.534201 (-0.512084) | 0.269651 / 0.579283 (-0.309632) | 0.272310 / 0.434364 (-0.162054) | 0.306434 / 0.540337 (-0.233903) | 0.408320 / 1.386936 (-0.978616) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004402 / 0.011353 (-0.006951) | 0.002732 / 0.011008 (-0.008277) | 0.062799 / 0.038508 (0.024291) | 0.029155 / 0.023109 (0.006046) | 0.241925 / 0.275898 (-0.033973) | 0.275694 / 0.323480 (-0.047786) | 0.003989 / 0.007986 (-0.003997) | 0.002528 / 0.004328 (-0.001801) | 0.048410 / 0.004250 (0.044160) | 0.043729 / 0.037052 (0.006677) | 0.248843 / 0.258489 (-0.009646) | 0.282980 / 0.293841 (-0.010860) | 0.023828 / 0.128546 (-0.104718) | 0.006972 / 0.075646 (-0.068675) | 0.213222 / 0.419271 (-0.206049) | 0.054883 / 0.043533 (0.011350) | 0.251353 / 0.255139 (-0.003786) | 0.269818 / 0.283200 (-0.013381) | 0.016906 / 0.141683 (-0.124777) | 1.114109 / 1.452155 (-0.338045) | 1.162942 / 1.492716 (-0.329774) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093724 / 0.018006 (0.075718) | 0.301989 / 0.000490 (0.301499) | 0.000213 / 0.000200 (0.000014) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018245 / 0.037411 (-0.019166) | 0.062237 / 0.014526 (0.047712) | 0.075644 / 0.176557 (-0.100913) | 0.119655 / 0.737135 (-0.617480) | 0.074525 / 0.296338 (-0.221814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.274534 / 0.215209 (0.059324) | 2.683678 / 2.077655 (0.606024) | 1.453306 / 1.504120 (-0.050814) | 1.347630 / 1.541195 (-0.193564) | 1.352875 / 1.468490 (-0.115615) | 0.398425 / 4.584777 (-4.186352) | 2.375738 / 3.745712 (-1.369974) | 2.591573 / 5.269862 (-2.678289) | 1.555527 / 4.565676 (-3.010150) | 0.045656 / 0.424275 (-0.378619) | 0.004898 / 0.007607 (-0.002709) | 0.330591 / 0.226044 (0.104547) | 3.247638 / 2.268929 (0.978710) | 1.816676 / 55.444624 (-53.627948) | 1.531754 / 6.876477 (-5.344723) | 1.543196 / 2.142072 (-0.598877) | 0.472489 / 4.805227 (-4.332739) | 0.099311 / 6.500664 (-6.401353) | 0.042139 / 0.075469 (-0.033330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945472 / 1.841788 (-0.896316) | 11.476550 / 8.074308 (3.402242) | 10.281157 / 10.191392 (0.089765) | 0.141062 / 0.680424 (-0.539362) | 0.013634 / 0.534201 (-0.520567) | 0.268778 / 0.579283 (-0.310505) | 0.263542 / 0.434364 (-0.170822) | 0.307918 / 0.540337 (-0.232420) | 0.421231 / 1.386936 (-0.965705) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005090 / 0.011353 (-0.006263) | 0.003135 / 0.011008 (-0.007873) | 0.048058 / 0.038508 (0.009550) | 0.052898 / 0.023109 (0.029789) | 0.273233 / 0.275898 (-0.002665) | 0.299516 / 0.323480 (-0.023964) | 0.004126 / 0.007986 (-0.003860) | 0.002331 / 0.004328 (-0.001997) | 0.047627 / 0.004250 (0.043376) | 0.039076 / 0.037052 (0.002023) | 0.276625 / 0.258489 (0.018136) | 0.308180 / 0.293841 (0.014340) | 0.024929 / 0.128546 (-0.103618) | 0.007396 / 0.075646 (-0.068251) | 0.053408 / 0.419271 (-0.365863) | 0.032896 / 0.043533 (-0.010637) | 0.275412 / 0.255139 (0.020273) | 0.292014 / 0.283200 (0.008814) | 0.018336 / 0.141683 (-0.123347) | 1.123565 / 1.452155 (-0.328589) | 1.175382 / 1.492716 (-0.317334) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093799 / 0.018006 (0.075793) | 0.304219 / 0.000490 (0.303729) | 0.000231 / 0.000200 (0.000031) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021034 / 0.037411 (-0.016377) | 0.069961 / 0.014526 (0.055435) | 0.080311 / 0.176557 (-0.096246) | 0.118603 / 0.737135 (-0.618532) | 0.084003 / 0.296338 (-0.212335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305610 / 0.215209 (0.090401) | 2.962027 / 2.077655 (0.884372) | 1.598604 / 1.504120 (0.094484) | 1.476227 / 1.541195 (-0.064967) | 1.528960 / 1.468490 (0.060470) | 0.404545 / 4.584777 (-4.180232) | 2.423147 / 3.745712 (-1.322565) | 2.516632 / 5.269862 (-2.753229) | 1.529000 / 4.565676 (-3.036677) | 0.045780 / 0.424275 (-0.378495) | 0.004784 / 0.007607 (-0.002823) | 0.358836 / 0.226044 (0.132792) | 3.508782 / 2.268929 (1.239853) | 1.954513 / 55.444624 (-53.490111) | 1.672824 / 6.876477 (-5.203653) | 1.683482 / 2.142072 (-0.458590) | 0.479014 / 4.805227 (-4.326213) | 0.098325 / 6.500664 (-6.402340) | 0.040934 / 0.075469 (-0.034536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974770 / 1.841788 (-0.867017) | 11.956137 / 8.074308 (3.881829) | 10.956458 / 10.191392 (0.765066) | 0.141800 / 0.680424 (-0.538624) | 0.015439 / 0.534201 (-0.518762) | 0.271783 / 0.579283 (-0.307500) | 0.278058 / 0.434364 (-0.156306) | 0.305823 / 0.540337 (-0.234514) | 0.415677 / 1.386936 (-0.971259) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004483 / 0.011353 (-0.006870) | 0.002560 / 0.011008 (-0.008448) | 0.061428 / 0.038508 (0.022920) | 0.029460 / 0.023109 (0.006351) | 0.238971 / 0.275898 (-0.036927) | 0.271768 / 0.323480 (-0.051712) | 0.003970 / 0.007986 (-0.004016) | 0.002408 / 0.004328 (-0.001921) | 0.047755 / 0.004250 (0.043505) | 0.043358 / 0.037052 (0.006306) | 0.245543 / 0.258489 (-0.012946) | 0.278230 / 0.293841 (-0.015611) | 0.023819 / 0.128546 (-0.104727) | 0.006856 / 0.075646 (-0.068790) | 0.204603 / 0.419271 (-0.214668) | 0.054521 / 0.043533 (0.010989) | 0.246277 / 0.255139 (-0.008862) | 0.271230 / 0.283200 (-0.011969) | 0.017283 / 0.141683 (-0.124400) | 1.088955 / 1.452155 (-0.363200) | 1.245141 / 1.492716 (-0.247575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091534 / 0.018006 (0.073528) | 0.299517 / 0.000490 (0.299027) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018105 / 0.037411 (-0.019306) | 0.061860 / 0.014526 (0.047334) | 0.074494 / 0.176557 (-0.102063) | 0.120107 / 0.737135 (-0.617029) | 0.073406 / 0.296338 (-0.222932) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278140 / 0.215209 (0.062931) | 2.746208 / 2.077655 (0.668553) | 1.476264 / 1.504120 (-0.027856) | 1.356498 / 1.541195 (-0.184697) | 1.362998 / 1.468490 (-0.105492) | 0.401884 / 4.584777 (-4.182893) | 2.409836 / 3.745712 (-1.335877) | 2.579087 / 5.269862 (-2.690775) | 1.545021 / 4.565676 (-3.020656) | 0.046001 / 0.424275 (-0.378274) | 0.004812 / 0.007607 (-0.002795) | 0.339767 / 0.226044 (0.113722) | 3.341599 / 2.268929 (1.072670) | 1.821498 / 55.444624 (-53.623127) | 1.559311 / 6.876477 (-5.317166) | 1.570368 / 2.142072 (-0.571704) | 0.472688 / 4.805227 (-4.332539) | 0.099549 / 6.500664 (-6.401115) | 0.041644 / 0.075469 (-0.033825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951988 / 1.841788 (-0.889799) | 11.371459 / 8.074308 (3.297150) | 10.229446 / 10.191392 (0.038054) | 0.128105 / 0.680424 (-0.552319) | 0.014418 / 0.534201 (-0.519783) | 0.268615 / 0.579283 (-0.310668) | 0.263956 / 0.434364 (-0.170407) | 0.302213 / 0.540337 (-0.238125) | 0.427224 / 1.386936 (-0.959712) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005150 / 0.011353 (-0.006203) | 0.002557 / 0.011008 (-0.008451) | 0.048092 / 0.038508 (0.009584) | 0.050522 / 0.023109 (0.027413) | 0.272195 / 0.275898 (-0.003703) | 0.294191 / 0.323480 (-0.029289) | 0.004098 / 0.007986 (-0.003887) | 0.002350 / 0.004328 (-0.001978) | 0.048682 / 0.004250 (0.044432) | 0.038381 / 0.037052 (0.001328) | 0.275530 / 0.258489 (0.017041) | 0.303991 / 0.293841 (0.010150) | 0.024734 / 0.128546 (-0.103812) | 0.006926 / 0.075646 (-0.068720) | 0.053683 / 0.419271 (-0.365588) | 0.032675 / 0.043533 (-0.010858) | 0.272816 / 0.255139 (0.017677) | 0.291754 / 0.283200 (0.008554) | 0.018290 / 0.141683 (-0.123392) | 1.127696 / 1.452155 (-0.324459) | 1.187080 / 1.492716 (-0.305636) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091224 / 0.018006 (0.073218) | 0.288838 / 0.000490 (0.288348) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069846 / 0.014526 (0.055320) | 0.079962 / 0.176557 (-0.096594) | 0.118575 / 0.737135 (-0.618560) | 0.080223 / 0.296338 (-0.216115) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290835 / 0.215209 (0.075626) | 2.831787 / 2.077655 (0.754133) | 1.587728 / 1.504120 (0.083608) | 1.461939 / 1.541195 (-0.079256) | 1.495257 / 1.468490 (0.026767) | 0.397653 / 4.584777 (-4.187124) | 2.399903 / 3.745712 (-1.345809) | 2.527615 / 5.269862 (-2.742247) | 1.501555 / 4.565676 (-3.064121) | 0.045742 / 0.424275 (-0.378533) | 0.004797 / 0.007607 (-0.002811) | 0.339139 / 0.226044 (0.113094) | 3.358340 / 2.268929 (1.089412) | 1.968955 / 55.444624 (-53.475670) | 1.663598 / 6.876477 (-5.212879) | 1.673995 / 2.142072 (-0.468078) | 0.463444 / 4.805227 (-4.341783) | 0.098008 / 6.500664 (-6.402656) | 0.040836 / 0.075469 (-0.034633) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974033 / 1.841788 (-0.867755) | 11.863206 / 8.074308 (3.788897) | 10.892389 / 10.191392 (0.700997) | 0.128884 / 0.680424 (-0.551540) | 0.015319 / 0.534201 (-0.518882) | 0.268931 / 0.579283 (-0.310353) | 0.274148 / 0.434364 (-0.160216) | 0.305407 / 0.540337 (-0.234930) | 0.410574 / 1.386936 (-0.976362) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n" ]
2023-11-16T07:37:20
2023-11-16T08:12:12
2023-11-16T07:43:05
MEMBER
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More robust temporary directory deletion
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6426). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004750 / 0.011353 (-0.006603) | 0.002928 / 0.011008 (-0.008080) | 0.061962 / 0.038508 (0.023454) | 0.029878 / 0.023109 (0.006768) | 0.233380 / 0.275898 (-0.042518) | 0.262221 / 0.323480 (-0.061259) | 0.002982 / 0.007986 (-0.005004) | 0.003698 / 0.004328 (-0.000630) | 0.048565 / 0.004250 (0.044314) | 0.046107 / 0.037052 (0.009055) | 0.240090 / 0.258489 (-0.018399) | 0.267294 / 0.293841 (-0.026547) | 0.023335 / 0.128546 (-0.105211) | 0.007221 / 0.075646 (-0.068425) | 0.200903 / 0.419271 (-0.218369) | 0.059237 / 0.043533 (0.015705) | 0.234929 / 0.255139 (-0.020210) | 0.256326 / 0.283200 (-0.026874) | 0.018549 / 0.141683 (-0.123134) | 1.103519 / 1.452155 (-0.348635) | 1.156573 / 1.492716 (-0.336143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091205 / 0.018006 (0.073199) | 0.303533 / 0.000490 (0.303043) | 0.000204 / 0.000200 (0.000004) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018572 / 0.037411 (-0.018839) | 0.062323 / 0.014526 (0.047797) | 0.074528 / 0.176557 (-0.102029) | 0.120295 / 0.737135 (-0.616841) | 0.076786 / 0.296338 (-0.219552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278814 / 0.215209 (0.063605) | 2.745483 / 2.077655 (0.667829) | 1.486073 / 1.504120 (-0.018047) | 1.385334 / 1.541195 (-0.155861) | 1.386351 / 1.468490 (-0.082139) | 0.395545 / 4.584777 (-4.189232) | 2.409468 / 3.745712 (-1.336244) | 2.670702 / 5.269862 (-2.599159) | 1.629245 / 4.565676 (-2.936432) | 0.045990 / 0.424275 (-0.378286) | 0.004782 / 0.007607 (-0.002825) | 0.332912 / 0.226044 (0.106867) | 3.249277 / 2.268929 (0.980349) | 1.888690 / 55.444624 (-53.555934) | 1.533462 / 6.876477 (-5.343015) | 1.576045 / 2.142072 (-0.566027) | 0.473090 / 4.805227 (-4.332138) | 0.099448 / 6.500664 (-6.401216) | 0.042613 / 0.075469 (-0.032857) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944229 / 1.841788 (-0.897559) | 12.103621 / 8.074308 (4.029313) | 10.643471 / 10.191392 (0.452079) | 0.143004 / 0.680424 (-0.537420) | 0.013872 / 0.534201 (-0.520329) | 0.272026 / 0.579283 (-0.307257) | 0.298701 / 0.434364 (-0.135663) | 0.310299 / 0.540337 (-0.230038) | 0.420934 / 1.386936 (-0.966002) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004904 / 0.011353 (-0.006449) | 0.003064 / 0.011008 (-0.007945) | 0.047982 / 0.038508 (0.009474) | 0.056354 / 0.023109 (0.033245) | 0.292893 / 0.275898 (0.016995) | 0.348744 / 0.323480 (0.025264) | 0.003988 / 0.007986 (-0.003997) | 0.002431 / 0.004328 (-0.001898) | 0.049108 / 0.004250 (0.044857) | 0.039055 / 0.037052 (0.002002) | 0.278129 / 0.258489 (0.019640) | 0.318547 / 0.293841 (0.024706) | 0.025040 / 0.128546 (-0.103507) | 0.007166 / 0.075646 (-0.068480) | 0.053967 / 0.419271 (-0.365305) | 0.033128 / 0.043533 (-0.010405) | 0.272849 / 0.255139 (0.017710) | 0.312143 / 0.283200 (0.028943) | 0.017942 / 0.141683 (-0.123741) | 1.192297 / 1.452155 (-0.259857) | 1.328102 / 1.492716 (-0.164615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090903 / 0.018006 (0.072896) | 0.301260 / 0.000490 (0.300770) | 0.000215 / 0.000200 (0.000015) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021112 / 0.037411 (-0.016300) | 0.070181 / 0.014526 (0.055656) | 0.082431 / 0.176557 (-0.094126) | 0.121973 / 0.737135 (-0.615163) | 0.083617 / 0.296338 (-0.212721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289587 / 0.215209 (0.074378) | 2.877895 / 2.077655 (0.800240) | 1.721417 / 1.504120 (0.217297) | 1.536023 / 1.541195 (-0.005171) | 1.550917 / 1.468490 (0.082427) | 0.402978 / 4.584777 (-4.181799) | 2.431767 / 3.745712 (-1.313946) | 2.544419 / 5.269862 (-2.725442) | 1.554562 / 4.565676 (-3.011115) | 0.046260 / 0.424275 (-0.378015) | 0.004923 / 0.007607 (-0.002684) | 0.341584 / 0.226044 (0.115540) | 3.362133 / 2.268929 (1.093205) | 1.928741 / 55.444624 (-53.515884) | 1.654798 / 6.876477 (-5.221679) | 1.715111 / 2.142072 (-0.426962) | 0.471029 / 4.805227 (-4.334198) | 0.098912 / 6.500664 (-6.401752) | 0.041018 / 0.075469 (-0.034451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992880 / 1.841788 (-0.848907) | 12.083890 / 8.074308 (4.009582) | 11.023833 / 10.191392 (0.832441) | 0.139217 / 0.680424 (-0.541207) | 0.015183 / 0.534201 (-0.519018) | 0.271637 / 0.579283 (-0.307646) | 0.278910 / 0.434364 (-0.155454) | 0.306891 / 0.540337 (-0.233447) | 0.424412 / 1.386936 (-0.962524) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d51f37eb9996d4c52250ee6e987ccce0d74f2f4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004545 / 0.011353 (-0.006808) | 0.002955 / 0.011008 (-0.008054) | 0.062119 / 0.038508 (0.023611) | 0.029357 / 0.023109 (0.006248) | 0.240068 / 0.275898 (-0.035830) | 0.273376 / 0.323480 (-0.050104) | 0.003884 / 0.007986 (-0.004102) | 0.002390 / 0.004328 (-0.001938) | 0.048621 / 0.004250 (0.044371) | 0.043867 / 0.037052 (0.006815) | 0.247240 / 0.258489 (-0.011249) | 0.279187 / 0.293841 (-0.014654) | 0.023377 / 0.128546 (-0.105169) | 0.007261 / 0.075646 (-0.068385) | 0.201913 / 0.419271 (-0.217359) | 0.057063 / 0.043533 (0.013530) | 0.245698 / 0.255139 (-0.009441) | 0.265644 / 0.283200 (-0.017556) | 0.018077 / 0.141683 (-0.123606) | 1.133225 / 1.452155 (-0.318930) | 1.186380 / 1.492716 (-0.306336) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089639 / 0.018006 (0.071632) | 0.298918 / 0.000490 (0.298428) | 0.000198 / 0.000200 (-0.000002) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019037 / 0.037411 (-0.018374) | 0.062580 / 0.014526 (0.048055) | 0.072974 / 0.176557 (-0.103582) | 0.119909 / 0.737135 (-0.617226) | 0.075021 / 0.296338 (-0.221317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276561 / 0.215209 (0.061352) | 2.697281 / 2.077655 (0.619626) | 1.419772 / 1.504120 (-0.084348) | 1.302079 / 1.541195 (-0.239115) | 1.329143 / 1.468490 (-0.139347) | 0.395528 / 4.584777 (-4.189249) | 2.365788 / 3.745712 (-1.379925) | 2.583802 / 5.269862 (-2.686059) | 1.561983 / 4.565676 (-3.003694) | 0.045269 / 0.424275 (-0.379006) | 0.004826 / 0.007607 (-0.002781) | 0.331041 / 0.226044 (0.104996) | 3.292523 / 2.268929 (1.023595) | 1.797865 / 55.444624 (-53.646759) | 1.509229 / 6.876477 (-5.367248) | 1.498884 / 2.142072 (-0.643188) | 0.458518 / 4.805227 (-4.346709) | 0.098076 / 6.500664 (-6.402588) | 0.042290 / 0.075469 (-0.033179) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.922331 / 1.841788 (-0.919457) | 11.605041 / 8.074308 (3.530732) | 10.471664 / 10.191392 (0.280272) | 0.130325 / 0.680424 (-0.550098) | 0.014084 / 0.534201 (-0.520117) | 0.278877 / 0.579283 (-0.300406) | 0.263104 / 0.434364 (-0.171259) | 0.306723 / 0.540337 (-0.233615) | 0.416238 / 1.386936 (-0.970698) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005094 / 0.011353 (-0.006259) | 0.002794 / 0.011008 (-0.008214) | 0.048189 / 0.038508 (0.009680) | 0.050409 / 0.023109 (0.027300) | 0.272618 / 0.275898 (-0.003280) | 0.293589 / 0.323480 (-0.029891) | 0.003995 / 0.007986 (-0.003991) | 0.002373 / 0.004328 (-0.001956) | 0.048269 / 0.004250 (0.044018) | 0.038751 / 0.037052 (0.001698) | 0.273495 / 0.258489 (0.015006) | 0.309244 / 0.293841 (0.015403) | 0.024681 / 0.128546 (-0.103866) | 0.007390 / 0.075646 (-0.068256) | 0.053844 / 0.419271 (-0.365427) | 0.032395 / 0.043533 (-0.011137) | 0.271963 / 0.255139 (0.016824) | 0.289557 / 0.283200 (0.006357) | 0.018659 / 0.141683 (-0.123024) | 1.154478 / 1.452155 (-0.297676) | 1.199772 / 1.492716 (-0.292944) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089771 / 0.018006 (0.071764) | 0.299468 / 0.000490 (0.298978) | 0.000219 / 0.000200 (0.000020) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021854 / 0.037411 (-0.015558) | 0.070280 / 0.014526 (0.055754) | 0.080956 / 0.176557 (-0.095600) | 0.119430 / 0.737135 (-0.617705) | 0.082778 / 0.296338 (-0.213561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304273 / 0.215209 (0.089064) | 2.968264 / 2.077655 (0.890609) | 1.592363 / 1.504120 (0.088243) | 1.460795 / 1.541195 (-0.080400) | 1.501545 / 1.468490 (0.033055) | 0.411001 / 4.584777 (-4.173776) | 2.464273 / 3.745712 (-1.281439) | 2.524585 / 5.269862 (-2.745277) | 1.537443 / 4.565676 (-3.028234) | 0.046163 / 0.424275 (-0.378112) | 0.004783 / 0.007607 (-0.002824) | 0.354251 / 0.226044 (0.128206) | 3.512087 / 2.268929 (1.243158) | 1.968156 / 55.444624 (-53.476468) | 1.664966 / 6.876477 (-5.211510) | 1.685013 / 2.142072 (-0.457060) | 0.485793 / 4.805227 (-4.319435) | 0.099789 / 6.500664 (-6.400875) | 0.040705 / 0.075469 (-0.034764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966570 / 1.841788 (-0.875218) | 12.023188 / 8.074308 (3.948880) | 11.122602 / 10.191392 (0.931210) | 0.141002 / 0.680424 (-0.539422) | 0.015955 / 0.534201 (-0.518246) | 0.270293 / 0.579283 (-0.308990) | 0.281839 / 0.434364 (-0.152525) | 0.307279 / 0.540337 (-0.233058) | 0.434687 / 1.386936 (-0.952249) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eaad71464e85c7358eaa36494227a43257ffcd8 \"CML watermark\")\n", "What would be the impact for non-windows users ?\r\n\r\nAlso I wonder if a gc.collect() after the `del` could help to remove the PermissionError ? Or register the dataset for deletion on copy/pickle maybe ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004973 / 0.011353 (-0.006380) | 0.002753 / 0.011008 (-0.008256) | 0.061489 / 0.038508 (0.022981) | 0.051122 / 0.023109 (0.028012) | 0.228783 / 0.275898 (-0.047115) | 0.256982 / 0.323480 (-0.066498) | 0.002873 / 0.007986 (-0.005112) | 0.003544 / 0.004328 (-0.000784) | 0.048721 / 0.004250 (0.044471) | 0.039137 / 0.037052 (0.002085) | 0.244988 / 0.258489 (-0.013501) | 0.275230 / 0.293841 (-0.018611) | 0.023034 / 0.128546 (-0.105513) | 0.006988 / 0.075646 (-0.068658) | 0.202780 / 0.419271 (-0.216492) | 0.035325 / 0.043533 (-0.008207) | 0.241722 / 0.255139 (-0.013417) | 0.259671 / 0.283200 (-0.023528) | 0.019875 / 0.141683 (-0.121808) | 1.098667 / 1.452155 (-0.353488) | 1.161444 / 1.492716 (-0.331272) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093591 / 0.018006 (0.075585) | 0.298703 / 0.000490 (0.298213) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018319 / 0.037411 (-0.019092) | 0.062993 / 0.014526 (0.048467) | 0.074313 / 0.176557 (-0.102244) | 0.123089 / 0.737135 (-0.614046) | 0.075177 / 0.296338 (-0.221162) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.268584 / 0.215209 (0.053375) | 2.633116 / 2.077655 (0.555461) | 1.390743 / 1.504120 (-0.113377) | 1.277385 / 1.541195 (-0.263810) | 1.287934 / 1.468490 (-0.180556) | 0.387934 / 4.584777 (-4.196843) | 2.345819 / 3.745712 (-1.399893) | 2.558169 / 5.269862 (-2.711693) | 1.569812 / 4.565676 (-2.995865) | 0.045297 / 0.424275 (-0.378978) | 0.005238 / 0.007607 (-0.002369) | 0.359704 / 0.226044 (0.133659) | 3.204688 / 2.268929 (0.935759) | 1.753321 / 55.444624 (-53.691303) | 1.492223 / 6.876477 (-5.384254) | 1.498207 / 2.142072 (-0.643865) | 0.459830 / 4.805227 (-4.345397) | 0.098194 / 6.500664 (-6.402470) | 0.042632 / 0.075469 (-0.032837) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963020 / 1.841788 (-0.878768) | 11.500470 / 8.074308 (3.426161) | 10.451882 / 10.191392 (0.260490) | 0.127706 / 0.680424 (-0.552718) | 0.014084 / 0.534201 (-0.520117) | 0.269728 / 0.579283 (-0.309555) | 0.260283 / 0.434364 (-0.174080) | 0.303717 / 0.540337 (-0.236620) | 0.397028 / 1.386936 (-0.989908) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004823 / 0.011353 (-0.006529) | 0.002751 / 0.011008 (-0.008257) | 0.048719 / 0.038508 (0.010211) | 0.051409 / 0.023109 (0.028300) | 0.267139 / 0.275898 (-0.008759) | 0.287659 / 0.323480 (-0.035821) | 0.003959 / 0.007986 (-0.004027) | 0.002376 / 0.004328 (-0.001953) | 0.047942 / 0.004250 (0.043692) | 0.039742 / 0.037052 (0.002690) | 0.268348 / 0.258489 (0.009859) | 0.297201 / 0.293841 (0.003360) | 0.024226 / 0.128546 (-0.104320) | 0.007103 / 0.075646 (-0.068544) | 0.053310 / 0.419271 (-0.365961) | 0.032716 / 0.043533 (-0.010816) | 0.269469 / 0.255139 (0.014330) | 0.287752 / 0.283200 (0.004553) | 0.018191 / 0.141683 (-0.123492) | 1.114086 / 1.452155 (-0.338069) | 1.188054 / 1.492716 (-0.304662) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091072 / 0.018006 (0.073066) | 0.300367 / 0.000490 (0.299877) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020970 / 0.037411 (-0.016441) | 0.070356 / 0.014526 (0.055830) | 0.081339 / 0.176557 (-0.095218) | 0.120741 / 0.737135 (-0.616394) | 0.081677 / 0.296338 (-0.214662) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290405 / 0.215209 (0.075196) | 2.863877 / 2.077655 (0.786222) | 1.524603 / 1.504120 (0.020483) | 1.397917 / 1.541195 (-0.143278) | 1.402635 / 1.468490 (-0.065855) | 0.405525 / 4.584777 (-4.179252) | 2.432474 / 3.745712 (-1.313239) | 2.446277 / 5.269862 (-2.823585) | 1.550300 / 4.565676 (-3.015377) | 0.046545 / 0.424275 (-0.377730) | 0.004824 / 0.007607 (-0.002783) | 0.343578 / 0.226044 (0.117534) | 3.436850 / 2.268929 (1.167922) | 1.897200 / 55.444624 (-53.547425) | 1.625222 / 6.876477 (-5.251255) | 1.730488 / 2.142072 (-0.411585) | 0.482099 / 4.805227 (-4.323129) | 0.097828 / 6.500664 (-6.402836) | 0.040385 / 0.075469 (-0.035084) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950975 / 1.841788 (-0.890812) | 11.875024 / 8.074308 (3.800715) | 10.430301 / 10.191392 (0.238909) | 0.130546 / 0.680424 (-0.549878) | 0.015423 / 0.534201 (-0.518778) | 0.269592 / 0.579283 (-0.309691) | 0.282505 / 0.434364 (-0.151859) | 0.305567 / 0.540337 (-0.234771) | 0.522142 / 1.386936 (-0.864794) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c166692aa955528180dd4d55474a984f6044896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004983 / 0.011353 (-0.006369) | 0.003346 / 0.011008 (-0.007662) | 0.062233 / 0.038508 (0.023725) | 0.050246 / 0.023109 (0.027137) | 0.305738 / 0.275898 (0.029839) | 0.321863 / 0.323480 (-0.001617) | 0.003870 / 0.007986 (-0.004116) | 0.002610 / 0.004328 (-0.001718) | 0.047734 / 0.004250 (0.043483) | 0.037611 / 0.037052 (0.000559) | 0.299121 / 0.258489 (0.040632) | 0.327370 / 0.293841 (0.033529) | 0.027009 / 0.128546 (-0.101537) | 0.010816 / 0.075646 (-0.064830) | 0.204627 / 0.419271 (-0.214645) | 0.035708 / 0.043533 (-0.007825) | 0.291837 / 0.255139 (0.036698) | 0.313646 / 0.283200 (0.030447) | 0.017277 / 0.141683 (-0.124405) | 1.097907 / 1.452155 (-0.354248) | 1.163203 / 1.492716 (-0.329513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091933 / 0.018006 (0.073926) | 0.298787 / 0.000490 (0.298297) | 0.000204 / 0.000200 (0.000004) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018349 / 0.037411 (-0.019062) | 0.061520 / 0.014526 (0.046994) | 0.073159 / 0.176557 (-0.103397) | 0.118657 / 0.737135 (-0.618478) | 0.073601 / 0.296338 (-0.222737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276297 / 0.215209 (0.061088) | 2.725668 / 2.077655 (0.648013) | 1.458079 / 1.504120 (-0.046041) | 1.331236 / 1.541195 (-0.209959) | 1.347919 / 1.468490 (-0.120571) | 0.565954 / 4.584777 (-4.018823) | 2.380883 / 3.745712 (-1.364829) | 2.800533 / 5.269862 (-2.469329) | 1.740534 / 4.565676 (-2.825142) | 0.065617 / 0.424275 (-0.358658) | 0.004907 / 0.007607 (-0.002700) | 0.335973 / 0.226044 (0.109929) | 3.337405 / 2.268929 (1.068476) | 1.819852 / 55.444624 (-53.624772) | 1.542724 / 6.876477 (-5.333752) | 1.509508 / 2.142072 (-0.632565) | 0.648618 / 4.805227 (-4.156609) | 0.116812 / 6.500664 (-6.383852) | 0.041561 / 0.075469 (-0.033909) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943488 / 1.841788 (-0.898299) | 11.184770 / 8.074308 (3.110462) | 10.406311 / 10.191392 (0.214919) | 0.129841 / 0.680424 (-0.550583) | 0.013736 / 0.534201 (-0.520465) | 0.287281 / 0.579283 (-0.292002) | 0.267403 / 0.434364 (-0.166961) | 0.325319 / 0.540337 (-0.215019) | 0.454207 / 1.386936 (-0.932729) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005169 / 0.011353 (-0.006183) | 0.003155 / 0.011008 (-0.007854) | 0.048101 / 0.038508 (0.009593) | 0.048726 / 0.023109 (0.025617) | 0.275768 / 0.275898 (-0.000130) | 0.291209 / 0.323480 (-0.032271) | 0.003984 / 0.007986 (-0.004001) | 0.002586 / 0.004328 (-0.001742) | 0.047751 / 0.004250 (0.043500) | 0.040176 / 0.037052 (0.003124) | 0.279161 / 0.258489 (0.020672) | 0.297371 / 0.293841 (0.003530) | 0.028502 / 0.128546 (-0.100044) | 0.010103 / 0.075646 (-0.065544) | 0.056920 / 0.419271 (-0.362351) | 0.032174 / 0.043533 (-0.011359) | 0.271925 / 0.255139 (0.016786) | 0.289572 / 0.283200 (0.006372) | 0.017981 / 0.141683 (-0.123702) | 1.192972 / 1.452155 (-0.259183) | 1.223231 / 1.492716 (-0.269485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091363 / 0.018006 (0.073356) | 0.298106 / 0.000490 (0.297616) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021509 / 0.037411 (-0.015902) | 0.068377 / 0.014526 (0.053851) | 0.079798 / 0.176557 (-0.096759) | 0.120546 / 0.737135 (-0.616589) | 0.080602 / 0.296338 (-0.215737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300809 / 0.215209 (0.085600) | 2.921144 / 2.077655 (0.843489) | 1.621096 / 1.504120 (0.116976) | 1.504265 / 1.541195 (-0.036930) | 1.508050 / 1.468490 (0.039560) | 0.554291 / 4.584777 (-4.030486) | 2.418798 / 3.745712 (-1.326914) | 2.768088 / 5.269862 (-2.501773) | 1.728267 / 4.565676 (-2.837410) | 0.062943 / 0.424275 (-0.361332) | 0.004891 / 0.007607 (-0.002716) | 0.350298 / 0.226044 (0.124254) | 3.442782 / 2.268929 (1.173853) | 1.960163 / 55.444624 (-53.484461) | 1.682000 / 6.876477 (-5.194477) | 1.680311 / 2.142072 (-0.461761) | 0.631201 / 4.805227 (-4.174026) | 0.115211 / 6.500664 (-6.385453) | 0.041279 / 0.075469 (-0.034190) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962478 / 1.841788 (-0.879310) | 11.671463 / 8.074308 (3.597155) | 10.640129 / 10.191392 (0.448737) | 0.130649 / 0.680424 (-0.549775) | 0.016169 / 0.534201 (-0.518032) | 0.286894 / 0.579283 (-0.292389) | 0.269319 / 0.434364 (-0.165045) | 0.324512 / 0.540337 (-0.215825) | 0.550874 / 1.386936 (-0.836062) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69f135121beb1616f1d7c7584b317d4e41e21275 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005078 / 0.011353 (-0.006275) | 0.003950 / 0.011008 (-0.007058) | 0.063345 / 0.038508 (0.024837) | 0.054486 / 0.023109 (0.031377) | 0.243213 / 0.275898 (-0.032685) | 0.264079 / 0.323480 (-0.059401) | 0.003922 / 0.007986 (-0.004064) | 0.002631 / 0.004328 (-0.001698) | 0.048660 / 0.004250 (0.044409) | 0.037205 / 0.037052 (0.000153) | 0.244577 / 0.258489 (-0.013912) | 0.276025 / 0.293841 (-0.017816) | 0.027134 / 0.128546 (-0.101412) | 0.010921 / 0.075646 (-0.064726) | 0.209792 / 0.419271 (-0.209479) | 0.035999 / 0.043533 (-0.007534) | 0.245671 / 0.255139 (-0.009468) | 0.262807 / 0.283200 (-0.020393) | 0.018173 / 0.141683 (-0.123510) | 1.084417 / 1.452155 (-0.367738) | 1.148284 / 1.492716 (-0.344432) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093128 / 0.018006 (0.075122) | 0.301606 / 0.000490 (0.301117) | 0.000221 / 0.000200 (0.000021) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018718 / 0.037411 (-0.018693) | 0.060819 / 0.014526 (0.046293) | 0.073050 / 0.176557 (-0.103507) | 0.120043 / 0.737135 (-0.617092) | 0.075374 / 0.296338 (-0.220965) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291080 / 0.215209 (0.075871) | 2.808802 / 2.077655 (0.731148) | 1.485686 / 1.504120 (-0.018434) | 1.354356 / 1.541195 (-0.186839) | 1.347863 / 1.468490 (-0.120627) | 0.571501 / 4.584777 (-4.013276) | 2.377960 / 3.745712 (-1.367752) | 2.768023 / 5.269862 (-2.501839) | 1.754360 / 4.565676 (-2.811316) | 0.063115 / 0.424275 (-0.361160) | 0.004941 / 0.007607 (-0.002666) | 0.338281 / 0.226044 (0.112237) | 3.340587 / 2.268929 (1.071658) | 1.849479 / 55.444624 (-53.595145) | 1.551846 / 6.876477 (-5.324631) | 1.539090 / 2.142072 (-0.602983) | 0.644522 / 4.805227 (-4.160705) | 0.117398 / 6.500664 (-6.383266) | 0.042239 / 0.075469 (-0.033230) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949496 / 1.841788 (-0.892291) | 11.548352 / 8.074308 (3.474044) | 10.478065 / 10.191392 (0.286673) | 0.129534 / 0.680424 (-0.550890) | 0.015378 / 0.534201 (-0.518822) | 0.287221 / 0.579283 (-0.292062) | 0.262944 / 0.434364 (-0.171419) | 0.321727 / 0.540337 (-0.218611) | 0.432354 / 1.386936 (-0.954582) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005256 / 0.011353 (-0.006097) | 0.003491 / 0.011008 (-0.007517) | 0.048647 / 0.038508 (0.010139) | 0.054011 / 0.023109 (0.030901) | 0.271786 / 0.275898 (-0.004112) | 0.291964 / 0.323480 (-0.031516) | 0.004035 / 0.007986 (-0.003950) | 0.002671 / 0.004328 (-0.001657) | 0.048108 / 0.004250 (0.043857) | 0.040421 / 0.037052 (0.003368) | 0.278594 / 0.258489 (0.020105) | 0.300707 / 0.293841 (0.006867) | 0.028924 / 0.128546 (-0.099623) | 0.010600 / 0.075646 (-0.065047) | 0.057649 / 0.419271 (-0.361623) | 0.034221 / 0.043533 (-0.009312) | 0.276692 / 0.255139 (0.021553) | 0.293545 / 0.283200 (0.010345) | 0.017908 / 0.141683 (-0.123775) | 1.135108 / 1.452155 (-0.317047) | 1.190823 / 1.492716 (-0.301893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095243 / 0.018006 (0.077237) | 0.301885 / 0.000490 (0.301396) | 0.000235 / 0.000200 (0.000035) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021561 / 0.037411 (-0.015850) | 0.069054 / 0.014526 (0.054529) | 0.080466 / 0.176557 (-0.096091) | 0.121323 / 0.737135 (-0.615812) | 0.081891 / 0.296338 (-0.214448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293957 / 0.215209 (0.078748) | 2.869035 / 2.077655 (0.791380) | 1.608837 / 1.504120 (0.104717) | 1.440594 / 1.541195 (-0.100601) | 1.464775 / 1.468490 (-0.003715) | 0.565663 / 4.584777 (-4.019114) | 2.439456 / 3.745712 (-1.306256) | 2.794775 / 5.269862 (-2.475087) | 1.750026 / 4.565676 (-2.815651) | 0.063291 / 0.424275 (-0.360984) | 0.004930 / 0.007607 (-0.002677) | 0.347169 / 0.226044 (0.121125) | 3.408260 / 2.268929 (1.139331) | 1.920933 / 55.444624 (-53.523691) | 1.648821 / 6.876477 (-5.227656) | 1.639022 / 2.142072 (-0.503051) | 0.642870 / 4.805227 (-4.162357) | 0.117077 / 6.500664 (-6.383587) | 0.040784 / 0.075469 (-0.034685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993501 / 1.841788 (-0.848287) | 12.012423 / 8.074308 (3.938115) | 10.740932 / 10.191392 (0.549540) | 0.132409 / 0.680424 (-0.548015) | 0.015294 / 0.534201 (-0.518907) | 0.287902 / 0.579283 (-0.291381) | 0.281350 / 0.434364 (-0.153014) | 0.329201 / 0.540337 (-0.211137) | 0.553199 / 1.386936 (-0.833737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ecd3a22c5dec2133491a320515e12956512439eb \"CML watermark\")\n" ]
2023-11-15T19:06:42
2023-12-01T15:37:32
2023-12-01T15:31:19
COLLABORATOR
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While fixing the Windows errors in #6362, I noticed that `PermissionError` can still easily be thrown on the session exit by the temporary cache directory's finalizer (we would also have to keep track of intermediate datasets, copies, etc.). ~~Due to the low usage of `datasets` on Windows, this PR takes a simpler approach to the issue than https://github.com/huggingface/datasets/pull/2403 - it tries to delete the temporary cache directory, and if this fails, logs a warning message about using a `delete-temp-cache` CLI command to delete it manually. The problematic references are freed after the session exits, so the CLI command should then succeed.~~ This PR implements `Dataset.__setstate__` to register datasets with temporary cache files for deletion.
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6,425
Fix deprecation warning when building conda package
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004811 / 0.011353 (-0.006542) | 0.002478 / 0.011008 (-0.008530) | 0.062241 / 0.038508 (0.023733) | 0.031153 / 0.023109 (0.008044) | 0.248896 / 0.275898 (-0.027002) | 0.276860 / 0.323480 (-0.046620) | 0.002934 / 0.007986 (-0.005052) | 0.002428 / 0.004328 (-0.001901) | 0.048507 / 0.004250 (0.044257) | 0.044567 / 0.037052 (0.007515) | 0.253570 / 0.258489 (-0.004919) | 0.280762 / 0.293841 (-0.013079) | 0.023549 / 0.128546 (-0.104997) | 0.006985 / 0.075646 (-0.068661) | 0.206227 / 0.419271 (-0.213044) | 0.054027 / 0.043533 (0.010494) | 0.257655 / 0.255139 (0.002516) | 0.273498 / 0.283200 (-0.009702) | 0.018997 / 0.141683 (-0.122685) | 1.111732 / 1.452155 (-0.340422) | 1.162078 / 1.492716 (-0.330639) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091816 / 0.018006 (0.073810) | 0.299428 / 0.000490 (0.298938) | 0.000211 / 0.000200 (0.000012) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018503 / 0.037411 (-0.018908) | 0.062933 / 0.014526 (0.048407) | 0.076349 / 0.176557 (-0.100208) | 0.123291 / 0.737135 (-0.613844) | 0.077491 / 0.296338 (-0.218847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280770 / 0.215209 (0.065561) | 2.762185 / 2.077655 (0.684530) | 1.429124 / 1.504120 (-0.074996) | 1.303162 / 1.541195 (-0.238033) | 1.307523 / 1.468490 (-0.160967) | 0.405593 / 4.584777 (-4.179184) | 2.396992 / 3.745712 (-1.348721) | 2.550968 / 5.269862 (-2.718894) | 1.557358 / 4.565676 (-3.008318) | 0.046149 / 0.424275 (-0.378126) | 0.004808 / 0.007607 (-0.002799) | 0.341870 / 0.226044 (0.115825) | 3.362478 / 2.268929 (1.093550) | 1.786360 / 55.444624 (-53.658264) | 1.483419 / 6.876477 (-5.393058) | 1.493463 / 2.142072 (-0.648609) | 0.470605 / 4.805227 (-4.334623) | 0.098372 / 6.500664 (-6.402292) | 0.041722 / 0.075469 (-0.033748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938148 / 1.841788 (-0.903640) | 11.219184 / 8.074308 (3.144876) | 10.454439 / 10.191392 (0.263047) | 0.139645 / 0.680424 (-0.540778) | 0.014453 / 0.534201 (-0.519748) | 0.268975 / 0.579283 (-0.310308) | 0.262060 / 0.434364 (-0.172304) | 0.313652 / 0.540337 (-0.226686) | 0.423992 / 1.386936 (-0.962944) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004829 / 0.011353 (-0.006524) | 0.002426 / 0.011008 (-0.008582) | 0.049064 / 0.038508 (0.010555) | 0.049728 / 0.023109 (0.026619) | 0.273263 / 0.275898 (-0.002635) | 0.295645 / 0.323480 (-0.027835) | 0.004156 / 0.007986 (-0.003830) | 0.002397 / 0.004328 (-0.001932) | 0.048902 / 0.004250 (0.044652) | 0.038414 / 0.037052 (0.001362) | 0.276176 / 0.258489 (0.017687) | 0.306844 / 0.293841 (0.013003) | 0.024546 / 0.128546 (-0.104000) | 0.006946 / 0.075646 (-0.068701) | 0.054024 / 0.419271 (-0.365247) | 0.032444 / 0.043533 (-0.011089) | 0.274125 / 0.255139 (0.018986) | 0.293226 / 0.283200 (0.010027) | 0.018003 / 0.141683 (-0.123680) | 1.130402 / 1.452155 (-0.321752) | 1.195969 / 1.492716 (-0.296748) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090043 / 0.018006 (0.072037) | 0.298699 / 0.000490 (0.298209) | 0.000214 / 0.000200 (0.000014) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021284 / 0.037411 (-0.016127) | 0.069954 / 0.014526 (0.055428) | 0.080445 / 0.176557 (-0.096111) | 0.119461 / 0.737135 (-0.617674) | 0.080632 / 0.296338 (-0.215706) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302246 / 0.215209 (0.087037) | 2.991936 / 2.077655 (0.914281) | 1.662969 / 1.504120 (0.158850) | 1.533141 / 1.541195 (-0.008054) | 1.583183 / 1.468490 (0.114693) | 0.402864 / 4.584777 (-4.181913) | 2.424119 / 3.745712 (-1.321593) | 2.489558 / 5.269862 (-2.780303) | 1.502196 / 4.565676 (-3.063481) | 0.045980 / 0.424275 (-0.378295) | 0.004768 / 0.007607 (-0.002839) | 0.356089 / 0.226044 (0.130044) | 3.481333 / 2.268929 (1.212404) | 2.009713 / 55.444624 (-53.434912) | 1.730021 / 6.876477 (-5.146455) | 1.704656 / 2.142072 (-0.437416) | 0.470832 / 4.805227 (-4.334395) | 0.097473 / 6.500664 (-6.403191) | 0.040437 / 0.075469 (-0.035032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981497 / 1.841788 (-0.860291) | 11.827242 / 8.074308 (3.752933) | 10.888324 / 10.191392 (0.696932) | 0.129249 / 0.680424 (-0.551174) | 0.015812 / 0.534201 (-0.518389) | 0.269657 / 0.579283 (-0.309626) | 0.275585 / 0.434364 (-0.158779) | 0.305698 / 0.540337 (-0.234639) | 0.411497 / 1.386936 (-0.975439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005402 / 0.011353 (-0.005951) | 0.003955 / 0.011008 (-0.007053) | 0.064096 / 0.038508 (0.025588) | 0.062330 / 0.023109 (0.039221) | 0.254729 / 0.275898 (-0.021169) | 0.276259 / 0.323480 (-0.047221) | 0.003052 / 0.007986 (-0.004934) | 0.003474 / 0.004328 (-0.000854) | 0.048938 / 0.004250 (0.044687) | 0.038635 / 0.037052 (0.001583) | 0.267953 / 0.258489 (0.009464) | 0.293725 / 0.293841 (-0.000116) | 0.028266 / 0.128546 (-0.100280) | 0.011188 / 0.075646 (-0.064458) | 0.221204 / 0.419271 (-0.198067) | 0.036549 / 0.043533 (-0.006984) | 0.252484 / 0.255139 (-0.002655) | 0.273855 / 0.283200 (-0.009345) | 0.017975 / 0.141683 (-0.123708) | 1.112265 / 1.452155 (-0.339890) | 1.185647 / 1.492716 (-0.307069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096223 / 0.018006 (0.078217) | 0.305010 / 0.000490 (0.304520) | 0.000227 / 0.000200 (0.000027) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018924 / 0.037411 (-0.018488) | 0.061910 / 0.014526 (0.047384) | 0.073751 / 0.176557 (-0.102806) | 0.120956 / 0.737135 (-0.616179) | 0.075090 / 0.296338 (-0.221249) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293277 / 0.215209 (0.078068) | 2.867468 / 2.077655 (0.789813) | 1.518218 / 1.504120 (0.014098) | 1.393741 / 1.541195 (-0.147454) | 1.424979 / 1.468490 (-0.043511) | 0.579766 / 4.584777 (-4.005011) | 2.434951 / 3.745712 (-1.310761) | 2.909924 / 5.269862 (-2.359937) | 1.838123 / 4.565676 (-2.727554) | 0.064260 / 0.424275 (-0.360015) | 0.005169 / 0.007607 (-0.002438) | 0.348228 / 0.226044 (0.122184) | 3.447558 / 2.268929 (1.178629) | 1.884988 / 55.444624 (-53.559636) | 1.570921 / 6.876477 (-5.305556) | 1.646341 / 2.142072 (-0.495732) | 0.660189 / 4.805227 (-4.145038) | 0.120026 / 6.500664 (-6.380638) | 0.043715 / 0.075469 (-0.031754) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953253 / 1.841788 (-0.888535) | 12.576112 / 8.074308 (4.501804) | 11.132637 / 10.191392 (0.941245) | 0.132870 / 0.680424 (-0.547553) | 0.014720 / 0.534201 (-0.519481) | 0.291866 / 0.579283 (-0.287417) | 0.265456 / 0.434364 (-0.168908) | 0.338629 / 0.540337 (-0.201709) | 0.456323 / 1.386936 (-0.930613) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005644 / 0.011353 (-0.005709) | 0.003624 / 0.011008 (-0.007384) | 0.049043 / 0.038508 (0.010535) | 0.059572 / 0.023109 (0.036463) | 0.277159 / 0.275898 (0.001261) | 0.303933 / 0.323480 (-0.019547) | 0.004294 / 0.007986 (-0.003692) | 0.002744 / 0.004328 (-0.001584) | 0.048187 / 0.004250 (0.043937) | 0.043655 / 0.037052 (0.006603) | 0.282441 / 0.258489 (0.023952) | 0.317130 / 0.293841 (0.023289) | 0.030159 / 0.128546 (-0.098387) | 0.011300 / 0.075646 (-0.064346) | 0.057451 / 0.419271 (-0.361821) | 0.033666 / 0.043533 (-0.009866) | 0.274554 / 0.255139 (0.019415) | 0.292470 / 0.283200 (0.009270) | 0.018757 / 0.141683 (-0.122926) | 1.170094 / 1.452155 (-0.282060) | 1.244626 / 1.492716 (-0.248090) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094920 / 0.018006 (0.076914) | 0.304156 / 0.000490 (0.303666) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022297 / 0.037411 (-0.015115) | 0.068908 / 0.014526 (0.054383) | 0.081520 / 0.176557 (-0.095037) | 0.122422 / 0.737135 (-0.614714) | 0.082533 / 0.296338 (-0.213806) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296080 / 0.215209 (0.080871) | 2.883120 / 2.077655 (0.805465) | 1.607950 / 1.504120 (0.103830) | 1.496191 / 1.541195 (-0.045004) | 1.520549 / 1.468490 (0.052059) | 0.562081 / 4.584777 (-4.022696) | 2.453447 / 3.745712 (-1.292265) | 2.943676 / 5.269862 (-2.326186) | 1.820581 / 4.565676 (-2.745096) | 0.064518 / 0.424275 (-0.359757) | 0.005406 / 0.007607 (-0.002201) | 0.349022 / 0.226044 (0.122978) | 3.472117 / 2.268929 (1.203188) | 2.006928 / 55.444624 (-53.437696) | 1.704800 / 6.876477 (-5.171677) | 1.719025 / 2.142072 (-0.423048) | 0.643719 / 4.805227 (-4.161508) | 0.117723 / 6.500664 (-6.382941) | 0.043158 / 0.075469 (-0.032311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981229 / 1.841788 (-0.860559) | 12.637620 / 8.074308 (4.563312) | 10.848775 / 10.191392 (0.657383) | 0.143981 / 0.680424 (-0.536443) | 0.015950 / 0.534201 (-0.518251) | 0.287542 / 0.579283 (-0.291741) | 0.278989 / 0.434364 (-0.155375) | 0.331786 / 0.540337 (-0.208552) | 0.607238 / 1.386936 (-0.779698) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n" ]
2023-11-15T18:00:11
2023-12-13T14:22:30
2023-12-13T14:16:00
MEMBER
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false
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When building/releasing conda package, we get this deprecation warning: ``` /usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead. ``` This PR fixes the deprecation warning by using `conda build` instead.
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005323 / 0.011353 (-0.006030) | 0.003560 / 0.011008 (-0.007448) | 0.062572 / 0.038508 (0.024064) | 0.049549 / 0.023109 (0.026440) | 0.236522 / 0.275898 (-0.039376) | 0.260601 / 0.323480 (-0.062879) | 0.002887 / 0.007986 (-0.005099) | 0.003225 / 0.004328 (-0.001103) | 0.048210 / 0.004250 (0.043960) | 0.038783 / 0.037052 (0.001731) | 0.242506 / 0.258489 (-0.015983) | 0.273906 / 0.293841 (-0.019935) | 0.027202 / 0.128546 (-0.101344) | 0.010577 / 0.075646 (-0.065069) | 0.211669 / 0.419271 (-0.207603) | 0.035727 / 0.043533 (-0.007806) | 0.242303 / 0.255139 (-0.012836) | 0.260468 / 0.283200 (-0.022732) | 0.020109 / 0.141683 (-0.121573) | 1.089603 / 1.452155 (-0.362552) | 1.149899 / 1.492716 (-0.342817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088768 / 0.018006 (0.070761) | 0.300300 / 0.000490 (0.299810) | 0.000212 / 0.000200 (0.000013) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018758 / 0.037411 (-0.018653) | 0.060097 / 0.014526 (0.045571) | 0.074060 / 0.176557 (-0.102496) | 0.119977 / 0.737135 (-0.617158) | 0.075298 / 0.296338 (-0.221040) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278640 / 0.215209 (0.063431) | 2.715574 / 2.077655 (0.637919) | 1.466644 / 1.504120 (-0.037476) | 1.344470 / 1.541195 (-0.196725) | 1.386984 / 1.468490 (-0.081506) | 0.575796 / 4.584777 (-4.008981) | 2.392324 / 3.745712 (-1.353388) | 2.826284 / 5.269862 (-2.443578) | 1.758997 / 4.565676 (-2.806679) | 0.062474 / 0.424275 (-0.361801) | 0.004930 / 0.007607 (-0.002678) | 0.332595 / 0.226044 (0.106551) | 3.240076 / 2.268929 (0.971147) | 1.785283 / 55.444624 (-53.659341) | 1.527594 / 6.876477 (-5.348882) | 1.562840 / 2.142072 (-0.579233) | 0.655474 / 4.805227 (-4.149754) | 0.116682 / 6.500664 (-6.383983) | 0.042664 / 0.075469 (-0.032805) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936306 / 1.841788 (-0.905481) | 11.561239 / 8.074308 (3.486931) | 10.341918 / 10.191392 (0.150526) | 0.140602 / 0.680424 (-0.539822) | 0.013857 / 0.534201 (-0.520344) | 0.294241 / 0.579283 (-0.285042) | 0.268359 / 0.434364 (-0.166005) | 0.326344 / 0.540337 (-0.213993) | 0.430936 / 1.386936 (-0.956000) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005197 / 0.011353 (-0.006156) | 0.003543 / 0.011008 (-0.007465) | 0.049051 / 0.038508 (0.010542) | 0.052742 / 0.023109 (0.029633) | 0.277032 / 0.275898 (0.001134) | 0.300799 / 0.323480 (-0.022681) | 0.003922 / 0.007986 (-0.004064) | 0.002573 / 0.004328 (-0.001755) | 0.047270 / 0.004250 (0.043019) | 0.039782 / 0.037052 (0.002730) | 0.282780 / 0.258489 (0.024291) | 0.308858 / 0.293841 (0.015017) | 0.028641 / 0.128546 (-0.099905) | 0.010516 / 0.075646 (-0.065131) | 0.056367 / 0.419271 (-0.362904) | 0.032346 / 0.043533 (-0.011186) | 0.277591 / 0.255139 (0.022452) | 0.298539 / 0.283200 (0.015339) | 0.018168 / 0.141683 (-0.123515) | 1.104331 / 1.452155 (-0.347823) | 1.187691 / 1.492716 (-0.305025) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089511 / 0.018006 (0.071505) | 0.301309 / 0.000490 (0.300820) | 0.000213 / 0.000200 (0.000013) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021466 / 0.037411 (-0.015945) | 0.069917 / 0.014526 (0.055391) | 0.081105 / 0.176557 (-0.095452) | 0.119619 / 0.737135 (-0.617516) | 0.083928 / 0.296338 (-0.212410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296471 / 0.215209 (0.081262) | 2.912139 / 2.077655 (0.834484) | 1.588861 / 1.504120 (0.084741) | 1.452148 / 1.541195 (-0.089047) | 1.475388 / 1.468490 (0.006898) | 0.555779 / 4.584777 (-4.028998) | 2.425599 / 3.745712 (-1.320113) | 2.792848 / 5.269862 (-2.477013) | 1.718757 / 4.565676 (-2.846919) | 0.077687 / 0.424275 (-0.346588) | 0.007522 / 0.007607 (-0.000085) | 0.348254 / 0.226044 (0.122210) | 3.439315 / 2.268929 (1.170386) | 1.925907 / 55.444624 (-53.518717) | 1.646163 / 6.876477 (-5.230314) | 1.662148 / 2.142072 (-0.479924) | 0.637277 / 4.805227 (-4.167950) | 0.116159 / 6.500664 (-6.384505) | 0.041518 / 0.075469 (-0.033952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966358 / 1.841788 (-0.875430) | 12.125201 / 8.074308 (4.050892) | 10.629939 / 10.191392 (0.438547) | 0.132439 / 0.680424 (-0.547984) | 0.015622 / 0.534201 (-0.518579) | 0.288824 / 0.579283 (-0.290459) | 0.277634 / 0.434364 (-0.156730) | 0.327200 / 0.540337 (-0.213138) | 0.549679 / 1.386936 (-0.837257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f \"CML watermark\")\n" ]
2023-11-15T17:28:14
2023-11-30T17:29:55
2023-11-30T17:23:46
CONTRIBUTOR
null
false
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Hi all! This is a PR adding a troubleshooting guide for Datasets docs. I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are: - creating a dataset from a folder and not following the required format - authentication issues when using `push_to_hub` - `Too Many Requests` with `push_to_hub` - Pickling issues when using Dataset.from_generator() There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.
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PR_kwDODunzps5fhzD6
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Fix conda release by adding pyarrow-hotfix dependency
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004476 / 0.011353 (-0.006877) | 0.002691 / 0.011008 (-0.008317) | 0.061400 / 0.038508 (0.022892) | 0.030096 / 0.023109 (0.006986) | 0.279868 / 0.275898 (0.003970) | 0.310320 / 0.323480 (-0.013159) | 0.003873 / 0.007986 (-0.004112) | 0.002394 / 0.004328 (-0.001935) | 0.048307 / 0.004250 (0.044056) | 0.043326 / 0.037052 (0.006273) | 0.288256 / 0.258489 (0.029767) | 0.311449 / 0.293841 (0.017609) | 0.022970 / 0.128546 (-0.105576) | 0.006714 / 0.075646 (-0.068932) | 0.201656 / 0.419271 (-0.217615) | 0.052811 / 0.043533 (0.009278) | 0.285123 / 0.255139 (0.029984) | 0.301495 / 0.283200 (0.018295) | 0.017531 / 0.141683 (-0.124152) | 1.097660 / 1.452155 (-0.354494) | 1.161986 / 1.492716 (-0.330731) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089223 / 0.018006 (0.071217) | 0.297815 / 0.000490 (0.297326) | 0.000205 / 0.000200 (0.000005) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018679 / 0.037411 (-0.018732) | 0.062742 / 0.014526 (0.048216) | 0.072869 / 0.176557 (-0.103687) | 0.120730 / 0.737135 (-0.616406) | 0.074526 / 0.296338 (-0.221813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299977 / 0.215209 (0.084768) | 2.921029 / 2.077655 (0.843375) | 1.632283 / 1.504120 (0.128163) | 1.508008 / 1.541195 (-0.033187) | 1.513967 / 1.468490 (0.045477) | 0.403056 / 4.584777 (-4.181721) | 2.340011 / 3.745712 (-1.405701) | 2.552319 / 5.269862 (-2.717543) | 1.549741 / 4.565676 (-3.015935) | 0.046303 / 0.424275 (-0.377972) | 0.004768 / 0.007607 (-0.002839) | 0.356921 / 0.226044 (0.130877) | 3.506410 / 2.268929 (1.237482) | 1.975394 / 55.444624 (-53.469230) | 1.688683 / 6.876477 (-5.187794) | 1.715502 / 2.142072 (-0.426571) | 0.471016 / 4.805227 (-4.334212) | 0.099552 / 6.500664 (-6.401112) | 0.042095 / 0.075469 (-0.033374) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.955784 / 1.841788 (-0.886004) | 11.191802 / 8.074308 (3.117494) | 10.127818 / 10.191392 (-0.063574) | 0.141225 / 0.680424 (-0.539199) | 0.014486 / 0.534201 (-0.519715) | 0.267204 / 0.579283 (-0.312079) | 0.289108 / 0.434364 (-0.145256) | 0.309458 / 0.540337 (-0.230880) | 0.422802 / 1.386936 (-0.964134) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004797 / 0.011353 (-0.006556) | 0.002907 / 0.011008 (-0.008101) | 0.047666 / 0.038508 (0.009158) | 0.051183 / 0.023109 (0.028074) | 0.266315 / 0.275898 (-0.009583) | 0.286429 / 0.323480 (-0.037051) | 0.003954 / 0.007986 (-0.004031) | 0.002041 / 0.004328 (-0.002288) | 0.047652 / 0.004250 (0.043401) | 0.038211 / 0.037052 (0.001158) | 0.272210 / 0.258489 (0.013721) | 0.299425 / 0.293841 (0.005584) | 0.024266 / 0.128546 (-0.104280) | 0.006747 / 0.075646 (-0.068900) | 0.052959 / 0.419271 (-0.366312) | 0.032094 / 0.043533 (-0.011439) | 0.265677 / 0.255139 (0.010538) | 0.285373 / 0.283200 (0.002174) | 0.017577 / 0.141683 (-0.124106) | 1.114514 / 1.452155 (-0.337640) | 1.212970 / 1.492716 (-0.279746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088347 / 0.018006 (0.070341) | 0.296678 / 0.000490 (0.296188) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021159 / 0.037411 (-0.016253) | 0.069886 / 0.014526 (0.055360) | 0.079832 / 0.176557 (-0.096725) | 0.115512 / 0.737135 (-0.621623) | 0.081600 / 0.296338 (-0.214739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292659 / 0.215209 (0.077450) | 2.872556 / 2.077655 (0.794901) | 1.573017 / 1.504120 (0.068897) | 1.445122 / 1.541195 (-0.096072) | 1.485584 / 1.468490 (0.017094) | 0.388638 / 4.584777 (-4.196139) | 2.434847 / 3.745712 (-1.310865) | 2.518167 / 5.269862 (-2.751695) | 1.503000 / 4.565676 (-3.062676) | 0.045123 / 0.424275 (-0.379153) | 0.004778 / 0.007607 (-0.002829) | 0.347955 / 0.226044 (0.121910) | 3.384819 / 2.268929 (1.115891) | 1.920185 / 55.444624 (-53.524439) | 1.646910 / 6.876477 (-5.229567) | 1.638092 / 2.142072 (-0.503980) | 0.450535 / 4.805227 (-4.354692) | 0.095301 / 6.500664 (-6.405363) | 0.040275 / 0.075469 (-0.035194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956088 / 1.841788 (-0.885700) | 11.776642 / 8.074308 (3.702334) | 10.651063 / 10.191392 (0.459671) | 0.127079 / 0.680424 (-0.553345) | 0.015080 / 0.534201 (-0.519121) | 0.273737 / 0.579283 (-0.305546) | 0.271434 / 0.434364 (-0.162929) | 0.308448 / 0.540337 (-0.231889) | 0.412467 / 1.386936 (-0.974469) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af014830363401a0166a2b8435ca2f863cb468d4 \"CML watermark\")\n", "Once this PR is merged, we should upload the missing version to conda.\r\n\r\n@lhoestq you did this in the past. If you tell me your approach (I see a tag called `VERSION`...), I could do it myself.", "Maybe open a PR against the 2.14 branch and update `release-conda.yml` like this ?\r\n\r\n```diff\r\n- on:\r\n- push:\r\n- tags:\r\n- - \"[0-9]+.[0-9]+.[0-9]+*\"\r\n+ on: push\r\n```\r\n\r\nand then set it back to normal after the release is done", "After having cherry-picked the commit in this PR, I have released the conda package. See: \r\n- https://github.com/huggingface/datasets/actions/runs/6880182419/job/18713812449\r\n- https://anaconda.org/HuggingFace/datasets/files?version=2.14.7\r\n\r\nI am merging this PR.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004993 / 0.011353 (-0.006360) | 0.002964 / 0.011008 (-0.008044) | 0.062588 / 0.038508 (0.024080) | 0.030794 / 0.023109 (0.007685) | 0.234856 / 0.275898 (-0.041042) | 0.264807 / 0.323480 (-0.058673) | 0.003139 / 0.007986 (-0.004847) | 0.002498 / 0.004328 (-0.001831) | 0.048058 / 0.004250 (0.043807) | 0.048349 / 0.037052 (0.011296) | 0.238210 / 0.258489 (-0.020279) | 0.278144 / 0.293841 (-0.015697) | 0.023219 / 0.128546 (-0.105327) | 0.007296 / 0.075646 (-0.068351) | 0.203263 / 0.419271 (-0.216008) | 0.058844 / 0.043533 (0.015311) | 0.246330 / 0.255139 (-0.008809) | 0.264550 / 0.283200 (-0.018649) | 0.018580 / 0.141683 (-0.123103) | 1.084163 / 1.452155 (-0.367992) | 1.154891 / 1.492716 (-0.337825) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092393 / 0.018006 (0.074387) | 0.300545 / 0.000490 (0.300055) | 0.000203 / 0.000200 (0.000003) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018648 / 0.037411 (-0.018763) | 0.063151 / 0.014526 (0.048625) | 0.074206 / 0.176557 (-0.102350) | 0.120929 / 0.737135 (-0.616207) | 0.075970 / 0.296338 (-0.220368) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278489 / 0.215209 (0.063279) | 2.664804 / 2.077655 (0.587150) | 1.433040 / 1.504120 (-0.071080) | 1.321416 / 1.541195 (-0.219779) | 1.320964 / 1.468490 (-0.147526) | 0.401289 / 4.584777 (-4.183488) | 2.365310 / 3.745712 (-1.380402) | 2.635798 / 5.269862 (-2.634063) | 1.584384 / 4.565676 (-2.981293) | 0.045675 / 0.424275 (-0.378600) | 0.004854 / 0.007607 (-0.002753) | 0.337592 / 0.226044 (0.111548) | 3.330462 / 2.268929 (1.061534) | 1.794507 / 55.444624 (-53.650117) | 1.531284 / 6.876477 (-5.345193) | 1.507165 / 2.142072 (-0.634908) | 0.478622 / 4.805227 (-4.326606) | 0.099105 / 6.500664 (-6.401560) | 0.041575 / 0.075469 (-0.033894) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941790 / 1.841788 (-0.899997) | 11.609871 / 8.074308 (3.535563) | 10.770869 / 10.191392 (0.579477) | 0.138931 / 0.680424 (-0.541493) | 0.014406 / 0.534201 (-0.519795) | 0.269681 / 0.579283 (-0.309602) | 0.260556 / 0.434364 (-0.173808) | 0.308244 / 0.540337 (-0.232093) | 0.428867 / 1.386936 (-0.958069) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004803 / 0.011353 (-0.006550) | 0.003263 / 0.011008 (-0.007745) | 0.049143 / 0.038508 (0.010635) | 0.052033 / 0.023109 (0.028924) | 0.267815 / 0.275898 (-0.008083) | 0.288733 / 0.323480 (-0.034747) | 0.004159 / 0.007986 (-0.003826) | 0.002407 / 0.004328 (-0.001921) | 0.048978 / 0.004250 (0.044728) | 0.038994 / 0.037052 (0.001942) | 0.264028 / 0.258489 (0.005539) | 0.303930 / 0.293841 (0.010090) | 0.024283 / 0.128546 (-0.104263) | 0.007201 / 0.075646 (-0.068446) | 0.053810 / 0.419271 (-0.365461) | 0.032611 / 0.043533 (-0.010922) | 0.266730 / 0.255139 (0.011591) | 0.281564 / 0.283200 (-0.001635) | 0.018720 / 0.141683 (-0.122963) | 1.140676 / 1.452155 (-0.311479) | 1.206604 / 1.492716 (-0.286113) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.109390 / 0.018006 (0.091384) | 0.313783 / 0.000490 (0.313294) | 0.000228 / 0.000200 (0.000028) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021228 / 0.037411 (-0.016183) | 0.070505 / 0.014526 (0.055979) | 0.081961 / 0.176557 (-0.094595) | 0.119943 / 0.737135 (-0.617193) | 0.083582 / 0.296338 (-0.212757) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295702 / 0.215209 (0.080493) | 2.886865 / 2.077655 (0.809210) | 1.583206 / 1.504120 (0.079086) | 1.451129 / 1.541195 (-0.090065) | 1.486253 / 1.468490 (0.017763) | 0.403207 / 4.584777 (-4.181570) | 2.408889 / 3.745712 (-1.336824) | 2.578480 / 5.269862 (-2.691381) | 1.533066 / 4.565676 (-3.032610) | 0.046075 / 0.424275 (-0.378200) | 0.004877 / 0.007607 (-0.002730) | 0.345995 / 0.226044 (0.119950) | 3.377039 / 2.268929 (1.108110) | 1.944614 / 55.444624 (-53.500010) | 1.677691 / 6.876477 (-5.198786) | 1.672828 / 2.142072 (-0.469244) | 0.468426 / 4.805227 (-4.336802) | 0.097290 / 6.500664 (-6.403374) | 0.040695 / 0.075469 (-0.034774) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965778 / 1.841788 (-0.876010) | 12.092639 / 8.074308 (4.018331) | 11.210968 / 10.191392 (1.019576) | 0.131212 / 0.680424 (-0.549212) | 0.015865 / 0.534201 (-0.518336) | 0.285702 / 0.579283 (-0.293581) | 0.278319 / 0.434364 (-0.156045) | 0.336063 / 0.540337 (-0.204275) | 0.426265 / 1.386936 (-0.960671) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d122b3ddc67705cc2b622bcbd79de9ff943a5742 \"CML watermark\")\n" ]
2023-11-15T14:57:12
2023-11-15T17:15:33
2023-11-15T17:09:24
MEMBER
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Fix conda release by adding pyarrow-hotfix dependency. Note that conda release failed in latest 2.14.7 release: https://github.com/huggingface/datasets/actions/runs/6874667214/job/18696761723 ``` Traceback (most recent call last): File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/test_tmp/run_test.py", line 2, in <module> import datasets File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 67, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/__init__.py", line 18, in <module> from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/features.py", line 34, in <module> import pyarrow_hotfix # noqa: F401 # to fix vulnerability on pyarrow<14.0.1 ^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'pyarrow_hotfix' ```
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I_kwDODunzps524t1D
6,422
Allow to choose the `writer_batch_size` when using `save_to_disk`
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[ "We have a config variable that controls the batch size in `save_to_disk`:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = <smaller_batch_size>\r\n...\r\nds.save_to_disk(...)\r\n```", "Thank you for your answer!\r\n\r\nFrom what I am reading in `https://github.com/huggingface/datasets/blob/2.14.5/src/datasets/arrow_dataset.py`, every function involved (`select`, `shard`, ...) has a default hardcoded batch size of 1000, as such:\r\n```python\r\ndef select(\r\n self,\r\n indices: Iterable,\r\n keep_in_memory: bool = False,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n...\r\n```\r\nThen, `ArrowWriter` is instantiated with the specified `writer_batch_size`. In `ArrowWriter`, `writer_batch_size` is set to `datasets.config.DEFAULT_MAX_BATCH_SIZE` if it is `None`(https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L345C14-L345C31). However, in our case, it is already set to 1000 by \"parent\" methods, so it won't happen.\r\n\r\nNevertheless, due to this: \r\n```python\r\ndef _save_to_disk_single(job_id: int, shard: \"Dataset\", fpath: str, storage_options: Optional[dict]):\r\n batch_size = config.DEFAULT_MAX_BATCH_SIZE\r\n...\r\n```\r\nit seems to work. I will use it as such, but it should maybe be added to documentation? And maybe improved in next versions?" ]
2023-11-15T11:18:34
2023-11-16T10:00:21
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### Feature request Add an argument in `save_to_disk` regarding batch size, which would be passed to `shard` and other methods. ### Motivation The `Dataset.save_to_disk` method currently calls `shard` without passing a `writer_batch_size` argument, thus implicitly using the default value (1000). This can result in RAM saturation when using a lot of processes on long text sequences or other modalities, or for specific IO configs. ### Your contribution I would be glad to submit a PR, as long as it does not imply extensive tests refactoring.
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1,994,451,553
PR_kwDODunzps5fgG1h
6,421
Add pyarrow-hotfix to release docs
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004755 / 0.011353 (-0.006598) | 0.002683 / 0.011008 (-0.008325) | 0.061701 / 0.038508 (0.023193) | 0.030123 / 0.023109 (0.007013) | 0.238186 / 0.275898 (-0.037712) | 0.266570 / 0.323480 (-0.056910) | 0.002898 / 0.007986 (-0.005088) | 0.002381 / 0.004328 (-0.001948) | 0.048033 / 0.004250 (0.043782) | 0.044529 / 0.037052 (0.007477) | 0.246728 / 0.258489 (-0.011761) | 0.302066 / 0.293841 (0.008225) | 0.024008 / 0.128546 (-0.104539) | 0.006626 / 0.075646 (-0.069020) | 0.202000 / 0.419271 (-0.217272) | 0.056492 / 0.043533 (0.012959) | 0.243417 / 0.255139 (-0.011722) | 0.263947 / 0.283200 (-0.019253) | 0.020481 / 0.141683 (-0.121202) | 1.130635 / 1.452155 (-0.321520) | 1.180570 / 1.492716 (-0.312146) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095541 / 0.018006 (0.077535) | 0.306152 / 0.000490 (0.305662) | 0.000217 / 0.000200 (0.000017) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018593 / 0.037411 (-0.018818) | 0.063029 / 0.014526 (0.048503) | 0.074312 / 0.176557 (-0.102245) | 0.119882 / 0.737135 (-0.617254) | 0.074066 / 0.296338 (-0.222273) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275409 / 0.215209 (0.060200) | 2.727061 / 2.077655 (0.649407) | 1.415632 / 1.504120 (-0.088488) | 1.294922 / 1.541195 (-0.246273) | 1.341636 / 1.468490 (-0.126854) | 0.403250 / 4.584777 (-4.181527) | 2.384657 / 3.745712 (-1.361055) | 2.604131 / 5.269862 (-2.665731) | 1.558888 / 4.565676 (-3.006789) | 0.046008 / 0.424275 (-0.378267) | 0.004819 / 0.007607 (-0.002789) | 0.331046 / 0.226044 (0.105002) | 3.340950 / 2.268929 (1.072021) | 1.801077 / 55.444624 (-53.643548) | 1.479162 / 6.876477 (-5.397315) | 1.503713 / 2.142072 (-0.638359) | 0.474931 / 4.805227 (-4.330296) | 0.101869 / 6.500664 (-6.398795) | 0.041946 / 0.075469 (-0.033523) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.955641 / 1.841788 (-0.886147) | 11.441032 / 8.074308 (3.366724) | 10.267731 / 10.191392 (0.076339) | 0.128735 / 0.680424 (-0.551689) | 0.013942 / 0.534201 (-0.520259) | 0.266620 / 0.579283 (-0.312663) | 0.262334 / 0.434364 (-0.172029) | 0.302713 / 0.540337 (-0.237624) | 0.430323 / 1.386936 (-0.956613) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004670 / 0.011353 (-0.006683) | 0.002671 / 0.011008 (-0.008338) | 0.048949 / 0.038508 (0.010441) | 0.052520 / 0.023109 (0.029411) | 0.272614 / 0.275898 (-0.003284) | 0.292618 / 0.323480 (-0.030862) | 0.004016 / 0.007986 (-0.003969) | 0.002430 / 0.004328 (-0.001899) | 0.048313 / 0.004250 (0.044063) | 0.038647 / 0.037052 (0.001595) | 0.279893 / 0.258489 (0.021404) | 0.305371 / 0.293841 (0.011530) | 0.023710 / 0.128546 (-0.104836) | 0.006999 / 0.075646 (-0.068648) | 0.053315 / 0.419271 (-0.365956) | 0.032417 / 0.043533 (-0.011115) | 0.272066 / 0.255139 (0.016927) | 0.291717 / 0.283200 (0.008518) | 0.018127 / 0.141683 (-0.123556) | 1.173611 / 1.452155 (-0.278544) | 1.183659 / 1.492716 (-0.309057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094831 / 0.018006 (0.076824) | 0.304911 / 0.000490 (0.304421) | 0.000225 / 0.000200 (0.000025) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020948 / 0.037411 (-0.016463) | 0.070255 / 0.014526 (0.055729) | 0.081371 / 0.176557 (-0.095186) | 0.118932 / 0.737135 (-0.618203) | 0.082207 / 0.296338 (-0.214132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294067 / 0.215209 (0.078858) | 2.856981 / 2.077655 (0.779326) | 1.598392 / 1.504120 (0.094273) | 1.479093 / 1.541195 (-0.062102) | 1.509495 / 1.468490 (0.041005) | 0.396303 / 4.584777 (-4.188473) | 2.429077 / 3.745712 (-1.316635) | 2.525037 / 5.269862 (-2.744824) | 1.503332 / 4.565676 (-3.062345) | 0.046191 / 0.424275 (-0.378084) | 0.004858 / 0.007607 (-0.002750) | 0.349528 / 0.226044 (0.123484) | 3.401451 / 2.268929 (1.132522) | 1.989613 / 55.444624 (-53.455012) | 1.664528 / 6.876477 (-5.211949) | 1.669076 / 2.142072 (-0.472997) | 0.467090 / 4.805227 (-4.338137) | 0.098137 / 6.500664 (-6.402527) | 0.040448 / 0.075469 (-0.035021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969578 / 1.841788 (-0.872210) | 12.064705 / 8.074308 (3.990396) | 10.991438 / 10.191392 (0.800046) | 0.130149 / 0.680424 (-0.550275) | 0.015357 / 0.534201 (-0.518844) | 0.266567 / 0.579283 (-0.312717) | 0.270619 / 0.434364 (-0.163744) | 0.305978 / 0.540337 (-0.234359) | 0.411164 / 1.386936 (-0.975772) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#86a2cf3174c55899535ee5f1707892a430ee53bc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009810 / 0.011353 (-0.001543) | 0.005411 / 0.011008 (-0.005598) | 0.111670 / 0.038508 (0.073162) | 0.050288 / 0.023109 (0.027179) | 0.415625 / 0.275898 (0.139727) | 0.479382 / 0.323480 (0.155902) | 0.005104 / 0.007986 (-0.002882) | 0.007122 / 0.004328 (0.002793) | 0.079626 / 0.004250 (0.075375) | 0.079421 / 0.037052 (0.042369) | 0.406722 / 0.258489 (0.148233) | 0.461511 / 0.293841 (0.167670) | 0.053812 / 0.128546 (-0.074734) | 0.014315 / 0.075646 (-0.061331) | 0.389636 / 0.419271 (-0.029636) | 0.111859 / 0.043533 (0.068326) | 0.411703 / 0.255139 (0.156564) | 0.457072 / 0.283200 (0.173872) | 0.039807 / 0.141683 (-0.101876) | 1.744064 / 1.452155 (0.291909) | 1.968321 / 1.492716 (0.475604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.341839 / 0.018006 (0.323833) | 0.628083 / 0.000490 (0.627593) | 0.023787 / 0.000200 (0.023587) | 0.000601 / 0.000054 (0.000547) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034170 / 0.037411 (-0.003241) | 0.091159 / 0.014526 (0.076633) | 0.108993 / 0.176557 (-0.067563) | 0.186906 / 0.737135 (-0.550229) | 0.109753 / 0.296338 (-0.186586) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.684138 / 0.215209 (0.468929) | 6.634852 / 2.077655 (4.557198) | 3.102870 / 1.504120 (1.598750) | 2.831023 / 1.541195 (1.289828) | 2.831597 / 1.468490 (1.363107) | 0.903584 / 4.584777 (-3.681193) | 5.503341 / 3.745712 (1.757629) | 4.970283 / 5.269862 (-0.299579) | 3.139413 / 4.565676 (-1.426264) | 0.109848 / 0.424275 (-0.314427) | 0.008501 / 0.007607 (0.000894) | 0.823815 / 0.226044 (0.597770) | 7.963355 / 2.268929 (5.694426) | 4.002010 / 55.444624 (-51.442614) | 3.229390 / 6.876477 (-3.647087) | 3.166413 / 2.142072 (1.024341) | 1.030313 / 4.805227 (-3.774914) | 0.219394 / 6.500664 (-6.281270) | 0.077760 / 0.075469 (0.002291) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.580309 / 1.841788 (-0.261479) | 24.279185 / 8.074308 (16.204877) | 22.305293 / 10.191392 (12.113901) | 0.235711 / 0.680424 (-0.444713) | 0.030342 / 0.534201 (-0.503859) | 0.498137 / 0.579283 (-0.081146) | 0.619173 / 0.434364 (0.184809) | 0.529904 / 0.540337 (-0.010434) | 0.822547 / 1.386936 (-0.564389) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009375 / 0.011353 (-0.001978) | 0.006009 / 0.011008 (-0.004999) | 0.074080 / 0.038508 (0.035572) | 0.089454 / 0.023109 (0.066345) | 0.473458 / 0.275898 (0.197560) | 0.462558 / 0.323480 (0.139078) | 0.006415 / 0.007986 (-0.001571) | 0.004777 / 0.004328 (0.000448) | 0.076563 / 0.004250 (0.072313) | 0.062793 / 0.037052 (0.025741) | 0.455860 / 0.258489 (0.197371) | 0.485281 / 0.293841 (0.191440) | 0.052966 / 0.128546 (-0.075580) | 0.021600 / 0.075646 (-0.054046) | 0.090407 / 0.419271 (-0.328864) | 0.063951 / 0.043533 (0.020418) | 0.487561 / 0.255139 (0.232422) | 0.479958 / 0.283200 (0.196758) | 0.039263 / 0.141683 (-0.102420) | 1.727215 / 1.452155 (0.275061) | 1.962039 / 1.492716 (0.469323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296267 / 0.018006 (0.278261) | 0.604982 / 0.000490 (0.604493) | 0.007842 / 0.000200 (0.007642) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034317 / 0.037411 (-0.003094) | 0.097796 / 0.014526 (0.083270) | 0.126034 / 0.176557 (-0.050522) | 0.180873 / 0.737135 (-0.556262) | 0.125410 / 0.296338 (-0.170928) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.608278 / 0.215209 (0.393069) | 6.154006 / 2.077655 (4.076351) | 2.822342 / 1.504120 (1.318222) | 2.568263 / 1.541195 (1.027068) | 2.518545 / 1.468490 (1.050055) | 0.863186 / 4.584777 (-3.721591) | 5.367969 / 3.745712 (1.622257) | 4.737691 / 5.269862 (-0.532170) | 2.917620 / 4.565676 (-1.648056) | 0.100731 / 0.424275 (-0.323544) | 0.008611 / 0.007607 (0.001004) | 0.735523 / 0.226044 (0.509479) | 7.552790 / 2.268929 (5.283862) | 3.821835 / 55.444624 (-51.622789) | 2.878259 / 6.876477 (-3.998217) | 2.957686 / 2.142072 (0.815613) | 0.964630 / 4.805227 (-3.840598) | 0.207098 / 6.500664 (-6.293566) | 0.084215 / 0.075469 (0.008746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.711020 / 1.841788 (-0.130768) | 24.034122 / 8.074308 (15.959814) | 21.378504 / 10.191392 (11.187112) | 0.233433 / 0.680424 (-0.446990) | 0.037214 / 0.534201 (-0.496987) | 0.511952 / 0.579283 (-0.067332) | 0.591486 / 0.434364 (0.157123) | 0.606549 / 0.540337 (0.066211) | 0.833773 / 1.386936 (-0.553163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#671f9b32fc559a35996c1b9070fad1a2647a7fef \"CML watermark\")\n" ]
2023-11-15T10:06:44
2023-11-15T13:49:55
2023-11-15T13:38:22
MEMBER
null
false
{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6421", "html_url": "https://github.com/huggingface/datasets/pull/6421", "diff_url": "https://github.com/huggingface/datasets/pull/6421.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6421.patch", "merged_at": "2023-11-15T13:38:22" }
Add `pyarrow-hotfix` to release docs.
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6420). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004536 / 0.011353 (-0.006816) | 0.002979 / 0.011008 (-0.008030) | 0.061984 / 0.038508 (0.023476) | 0.029382 / 0.023109 (0.006273) | 0.245237 / 0.275898 (-0.030661) | 0.270571 / 0.323480 (-0.052909) | 0.003956 / 0.007986 (-0.004029) | 0.002453 / 0.004328 (-0.001876) | 0.047967 / 0.004250 (0.043717) | 0.043695 / 0.037052 (0.006643) | 0.248457 / 0.258489 (-0.010032) | 0.283293 / 0.293841 (-0.010548) | 0.023603 / 0.128546 (-0.104943) | 0.007225 / 0.075646 (-0.068422) | 0.200533 / 0.419271 (-0.218739) | 0.055310 / 0.043533 (0.011777) | 0.245152 / 0.255139 (-0.009987) | 0.267187 / 0.283200 (-0.016012) | 0.018158 / 0.141683 (-0.123525) | 1.126079 / 1.452155 (-0.326075) | 1.185137 / 1.492716 (-0.307580) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092436 / 0.018006 (0.074430) | 0.300132 / 0.000490 (0.299642) | 0.000206 / 0.000200 (0.000006) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018476 / 0.037411 (-0.018935) | 0.062827 / 0.014526 (0.048301) | 0.074605 / 0.176557 (-0.101952) | 0.119768 / 0.737135 (-0.617368) | 0.076044 / 0.296338 (-0.220294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279717 / 0.215209 (0.064508) | 2.752308 / 2.077655 (0.674654) | 1.434954 / 1.504120 (-0.069166) | 1.314700 / 1.541195 (-0.226495) | 1.347689 / 1.468490 (-0.120802) | 0.400332 / 4.584777 (-4.184445) | 2.383024 / 3.745712 (-1.362689) | 2.583130 / 5.269862 (-2.686732) | 1.567670 / 4.565676 (-2.998007) | 0.045446 / 0.424275 (-0.378829) | 0.004813 / 0.007607 (-0.002794) | 0.336191 / 0.226044 (0.110147) | 3.319837 / 2.268929 (1.050909) | 1.816808 / 55.444624 (-53.627817) | 1.539052 / 6.876477 (-5.337424) | 1.550765 / 2.142072 (-0.591307) | 0.484253 / 4.805227 (-4.320974) | 0.100494 / 6.500664 (-6.400170) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940857 / 1.841788 (-0.900931) | 11.784946 / 8.074308 (3.710638) | 10.397038 / 10.191392 (0.205646) | 0.141458 / 0.680424 (-0.538965) | 0.014193 / 0.534201 (-0.520008) | 0.268304 / 0.579283 (-0.310979) | 0.267059 / 0.434364 (-0.167305) | 0.309389 / 0.540337 (-0.230949) | 0.420628 / 1.386936 (-0.966308) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004776 / 0.011353 (-0.006577) | 0.002941 / 0.011008 (-0.008067) | 0.048659 / 0.038508 (0.010151) | 0.053334 / 0.023109 (0.030225) | 0.273342 / 0.275898 (-0.002556) | 0.302278 / 0.323480 (-0.021202) | 0.004001 / 0.007986 (-0.003984) | 0.002414 / 0.004328 (-0.001914) | 0.047504 / 0.004250 (0.043254) | 0.038581 / 0.037052 (0.001529) | 0.277768 / 0.258489 (0.019279) | 0.306772 / 0.293841 (0.012931) | 0.024146 / 0.128546 (-0.104400) | 0.007233 / 0.075646 (-0.068413) | 0.053308 / 0.419271 (-0.365964) | 0.032617 / 0.043533 (-0.010916) | 0.277390 / 0.255139 (0.022251) | 0.296015 / 0.283200 (0.012816) | 0.018733 / 0.141683 (-0.122950) | 1.124895 / 1.452155 (-0.327260) | 1.182579 / 1.492716 (-0.310137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093375 / 0.018006 (0.075369) | 0.301555 / 0.000490 (0.301066) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021284 / 0.037411 (-0.016127) | 0.070158 / 0.014526 (0.055632) | 0.080187 / 0.176557 (-0.096370) | 0.119282 / 0.737135 (-0.617854) | 0.081672 / 0.296338 (-0.214666) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.314396 / 0.215209 (0.099187) | 2.975114 / 2.077655 (0.897459) | 1.724658 / 1.504120 (0.220539) | 1.604464 / 1.541195 (0.063269) | 1.652736 / 1.468490 (0.184246) | 0.395064 / 4.584777 (-4.189713) | 2.412768 / 3.745712 (-1.332944) | 2.564427 / 5.269862 (-2.705435) | 1.507627 / 4.565676 (-3.058050) | 0.045463 / 0.424275 (-0.378812) | 0.004797 / 0.007607 (-0.002810) | 0.383115 / 0.226044 (0.157071) | 3.501976 / 2.268929 (1.233048) | 2.087512 / 55.444624 (-53.357113) | 1.793132 / 6.876477 (-5.083345) | 1.804178 / 2.142072 (-0.337895) | 0.468287 / 4.805227 (-4.336940) | 0.097247 / 6.500664 (-6.403417) | 0.041139 / 0.075469 (-0.034330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976034 / 1.841788 (-0.865754) | 12.431248 / 8.074308 (4.356940) | 10.896064 / 10.191392 (0.704672) | 0.129137 / 0.680424 (-0.551287) | 0.015636 / 0.534201 (-0.518565) | 0.268219 / 0.579283 (-0.311064) | 0.278345 / 0.434364 (-0.156019) | 0.302696 / 0.540337 (-0.237642) | 0.408465 / 1.386936 (-0.978471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51c53e94acd7a273c24899c045446df021314cd2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007703 / 0.011353 (-0.003650) | 0.004614 / 0.011008 (-0.006394) | 0.101425 / 0.038508 (0.062917) | 0.040122 / 0.023109 (0.017013) | 0.398890 / 0.275898 (0.122992) | 0.424392 / 0.323480 (0.100912) | 0.005411 / 0.007986 (-0.002575) | 0.003747 / 0.004328 (-0.000582) | 0.080494 / 0.004250 (0.076243) | 0.059392 / 0.037052 (0.022340) | 0.398025 / 0.258489 (0.139536) | 0.454293 / 0.293841 (0.160452) | 0.043662 / 0.128546 (-0.084884) | 0.013726 / 0.075646 (-0.061920) | 0.352910 / 0.419271 (-0.066362) | 0.088572 / 0.043533 (0.045039) | 0.401677 / 0.255139 (0.146538) | 0.421774 / 0.283200 (0.138575) | 0.033377 / 0.141683 (-0.108305) | 1.728499 / 1.452155 (0.276344) | 1.821557 / 1.492716 (0.328841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230744 / 0.018006 (0.212738) | 0.496188 / 0.000490 (0.495698) | 0.010315 / 0.000200 (0.010115) | 0.000402 / 0.000054 (0.000348) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028859 / 0.037411 (-0.008552) | 0.089688 / 0.014526 (0.075163) | 0.111697 / 0.176557 (-0.064860) | 0.183238 / 0.737135 (-0.553898) | 0.112407 / 0.296338 (-0.183931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.558394 / 0.215209 (0.343185) | 5.643048 / 2.077655 (3.565393) | 2.454622 / 1.504120 (0.950502) | 2.183338 / 1.541195 (0.642143) | 2.324793 / 1.468490 (0.856303) | 0.859482 / 4.584777 (-3.725295) | 4.959346 / 3.745712 (1.213634) | 4.599224 / 5.269862 (-0.670638) | 2.764382 / 4.565676 (-1.801295) | 0.089976 / 0.424275 (-0.334299) | 0.008144 / 0.007607 (0.000537) | 0.634675 / 0.226044 (0.408631) | 6.555693 / 2.268929 (4.286765) | 3.080252 / 55.444624 (-52.364373) | 2.442715 / 6.876477 (-4.433762) | 2.475126 / 2.142072 (0.333053) | 0.986459 / 4.805227 (-3.818768) | 0.193859 / 6.500664 (-6.306805) | 0.063652 / 0.075469 (-0.011817) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.545318 / 1.841788 (-0.296469) | 21.928751 / 8.074308 (13.854442) | 20.598229 / 10.191392 (10.406837) | 0.234046 / 0.680424 (-0.446377) | 0.025947 / 0.534201 (-0.508254) | 0.459773 / 0.579283 (-0.119510) | 0.598026 / 0.434364 (0.163662) | 0.555260 / 0.540337 (0.014922) | 0.782767 / 1.386936 (-0.604169) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009322 / 0.011353 (-0.002030) | 0.004650 / 0.011008 (-0.006358) | 0.079326 / 0.038508 (0.040818) | 0.079112 / 0.023109 (0.056003) | 0.428708 / 0.275898 (0.152810) | 0.481647 / 0.323480 (0.158168) | 0.006419 / 0.007986 (-0.001566) | 0.003878 / 0.004328 (-0.000450) | 0.079013 / 0.004250 (0.074762) | 0.058107 / 0.037052 (0.021055) | 0.436967 / 0.258489 (0.178478) | 0.501120 / 0.293841 (0.207279) | 0.052972 / 0.128546 (-0.075574) | 0.014414 / 0.075646 (-0.061232) | 0.098587 / 0.419271 (-0.320685) | 0.061626 / 0.043533 (0.018093) | 0.451623 / 0.255139 (0.196484) | 0.468893 / 0.283200 (0.185693) | 0.032479 / 0.141683 (-0.109203) | 1.911743 / 1.452155 (0.459588) | 1.969024 / 1.492716 (0.476308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232015 / 0.018006 (0.214009) | 0.508637 / 0.000490 (0.508147) | 0.005470 / 0.000200 (0.005270) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035345 / 0.037411 (-0.002066) | 0.106319 / 0.014526 (0.091794) | 0.117205 / 0.176557 (-0.059352) | 0.176527 / 0.737135 (-0.560608) | 0.121566 / 0.296338 (-0.174773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584920 / 0.215209 (0.369711) | 5.745688 / 2.077655 (3.668034) | 2.519875 / 1.504120 (1.015755) | 2.197593 / 1.541195 (0.656398) | 2.296670 / 1.468490 (0.828180) | 0.831938 / 4.584777 (-3.752839) | 5.130594 / 3.745712 (1.384882) | 4.581385 / 5.269862 (-0.688476) | 2.829516 / 4.565676 (-1.736161) | 0.099015 / 0.424275 (-0.325260) | 0.011468 / 0.007607 (0.003861) | 0.702717 / 0.226044 (0.476672) | 6.856099 / 2.268929 (4.587170) | 3.372966 / 55.444624 (-52.071658) | 2.567664 / 6.876477 (-4.308812) | 2.699200 / 2.142072 (0.557127) | 0.992316 / 4.805227 (-3.812911) | 0.190463 / 6.500664 (-6.310201) | 0.063305 / 0.075469 (-0.012165) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591491 / 1.841788 (-0.250296) | 21.696492 / 8.074308 (13.622184) | 19.695404 / 10.191392 (9.504012) | 0.222853 / 0.680424 (-0.457571) | 0.032936 / 0.534201 (-0.501265) | 0.431209 / 0.579283 (-0.148074) | 0.543101 / 0.434364 (0.108737) | 0.543427 / 0.540337 (0.003089) | 0.742102 / 1.386936 (-0.644834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#534a227179265df9093230885613c95390325705 \"CML watermark\")\n" ]
2023-11-15T08:22:19
2023-11-15T08:33:36
2023-11-15T08:22:33
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004943 / 0.011353 (-0.006410) | 0.002900 / 0.011008 (-0.008109) | 0.061495 / 0.038508 (0.022987) | 0.053575 / 0.023109 (0.030466) | 0.249318 / 0.275898 (-0.026580) | 0.271773 / 0.323480 (-0.051706) | 0.003074 / 0.007986 (-0.004911) | 0.003738 / 0.004328 (-0.000590) | 0.047624 / 0.004250 (0.043373) | 0.045141 / 0.037052 (0.008089) | 0.255467 / 0.258489 (-0.003022) | 0.286577 / 0.293841 (-0.007264) | 0.023113 / 0.128546 (-0.105433) | 0.007189 / 0.075646 (-0.068458) | 0.204441 / 0.419271 (-0.214830) | 0.036829 / 0.043533 (-0.006704) | 0.252474 / 0.255139 (-0.002665) | 0.270960 / 0.283200 (-0.012239) | 0.019666 / 0.141683 (-0.122017) | 1.095139 / 1.452155 (-0.357015) | 1.158659 / 1.492716 (-0.334057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091046 / 0.018006 (0.073040) | 0.298346 / 0.000490 (0.297856) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018702 / 0.037411 (-0.018709) | 0.062213 / 0.014526 (0.047687) | 0.073364 / 0.176557 (-0.103193) | 0.119841 / 0.737135 (-0.617294) | 0.074070 / 0.296338 (-0.222268) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282388 / 0.215209 (0.067179) | 2.792029 / 2.077655 (0.714375) | 1.471483 / 1.504120 (-0.032637) | 1.386236 / 1.541195 (-0.154959) | 1.377489 / 1.468490 (-0.091001) | 0.410335 / 4.584777 (-4.174442) | 2.424866 / 3.745712 (-1.320846) | 2.610609 / 5.269862 (-2.659253) | 1.574636 / 4.565676 (-2.991041) | 0.046716 / 0.424275 (-0.377559) | 0.004768 / 0.007607 (-0.002839) | 0.339831 / 0.226044 (0.113787) | 3.297579 / 2.268929 (1.028651) | 1.851410 / 55.444624 (-53.593214) | 1.550048 / 6.876477 (-5.326428) | 1.576647 / 2.142072 (-0.565425) | 0.482538 / 4.805227 (-4.322689) | 0.101381 / 6.500664 (-6.399283) | 0.042066 / 0.075469 (-0.033403) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972664 / 1.841788 (-0.869123) | 11.580700 / 8.074308 (3.506392) | 10.586747 / 10.191392 (0.395355) | 0.127844 / 0.680424 (-0.552580) | 0.014270 / 0.534201 (-0.519931) | 0.269678 / 0.579283 (-0.309605) | 0.264022 / 0.434364 (-0.170342) | 0.309395 / 0.540337 (-0.230942) | 0.429228 / 1.386936 (-0.957708) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004815 / 0.011353 (-0.006538) | 0.002890 / 0.011008 (-0.008119) | 0.048039 / 0.038508 (0.009531) | 0.053029 / 0.023109 (0.029920) | 0.271346 / 0.275898 (-0.004552) | 0.294488 / 0.323480 (-0.028992) | 0.003983 / 0.007986 (-0.004003) | 0.002439 / 0.004328 (-0.001889) | 0.048250 / 0.004250 (0.044000) | 0.038855 / 0.037052 (0.001803) | 0.284723 / 0.258489 (0.026234) | 0.303604 / 0.293841 (0.009763) | 0.024384 / 0.128546 (-0.104163) | 0.007021 / 0.075646 (-0.068625) | 0.053850 / 0.419271 (-0.365422) | 0.032177 / 0.043533 (-0.011356) | 0.270039 / 0.255139 (0.014900) | 0.289669 / 0.283200 (0.006469) | 0.018840 / 0.141683 (-0.122842) | 1.122191 / 1.452155 (-0.329963) | 1.187083 / 1.492716 (-0.305634) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090609 / 0.018006 (0.072603) | 0.298915 / 0.000490 (0.298425) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020919 / 0.037411 (-0.016492) | 0.070474 / 0.014526 (0.055948) | 0.082421 / 0.176557 (-0.094135) | 0.126967 / 0.737135 (-0.610168) | 0.083447 / 0.296338 (-0.212892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300153 / 0.215209 (0.084944) | 2.958992 / 2.077655 (0.881337) | 1.631228 / 1.504120 (0.127108) | 1.497991 / 1.541195 (-0.043204) | 1.536963 / 1.468490 (0.068473) | 0.403047 / 4.584777 (-4.181730) | 2.448782 / 3.745712 (-1.296930) | 2.571954 / 5.269862 (-2.697908) | 1.556346 / 4.565676 (-3.009331) | 0.045992 / 0.424275 (-0.378283) | 0.004785 / 0.007607 (-0.002822) | 0.357448 / 0.226044 (0.131404) | 3.558808 / 2.268929 (1.289880) | 1.992624 / 55.444624 (-53.452001) | 1.695027 / 6.876477 (-5.181450) | 1.695183 / 2.142072 (-0.446889) | 0.477001 / 4.805227 (-4.328226) | 0.097485 / 6.500664 (-6.403179) | 0.040530 / 0.075469 (-0.034939) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976342 / 1.841788 (-0.865445) | 12.141698 / 8.074308 (4.067390) | 10.881101 / 10.191392 (0.689709) | 0.142443 / 0.680424 (-0.537981) | 0.015583 / 0.534201 (-0.518618) | 0.269727 / 0.579283 (-0.309556) | 0.275890 / 0.434364 (-0.158474) | 0.306351 / 0.540337 (-0.233987) | 0.412003 / 1.386936 (-0.974933) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c744261000fd684f54c54de8ac4f15a726092d7 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004946 / 0.011353 (-0.006407) | 0.002863 / 0.011008 (-0.008146) | 0.061888 / 0.038508 (0.023380) | 0.050664 / 0.023109 (0.027554) | 0.242635 / 0.275898 (-0.033263) | 0.271741 / 0.323480 (-0.051739) | 0.003023 / 0.007986 (-0.004963) | 0.003088 / 0.004328 (-0.001241) | 0.049286 / 0.004250 (0.045036) | 0.044699 / 0.037052 (0.007647) | 0.249581 / 0.258489 (-0.008908) | 0.285633 / 0.293841 (-0.008208) | 0.023048 / 0.128546 (-0.105499) | 0.007235 / 0.075646 (-0.068412) | 0.202989 / 0.419271 (-0.216282) | 0.036357 / 0.043533 (-0.007175) | 0.245980 / 0.255139 (-0.009159) | 0.277486 / 0.283200 (-0.005713) | 0.019215 / 0.141683 (-0.122468) | 1.096456 / 1.452155 (-0.355699) | 1.152196 / 1.492716 (-0.340520) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092026 / 0.018006 (0.074020) | 0.303038 / 0.000490 (0.302549) | 0.000209 / 0.000200 (0.000009) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018670 / 0.037411 (-0.018741) | 0.061972 / 0.014526 (0.047446) | 0.072963 / 0.176557 (-0.103594) | 0.119984 / 0.737135 (-0.617151) | 0.074074 / 0.296338 (-0.222265) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282444 / 0.215209 (0.067235) | 2.754571 / 2.077655 (0.676916) | 1.482635 / 1.504120 (-0.021485) | 1.352039 / 1.541195 (-0.189155) | 1.359333 / 1.468490 (-0.109157) | 0.399690 / 4.584777 (-4.185087) | 2.364844 / 3.745712 (-1.380868) | 2.603942 / 5.269862 (-2.665919) | 1.569512 / 4.565676 (-2.996164) | 0.046074 / 0.424275 (-0.378201) | 0.004745 / 0.007607 (-0.002862) | 0.339066 / 0.226044 (0.113022) | 3.363456 / 2.268929 (1.094527) | 1.822213 / 55.444624 (-53.622411) | 1.536622 / 6.876477 (-5.339854) | 1.574772 / 2.142072 (-0.567300) | 0.474418 / 4.805227 (-4.330809) | 0.099572 / 6.500664 (-6.401092) | 0.041824 / 0.075469 (-0.033645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956300 / 1.841788 (-0.885487) | 11.648886 / 8.074308 (3.574578) | 10.645700 / 10.191392 (0.454308) | 0.138924 / 0.680424 (-0.541499) | 0.013936 / 0.534201 (-0.520265) | 0.270319 / 0.579283 (-0.308964) | 0.269735 / 0.434364 (-0.164629) | 0.309699 / 0.540337 (-0.230639) | 0.429139 / 1.386936 (-0.957797) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004838 / 0.011353 (-0.006515) | 0.002937 / 0.011008 (-0.008072) | 0.048094 / 0.038508 (0.009586) | 0.053131 / 0.023109 (0.030022) | 0.271893 / 0.275898 (-0.004005) | 0.291025 / 0.323480 (-0.032454) | 0.004058 / 0.007986 (-0.003928) | 0.002410 / 0.004328 (-0.001919) | 0.047939 / 0.004250 (0.043689) | 0.038996 / 0.037052 (0.001944) | 0.274983 / 0.258489 (0.016494) | 0.306175 / 0.293841 (0.012334) | 0.024388 / 0.128546 (-0.104159) | 0.007242 / 0.075646 (-0.068404) | 0.054011 / 0.419271 (-0.365261) | 0.032750 / 0.043533 (-0.010783) | 0.271147 / 0.255139 (0.016008) | 0.288163 / 0.283200 (0.004963) | 0.018383 / 0.141683 (-0.123299) | 1.116134 / 1.452155 (-0.336021) | 1.185964 / 1.492716 (-0.306752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093289 / 0.018006 (0.075283) | 0.303058 / 0.000490 (0.302568) | 0.000241 / 0.000200 (0.000041) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021422 / 0.037411 (-0.015990) | 0.069974 / 0.014526 (0.055449) | 0.081164 / 0.176557 (-0.095392) | 0.119991 / 0.737135 (-0.617144) | 0.082154 / 0.296338 (-0.214184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292298 / 0.215209 (0.077089) | 2.851475 / 2.077655 (0.773821) | 1.558283 / 1.504120 (0.054163) | 1.432431 / 1.541195 (-0.108764) | 1.479282 / 1.468490 (0.010792) | 0.413124 / 4.584777 (-4.171653) | 2.473005 / 3.745712 (-1.272707) | 2.548779 / 5.269862 (-2.721082) | 1.520776 / 4.565676 (-3.044900) | 0.046476 / 0.424275 (-0.377799) | 0.004814 / 0.007607 (-0.002794) | 0.347036 / 0.226044 (0.120992) | 3.424928 / 2.268929 (1.155999) | 1.963274 / 55.444624 (-53.481351) | 1.653794 / 6.876477 (-5.222683) | 1.643874 / 2.142072 (-0.498198) | 0.469086 / 4.805227 (-4.336141) | 0.097417 / 6.500664 (-6.403247) | 0.040468 / 0.075469 (-0.035002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972783 / 1.841788 (-0.869005) | 12.122994 / 8.074308 (4.048686) | 10.876396 / 10.191392 (0.685004) | 0.130573 / 0.680424 (-0.549850) | 0.016693 / 0.534201 (-0.517508) | 0.270952 / 0.579283 (-0.308331) | 0.273834 / 0.434364 (-0.160530) | 0.305049 / 0.540337 (-0.235289) | 0.408776 / 1.386936 (-0.978160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4216e5d57ea07e6b1ed73a3ec2cf845c6e59f70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004606 / 0.011353 (-0.006747) | 0.002433 / 0.011008 (-0.008576) | 0.061985 / 0.038508 (0.023477) | 0.048853 / 0.023109 (0.025744) | 0.244506 / 0.275898 (-0.031392) | 0.270159 / 0.323480 (-0.053321) | 0.003962 / 0.007986 (-0.004024) | 0.002376 / 0.004328 (-0.001952) | 0.048067 / 0.004250 (0.043817) | 0.041864 / 0.037052 (0.004812) | 0.249743 / 0.258489 (-0.008746) | 0.287723 / 0.293841 (-0.006117) | 0.022954 / 0.128546 (-0.105593) | 0.006845 / 0.075646 (-0.068801) | 0.206313 / 0.419271 (-0.212959) | 0.035780 / 0.043533 (-0.007753) | 0.244286 / 0.255139 (-0.010853) | 0.270026 / 0.283200 (-0.013173) | 0.018177 / 0.141683 (-0.123506) | 1.083998 / 1.452155 (-0.368157) | 1.156086 / 1.492716 (-0.336630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093754 / 0.018006 (0.075748) | 0.302157 / 0.000490 (0.301667) | 0.000215 / 0.000200 (0.000015) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018745 / 0.037411 (-0.018666) | 0.061707 / 0.014526 (0.047181) | 0.074356 / 0.176557 (-0.102200) | 0.121643 / 0.737135 (-0.615492) | 0.075885 / 0.296338 (-0.220454) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289156 / 0.215209 (0.073947) | 2.881327 / 2.077655 (0.803672) | 1.483568 / 1.504120 (-0.020552) | 1.355933 / 1.541195 (-0.185262) | 1.389693 / 1.468490 (-0.078797) | 0.402834 / 4.584777 (-4.181943) | 2.390634 / 3.745712 (-1.355078) | 2.596761 / 5.269862 (-2.673101) | 1.527602 / 4.565676 (-3.038074) | 0.046434 / 0.424275 (-0.377841) | 0.004783 / 0.007607 (-0.002824) | 0.341017 / 0.226044 (0.114972) | 3.429023 / 2.268929 (1.160095) | 1.832988 / 55.444624 (-53.611637) | 1.526510 / 6.876477 (-5.349967) | 1.539382 / 2.142072 (-0.602690) | 0.475734 / 4.805227 (-4.329493) | 0.098710 / 6.500664 (-6.401954) | 0.041136 / 0.075469 (-0.034333) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.922023 / 1.841788 (-0.919765) | 11.428215 / 8.074308 (3.353907) | 10.356668 / 10.191392 (0.165276) | 0.139575 / 0.680424 (-0.540848) | 0.014541 / 0.534201 (-0.519660) | 0.271359 / 0.579283 (-0.307924) | 0.266701 / 0.434364 (-0.167663) | 0.309449 / 0.540337 (-0.230888) | 0.422047 / 1.386936 (-0.964889) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004892 / 0.011353 (-0.006461) | 0.002792 / 0.011008 (-0.008216) | 0.048027 / 0.038508 (0.009519) | 0.059256 / 0.023109 (0.036147) | 0.270150 / 0.275898 (-0.005748) | 0.294530 / 0.323480 (-0.028950) | 0.004162 / 0.007986 (-0.003823) | 0.002470 / 0.004328 (-0.001858) | 0.047993 / 0.004250 (0.043743) | 0.040380 / 0.037052 (0.003328) | 0.275247 / 0.258489 (0.016758) | 0.305684 / 0.293841 (0.011843) | 0.025072 / 0.128546 (-0.103474) | 0.007183 / 0.075646 (-0.068463) | 0.054875 / 0.419271 (-0.364397) | 0.033053 / 0.043533 (-0.010480) | 0.271281 / 0.255139 (0.016142) | 0.288057 / 0.283200 (0.004858) | 0.018692 / 0.141683 (-0.122991) | 1.125224 / 1.452155 (-0.326930) | 1.171083 / 1.492716 (-0.321633) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103102 / 0.018006 (0.085096) | 0.309099 / 0.000490 (0.308609) | 0.000232 / 0.000200 (0.000032) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021532 / 0.037411 (-0.015879) | 0.069927 / 0.014526 (0.055401) | 0.080920 / 0.176557 (-0.095637) | 0.122214 / 0.737135 (-0.614921) | 0.082268 / 0.296338 (-0.214071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298121 / 0.215209 (0.082912) | 2.933000 / 2.077655 (0.855345) | 1.608782 / 1.504120 (0.104662) | 1.554083 / 1.541195 (0.012889) | 1.552700 / 1.468490 (0.084209) | 0.400576 / 4.584777 (-4.184201) | 2.412914 / 3.745712 (-1.332798) | 2.545706 / 5.269862 (-2.724155) | 1.548797 / 4.565676 (-3.016879) | 0.045553 / 0.424275 (-0.378722) | 0.004751 / 0.007607 (-0.002857) | 0.343002 / 0.226044 (0.116958) | 3.402866 / 2.268929 (1.133937) | 1.969910 / 55.444624 (-53.474715) | 1.686639 / 6.876477 (-5.189838) | 1.768474 / 2.142072 (-0.373599) | 0.471299 / 4.805227 (-4.333928) | 0.097696 / 6.500664 (-6.402968) | 0.041693 / 0.075469 (-0.033776) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971380 / 1.841788 (-0.870408) | 12.686033 / 8.074308 (4.611725) | 11.370946 / 10.191392 (1.179554) | 0.138377 / 0.680424 (-0.542047) | 0.015623 / 0.534201 (-0.518578) | 0.270935 / 0.579283 (-0.308348) | 0.276235 / 0.434364 (-0.158129) | 0.310196 / 0.540337 (-0.230141) | 0.416908 / 1.386936 (-0.970028) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004581 / 0.011353 (-0.006772) | 0.002468 / 0.011008 (-0.008541) | 0.061420 / 0.038508 (0.022912) | 0.047685 / 0.023109 (0.024575) | 0.237756 / 0.275898 (-0.038142) | 0.267548 / 0.323480 (-0.055932) | 0.003899 / 0.007986 (-0.004086) | 0.002338 / 0.004328 (-0.001990) | 0.048794 / 0.004250 (0.044543) | 0.042485 / 0.037052 (0.005433) | 0.250165 / 0.258489 (-0.008324) | 0.278791 / 0.293841 (-0.015050) | 0.022371 / 0.128546 (-0.106175) | 0.006923 / 0.075646 (-0.068723) | 0.201401 / 0.419271 (-0.217870) | 0.035867 / 0.043533 (-0.007665) | 0.244628 / 0.255139 (-0.010511) | 0.271137 / 0.283200 (-0.012063) | 0.017257 / 0.141683 (-0.124426) | 1.097261 / 1.452155 (-0.354894) | 1.163314 / 1.492716 (-0.329402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089060 / 0.018006 (0.071054) | 0.297489 / 0.000490 (0.296999) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018583 / 0.037411 (-0.018828) | 0.061974 / 0.014526 (0.047449) | 0.073300 / 0.176557 (-0.103256) | 0.118871 / 0.737135 (-0.618264) | 0.075513 / 0.296338 (-0.220826) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285544 / 0.215209 (0.070335) | 2.799871 / 2.077655 (0.722216) | 1.479871 / 1.504120 (-0.024249) | 1.351128 / 1.541195 (-0.190067) | 1.377540 / 1.468490 (-0.090950) | 0.393056 / 4.584777 (-4.191721) | 2.341791 / 3.745712 (-1.403921) | 2.546854 / 5.269862 (-2.723007) | 1.547368 / 4.565676 (-3.018309) | 0.046056 / 0.424275 (-0.378219) | 0.004765 / 0.007607 (-0.002842) | 0.336384 / 0.226044 (0.110339) | 3.283277 / 2.268929 (1.014348) | 1.784535 / 55.444624 (-53.660089) | 1.557809 / 6.876477 (-5.318667) | 1.581728 / 2.142072 (-0.560344) | 0.470527 / 4.805227 (-4.334700) | 0.098383 / 6.500664 (-6.402281) | 0.041563 / 0.075469 (-0.033906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946924 / 1.841788 (-0.894863) | 11.202775 / 8.074308 (3.128467) | 10.249760 / 10.191392 (0.058368) | 0.142337 / 0.680424 (-0.538087) | 0.013784 / 0.534201 (-0.520417) | 0.267237 / 0.579283 (-0.312046) | 0.264142 / 0.434364 (-0.170222) | 0.306343 / 0.540337 (-0.233994) | 0.423681 / 1.386936 (-0.963255) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004786 / 0.011353 (-0.006567) | 0.002398 / 0.011008 (-0.008610) | 0.047325 / 0.038508 (0.008817) | 0.050753 / 0.023109 (0.027644) | 0.271132 / 0.275898 (-0.004766) | 0.290854 / 0.323480 (-0.032626) | 0.003953 / 0.007986 (-0.004033) | 0.002238 / 0.004328 (-0.002090) | 0.047463 / 0.004250 (0.043213) | 0.038504 / 0.037052 (0.001451) | 0.273182 / 0.258489 (0.014693) | 0.303449 / 0.293841 (0.009608) | 0.024069 / 0.128546 (-0.104477) | 0.006712 / 0.075646 (-0.068934) | 0.053032 / 0.419271 (-0.366239) | 0.032221 / 0.043533 (-0.011312) | 0.271770 / 0.255139 (0.016631) | 0.287876 / 0.283200 (0.004677) | 0.018040 / 0.141683 (-0.123643) | 1.138749 / 1.452155 (-0.313405) | 1.192048 / 1.492716 (-0.300668) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089132 / 0.018006 (0.071126) | 0.298636 / 0.000490 (0.298146) | 0.000220 / 0.000200 (0.000020) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020808 / 0.037411 (-0.016603) | 0.069506 / 0.014526 (0.054980) | 0.079412 / 0.176557 (-0.097145) | 0.118188 / 0.737135 (-0.618947) | 0.083044 / 0.296338 (-0.213294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293502 / 0.215209 (0.078293) | 2.863692 / 2.077655 (0.786037) | 1.590877 / 1.504120 (0.086757) | 1.483634 / 1.541195 (-0.057561) | 1.502113 / 1.468490 (0.033623) | 0.402170 / 4.584777 (-4.182607) | 2.414188 / 3.745712 (-1.331524) | 2.500146 / 5.269862 (-2.769716) | 1.506977 / 4.565676 (-3.058699) | 0.045849 / 0.424275 (-0.378426) | 0.004755 / 0.007607 (-0.002852) | 0.343073 / 0.226044 (0.117029) | 3.354985 / 2.268929 (1.086056) | 1.952594 / 55.444624 (-53.492030) | 1.664084 / 6.876477 (-5.212392) | 1.664203 / 2.142072 (-0.477869) | 0.475858 / 4.805227 (-4.329370) | 0.097539 / 6.500664 (-6.403125) | 0.040201 / 0.075469 (-0.035268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980051 / 1.841788 (-0.861736) | 11.615291 / 8.074308 (3.540983) | 10.492092 / 10.191392 (0.300700) | 0.130450 / 0.680424 (-0.549974) | 0.015883 / 0.534201 (-0.518318) | 0.267575 / 0.579283 (-0.311708) | 0.276981 / 0.434364 (-0.157383) | 0.310221 / 0.540337 (-0.230116) | 0.417143 / 1.386936 (-0.969793) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004721 / 0.011353 (-0.006632) | 0.002931 / 0.011008 (-0.008077) | 0.061948 / 0.038508 (0.023440) | 0.051066 / 0.023109 (0.027957) | 0.245431 / 0.275898 (-0.030467) | 0.295627 / 0.323480 (-0.027852) | 0.003997 / 0.007986 (-0.003988) | 0.002408 / 0.004328 (-0.001920) | 0.048292 / 0.004250 (0.044041) | 0.044716 / 0.037052 (0.007664) | 0.255119 / 0.258489 (-0.003371) | 0.287384 / 0.293841 (-0.006457) | 0.022835 / 0.128546 (-0.105711) | 0.007162 / 0.075646 (-0.068484) | 0.201352 / 0.419271 (-0.217920) | 0.036626 / 0.043533 (-0.006906) | 0.249590 / 0.255139 (-0.005549) | 0.270822 / 0.283200 (-0.012378) | 0.018152 / 0.141683 (-0.123531) | 1.097046 / 1.452155 (-0.355109) | 1.160461 / 1.492716 (-0.332255) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091712 / 0.018006 (0.073705) | 0.299121 / 0.000490 (0.298631) | 0.000244 / 0.000200 (0.000044) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018998 / 0.037411 (-0.018413) | 0.062811 / 0.014526 (0.048285) | 0.076348 / 0.176557 (-0.100209) | 0.123898 / 0.737135 (-0.613238) | 0.076249 / 0.296338 (-0.220090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282780 / 0.215209 (0.067571) | 2.739028 / 2.077655 (0.661373) | 1.472564 / 1.504120 (-0.031556) | 1.347343 / 1.541195 (-0.193852) | 1.387130 / 1.468490 (-0.081360) | 0.403348 / 4.584777 (-4.181429) | 2.369924 / 3.745712 (-1.375788) | 2.612875 / 5.269862 (-2.656987) | 1.588079 / 4.565676 (-2.977598) | 0.045233 / 0.424275 (-0.379042) | 0.004767 / 0.007607 (-0.002840) | 0.336614 / 0.226044 (0.110570) | 3.300485 / 2.268929 (1.031556) | 1.834365 / 55.444624 (-53.610259) | 1.559799 / 6.876477 (-5.316677) | 1.601265 / 2.142072 (-0.540808) | 0.468158 / 4.805227 (-4.337069) | 0.099811 / 6.500664 (-6.400853) | 0.042688 / 0.075469 (-0.032782) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.934097 / 1.841788 (-0.907691) | 11.687713 / 8.074308 (3.613405) | 10.412723 / 10.191392 (0.221331) | 0.139276 / 0.680424 (-0.541148) | 0.014042 / 0.534201 (-0.520159) | 0.270306 / 0.579283 (-0.308978) | 0.266609 / 0.434364 (-0.167755) | 0.314179 / 0.540337 (-0.226158) | 0.437744 / 1.386936 (-0.949192) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004893 / 0.011353 (-0.006460) | 0.002952 / 0.011008 (-0.008056) | 0.050441 / 0.038508 (0.011933) | 0.051838 / 0.023109 (0.028729) | 0.271163 / 0.275898 (-0.004735) | 0.293031 / 0.323480 (-0.030449) | 0.003976 / 0.007986 (-0.004010) | 0.002396 / 0.004328 (-0.001933) | 0.048103 / 0.004250 (0.043852) | 0.038732 / 0.037052 (0.001680) | 0.274276 / 0.258489 (0.015787) | 0.305112 / 0.293841 (0.011271) | 0.024112 / 0.128546 (-0.104434) | 0.007203 / 0.075646 (-0.068443) | 0.053502 / 0.419271 (-0.365770) | 0.032360 / 0.043533 (-0.011173) | 0.270154 / 0.255139 (0.015015) | 0.286689 / 0.283200 (0.003489) | 0.018285 / 0.141683 (-0.123397) | 1.141421 / 1.452155 (-0.310734) | 1.244062 / 1.492716 (-0.248654) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090960 / 0.018006 (0.072954) | 0.286134 / 0.000490 (0.285644) | 0.000207 / 0.000200 (0.000007) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020789 / 0.037411 (-0.016622) | 0.070850 / 0.014526 (0.056324) | 0.080750 / 0.176557 (-0.095807) | 0.120046 / 0.737135 (-0.617089) | 0.083630 / 0.296338 (-0.212708) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290654 / 0.215209 (0.075445) | 2.846669 / 2.077655 (0.769014) | 1.561752 / 1.504120 (0.057632) | 1.442968 / 1.541195 (-0.098227) | 1.503551 / 1.468490 (0.035061) | 0.399731 / 4.584777 (-4.185046) | 2.430099 / 3.745712 (-1.315613) | 2.556169 / 5.269862 (-2.713692) | 1.545591 / 4.565676 (-3.020085) | 0.045967 / 0.424275 (-0.378309) | 0.004851 / 0.007607 (-0.002756) | 0.340167 / 0.226044 (0.114122) | 3.392738 / 2.268929 (1.123809) | 1.943577 / 55.444624 (-53.501047) | 1.650057 / 6.876477 (-5.226420) | 1.686872 / 2.142072 (-0.455201) | 0.470305 / 4.805227 (-4.334923) | 0.097296 / 6.500664 (-6.403368) | 0.041399 / 0.075469 (-0.034070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985660 / 1.841788 (-0.856128) | 12.300826 / 8.074308 (4.226518) | 10.972591 / 10.191392 (0.781199) | 0.131512 / 0.680424 (-0.548912) | 0.015742 / 0.534201 (-0.518459) | 0.270630 / 0.579283 (-0.308653) | 0.276039 / 0.434364 (-0.158325) | 0.302288 / 0.540337 (-0.238050) | 0.409415 / 1.386936 (-0.977521) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n" ]
2023-11-15T08:07:37
2023-11-15T17:35:30
2023-11-15T08:12:59
MEMBER
null
false
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Release 2.14.7.
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https://github.com/huggingface/datasets/pull/6418
1,993,224,629
PR_kwDODunzps5fb7lu
6,418
Remove token value from warnings
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005135 / 0.011353 (-0.006218) | 0.002950 / 0.011008 (-0.008058) | 0.062316 / 0.038508 (0.023808) | 0.030068 / 0.023109 (0.006959) | 0.251998 / 0.275898 (-0.023900) | 0.274806 / 0.323480 (-0.048674) | 0.003067 / 0.007986 (-0.004919) | 0.003082 / 0.004328 (-0.001247) | 0.048503 / 0.004250 (0.044253) | 0.045167 / 0.037052 (0.008114) | 0.254277 / 0.258489 (-0.004212) | 0.290528 / 0.293841 (-0.003313) | 0.023666 / 0.128546 (-0.104880) | 0.007049 / 0.075646 (-0.068597) | 0.202367 / 0.419271 (-0.216905) | 0.056291 / 0.043533 (0.012758) | 0.251923 / 0.255139 (-0.003216) | 0.273595 / 0.283200 (-0.009605) | 0.019065 / 0.141683 (-0.122618) | 1.100832 / 1.452155 (-0.351322) | 1.266758 / 1.492716 (-0.225959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094311 / 0.018006 (0.076305) | 0.303199 / 0.000490 (0.302709) | 0.000238 / 0.000200 (0.000039) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019413 / 0.037411 (-0.017999) | 0.062618 / 0.014526 (0.048092) | 0.072850 / 0.176557 (-0.103707) | 0.119124 / 0.737135 (-0.618012) | 0.074044 / 0.296338 (-0.222294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273660 / 0.215209 (0.058451) | 2.682371 / 2.077655 (0.604716) | 1.426041 / 1.504120 (-0.078079) | 1.317186 / 1.541195 (-0.224009) | 1.332385 / 1.468490 (-0.136106) | 0.394599 / 4.584777 (-4.190178) | 2.368167 / 3.745712 (-1.377545) | 2.683728 / 5.269862 (-2.586134) | 1.668348 / 4.565676 (-2.897329) | 0.046177 / 0.424275 (-0.378098) | 0.004833 / 0.007607 (-0.002774) | 0.331413 / 0.226044 (0.105369) | 3.278984 / 2.268929 (1.010055) | 1.797600 / 55.444624 (-53.647024) | 1.492202 / 6.876477 (-5.384274) | 1.536039 / 2.142072 (-0.606034) | 0.470601 / 4.805227 (-4.334626) | 0.100833 / 6.500664 (-6.399831) | 0.042787 / 0.075469 (-0.032682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959036 / 1.841788 (-0.882752) | 11.632956 / 8.074308 (3.558648) | 10.384574 / 10.191392 (0.193182) | 0.127477 / 0.680424 (-0.552946) | 0.014072 / 0.534201 (-0.520129) | 0.269534 / 0.579283 (-0.309749) | 0.259753 / 0.434364 (-0.174611) | 0.313450 / 0.540337 (-0.226888) | 0.431799 / 1.386936 (-0.955137) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004964 / 0.011353 (-0.006389) | 0.002906 / 0.011008 (-0.008102) | 0.048145 / 0.038508 (0.009637) | 0.056457 / 0.023109 (0.033348) | 0.274131 / 0.275898 (-0.001767) | 0.298534 / 0.323480 (-0.024946) | 0.004145 / 0.007986 (-0.003841) | 0.002415 / 0.004328 (-0.001913) | 0.048558 / 0.004250 (0.044308) | 0.039031 / 0.037052 (0.001978) | 0.278948 / 0.258489 (0.020459) | 0.312358 / 0.293841 (0.018517) | 0.024902 / 0.128546 (-0.103645) | 0.007286 / 0.075646 (-0.068360) | 0.053839 / 0.419271 (-0.365433) | 0.032510 / 0.043533 (-0.011023) | 0.272023 / 0.255139 (0.016884) | 0.293420 / 0.283200 (0.010221) | 0.018932 / 0.141683 (-0.122750) | 1.122792 / 1.452155 (-0.329362) | 1.167385 / 1.492716 (-0.325331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094574 / 0.018006 (0.076567) | 0.303810 / 0.000490 (0.303321) | 0.000227 / 0.000200 (0.000027) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021675 / 0.037411 (-0.015737) | 0.070289 / 0.014526 (0.055763) | 0.080345 / 0.176557 (-0.096211) | 0.120220 / 0.737135 (-0.616915) | 0.084080 / 0.296338 (-0.212259) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300134 / 0.215209 (0.084925) | 2.945831 / 2.077655 (0.868176) | 1.605303 / 1.504120 (0.101183) | 1.480135 / 1.541195 (-0.061059) | 1.526039 / 1.468490 (0.057549) | 0.398264 / 4.584777 (-4.186512) | 2.461391 / 3.745712 (-1.284321) | 2.559929 / 5.269862 (-2.709933) | 1.541391 / 4.565676 (-3.024286) | 0.045319 / 0.424275 (-0.378957) | 0.004834 / 0.007607 (-0.002773) | 0.352186 / 0.226044 (0.126141) | 3.500108 / 2.268929 (1.231180) | 1.966394 / 55.444624 (-53.478230) | 1.675500 / 6.876477 (-5.200977) | 1.683134 / 2.142072 (-0.458938) | 0.465085 / 4.805227 (-4.340142) | 0.097235 / 6.500664 (-6.403429) | 0.040764 / 0.075469 (-0.034705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982813 / 1.841788 (-0.858975) | 12.382529 / 8.074308 (4.308221) | 11.082660 / 10.191392 (0.891268) | 0.129113 / 0.680424 (-0.551310) | 0.015718 / 0.534201 (-0.518483) | 0.272776 / 0.579283 (-0.306507) | 0.275513 / 0.434364 (-0.158850) | 0.304933 / 0.540337 (-0.235404) | 0.414591 / 1.386936 (-0.972345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8723b129a64928eba40baf70ffd462060ade9f97 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004400 / 0.011353 (-0.006953) | 0.002580 / 0.011008 (-0.008428) | 0.060975 / 0.038508 (0.022467) | 0.029337 / 0.023109 (0.006228) | 0.248643 / 0.275898 (-0.027255) | 0.274476 / 0.323480 (-0.049004) | 0.003925 / 0.007986 (-0.004061) | 0.002332 / 0.004328 (-0.001997) | 0.049501 / 0.004250 (0.045251) | 0.042730 / 0.037052 (0.005678) | 0.255823 / 0.258489 (-0.002666) | 0.281748 / 0.293841 (-0.012093) | 0.023118 / 0.128546 (-0.105428) | 0.006957 / 0.075646 (-0.068690) | 0.201630 / 0.419271 (-0.217641) | 0.054258 / 0.043533 (0.010725) | 0.252289 / 0.255139 (-0.002850) | 0.267561 / 0.283200 (-0.015639) | 0.016903 / 0.141683 (-0.124780) | 1.104322 / 1.452155 (-0.347833) | 1.160027 / 1.492716 (-0.332689) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096340 / 0.018006 (0.078333) | 0.305187 / 0.000490 (0.304697) | 0.000222 / 0.000200 (0.000022) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018733 / 0.037411 (-0.018678) | 0.062382 / 0.014526 (0.047856) | 0.072309 / 0.176557 (-0.104248) | 0.119772 / 0.737135 (-0.617364) | 0.074655 / 0.296338 (-0.221683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286150 / 0.215209 (0.070941) | 2.770328 / 2.077655 (0.692673) | 1.494593 / 1.504120 (-0.009527) | 1.358611 / 1.541195 (-0.182583) | 1.396308 / 1.468490 (-0.072182) | 0.394806 / 4.584777 (-4.189971) | 2.349100 / 3.745712 (-1.396613) | 2.600541 / 5.269862 (-2.669321) | 1.568975 / 4.565676 (-2.996701) | 0.046212 / 0.424275 (-0.378063) | 0.004821 / 0.007607 (-0.002786) | 0.332286 / 0.226044 (0.106242) | 3.302643 / 2.268929 (1.033714) | 1.838992 / 55.444624 (-53.605633) | 1.571919 / 6.876477 (-5.304557) | 1.574956 / 2.142072 (-0.567117) | 0.464156 / 4.805227 (-4.341071) | 0.097983 / 6.500664 (-6.402681) | 0.042243 / 0.075469 (-0.033226) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941675 / 1.841788 (-0.900113) | 11.450326 / 8.074308 (3.376017) | 10.169943 / 10.191392 (-0.021449) | 0.137879 / 0.680424 (-0.542545) | 0.013765 / 0.534201 (-0.520436) | 0.268633 / 0.579283 (-0.310650) | 0.265083 / 0.434364 (-0.169281) | 0.302099 / 0.540337 (-0.238238) | 0.423033 / 1.386936 (-0.963903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004998 / 0.011353 (-0.006355) | 0.003174 / 0.011008 (-0.007834) | 0.047924 / 0.038508 (0.009416) | 0.057598 / 0.023109 (0.034489) | 0.278823 / 0.275898 (0.002925) | 0.334349 / 0.323480 (0.010869) | 0.004053 / 0.007986 (-0.003932) | 0.002554 / 0.004328 (-0.001774) | 0.047797 / 0.004250 (0.043547) | 0.039802 / 0.037052 (0.002749) | 0.278295 / 0.258489 (0.019806) | 0.319597 / 0.293841 (0.025757) | 0.024802 / 0.128546 (-0.103744) | 0.007362 / 0.075646 (-0.068284) | 0.066983 / 0.419271 (-0.352288) | 0.032707 / 0.043533 (-0.010826) | 0.277350 / 0.255139 (0.022211) | 0.296829 / 0.283200 (0.013629) | 0.017902 / 0.141683 (-0.123781) | 1.129765 / 1.452155 (-0.322390) | 1.201940 / 1.492716 (-0.290777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095631 / 0.018006 (0.077625) | 0.296999 / 0.000490 (0.296510) | 0.000234 / 0.000200 (0.000034) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021547 / 0.037411 (-0.015865) | 0.070003 / 0.014526 (0.055477) | 0.083173 / 0.176557 (-0.093384) | 0.121676 / 0.737135 (-0.615459) | 0.082974 / 0.296338 (-0.213364) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298982 / 0.215209 (0.083773) | 2.918666 / 2.077655 (0.841011) | 1.582054 / 1.504120 (0.077934) | 1.463804 / 1.541195 (-0.077391) | 1.484384 / 1.468490 (0.015893) | 0.399443 / 4.584777 (-4.185334) | 2.393515 / 3.745712 (-1.352197) | 2.533004 / 5.269862 (-2.736858) | 1.490411 / 4.565676 (-3.075266) | 0.045274 / 0.424275 (-0.379002) | 0.004783 / 0.007607 (-0.002824) | 0.350510 / 0.226044 (0.124465) | 3.437927 / 2.268929 (1.168998) | 1.940115 / 55.444624 (-53.504509) | 1.662025 / 6.876477 (-5.214452) | 1.640621 / 2.142072 (-0.501452) | 0.464014 / 4.805227 (-4.341214) | 0.095506 / 6.500664 (-6.405158) | 0.040172 / 0.075469 (-0.035297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975618 / 1.841788 (-0.866169) | 12.561067 / 8.074308 (4.486759) | 11.408037 / 10.191392 (1.216645) | 0.130699 / 0.680424 (-0.549725) | 0.016796 / 0.534201 (-0.517405) | 0.271130 / 0.579283 (-0.308153) | 0.283506 / 0.434364 (-0.150857) | 0.304482 / 0.540337 (-0.235856) | 0.413673 / 1.386936 (-0.973263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#723038a73248dd12dc0673d2b341e9295c441ea3 \"CML watermark\")\n" ]
2023-11-14T17:34:06
2023-11-14T22:26:04
2023-11-14T22:19:45
COLLABORATOR
null
false
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Fix #6412
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Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error
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[ "Very strange: `datasets-cli env`\r\n> \r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.9.0\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.13\r\n> - PyArrow version: 8.0.0\r\n> - Pandas version: 1.3.5\r\n\r\nAfter updating datasets and pyarrow on base environment, although I am using a different one called layoutLM\r\n\r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.14.6\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.18\r\n> - Huggingface_hub version: 0.17.3\r\n> - PyArrow version: 14.0.1\r\n> - Pandas version: 2.1.3", "Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.", "> Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.\r\n\r\nThanks for the info and the latest release, it seems this has also solved my issue. First run after the update worked and I am training right now :D\r\nWill close the Issu" ]
2023-11-14T16:53:20
2023-11-16T20:23:41
2023-11-16T20:23:41
NONE
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### Describe the bug Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab. **Notebook**: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb **Error**: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.` **Caused by**: ``` # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) ``` ### Steps to reproduce the bug Run the notebook provided, locally. If possible also on M1. ### Expected behavior The cell where features are mapped to Array2D and Array3D should work without any issues. ### Environment info Tried with Python 3.9 and 3.10 conda envs. Running Mac M1. `pip show datasets` > Name: datasets Version: 2.14.6 Summary: HuggingFace community-driven open-source library of datasets `pip list` > Package Version > ------------------------- ------------ > accelerate 0.24.1 > aiohttp 3.8.6 > aiosignal 1.3.1 > anyio 3.5.0 > appnope 0.1.2 > argon2-cffi 21.3.0 > argon2-cffi-bindings 21.2.0 > asttokens 2.0.5 > async-timeout 4.0.3 > attrs 23.1.0 > backcall 0.2.0 > beautifulsoup4 4.12.2 > bleach 4.1.0 > certifi 2023.7.22 > cffi 1.15.1 > charset-normalizer 3.3.2 > comm 0.1.2 > datasets 2.14.6 > debugpy 1.6.7 > decorator 5.1.1 > defusedxml 0.7.1 > dill 0.3.7 > entrypoints 0.4 > exceptiongroup 1.0.4 > executing 0.8.3 > fastjsonschema 2.16.2 > filelock 3.13.1 > frozenlist 1.4.0 > fsspec 2023.10.0 > huggingface-hub 0.17.3 > idna 3.4 > importlib-metadata 6.0.0 > IProgress 0.4 > ipykernel 6.25.0 > ipython 8.15.0 > ipython-genutils 0.2.0 > jedi 0.18.1 > Jinja2 3.1.2 > joblib 1.3.2 > jsonschema 4.19.2 > jsonschema-specifications 2023.7.1 > jupyter_client 7.4.9 > jupyter_core 5.5.0 > jupyter-server 1.23.4 > jupyterlab-pygments 0.1.2 > MarkupSafe 2.1.1 > matplotlib-inline 0.1.6 > mistune 2.0.4 > mpmath 1.3.0 > multidict 6.0.4 > multiprocess 0.70.15 > nbclassic 1.0.0 > nbclient 0.8.0 > nbconvert 7.10.0 > nbformat 5.9.2 > nest-asyncio 1.5.6 > networkx 3.2.1 > notebook 6.5.4 > notebook_shim 0.2.3 > numpy 1.26.1 > packaging 23.1 > pandas 2.1.3 > pandocfilters 1.5.0 > parso 0.8.3 > pexpect 4.8.0 > pickleshare 0.7.5 > Pillow 10.1.0 > pip 23.3 > platformdirs 3.10.0 > prometheus-client 0.14.1 > prompt-toolkit 3.0.36 > psutil 5.9.0 > ptyprocess 0.7.0 > pure-eval 0.2.2 > pyarrow 14.0.1 > pycparser 2.21 > Pygments 2.15.1 > python-dateutil 2.8.2 > pytz 2023.3.post1 > PyYAML 6.0.1 > pyzmq 23.2.0 > referencing 0.30.2 > regex 2023.10.3 > requests 2.31.0 > rpds-py 0.10.6 > safetensors 0.4.0 > scikit-learn 1.3.2 > scipy 1.11.3 > Send2Trash 1.8.2 > seqeval 1.2.2 > setuptools 68.0.0 > six 1.16.0 > sniffio 1.2.0 > soupsieve 2.5 > stack-data 0.2.0 > sympy 1.12 > terminado 0.17.1 > threadpoolctl 3.2.0 > tinycss2 1.2.1 > tokenizers 0.14.1 > torch 2.1.0 > tornado 6.3.3 > tqdm 4.66.1 > traitlets 5.7.1 > transformers 4.36.0.dev0 > typing_extensions 4.7.1 > tzdata 2023.3 > urllib3 2.0.7 > wcwidth 0.2.5 > webencodings 0.5.1 > websocket-client 0.58.0 > wheel 0.41.2 > xxhash 3.4.1 > yarl 1.9.2 > zipp 3.11.0
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Rename audio_classificiation.py to audio_classification.py
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[ "Oh good catch. Can you also rename it in `src/datasets/tasks/__init__.py` ?", "Fixed! \r\n\r\n(I think, tough word to spell right TBH)", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004737 / 0.011353 (-0.006616) | 0.002446 / 0.011008 (-0.008563) | 0.060928 / 0.038508 (0.022420) | 0.030479 / 0.023109 (0.007370) | 0.238385 / 0.275898 (-0.037513) | 0.265563 / 0.323480 (-0.057917) | 0.002910 / 0.007986 (-0.005076) | 0.002325 / 0.004328 (-0.002004) | 0.047817 / 0.004250 (0.043566) | 0.044243 / 0.037052 (0.007191) | 0.245190 / 0.258489 (-0.013299) | 0.275449 / 0.293841 (-0.018392) | 0.023384 / 0.128546 (-0.105162) | 0.006820 / 0.075646 (-0.068826) | 0.201488 / 0.419271 (-0.217783) | 0.057758 / 0.043533 (0.014225) | 0.245279 / 0.255139 (-0.009860) | 0.266094 / 0.283200 (-0.017106) | 0.019254 / 0.141683 (-0.122429) | 1.107497 / 1.452155 (-0.344658) | 1.161412 / 1.492716 (-0.331304) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094909 / 0.018006 (0.076903) | 0.305185 / 0.000490 (0.304695) | 0.000221 / 0.000200 (0.000021) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018352 / 0.037411 (-0.019059) | 0.062441 / 0.014526 (0.047915) | 0.072386 / 0.176557 (-0.104171) | 0.118836 / 0.737135 (-0.618299) | 0.074514 / 0.296338 (-0.221824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283632 / 0.215209 (0.068423) | 2.751845 / 2.077655 (0.674190) | 1.478620 / 1.504120 (-0.025499) | 1.357221 / 1.541195 (-0.183974) | 1.415297 / 1.468490 (-0.053194) | 0.400093 / 4.584777 (-4.184684) | 2.404607 / 3.745712 (-1.341105) | 2.617572 / 5.269862 (-2.652289) | 1.587622 / 4.565676 (-2.978055) | 0.045997 / 0.424275 (-0.378278) | 0.004872 / 0.007607 (-0.002735) | 0.338901 / 0.226044 (0.112856) | 3.371362 / 2.268929 (1.102434) | 1.870469 / 55.444624 (-53.574155) | 1.561670 / 6.876477 (-5.314807) | 1.573186 / 2.142072 (-0.568886) | 0.478735 / 4.805227 (-4.326492) | 0.098743 / 6.500664 (-6.401921) | 0.041780 / 0.075469 (-0.033689) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945422 / 1.841788 (-0.896366) | 11.563464 / 8.074308 (3.489156) | 10.368731 / 10.191392 (0.177339) | 0.129910 / 0.680424 (-0.550513) | 0.014014 / 0.534201 (-0.520187) | 0.269036 / 0.579283 (-0.310247) | 0.265516 / 0.434364 (-0.168848) | 0.311082 / 0.540337 (-0.229255) | 0.431510 / 1.386936 (-0.955426) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005068 / 0.011353 (-0.006284) | 0.002989 / 0.011008 (-0.008019) | 0.048213 / 0.038508 (0.009705) | 0.056133 / 0.023109 (0.033024) | 0.283347 / 0.275898 (0.007449) | 0.307505 / 0.323480 (-0.015975) | 0.004041 / 0.007986 (-0.003944) | 0.002477 / 0.004328 (-0.001852) | 0.047771 / 0.004250 (0.043521) | 0.039361 / 0.037052 (0.002309) | 0.283764 / 0.258489 (0.025275) | 0.320644 / 0.293841 (0.026803) | 0.024972 / 0.128546 (-0.103575) | 0.007599 / 0.075646 (-0.068048) | 0.054732 / 0.419271 (-0.364539) | 0.032774 / 0.043533 (-0.010759) | 0.285594 / 0.255139 (0.030455) | 0.301500 / 0.283200 (0.018300) | 0.018181 / 0.141683 (-0.123501) | 1.126311 / 1.452155 (-0.325843) | 1.187147 / 1.492716 (-0.305569) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097397 / 0.018006 (0.079391) | 0.315112 / 0.000490 (0.314622) | 0.000224 / 0.000200 (0.000024) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021529 / 0.037411 (-0.015882) | 0.073208 / 0.014526 (0.058682) | 0.081683 / 0.176557 (-0.094874) | 0.120475 / 0.737135 (-0.616660) | 0.083265 / 0.296338 (-0.213073) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289976 / 0.215209 (0.074767) | 2.839860 / 2.077655 (0.762205) | 1.592635 / 1.504120 (0.088515) | 1.466722 / 1.541195 (-0.074472) | 1.552850 / 1.468490 (0.084360) | 0.418693 / 4.584777 (-4.166084) | 2.526620 / 3.745712 (-1.219093) | 2.706182 / 5.269862 (-2.563680) | 1.618514 / 4.565676 (-2.947162) | 0.046303 / 0.424275 (-0.377972) | 0.004873 / 0.007607 (-0.002734) | 0.345146 / 0.226044 (0.119102) | 3.378448 / 2.268929 (1.109520) | 1.986393 / 55.444624 (-53.458231) | 1.681838 / 6.876477 (-5.194639) | 1.738093 / 2.142072 (-0.403980) | 0.484386 / 4.805227 (-4.320842) | 0.100693 / 6.500664 (-6.399971) | 0.043084 / 0.075469 (-0.032385) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976399 / 1.841788 (-0.865389) | 13.122968 / 8.074308 (5.048660) | 11.245031 / 10.191392 (1.053639) | 0.134433 / 0.680424 (-0.545991) | 0.017439 / 0.534201 (-0.516762) | 0.274083 / 0.579283 (-0.305200) | 0.287353 / 0.434364 (-0.147011) | 0.309231 / 0.540337 (-0.231106) | 0.418003 / 1.386936 (-0.968933) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939f136f255eab68a5bf6441db2a395f8af78511 \"CML watermark\")\n" ]
2023-11-14T15:15:29
2023-11-15T11:59:32
2023-11-15T11:53:20
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PR_kwDODunzps5fa4n7
6,415
Fix multi gpu map example
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004537 / 0.011353 (-0.006816) | 0.002844 / 0.011008 (-0.008164) | 0.062506 / 0.038508 (0.023998) | 0.029675 / 0.023109 (0.006566) | 0.238080 / 0.275898 (-0.037818) | 0.259858 / 0.323480 (-0.063622) | 0.004015 / 0.007986 (-0.003970) | 0.002432 / 0.004328 (-0.001897) | 0.049477 / 0.004250 (0.045227) | 0.045383 / 0.037052 (0.008331) | 0.241934 / 0.258489 (-0.016555) | 0.270759 / 0.293841 (-0.023082) | 0.023207 / 0.128546 (-0.105339) | 0.007107 / 0.075646 (-0.068539) | 0.207626 / 0.419271 (-0.211645) | 0.056706 / 0.043533 (0.013173) | 0.239713 / 0.255139 (-0.015426) | 0.256639 / 0.283200 (-0.026560) | 0.017514 / 0.141683 (-0.124169) | 1.105201 / 1.452155 (-0.346953) | 1.173087 / 1.492716 (-0.319629) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093391 / 0.018006 (0.075384) | 0.302673 / 0.000490 (0.302184) | 0.000218 / 0.000200 (0.000018) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019447 / 0.037411 (-0.017965) | 0.063349 / 0.014526 (0.048823) | 0.075600 / 0.176557 (-0.100957) | 0.121098 / 0.737135 (-0.616037) | 0.075028 / 0.296338 (-0.221311) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291479 / 0.215209 (0.076270) | 2.787231 / 2.077655 (0.709576) | 1.480205 / 1.504120 (-0.023915) | 1.417656 / 1.541195 (-0.123538) | 1.394529 / 1.468490 (-0.073962) | 0.408843 / 4.584777 (-4.175934) | 2.398691 / 3.745712 (-1.347021) | 2.635457 / 5.269862 (-2.634404) | 1.591722 / 4.565676 (-2.973955) | 0.048445 / 0.424275 (-0.375830) | 0.004864 / 0.007607 (-0.002743) | 0.349014 / 0.226044 (0.122969) | 3.436962 / 2.268929 (1.168033) | 1.839266 / 55.444624 (-53.605359) | 1.535252 / 6.876477 (-5.341225) | 1.581048 / 2.142072 (-0.561025) | 0.491150 / 4.805227 (-4.314078) | 0.101279 / 6.500664 (-6.399385) | 0.041938 / 0.075469 (-0.033532) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946986 / 1.841788 (-0.894801) | 11.766196 / 8.074308 (3.691888) | 10.425615 / 10.191392 (0.234223) | 0.129957 / 0.680424 (-0.550467) | 0.014859 / 0.534201 (-0.519342) | 0.268046 / 0.579283 (-0.311237) | 0.263724 / 0.434364 (-0.170640) | 0.311028 / 0.540337 (-0.229309) | 0.434715 / 1.386936 (-0.952221) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004874 / 0.011353 (-0.006479) | 0.002942 / 0.011008 (-0.008067) | 0.048250 / 0.038508 (0.009742) | 0.053726 / 0.023109 (0.030617) | 0.268870 / 0.275898 (-0.007028) | 0.289152 / 0.323480 (-0.034328) | 0.003982 / 0.007986 (-0.004004) | 0.002488 / 0.004328 (-0.001840) | 0.047902 / 0.004250 (0.043652) | 0.038732 / 0.037052 (0.001680) | 0.271021 / 0.258489 (0.012532) | 0.299967 / 0.293841 (0.006126) | 0.024672 / 0.128546 (-0.103874) | 0.007311 / 0.075646 (-0.068336) | 0.053721 / 0.419271 (-0.365550) | 0.032407 / 0.043533 (-0.011126) | 0.266604 / 0.255139 (0.011465) | 0.286816 / 0.283200 (0.003617) | 0.018973 / 0.141683 (-0.122710) | 1.122460 / 1.452155 (-0.329695) | 1.177720 / 1.492716 (-0.314997) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093968 / 0.018006 (0.075962) | 0.304010 / 0.000490 (0.303521) | 0.000228 / 0.000200 (0.000028) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021203 / 0.037411 (-0.016208) | 0.070318 / 0.014526 (0.055793) | 0.081688 / 0.176557 (-0.094869) | 0.120916 / 0.737135 (-0.616219) | 0.083452 / 0.296338 (-0.212886) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293961 / 0.215209 (0.078752) | 2.858514 / 2.077655 (0.780860) | 1.556169 / 1.504120 (0.052049) | 1.431523 / 1.541195 (-0.109671) | 1.478145 / 1.468490 (0.009654) | 0.408927 / 4.584777 (-4.175850) | 2.440630 / 3.745712 (-1.305082) | 2.586327 / 5.269862 (-2.683534) | 1.529495 / 4.565676 (-3.036182) | 0.047387 / 0.424275 (-0.376888) | 0.004817 / 0.007607 (-0.002790) | 0.345009 / 0.226044 (0.118965) | 3.386313 / 2.268929 (1.117384) | 1.922361 / 55.444624 (-53.522264) | 1.640814 / 6.876477 (-5.235663) | 1.657005 / 2.142072 (-0.485068) | 0.483844 / 4.805227 (-4.321383) | 0.099470 / 6.500664 (-6.401194) | 0.040735 / 0.075469 (-0.034734) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986311 / 1.841788 (-0.855476) | 12.327425 / 8.074308 (4.253117) | 10.995135 / 10.191392 (0.803743) | 0.146814 / 0.680424 (-0.533610) | 0.015820 / 0.534201 (-0.518381) | 0.272319 / 0.579283 (-0.306964) | 0.274858 / 0.434364 (-0.159506) | 0.305728 / 0.540337 (-0.234609) | 0.421400 / 1.386936 (-0.965536) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007995 / 0.011353 (-0.003358) | 0.004596 / 0.011008 (-0.006412) | 0.099818 / 0.038508 (0.061310) | 0.053539 / 0.023109 (0.030429) | 0.367757 / 0.275898 (0.091859) | 0.409351 / 0.323480 (0.085871) | 0.007423 / 0.007986 (-0.000563) | 0.003770 / 0.004328 (-0.000558) | 0.075635 / 0.004250 (0.071385) | 0.078844 / 0.037052 (0.041791) | 0.374523 / 0.258489 (0.116034) | 0.423378 / 0.293841 (0.129537) | 0.038901 / 0.128546 (-0.089645) | 0.009985 / 0.075646 (-0.065661) | 0.342793 / 0.419271 (-0.076479) | 0.098045 / 0.043533 (0.054512) | 0.368077 / 0.255139 (0.112938) | 0.394251 / 0.283200 (0.111051) | 0.030624 / 0.141683 (-0.111059) | 1.782728 / 1.452155 (0.330574) | 1.867571 / 1.492716 (0.374855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265550 / 0.018006 (0.247544) | 0.504045 / 0.000490 (0.503555) | 0.016523 / 0.000200 (0.016323) | 0.000757 / 0.000054 (0.000702) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034239 / 0.037411 (-0.003172) | 0.099953 / 0.014526 (0.085427) | 0.113728 / 0.176557 (-0.062829) | 0.180113 / 0.737135 (-0.557023) | 0.114506 / 0.296338 (-0.181833) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507186 / 0.215209 (0.291977) | 5.033590 / 2.077655 (2.955935) | 2.480111 / 1.504120 (0.975991) | 2.258966 / 1.541195 (0.717771) | 2.316045 / 1.468490 (0.847555) | 0.622482 / 4.584777 (-3.962295) | 4.400909 / 3.745712 (0.655197) | 4.012443 / 5.269862 (-1.257419) | 2.408294 / 4.565676 (-2.157383) | 0.067608 / 0.424275 (-0.356668) | 0.008638 / 0.007607 (0.001031) | 0.546558 / 0.226044 (0.320513) | 5.472973 / 2.268929 (3.204044) | 2.795147 / 55.444624 (-52.649477) | 2.371153 / 6.876477 (-4.505324) | 2.440883 / 2.142072 (0.298811) | 0.682380 / 4.805227 (-4.122847) | 0.156819 / 6.500664 (-6.343845) | 0.071969 / 0.075469 (-0.003500) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.500200 / 1.841788 (-0.341588) | 22.854103 / 8.074308 (14.779795) | 16.691945 / 10.191392 (6.500553) | 0.210945 / 0.680424 (-0.469479) | 0.023234 / 0.534201 (-0.510967) | 0.475641 / 0.579283 (-0.103642) | 0.491553 / 0.434364 (0.057189) | 0.549311 / 0.540337 (0.008974) | 0.858498 / 1.386936 (-0.528439) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009020 / 0.011353 (-0.002333) | 0.004768 / 0.011008 (-0.006240) | 0.082841 / 0.038508 (0.044333) | 0.095111 / 0.023109 (0.072002) | 0.486050 / 0.275898 (0.210151) | 0.527074 / 0.323480 (0.203594) | 0.006622 / 0.007986 (-0.001364) | 0.003961 / 0.004328 (-0.000367) | 0.083361 / 0.004250 (0.079111) | 0.068571 / 0.037052 (0.031518) | 0.494575 / 0.258489 (0.236086) | 0.545593 / 0.293841 (0.251752) | 0.047671 / 0.128546 (-0.080875) | 0.010715 / 0.075646 (-0.064932) | 0.096239 / 0.419271 (-0.323033) | 0.061556 / 0.043533 (0.018023) | 0.484301 / 0.255139 (0.229162) | 0.492189 / 0.283200 (0.208989) | 0.029374 / 0.141683 (-0.112309) | 1.911833 / 1.452155 (0.459678) | 2.005744 / 1.492716 (0.513028) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265402 / 0.018006 (0.247396) | 0.501034 / 0.000490 (0.500545) | 0.004039 / 0.000200 (0.003839) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.041005 / 0.037411 (0.003594) | 0.119204 / 0.014526 (0.104678) | 0.134583 / 0.176557 (-0.041973) | 0.195995 / 0.737135 (-0.541140) | 0.133125 / 0.296338 (-0.163214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503012 / 0.215209 (0.287803) | 5.021972 / 2.077655 (2.944318) | 2.912987 / 1.504120 (1.408867) | 2.707637 / 1.541195 (1.166442) | 2.824065 / 1.468490 (1.355575) | 0.664285 / 4.584777 (-3.920492) | 4.341905 / 3.745712 (0.596193) | 4.152839 / 5.269862 (-1.117022) | 2.438138 / 4.565676 (-2.127539) | 0.076169 / 0.424275 (-0.348106) | 0.010471 / 0.007607 (0.002864) | 0.680918 / 0.226044 (0.454874) | 6.424209 / 2.268929 (4.155281) | 3.285353 / 55.444624 (-52.159271) | 2.865458 / 6.876477 (-4.011019) | 2.946246 / 2.142072 (0.804173) | 0.700051 / 4.805227 (-4.105176) | 0.155299 / 6.500664 (-6.345365) | 0.069372 / 0.075469 (-0.006097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.749517 / 1.841788 (-0.092271) | 23.382582 / 8.074308 (15.308274) | 17.708718 / 10.191392 (7.517326) | 0.197042 / 0.680424 (-0.483382) | 0.023874 / 0.534201 (-0.510327) | 0.471631 / 0.579283 (-0.107652) | 0.512649 / 0.434364 (0.078285) | 0.614479 / 0.540337 (0.074142) | 0.771859 / 1.386936 (-0.615077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f \"CML watermark\")\n", "Merging this one, but lmk if you have more comments for subsequent improvements @NielsRogge ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004874 / 0.011353 (-0.006479) | 0.002866 / 0.011008 (-0.008142) | 0.061761 / 0.038508 (0.023253) | 0.052185 / 0.023109 (0.029076) | 0.242264 / 0.275898 (-0.033634) | 0.267816 / 0.323480 (-0.055664) | 0.002844 / 0.007986 (-0.005142) | 0.002349 / 0.004328 (-0.001979) | 0.048393 / 0.004250 (0.044142) | 0.038590 / 0.037052 (0.001538) | 0.257483 / 0.258489 (-0.001006) | 0.279704 / 0.293841 (-0.014137) | 0.023125 / 0.128546 (-0.105421) | 0.007044 / 0.075646 (-0.068602) | 0.203606 / 0.419271 (-0.215665) | 0.035489 / 0.043533 (-0.008044) | 0.248419 / 0.255139 (-0.006719) | 0.266357 / 0.283200 (-0.016843) | 0.020178 / 0.141683 (-0.121505) | 1.163674 / 1.452155 (-0.288481) | 1.191340 / 1.492716 (-0.301376) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092972 / 0.018006 (0.074966) | 0.295260 / 0.000490 (0.294770) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018109 / 0.037411 (-0.019302) | 0.061743 / 0.014526 (0.047217) | 0.073965 / 0.176557 (-0.102592) | 0.119493 / 0.737135 (-0.617642) | 0.075646 / 0.296338 (-0.220692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275700 / 0.215209 (0.060491) | 2.666846 / 2.077655 (0.589191) | 1.401452 / 1.504120 (-0.102668) | 1.276009 / 1.541195 (-0.265186) | 1.309914 / 1.468490 (-0.158576) | 0.396411 / 4.584777 (-4.188365) | 2.347193 / 3.745712 (-1.398519) | 2.568006 / 5.269862 (-2.701856) | 1.564572 / 4.565676 (-3.001105) | 0.045450 / 0.424275 (-0.378825) | 0.004827 / 0.007607 (-0.002780) | 0.333092 / 0.226044 (0.107048) | 3.284295 / 2.268929 (1.015367) | 1.809928 / 55.444624 (-53.634696) | 1.486041 / 6.876477 (-5.390436) | 1.528198 / 2.142072 (-0.613875) | 0.470053 / 4.805227 (-4.335174) | 0.098559 / 6.500664 (-6.402105) | 0.041637 / 0.075469 (-0.033832) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948915 / 1.841788 (-0.892873) | 11.513211 / 8.074308 (3.438903) | 10.386419 / 10.191392 (0.195027) | 0.129513 / 0.680424 (-0.550910) | 0.021772 / 0.534201 (-0.512429) | 0.295627 / 0.579283 (-0.283656) | 0.261008 / 0.434364 (-0.173355) | 0.305869 / 0.540337 (-0.234469) | 0.399676 / 1.386936 (-0.987260) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004799 / 0.011353 (-0.006553) | 0.002764 / 0.011008 (-0.008244) | 0.048469 / 0.038508 (0.009961) | 0.051346 / 0.023109 (0.028236) | 0.274853 / 0.275898 (-0.001045) | 0.300770 / 0.323480 (-0.022710) | 0.003986 / 0.007986 (-0.003999) | 0.002376 / 0.004328 (-0.001952) | 0.048545 / 0.004250 (0.044294) | 0.039854 / 0.037052 (0.002801) | 0.280053 / 0.258489 (0.021564) | 0.312797 / 0.293841 (0.018957) | 0.024513 / 0.128546 (-0.104033) | 0.006971 / 0.075646 (-0.068675) | 0.053030 / 0.419271 (-0.366241) | 0.035580 / 0.043533 (-0.007953) | 0.276078 / 0.255139 (0.020939) | 0.299345 / 0.283200 (0.016145) | 0.020423 / 0.141683 (-0.121260) | 1.103053 / 1.452155 (-0.349102) | 1.179747 / 1.492716 (-0.312969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093042 / 0.018006 (0.075036) | 0.299421 / 0.000490 (0.298932) | 0.000232 / 0.000200 (0.000033) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021966 / 0.037411 (-0.015445) | 0.070978 / 0.014526 (0.056452) | 0.083841 / 0.176557 (-0.092715) | 0.121223 / 0.737135 (-0.615912) | 0.082829 / 0.296338 (-0.213510) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289436 / 0.215209 (0.074227) | 2.838074 / 2.077655 (0.760419) | 1.597013 / 1.504120 (0.092893) | 1.476888 / 1.541195 (-0.064307) | 1.504582 / 1.468490 (0.036092) | 0.398050 / 4.584777 (-4.186727) | 2.434446 / 3.745712 (-1.311266) | 2.493545 / 5.269862 (-2.776316) | 1.584159 / 4.565676 (-2.981517) | 0.046461 / 0.424275 (-0.377814) | 0.004876 / 0.007607 (-0.002731) | 0.344166 / 0.226044 (0.118122) | 3.388530 / 2.268929 (1.119602) | 1.939585 / 55.444624 (-53.505039) | 1.672495 / 6.876477 (-5.203982) | 1.811825 / 2.142072 (-0.330247) | 0.470798 / 4.805227 (-4.334429) | 0.097522 / 6.500664 (-6.403142) | 0.040887 / 0.075469 (-0.034582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990081 / 1.841788 (-0.851707) | 12.619827 / 8.074308 (4.545519) | 10.748062 / 10.191392 (0.556670) | 0.130409 / 0.680424 (-0.550015) | 0.016624 / 0.534201 (-0.517577) | 0.272381 / 0.579283 (-0.306902) | 0.270597 / 0.434364 (-0.163767) | 0.306458 / 0.540337 (-0.233879) | 0.408700 / 1.386936 (-0.978236) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc44d2188a1baac50d28a6c8110d6e5497f409de \"CML watermark\")\n", "This is a little hard to follow — where is the documentation currently? I am trying to follow from snippets, here is what I have based on your convo in this thread:\r\n\r\n```>>> import os\r\n>>>\r\n>>> for i in range(torch.cuda.device_count()): # send model to every GPU\r\n... model.to(f\"cuda:{i}\")\r\n>>>\r\n>>> def gpu_computation(example, rank):\r\n... torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU\r\n... inputs = tokenizer(texts, truncation=True, return_tensors=\"pt\").to(f\"cuda:{rank}\")\r\n... outputs = model(**inputs)\r\n... .... \r\n```\r\n\r\nbut I'm getting device errors (data is on device 3, but it thinks model is on device 0, despite setting `torch.cuda.set_device`\r\n\r\nIs this correct? What version of Torch are you using for this? ", "Anyway, this didn't work for me:\r\n\r\n`torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU`\r\n\r\nbut substituting it for:\r\n\r\n`model.to(f\"cuda:{rank}\")`\r\n\r\n(`.to` doesn't make a million copies of the model on the device, which I was worried it would do... so you can use it in an inner process)\r\n\r\n(btw, versions: `torch==2.1.1`, `cuda=12.2`)", "Yeah for me this issue isn't resolved yet, we need a better code example", "Hi @alex2awesome, could you open a PR with your suggestion to improve this code snippet ?", "i'm happy to when i get it fully working, but i feel like there are some fundamentals that I'm not fully understanding...\r\n\r\nI've set it up twice now, for 2 GPU-processing pipelines. \r\n\r\nIn one pipelines, my memory usage is fine, it delivers me a huge speedup, and everything is great. In the second pipeline, I keep getting OOM errors when `num_proc > 1` that I don't get when `num_proc=1`. \r\n\r\nThere is a discussion here: https://github.com/pytorch/pytorch/issues/44156 about CUDA memory leaks in multiprocessing setups, and I haven't had the time to fully read the source code to `datasets.map` to understand whether the situations are parallel. Also, if they are, then I don't know what the solution is, not really knowing how it is implemented under the hood. In that discussion, one guy offers a work-around, but it doesn't look great.\r\n\r\nSo, I haven't fully tested out enough to see what the issue. If I feel comfortable over the next several days to generate a slimmed-down example that will generalize to real-world cases such as those I'm working with now, then I will contribute it.\r\n\r\n", "@lhoestq do you know how `datasets` does multiprocessing? Do we use:\r\nhttps://pytorch.org/docs/stable/multiprocessing.html#module-torch.multiprocessing?\r\n\r\nIf so, there are lots of points around memory usage, here:\r\nhttps://pytorch.org/docs/stable/notes/multiprocessing.html\r\n\r\nEDIT: ahh I see it is using python's native multiprocessing library: https://github.com/huggingface/datasets/blob/2.15.0/src/datasets/arrow_dataset.py#L3172-L3189", "After some more research and playing around, I can't pinpoint the source of my CUDA memory leak nor can I determine with confidence what works and what doesn't in this setup.\r\n\r\nI'm not really an expert on multiprocessing in general, but my gut is that the current set-up isn't ideal for multiprocessing and I'm not sure I would recommend users to do this. \r\n\r\nKinda unfortunate, because I don't see any great tools for distributed inference out there, and in theory, `datasets.map` could be the standard.\r\n\r\nAre either of you more experienced in this?", "Not sure about your GPU's OOM :/\r\n\r\nStill, I opened a PR with your suggestion here: https://github.com/huggingface/datasets/pull/6550", "I still get only 0 rank...\r\n\r\nHere is my code: https://pastebin.com/c6du8jaM\r\n\r\nfrom this ^ i just improt one function:\r\n\r\n\r\n```\r\nfrom test import map_train\r\nfrom multiprocess import set_start_method\r\n\r\n\r\nset_start_method(\"spawn\")\r\nmap_train()\r\n```\r\n\r\nAnd here is the traceback:\r\nhttps://pastebin.com/YijspwQK ", "Also this code from your docs is not valid (source: https://huggingface.co/docs/datasets/main/en/process#multiprocessing):\r\n```\r\nfor i in range(torch.cuda.device_count()):\r\n model.to(f\"cuda:{i}\")\r\n```\r\n\r\n\r\nThis for me sends the model only to the second GPU\r\n```\r\nvae = AutoencoderKL.from_pretrained(\r\n pretrained_model_name_or_path, subfolder=\"vae\"\r\n)\r\nvae.to(\"cuda:0\")\r\nvae.to(\"cuda:1\")\r\n```", "Could you please provide a working example of multi-GPU mapping?\r\n\r\nNot just an example in docs, but a real working example starting from all imports loading datasets and models.", "@alex2awesome the same issue with CUDA OOM. It should not be happening, since it should 2 different GPUs be handling different loads. But in fact something wrong is happening.", "I haven't experimented much with the multi-GPU code documentation.\r\n\r\nCan you try using the code example at https://github.com/huggingface/datasets/pull/6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n\r\nThough if you have some fixes/improvements ideas feel free to open a PR !", "@lhoestq the mapping does not start at all in this case:\r\n<img width=\"855\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/7f29a3c1-c6dc-4bab-9955-5311256aa217\">\r\n\r\nHere is the updated code: https://pastebin.com/Kn9aGfZr", "@lhoestq with this code: https://pastebin.com/muDm78kp\r\ni now getting this error:\r\n\r\n```\r\nMap (num_proc=2): 1%| | 26288/3043663 [06:11<11:51:08, 70.72 examples/s]\r\nTraceback (most recent call last):\r\n File \"/workspace/compute.py\", line 229, in <module>\r\n map_train()\r\n File \"/workspace/compute.py\", line 224, in map_train\r\n return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, with_rank=True, num_proc=2, keep_in_memory=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 3193, in map\r\n for rank, done, content in iflatmap_unordered(\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py\", line 658, in iflatmap_unordered\r\n raise RuntimeError(\r\nRuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing.\r\n```\r\n\r\nAlso when trying to download my dataset there were no issues from one machine, but from another:\r\n```\r\nSSLError: (MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/datasets/kopyl/3M_icons_monochrome_only_no_captioning/revision/753dca4be462dad7022f34cc273555ab6deb5832 (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\"), '(Request ID: 7d0881f3-1b93-4d73-bcb6-52e816d84529)')\r\n```\r\n\r\nCan't download my dataset at all...", "Hmm this is not good, do you know a way to make it work ?\r\n\r\nBasically `map` creates two subprocesses and runs the function in the subprocesses. Since each function has a parameter `rank` it should be possible to choose which GPU to use", "I can confirm that PR #6550 works. All GPUs are at full throttle. You have to manually move the model to all GPUs. \r\n\r\n> I haven't experimented much with the multi-GPU code documentation.\r\n> \r\n> Can you try using the code example at #6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n> \r\n> Though if you have some fixes/improvements ideas feel free to open a PR !\r\n\r\n", "I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example by compiling information from several PRs and issues here. Hope it can help. Let me know how it works. \r\n\r\n> Could you please provide a working example of multi-GPU mapping?\r\n> \r\n> Not just an example in docs, but a real working example starting from all imports loading datasets and models.\r\n\r\n" ]
2023-11-14T14:57:18
2024-01-31T00:49:15
2023-11-22T15:42:19
MEMBER
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- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES ` - add `if __name__ == "__main__"` fix https://github.com/huggingface/datasets/issues/6186
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Set `usedforsecurity=False` in hashlib methods (FIPS compliance)
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008434 / 0.011353 (-0.002919) | 0.006755 / 0.011008 (-0.004253) | 0.106169 / 0.038508 (0.067661) | 0.049329 / 0.023109 (0.026220) | 0.433610 / 0.275898 (0.157712) | 0.441993 / 0.323480 (0.118513) | 0.004703 / 0.007986 (-0.003282) | 0.006996 / 0.004328 (0.002667) | 0.080330 / 0.004250 (0.076080) | 0.066098 / 0.037052 (0.029045) | 0.435444 / 0.258489 (0.176955) | 0.490442 / 0.293841 (0.196601) | 0.047050 / 0.128546 (-0.081496) | 0.014520 / 0.075646 (-0.061127) | 0.339805 / 0.419271 (-0.079467) | 0.101161 / 0.043533 (0.057629) | 0.423236 / 0.255139 (0.168097) | 0.455627 / 0.283200 (0.172427) | 0.036218 / 0.141683 (-0.105465) | 1.766128 / 1.452155 (0.313973) | 1.923919 / 1.492716 (0.431203) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242939 / 0.018006 (0.224933) | 0.515582 / 0.000490 (0.515093) | 0.020271 / 0.000200 (0.020071) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030927 / 0.037411 (-0.006484) | 0.093951 / 0.014526 (0.079425) | 0.109028 / 0.176557 (-0.067529) | 0.174947 / 0.737135 (-0.562188) | 0.120538 / 0.296338 (-0.175800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.553884 / 0.215209 (0.338675) | 5.424566 / 2.077655 (3.346911) | 2.439420 / 1.504120 (0.935301) | 2.019324 / 1.541195 (0.478129) | 2.170781 / 1.468490 (0.702290) | 0.924424 / 4.584777 (-3.660353) | 5.706029 / 3.745712 (1.960317) | 5.096911 / 5.269862 (-0.172951) | 3.168261 / 4.565676 (-1.397416) | 0.094336 / 0.424275 (-0.329940) | 0.015899 / 0.007607 (0.008292) | 0.709684 / 0.226044 (0.483639) | 7.476865 / 2.268929 (5.207936) | 3.350983 / 55.444624 (-52.093641) | 2.653419 / 6.876477 (-4.223058) | 2.802201 / 2.142072 (0.660129) | 1.081442 / 4.805227 (-3.723785) | 0.217025 / 6.500664 (-6.283639) | 0.077248 / 0.075469 (0.001779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.598621 / 1.841788 (-0.243167) | 23.490338 / 8.074308 (15.416030) | 21.853488 / 10.191392 (11.662096) | 0.209625 / 0.680424 (-0.470799) | 0.028166 / 0.534201 (-0.506035) | 0.473883 / 0.579283 (-0.105400) | 0.584226 / 0.434364 (0.149862) | 0.538605 / 0.540337 (-0.001732) | 0.837060 / 1.386936 (-0.549876) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009029 / 0.011353 (-0.002324) | 0.004945 / 0.011008 (-0.006063) | 0.084539 / 0.038508 (0.046031) | 0.081014 / 0.023109 (0.057905) | 0.431291 / 0.275898 (0.155393) | 0.478913 / 0.323480 (0.155433) | 0.006107 / 0.007986 (-0.001879) | 0.003939 / 0.004328 (-0.000390) | 0.079932 / 0.004250 (0.075682) | 0.057936 / 0.037052 (0.020884) | 0.437295 / 0.258489 (0.178806) | 0.489790 / 0.293841 (0.195949) | 0.049544 / 0.128546 (-0.079003) | 0.013675 / 0.075646 (-0.061972) | 0.093143 / 0.419271 (-0.326128) | 0.064104 / 0.043533 (0.020571) | 0.444699 / 0.255139 (0.189560) | 0.443688 / 0.283200 (0.160489) | 0.034331 / 0.141683 (-0.107352) | 1.753014 / 1.452155 (0.300859) | 1.877274 / 1.492716 (0.384558) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250460 / 0.018006 (0.232454) | 0.527241 / 0.000490 (0.526752) | 0.007679 / 0.000200 (0.007479) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033269 / 0.037411 (-0.004142) | 0.111262 / 0.014526 (0.096736) | 0.133503 / 0.176557 (-0.043053) | 0.177998 / 0.737135 (-0.559137) | 0.117899 / 0.296338 (-0.178440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633588 / 0.215209 (0.418379) | 6.105283 / 2.077655 (4.027628) | 2.779309 / 1.504120 (1.275189) | 2.445788 / 1.541195 (0.904594) | 2.396443 / 1.468490 (0.927953) | 0.925928 / 4.584777 (-3.658849) | 5.266142 / 3.745712 (1.520430) | 4.868830 / 5.269862 (-0.401031) | 2.998768 / 4.565676 (-1.566909) | 0.103135 / 0.424275 (-0.321140) | 0.008059 / 0.007607 (0.000452) | 0.753159 / 0.226044 (0.527115) | 7.532170 / 2.268929 (5.263242) | 3.563941 / 55.444624 (-51.880683) | 2.829208 / 6.876477 (-4.047269) | 2.913954 / 2.142072 (0.771881) | 1.085843 / 4.805227 (-3.719384) | 0.214195 / 6.500664 (-6.286469) | 0.071509 / 0.075469 (-0.003960) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544819 / 1.841788 (-0.296968) | 23.790149 / 8.074308 (15.715841) | 23.086019 / 10.191392 (12.894627) | 0.242695 / 0.680424 (-0.437729) | 0.041706 / 0.534201 (-0.492495) | 0.552402 / 0.579283 (-0.026881) | 0.652518 / 0.434364 (0.218154) | 0.581876 / 0.540337 (0.041539) | 0.795425 / 1.386936 (-0.591511) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004573 / 0.011353 (-0.006780) | 0.002965 / 0.011008 (-0.008043) | 0.061913 / 0.038508 (0.023405) | 0.029474 / 0.023109 (0.006365) | 0.258117 / 0.275898 (-0.017781) | 0.279854 / 0.323480 (-0.043626) | 0.003954 / 0.007986 (-0.004031) | 0.002479 / 0.004328 (-0.001850) | 0.048685 / 0.004250 (0.044434) | 0.044733 / 0.037052 (0.007681) | 0.256659 / 0.258489 (-0.001830) | 0.285235 / 0.293841 (-0.008606) | 0.023566 / 0.128546 (-0.104981) | 0.007291 / 0.075646 (-0.068355) | 0.202701 / 0.419271 (-0.216570) | 0.055706 / 0.043533 (0.012173) | 0.258790 / 0.255139 (0.003651) | 0.278675 / 0.283200 (-0.004525) | 0.018574 / 0.141683 (-0.123109) | 1.109359 / 1.452155 (-0.342796) | 1.184434 / 1.492716 (-0.308282) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095048 / 0.018006 (0.077042) | 0.305027 / 0.000490 (0.304537) | 0.000310 / 0.000200 (0.000110) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018183 / 0.037411 (-0.019228) | 0.066130 / 0.014526 (0.051604) | 0.073948 / 0.176557 (-0.102608) | 0.120458 / 0.737135 (-0.616678) | 0.075995 / 0.296338 (-0.220343) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279419 / 0.215209 (0.064210) | 2.728591 / 2.077655 (0.650936) | 1.439016 / 1.504120 (-0.065104) | 1.325798 / 1.541195 (-0.215397) | 1.352050 / 1.468490 (-0.116440) | 0.395041 / 4.584777 (-4.189736) | 2.377651 / 3.745712 (-1.368061) | 2.618473 / 5.269862 (-2.651389) | 1.587580 / 4.565676 (-2.978096) | 0.045910 / 0.424275 (-0.378365) | 0.004843 / 0.007607 (-0.002764) | 0.335491 / 0.226044 (0.109447) | 3.378441 / 2.268929 (1.109512) | 1.827757 / 55.444624 (-53.616868) | 1.502360 / 6.876477 (-5.374117) | 1.508460 / 2.142072 (-0.633612) | 0.471309 / 4.805227 (-4.333918) | 0.098934 / 6.500664 (-6.401730) | 0.041705 / 0.075469 (-0.033764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945067 / 1.841788 (-0.896720) | 11.548209 / 8.074308 (3.473900) | 10.422628 / 10.191392 (0.231236) | 0.141494 / 0.680424 (-0.538929) | 0.014345 / 0.534201 (-0.519856) | 0.267750 / 0.579283 (-0.311533) | 0.261488 / 0.434364 (-0.172876) | 0.307192 / 0.540337 (-0.233145) | 0.427926 / 1.386936 (-0.959010) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004831 / 0.011353 (-0.006522) | 0.002876 / 0.011008 (-0.008132) | 0.048629 / 0.038508 (0.010121) | 0.055090 / 0.023109 (0.031981) | 0.271381 / 0.275898 (-0.004517) | 0.292350 / 0.323480 (-0.031130) | 0.004001 / 0.007986 (-0.003985) | 0.002389 / 0.004328 (-0.001939) | 0.047527 / 0.004250 (0.043277) | 0.038065 / 0.037052 (0.001012) | 0.277387 / 0.258489 (0.018898) | 0.307209 / 0.293841 (0.013368) | 0.025136 / 0.128546 (-0.103411) | 0.007309 / 0.075646 (-0.068338) | 0.054483 / 0.419271 (-0.364789) | 0.032807 / 0.043533 (-0.010726) | 0.274364 / 0.255139 (0.019225) | 0.290280 / 0.283200 (0.007080) | 0.017855 / 0.141683 (-0.123828) | 1.185912 / 1.452155 (-0.266243) | 1.228141 / 1.492716 (-0.264576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094787 / 0.018006 (0.076781) | 0.314191 / 0.000490 (0.313701) | 0.000217 / 0.000200 (0.000017) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020920 / 0.037411 (-0.016491) | 0.070446 / 0.014526 (0.055920) | 0.081371 / 0.176557 (-0.095186) | 0.119127 / 0.737135 (-0.618009) | 0.085658 / 0.296338 (-0.210680) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290601 / 0.215209 (0.075392) | 2.874091 / 2.077655 (0.796436) | 1.598934 / 1.504120 (0.094814) | 1.464329 / 1.541195 (-0.076866) | 1.504943 / 1.468490 (0.036453) | 0.410457 / 4.584777 (-4.174320) | 2.428706 / 3.745712 (-1.317006) | 2.596510 / 5.269862 (-2.673352) | 1.547084 / 4.565676 (-3.018592) | 0.047546 / 0.424275 (-0.376729) | 0.004740 / 0.007607 (-0.002867) | 0.351168 / 0.226044 (0.125123) | 3.424554 / 2.268929 (1.155626) | 1.969792 / 55.444624 (-53.474832) | 1.676731 / 6.876477 (-5.199745) | 1.668769 / 2.142072 (-0.473304) | 0.482486 / 4.805227 (-4.322741) | 0.100018 / 6.500664 (-6.400646) | 0.040956 / 0.075469 (-0.034513) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966306 / 1.841788 (-0.875482) | 12.158909 / 8.074308 (4.084601) | 10.926447 / 10.191392 (0.735055) | 0.130359 / 0.680424 (-0.550065) | 0.016162 / 0.534201 (-0.518039) | 0.269977 / 0.579283 (-0.309306) | 0.283366 / 0.434364 (-0.150997) | 0.304517 / 0.540337 (-0.235821) | 0.410398 / 1.386936 (-0.976539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004686 / 0.011353 (-0.006667) | 0.002764 / 0.011008 (-0.008244) | 0.061411 / 0.038508 (0.022902) | 0.030450 / 0.023109 (0.007341) | 0.247648 / 0.275898 (-0.028250) | 0.278033 / 0.323480 (-0.045447) | 0.002903 / 0.007986 (-0.005082) | 0.002350 / 0.004328 (-0.001979) | 0.047514 / 0.004250 (0.043264) | 0.044446 / 0.037052 (0.007393) | 0.256170 / 0.258489 (-0.002319) | 0.285977 / 0.293841 (-0.007864) | 0.023407 / 0.128546 (-0.105139) | 0.007223 / 0.075646 (-0.068423) | 0.201274 / 0.419271 (-0.217997) | 0.054022 / 0.043533 (0.010489) | 0.253841 / 0.255139 (-0.001298) | 0.278219 / 0.283200 (-0.004980) | 0.017796 / 0.141683 (-0.123886) | 1.105950 / 1.452155 (-0.346205) | 1.182021 / 1.492716 (-0.310695) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089584 / 0.018006 (0.071578) | 0.299338 / 0.000490 (0.298849) | 0.000202 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018974 / 0.037411 (-0.018437) | 0.062352 / 0.014526 (0.047826) | 0.073667 / 0.176557 (-0.102889) | 0.119225 / 0.737135 (-0.617911) | 0.075393 / 0.296338 (-0.220945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282749 / 0.215209 (0.067540) | 2.795822 / 2.077655 (0.718167) | 1.492946 / 1.504120 (-0.011174) | 1.382340 / 1.541195 (-0.158855) | 1.377281 / 1.468490 (-0.091209) | 0.397361 / 4.584777 (-4.187415) | 2.379416 / 3.745712 (-1.366296) | 2.552967 / 5.269862 (-2.716895) | 1.546347 / 4.565676 (-3.019330) | 0.045851 / 0.424275 (-0.378424) | 0.004830 / 0.007607 (-0.002777) | 0.351194 / 0.226044 (0.125150) | 3.407406 / 2.268929 (1.138478) | 1.852983 / 55.444624 (-53.591641) | 1.536381 / 6.876477 (-5.340095) | 1.542786 / 2.142072 (-0.599287) | 0.471960 / 4.805227 (-4.333267) | 0.098336 / 6.500664 (-6.402328) | 0.041569 / 0.075469 (-0.033900) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.912718 / 1.841788 (-0.929070) | 11.339404 / 8.074308 (3.265095) | 10.480593 / 10.191392 (0.289201) | 0.139508 / 0.680424 (-0.540916) | 0.014210 / 0.534201 (-0.519991) | 0.268152 / 0.579283 (-0.311131) | 0.260503 / 0.434364 (-0.173860) | 0.304735 / 0.540337 (-0.235602) | 0.422155 / 1.386936 (-0.964781) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004714 / 0.011353 (-0.006638) | 0.002638 / 0.011008 (-0.008370) | 0.047967 / 0.038508 (0.009459) | 0.050758 / 0.023109 (0.027649) | 0.265619 / 0.275898 (-0.010279) | 0.286920 / 0.323480 (-0.036560) | 0.003936 / 0.007986 (-0.004050) | 0.002351 / 0.004328 (-0.001977) | 0.047642 / 0.004250 (0.043392) | 0.038412 / 0.037052 (0.001360) | 0.269561 / 0.258489 (0.011072) | 0.302057 / 0.293841 (0.008216) | 0.023893 / 0.128546 (-0.104653) | 0.006793 / 0.075646 (-0.068854) | 0.053091 / 0.419271 (-0.366180) | 0.032228 / 0.043533 (-0.011305) | 0.267110 / 0.255139 (0.011971) | 0.287211 / 0.283200 (0.004011) | 0.017945 / 0.141683 (-0.123738) | 1.191770 / 1.452155 (-0.260384) | 1.269644 / 1.492716 (-0.223072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088067 / 0.018006 (0.070061) | 0.298383 / 0.000490 (0.297893) | 0.000202 / 0.000200 (0.000002) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020685 / 0.037411 (-0.016726) | 0.069883 / 0.014526 (0.055357) | 0.080107 / 0.176557 (-0.096450) | 0.119311 / 0.737135 (-0.617825) | 0.080791 / 0.296338 (-0.215548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295781 / 0.215209 (0.080572) | 2.905536 / 2.077655 (0.827881) | 1.579184 / 1.504120 (0.075064) | 1.475937 / 1.541195 (-0.065258) | 1.533708 / 1.468490 (0.065218) | 0.409851 / 4.584777 (-4.174926) | 2.443217 / 3.745712 (-1.302496) | 2.543980 / 5.269862 (-2.725882) | 1.512187 / 4.565676 (-3.053489) | 0.046390 / 0.424275 (-0.377885) | 0.004762 / 0.007607 (-0.002845) | 0.345066 / 0.226044 (0.119021) | 3.485133 / 2.268929 (1.216204) | 1.954690 / 55.444624 (-53.489934) | 1.671104 / 6.876477 (-5.205372) | 1.655330 / 2.142072 (-0.486743) | 0.487910 / 4.805227 (-4.317317) | 0.097707 / 6.500664 (-6.402957) | 0.040379 / 0.075469 (-0.035090) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981620 / 1.841788 (-0.860168) | 11.806530 / 8.074308 (3.732222) | 10.868275 / 10.191392 (0.676883) | 0.141230 / 0.680424 (-0.539194) | 0.015785 / 0.534201 (-0.518416) | 0.271416 / 0.579283 (-0.307867) | 0.276048 / 0.434364 (-0.158316) | 0.310988 / 0.540337 (-0.229349) | 0.410078 / 1.386936 (-0.976858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004803 / 0.011353 (-0.006550) | 0.002961 / 0.011008 (-0.008047) | 0.061431 / 0.038508 (0.022923) | 0.030189 / 0.023109 (0.007080) | 0.255755 / 0.275898 (-0.020143) | 0.277841 / 0.323480 (-0.045639) | 0.003083 / 0.007986 (-0.004902) | 0.002432 / 0.004328 (-0.001896) | 0.047674 / 0.004250 (0.043424) | 0.045066 / 0.037052 (0.008014) | 0.268701 / 0.258489 (0.010211) | 0.286673 / 0.293841 (-0.007168) | 0.023663 / 0.128546 (-0.104883) | 0.007148 / 0.075646 (-0.068499) | 0.201962 / 0.419271 (-0.217310) | 0.054953 / 0.043533 (0.011420) | 0.257155 / 0.255139 (0.002016) | 0.277769 / 0.283200 (-0.005431) | 0.017803 / 0.141683 (-0.123880) | 1.100270 / 1.452155 (-0.351884) | 1.146975 / 1.492716 (-0.345741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092776 / 0.018006 (0.074770) | 0.303786 / 0.000490 (0.303296) | 0.000237 / 0.000200 (0.000037) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019647 / 0.037411 (-0.017765) | 0.063211 / 0.014526 (0.048686) | 0.076684 / 0.176557 (-0.099873) | 0.121952 / 0.737135 (-0.615184) | 0.077202 / 0.296338 (-0.219137) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282087 / 0.215209 (0.066878) | 2.789204 / 2.077655 (0.711550) | 1.510376 / 1.504120 (0.006256) | 1.384241 / 1.541195 (-0.156954) | 1.414949 / 1.468490 (-0.053541) | 0.402206 / 4.584777 (-4.182570) | 2.377601 / 3.745712 (-1.368111) | 2.585354 / 5.269862 (-2.684508) | 1.592937 / 4.565676 (-2.972740) | 0.045217 / 0.424275 (-0.379058) | 0.004772 / 0.007607 (-0.002835) | 0.339584 / 0.226044 (0.113539) | 3.373184 / 2.268929 (1.104256) | 1.855196 / 55.444624 (-53.589428) | 1.599559 / 6.876477 (-5.276918) | 1.604421 / 2.142072 (-0.537651) | 0.467754 / 4.805227 (-4.337474) | 0.098244 / 6.500664 (-6.402420) | 0.042631 / 0.075469 (-0.032838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947680 / 1.841788 (-0.894108) | 11.539875 / 8.074308 (3.465567) | 10.340830 / 10.191392 (0.149438) | 0.145591 / 0.680424 (-0.534833) | 0.014367 / 0.534201 (-0.519834) | 0.270506 / 0.579283 (-0.308777) | 0.268825 / 0.434364 (-0.165539) | 0.308372 / 0.540337 (-0.231966) | 0.425039 / 1.386936 (-0.961897) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004813 / 0.011353 (-0.006540) | 0.002931 / 0.011008 (-0.008078) | 0.047997 / 0.038508 (0.009489) | 0.050753 / 0.023109 (0.027644) | 0.272704 / 0.275898 (-0.003194) | 0.294045 / 0.323480 (-0.029435) | 0.004059 / 0.007986 (-0.003927) | 0.002491 / 0.004328 (-0.001838) | 0.047621 / 0.004250 (0.043371) | 0.038824 / 0.037052 (0.001772) | 0.275322 / 0.258489 (0.016833) | 0.306447 / 0.293841 (0.012606) | 0.024402 / 0.128546 (-0.104145) | 0.007252 / 0.075646 (-0.068394) | 0.053346 / 0.419271 (-0.365925) | 0.032224 / 0.043533 (-0.011309) | 0.271468 / 0.255139 (0.016329) | 0.289429 / 0.283200 (0.006229) | 0.018285 / 0.141683 (-0.123398) | 1.116743 / 1.452155 (-0.335412) | 1.182724 / 1.492716 (-0.309993) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091899 / 0.018006 (0.073893) | 0.299161 / 0.000490 (0.298671) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021823 / 0.037411 (-0.015588) | 0.071227 / 0.014526 (0.056701) | 0.080503 / 0.176557 (-0.096053) | 0.120243 / 0.737135 (-0.616892) | 0.082328 / 0.296338 (-0.214010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.324951 / 0.215209 (0.109742) | 2.842358 / 2.077655 (0.764703) | 1.602317 / 1.504120 (0.098197) | 1.481103 / 1.541195 (-0.060091) | 1.497557 / 1.468490 (0.029067) | 0.406523 / 4.584777 (-4.178254) | 2.402743 / 3.745712 (-1.342970) | 2.545435 / 5.269862 (-2.724427) | 1.534071 / 4.565676 (-3.031605) | 0.046914 / 0.424275 (-0.377361) | 0.004728 / 0.007607 (-0.002879) | 0.341544 / 0.226044 (0.115499) | 3.412017 / 2.268929 (1.143089) | 1.937442 / 55.444624 (-53.507182) | 1.668774 / 6.876477 (-5.207703) | 1.668908 / 2.142072 (-0.473165) | 0.477398 / 4.805227 (-4.327829) | 0.098531 / 6.500664 (-6.402133) | 0.041077 / 0.075469 (-0.034392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983888 / 1.841788 (-0.857900) | 12.072703 / 8.074308 (3.998395) | 11.028622 / 10.191392 (0.837230) | 0.148097 / 0.680424 (-0.532327) | 0.015869 / 0.534201 (-0.518332) | 0.267609 / 0.579283 (-0.311674) | 0.272345 / 0.434364 (-0.162019) | 0.303840 / 0.540337 (-0.236497) | 0.409199 / 1.386936 (-0.977737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005016 / 0.011353 (-0.006337) | 0.002931 / 0.011008 (-0.008077) | 0.062142 / 0.038508 (0.023634) | 0.030758 / 0.023109 (0.007648) | 0.251689 / 0.275898 (-0.024209) | 0.272114 / 0.323480 (-0.051366) | 0.004102 / 0.007986 (-0.003884) | 0.002500 / 0.004328 (-0.001828) | 0.049187 / 0.004250 (0.044937) | 0.047150 / 0.037052 (0.010098) | 0.256497 / 0.258489 (-0.001992) | 0.288069 / 0.293841 (-0.005772) | 0.023915 / 0.128546 (-0.104632) | 0.007204 / 0.075646 (-0.068442) | 0.204257 / 0.419271 (-0.215015) | 0.063879 / 0.043533 (0.020346) | 0.253008 / 0.255139 (-0.002131) | 0.266554 / 0.283200 (-0.016645) | 0.018929 / 0.141683 (-0.122754) | 1.140547 / 1.452155 (-0.311608) | 1.197049 / 1.492716 (-0.295668) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094111 / 0.018006 (0.076105) | 0.301618 / 0.000490 (0.301128) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018614 / 0.037411 (-0.018797) | 0.062426 / 0.014526 (0.047900) | 0.073079 / 0.176557 (-0.103477) | 0.120313 / 0.737135 (-0.616823) | 0.076445 / 0.296338 (-0.219894) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285151 / 0.215209 (0.069942) | 2.754272 / 2.077655 (0.676617) | 1.485254 / 1.504120 (-0.018866) | 1.368412 / 1.541195 (-0.172783) | 1.402819 / 1.468490 (-0.065671) | 0.396561 / 4.584777 (-4.188216) | 2.375708 / 3.745712 (-1.370004) | 2.656088 / 5.269862 (-2.613773) | 1.588676 / 4.565676 (-2.977001) | 0.048662 / 0.424275 (-0.375613) | 0.004963 / 0.007607 (-0.002644) | 0.339747 / 0.226044 (0.113702) | 3.315841 / 2.268929 (1.046912) | 1.841439 / 55.444624 (-53.603186) | 1.547803 / 6.876477 (-5.328674) | 1.601872 / 2.142072 (-0.540200) | 0.468637 / 4.805227 (-4.336591) | 0.099423 / 6.500664 (-6.401241) | 0.041926 / 0.075469 (-0.033543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933058 / 1.841788 (-0.908730) | 11.680870 / 8.074308 (3.606561) | 10.239009 / 10.191392 (0.047617) | 0.129974 / 0.680424 (-0.550450) | 0.014081 / 0.534201 (-0.520120) | 0.273076 / 0.579283 (-0.306207) | 0.261914 / 0.434364 (-0.172450) | 0.305982 / 0.540337 (-0.234356) | 0.430623 / 1.386936 (-0.956313) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004969 / 0.011353 (-0.006384) | 0.003084 / 0.011008 (-0.007924) | 0.048686 / 0.038508 (0.010178) | 0.057234 / 0.023109 (0.034125) | 0.295408 / 0.275898 (0.019510) | 0.323774 / 0.323480 (0.000294) | 0.004014 / 0.007986 (-0.003972) | 0.002423 / 0.004328 (-0.001905) | 0.048000 / 0.004250 (0.043749) | 0.039872 / 0.037052 (0.002820) | 0.294717 / 0.258489 (0.036228) | 0.331149 / 0.293841 (0.037309) | 0.027884 / 0.128546 (-0.100662) | 0.007155 / 0.075646 (-0.068491) | 0.053812 / 0.419271 (-0.365460) | 0.032483 / 0.043533 (-0.011050) | 0.293402 / 0.255139 (0.038263) | 0.312553 / 0.283200 (0.029354) | 0.017848 / 0.141683 (-0.123835) | 1.125600 / 1.452155 (-0.326554) | 1.189469 / 1.492716 (-0.303248) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096198 / 0.018006 (0.078191) | 0.305096 / 0.000490 (0.304607) | 0.000229 / 0.000200 (0.000029) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021992 / 0.037411 (-0.015419) | 0.072082 / 0.014526 (0.057556) | 0.082704 / 0.176557 (-0.093853) | 0.124512 / 0.737135 (-0.612624) | 0.084541 / 0.296338 (-0.211797) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296440 / 0.215209 (0.081231) | 2.923392 / 2.077655 (0.845738) | 1.599057 / 1.504120 (0.094937) | 1.480473 / 1.541195 (-0.060722) | 1.551837 / 1.468490 (0.083347) | 0.418618 / 4.584777 (-4.166159) | 2.472727 / 3.745712 (-1.272985) | 2.796141 / 5.269862 (-2.473721) | 1.629139 / 4.565676 (-2.936538) | 0.047703 / 0.424275 (-0.376572) | 0.004971 / 0.007607 (-0.002636) | 0.354453 / 0.226044 (0.128408) | 3.514861 / 2.268929 (1.245932) | 1.993597 / 55.444624 (-53.451028) | 1.694386 / 6.876477 (-5.182090) | 1.748562 / 2.142072 (-0.393510) | 0.487158 / 4.805227 (-4.318070) | 0.102021 / 6.500664 (-6.398643) | 0.042648 / 0.075469 (-0.032821) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974950 / 1.841788 (-0.866837) | 13.391204 / 8.074308 (5.316896) | 11.474696 / 10.191392 (1.283304) | 0.142618 / 0.680424 (-0.537806) | 0.016163 / 0.534201 (-0.518038) | 0.271453 / 0.579283 (-0.307830) | 0.287049 / 0.434364 (-0.147315) | 0.309069 / 0.540337 (-0.231268) | 0.417117 / 1.386936 (-0.969819) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004974 / 0.011353 (-0.006379) | 0.002950 / 0.011008 (-0.008058) | 0.061856 / 0.038508 (0.023348) | 0.030539 / 0.023109 (0.007429) | 0.250105 / 0.275898 (-0.025793) | 0.276687 / 0.323480 (-0.046793) | 0.003077 / 0.007986 (-0.004908) | 0.002412 / 0.004328 (-0.001916) | 0.048336 / 0.004250 (0.044086) | 0.045849 / 0.037052 (0.008797) | 0.251757 / 0.258489 (-0.006732) | 0.284914 / 0.293841 (-0.008927) | 0.024033 / 0.128546 (-0.104513) | 0.007343 / 0.075646 (-0.068303) | 0.202867 / 0.419271 (-0.216405) | 0.061294 / 0.043533 (0.017762) | 0.263590 / 0.255139 (0.008451) | 0.272744 / 0.283200 (-0.010455) | 0.019613 / 0.141683 (-0.122070) | 1.104263 / 1.452155 (-0.347892) | 1.164128 / 1.492716 (-0.328588) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094261 / 0.018006 (0.076255) | 0.303340 / 0.000490 (0.302850) | 0.000215 / 0.000200 (0.000015) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018381 / 0.037411 (-0.019030) | 0.062727 / 0.014526 (0.048201) | 0.074955 / 0.176557 (-0.101602) | 0.124810 / 0.737135 (-0.612326) | 0.074335 / 0.296338 (-0.222004) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279368 / 0.215209 (0.064159) | 2.721641 / 2.077655 (0.643986) | 1.510773 / 1.504120 (0.006653) | 1.364349 / 1.541195 (-0.176845) | 1.386044 / 1.468490 (-0.082446) | 0.403051 / 4.584777 (-4.181726) | 2.416525 / 3.745712 (-1.329187) | 2.623198 / 5.269862 (-2.646663) | 1.560869 / 4.565676 (-3.004808) | 0.046613 / 0.424275 (-0.377662) | 0.004861 / 0.007607 (-0.002746) | 0.337875 / 0.226044 (0.111830) | 3.289956 / 2.268929 (1.021028) | 1.851707 / 55.444624 (-53.592917) | 1.571092 / 6.876477 (-5.305385) | 1.600328 / 2.142072 (-0.541745) | 0.480766 / 4.805227 (-4.324461) | 0.099138 / 6.500664 (-6.401526) | 0.041691 / 0.075469 (-0.033779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941162 / 1.841788 (-0.900626) | 11.745335 / 8.074308 (3.671027) | 10.645509 / 10.191392 (0.454117) | 0.132506 / 0.680424 (-0.547918) | 0.015192 / 0.534201 (-0.519009) | 0.272483 / 0.579283 (-0.306800) | 0.270269 / 0.434364 (-0.164094) | 0.309580 / 0.540337 (-0.230758) | 0.431513 / 1.386936 (-0.955423) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005068 / 0.011353 (-0.006285) | 0.003069 / 0.011008 (-0.007939) | 0.048605 / 0.038508 (0.010097) | 0.059557 / 0.023109 (0.036448) | 0.275092 / 0.275898 (-0.000806) | 0.298910 / 0.323480 (-0.024570) | 0.004198 / 0.007986 (-0.003788) | 0.002499 / 0.004328 (-0.001830) | 0.048248 / 0.004250 (0.043997) | 0.040302 / 0.037052 (0.003249) | 0.279539 / 0.258489 (0.021050) | 0.312500 / 0.293841 (0.018659) | 0.025407 / 0.128546 (-0.103140) | 0.007364 / 0.075646 (-0.068282) | 0.053086 / 0.419271 (-0.366186) | 0.033291 / 0.043533 (-0.010242) | 0.276521 / 0.255139 (0.021382) | 0.292943 / 0.283200 (0.009743) | 0.019416 / 0.141683 (-0.122267) | 1.151734 / 1.452155 (-0.300421) | 1.205021 / 1.492716 (-0.287695) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094112 / 0.018006 (0.076106) | 0.309534 / 0.000490 (0.309044) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021539 / 0.037411 (-0.015872) | 0.070325 / 0.014526 (0.055799) | 0.080468 / 0.176557 (-0.096089) | 0.121095 / 0.737135 (-0.616040) | 0.082008 / 0.296338 (-0.214331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302591 / 0.215209 (0.087382) | 2.943475 / 2.077655 (0.865820) | 1.597970 / 1.504120 (0.093850) | 1.468774 / 1.541195 (-0.072421) | 1.504812 / 1.468490 (0.036322) | 0.413715 / 4.584777 (-4.171062) | 2.418319 / 3.745712 (-1.327393) | 2.616656 / 5.269862 (-2.653206) | 1.558165 / 4.565676 (-3.007512) | 0.047169 / 0.424275 (-0.377106) | 0.004761 / 0.007607 (-0.002846) | 0.347225 / 0.226044 (0.121180) | 3.479624 / 2.268929 (1.210696) | 1.961253 / 55.444624 (-53.483371) | 1.673532 / 6.876477 (-5.202944) | 1.698900 / 2.142072 (-0.443172) | 0.488373 / 4.805227 (-4.316855) | 0.098322 / 6.500664 (-6.402342) | 0.040832 / 0.075469 (-0.034637) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009133 / 1.841788 (-0.832655) | 13.373258 / 8.074308 (5.298949) | 11.327360 / 10.191392 (1.135968) | 0.135778 / 0.680424 (-0.544646) | 0.015813 / 0.534201 (-0.518388) | 0.275404 / 0.579283 (-0.303879) | 0.282564 / 0.434364 (-0.151799) | 0.311830 / 0.540337 (-0.228507) | 0.419008 / 1.386936 (-0.967928) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004899 / 0.011353 (-0.006454) | 0.002780 / 0.011008 (-0.008229) | 0.061997 / 0.038508 (0.023489) | 0.029909 / 0.023109 (0.006800) | 0.233445 / 0.275898 (-0.042453) | 0.254128 / 0.323480 (-0.069351) | 0.002927 / 0.007986 (-0.005058) | 0.002396 / 0.004328 (-0.001932) | 0.048118 / 0.004250 (0.043868) | 0.044520 / 0.037052 (0.007468) | 0.237594 / 0.258489 (-0.020895) | 0.268407 / 0.293841 (-0.025434) | 0.023517 / 0.128546 (-0.105029) | 0.007035 / 0.075646 (-0.068612) | 0.202803 / 0.419271 (-0.216469) | 0.057692 / 0.043533 (0.014159) | 0.237058 / 0.255139 (-0.018081) | 0.252966 / 0.283200 (-0.030233) | 0.017934 / 0.141683 (-0.123748) | 1.096406 / 1.452155 (-0.355749) | 1.153509 / 1.492716 (-0.339207) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091812 / 0.018006 (0.073806) | 0.298410 / 0.000490 (0.297920) | 0.000228 / 0.000200 (0.000028) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018333 / 0.037411 (-0.019078) | 0.062685 / 0.014526 (0.048159) | 0.073295 / 0.176557 (-0.103261) | 0.119234 / 0.737135 (-0.617901) | 0.074603 / 0.296338 (-0.221736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279078 / 0.215209 (0.063869) | 2.768535 / 2.077655 (0.690880) | 1.457049 / 1.504120 (-0.047071) | 1.326870 / 1.541195 (-0.214325) | 1.349657 / 1.468490 (-0.118833) | 0.405003 / 4.584777 (-4.179774) | 2.428726 / 3.745712 (-1.316986) | 2.595776 / 5.269862 (-2.674086) | 1.557879 / 4.565676 (-3.007797) | 0.045985 / 0.424275 (-0.378291) | 0.004854 / 0.007607 (-0.002753) | 0.336437 / 0.226044 (0.110392) | 3.317330 / 2.268929 (1.048401) | 1.784525 / 55.444624 (-53.660100) | 1.500295 / 6.876477 (-5.376182) | 1.529869 / 2.142072 (-0.612203) | 0.473426 / 4.805227 (-4.331801) | 0.099609 / 6.500664 (-6.401055) | 0.042054 / 0.075469 (-0.033415) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937154 / 1.841788 (-0.904633) | 11.482383 / 8.074308 (3.408075) | 10.468769 / 10.191392 (0.277377) | 0.132724 / 0.680424 (-0.547700) | 0.015242 / 0.534201 (-0.518959) | 0.281124 / 0.579283 (-0.298159) | 0.268603 / 0.434364 (-0.165761) | 0.311410 / 0.540337 (-0.228928) | 0.431817 / 1.386936 (-0.955119) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004695 / 0.011353 (-0.006658) | 0.002873 / 0.011008 (-0.008135) | 0.048133 / 0.038508 (0.009625) | 0.052505 / 0.023109 (0.029396) | 0.271679 / 0.275898 (-0.004219) | 0.292530 / 0.323480 (-0.030950) | 0.003844 / 0.007986 (-0.004142) | 0.002417 / 0.004328 (-0.001912) | 0.048619 / 0.004250 (0.044369) | 0.039152 / 0.037052 (0.002100) | 0.276575 / 0.258489 (0.018086) | 0.307836 / 0.293841 (0.013995) | 0.023877 / 0.128546 (-0.104669) | 0.006897 / 0.075646 (-0.068749) | 0.053241 / 0.419271 (-0.366031) | 0.032487 / 0.043533 (-0.011046) | 0.274205 / 0.255139 (0.019066) | 0.289701 / 0.283200 (0.006502) | 0.018250 / 0.141683 (-0.123432) | 1.137902 / 1.452155 (-0.314253) | 1.202043 / 1.492716 (-0.290673) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091453 / 0.018006 (0.073446) | 0.297032 / 0.000490 (0.296543) | 0.000224 / 0.000200 (0.000024) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021062 / 0.037411 (-0.016349) | 0.069848 / 0.014526 (0.055322) | 0.084337 / 0.176557 (-0.092219) | 0.119951 / 0.737135 (-0.617184) | 0.082805 / 0.296338 (-0.213533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297056 / 0.215209 (0.081846) | 2.890110 / 2.077655 (0.812456) | 1.609918 / 1.504120 (0.105798) | 1.491184 / 1.541195 (-0.050011) | 1.529433 / 1.468490 (0.060943) | 0.396081 / 4.584777 (-4.188696) | 2.408310 / 3.745712 (-1.337402) | 2.567905 / 5.269862 (-2.701957) | 1.514465 / 4.565676 (-3.051212) | 0.045329 / 0.424275 (-0.378946) | 0.004738 / 0.007607 (-0.002869) | 0.344373 / 0.226044 (0.118328) | 3.428333 / 2.268929 (1.159404) | 1.981401 / 55.444624 (-53.463223) | 1.688007 / 6.876477 (-5.188470) | 1.685542 / 2.142072 (-0.456531) | 0.478045 / 4.805227 (-4.327182) | 0.096664 / 6.500664 (-6.404001) | 0.040335 / 0.075469 (-0.035135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972912 / 1.841788 (-0.868876) | 12.055045 / 8.074308 (3.980737) | 10.821073 / 10.191392 (0.629681) | 0.139177 / 0.680424 (-0.541247) | 0.015046 / 0.534201 (-0.519155) | 0.275670 / 0.579283 (-0.303613) | 0.280366 / 0.434364 (-0.153998) | 0.315781 / 0.540337 (-0.224556) | 0.424536 / 1.386936 (-0.962400) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n", "I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004435 / 0.011353 (-0.006918) | 0.002924 / 0.011008 (-0.008084) | 0.062159 / 0.038508 (0.023651) | 0.029639 / 0.023109 (0.006529) | 0.237470 / 0.275898 (-0.038428) | 0.269641 / 0.323480 (-0.053839) | 0.004124 / 0.007986 (-0.003862) | 0.002528 / 0.004328 (-0.001800) | 0.048114 / 0.004250 (0.043864) | 0.046055 / 0.037052 (0.009002) | 0.245844 / 0.258489 (-0.012645) | 0.278085 / 0.293841 (-0.015756) | 0.023152 / 0.128546 (-0.105394) | 0.007194 / 0.075646 (-0.068452) | 0.206493 / 0.419271 (-0.212778) | 0.055687 / 0.043533 (0.012155) | 0.243301 / 0.255139 (-0.011838) | 0.267645 / 0.283200 (-0.015555) | 0.017413 / 0.141683 (-0.124270) | 1.113071 / 1.452155 (-0.339083) | 1.201436 / 1.492716 (-0.291280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092576 / 0.018006 (0.074570) | 0.303516 / 0.000490 (0.303027) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019108 / 0.037411 (-0.018303) | 0.062326 / 0.014526 (0.047800) | 0.073711 / 0.176557 (-0.102846) | 0.120414 / 0.737135 (-0.616721) | 0.075837 / 0.296338 (-0.220501) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278267 / 0.215209 (0.063058) | 2.766231 / 2.077655 (0.688576) | 1.455613 / 1.504120 (-0.048507) | 1.337128 / 1.541195 (-0.204066) | 1.357659 / 1.468490 (-0.110831) | 0.404549 / 4.584777 (-4.180228) | 2.409084 / 3.745712 (-1.336628) | 2.645000 / 5.269862 (-2.624861) | 1.600475 / 4.565676 (-2.965201) | 0.046680 / 0.424275 (-0.377595) | 0.004887 / 0.007607 (-0.002720) | 0.340338 / 0.226044 (0.114294) | 3.332647 / 2.268929 (1.063719) | 1.852529 / 55.444624 (-53.592096) | 1.532442 / 6.876477 (-5.344035) | 1.550383 / 2.142072 (-0.591689) | 0.482702 / 4.805227 (-4.322525) | 0.101067 / 6.500664 (-6.399597) | 0.042132 / 0.075469 (-0.033337) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945481 / 1.841788 (-0.896307) | 11.886240 / 8.074308 (3.811932) | 10.484620 / 10.191392 (0.293228) | 0.130906 / 0.680424 (-0.549518) | 0.014880 / 0.534201 (-0.519321) | 0.268836 / 0.579283 (-0.310447) | 0.268112 / 0.434364 (-0.166251) | 0.304300 / 0.540337 (-0.236038) | 0.440262 / 1.386936 (-0.946674) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005028 / 0.011353 (-0.006325) | 0.002937 / 0.011008 (-0.008071) | 0.049038 / 0.038508 (0.010530) | 0.057763 / 0.023109 (0.034653) | 0.273196 / 0.275898 (-0.002702) | 0.295519 / 0.323480 (-0.027961) | 0.004102 / 0.007986 (-0.003883) | 0.002487 / 0.004328 (-0.001841) | 0.049148 / 0.004250 (0.044898) | 0.040303 / 0.037052 (0.003251) | 0.279187 / 0.258489 (0.020698) | 0.311086 / 0.293841 (0.017245) | 0.024961 / 0.128546 (-0.103585) | 0.007264 / 0.075646 (-0.068382) | 0.055711 / 0.419271 (-0.363561) | 0.032355 / 0.043533 (-0.011178) | 0.274304 / 0.255139 (0.019165) | 0.290953 / 0.283200 (0.007753) | 0.018358 / 0.141683 (-0.123325) | 1.115984 / 1.452155 (-0.336170) | 1.190409 / 1.492716 (-0.302308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095765 / 0.018006 (0.077759) | 0.287947 / 0.000490 (0.287457) | 0.000242 / 0.000200 (0.000042) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022165 / 0.037411 (-0.015246) | 0.070465 / 0.014526 (0.055940) | 0.082078 / 0.176557 (-0.094479) | 0.120209 / 0.737135 (-0.616926) | 0.084573 / 0.296338 (-0.211765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298492 / 0.215209 (0.083283) | 2.924981 / 2.077655 (0.847327) | 1.597326 / 1.504120 (0.093206) | 1.459132 / 1.541195 (-0.082062) | 1.511471 / 1.468490 (0.042981) | 0.406671 / 4.584777 (-4.178106) | 2.443154 / 3.745712 (-1.302558) | 2.591131 / 5.269862 (-2.678731) | 1.549931 / 4.565676 (-3.015745) | 0.047042 / 0.424275 (-0.377234) | 0.004891 / 0.007607 (-0.002716) | 0.346274 / 0.226044 (0.120230) | 3.456050 / 2.268929 (1.187121) | 1.959328 / 55.444624 (-53.485296) | 1.647631 / 6.876477 (-5.228845) | 1.692024 / 2.142072 (-0.450049) | 0.478307 / 4.805227 (-4.326920) | 0.098738 / 6.500664 (-6.401926) | 0.041743 / 0.075469 (-0.033726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984619 / 1.841788 (-0.857168) | 12.403984 / 8.074308 (4.329676) | 10.974347 / 10.191392 (0.782955) | 0.132893 / 0.680424 (-0.547530) | 0.015504 / 0.534201 (-0.518697) | 0.275354 / 0.579283 (-0.303929) | 0.283312 / 0.434364 (-0.151052) | 0.313661 / 0.540337 (-0.226677) | 0.419065 / 1.386936 (-0.967871) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n" ]
2023-11-14T10:47:09
2023-11-17T14:23:20
2023-11-17T14:17:00
CONTRIBUTOR
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Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782. **TL;DR:** `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that. **Note:** before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.
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1,992,401,594
I_kwDODunzps52waK6
6,412
User token is printed out!
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[ "Indeed, this is not a good practice. I've opened a PR that removes the token value from the (deprecation) warning." ]
2023-11-14T10:01:34
2023-11-14T22:19:46
2023-11-14T22:19:46
NONE
null
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This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091
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6,411
Fix dependency conflict within CI build documentation
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2023-11-14T09:52:51
2023-11-14T10:05:59
2023-11-14T10:05:35
MEMBER
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Manually fix dependency conflict on `typing-extensions` version originated by `apache-beam` + `pydantic` (now a dependency of `huggingface-hub`). This is a temporary hot fix of our CI build documentation until we stop using `apache-beam`. Fix #6406.
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1,992,100,209
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6,410
Datasets does not load HuggingFace Repository properly
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[ "Hi! You can avoid the error by requesting only the `jsonl` files. `dataset = load_dataset(\"ai4privacy/pii-masking-200k\", data_files=[\"*.jsonl\"])`.\r\n\r\nOur data file inference does not filter out (incompatible) `json` files because `json` and `jsonl` use the same builder. Still, I think the inference should differentiate these extensions because it's safe to assume that loading them together will lead to an error. WDYT @lhoestq? ", "Raising an error if there is a mix of json and jsonl in the builder makes sense yea" ]
2023-11-14T06:50:49
2023-11-16T06:54:36
null
NONE
null
null
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### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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I_kwDODunzps52uukh
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using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception
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2023-11-14T04:21:01
2023-11-22T16:42:09
2023-11-22T16:42:09
NONE
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### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64
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6,408
`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`
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2023-11-14T03:12:08
2023-11-16T06:24:10
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### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6
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Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"
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[ "I have encountered the same problem with `datasets-2.20.0`. \r\n\r\nI found the following workaround for this issue (including the fix from #6598):\r\n1. specify the AWS profile name in the `storage_options` instead of passing an existing session object\r\n2. use a custom `DownloadConfig` object to fix #6598\r\n3. pass the `storage_options` to the `DownloadConfig`\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n# Fix for DownloadConfig from https://github.com/huggingface/datasets/issues/6598#issuecomment-1986699619\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\nstorage_options={\"profile\": \"my-aws-profile-name\"}\r\n\r\nds = load_dataset(\r\n \"parquet\", \r\n data_files={\"train\": DATA_PATH}, \r\n storage_options=storage_options,\r\n download_config=ReviseDownloadConfig(storage_options=storage_options)\r\n)\r\n```" ]
2023-11-13T21:27:43
2024-07-30T12:35:09
null
NONE
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### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(*args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(*args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0
null
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6,406
CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'
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2023-11-13T11:36:10
2023-11-14T10:05:36
2023-11-14T10:05:36
MEMBER
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Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```
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ConfigNamesError on a simple CSV file
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[ "The viewer is working now. \r\n\r\nBased on the repo commit history, the bug was due to the incorrect format of the `features` field in the README YAML (`Value` requires `dtype`, e.g., `Value(\"string\")`, but it was not specified)", "Feel free to close the issue", "Oh, OK! Thanks. So, there was no reason to open an issue" ]
2023-11-13T10:28:29
2023-11-13T20:01:24
2023-11-13T20:01:24
COLLABORATOR
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See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv
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6,404
Support pyarrow 14.0.1 and fix vulnerability CVE-2023-47248
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005974 / 0.011353 (-0.005378) | 0.003707 / 0.011008 (-0.007301) | 0.079908 / 0.038508 (0.041399) | 0.036891 / 0.023109 (0.013781) | 0.390355 / 0.275898 (0.114457) | 0.424439 / 0.323480 (0.100960) | 0.004936 / 0.007986 (-0.003050) | 0.002886 / 0.004328 (-0.001442) | 0.062793 / 0.004250 (0.058542) | 0.054192 / 0.037052 (0.017139) | 0.394697 / 0.258489 (0.136208) | 0.437775 / 0.293841 (0.143934) | 0.027596 / 0.128546 (-0.100950) | 0.008006 / 0.075646 (-0.067640) | 0.262515 / 0.419271 (-0.156757) | 0.071014 / 0.043533 (0.027481) | 0.392964 / 0.255139 (0.137825) | 0.417449 / 0.283200 (0.134249) | 0.021819 / 0.141683 (-0.119864) | 1.458083 / 1.452155 (0.005929) | 1.489042 / 1.492716 (-0.003674) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230303 / 0.018006 (0.212297) | 0.439361 / 0.000490 (0.438871) | 0.010615 / 0.000200 (0.010415) | 0.000303 / 0.000054 (0.000249) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026600 / 0.037411 (-0.010811) | 0.078605 / 0.014526 (0.064079) | 0.088552 / 0.176557 (-0.088005) | 0.149429 / 0.737135 (-0.587706) | 0.087921 / 0.296338 (-0.208417) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422063 / 0.215209 (0.206854) | 4.201333 / 2.077655 (2.123678) | 1.982284 / 1.504120 (0.478164) | 1.779625 / 1.541195 (0.238431) | 1.872454 / 1.468490 (0.403964) | 0.502713 / 4.584777 (-4.082063) | 3.103372 / 3.745712 (-0.642340) | 3.030516 / 5.269862 (-2.239346) | 1.909123 / 4.565676 (-2.656554) | 0.057134 / 0.424275 (-0.367141) | 0.006405 / 0.007607 (-0.001202) | 0.494452 / 0.226044 (0.268408) | 4.839345 / 2.268929 (2.570417) | 2.424721 / 55.444624 (-53.019904) | 2.028618 / 6.876477 (-4.847859) | 2.082528 / 2.142072 (-0.059545) | 0.587396 / 4.805227 (-4.217831) | 0.125013 / 6.500664 (-6.375651) | 0.061369 / 0.075469 (-0.014100) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235799 / 1.841788 (-0.605989) | 17.919977 / 8.074308 (9.845669) | 13.868524 / 10.191392 (3.677132) | 0.146058 / 0.680424 (-0.534366) | 0.016826 / 0.534201 (-0.517375) | 0.337512 / 0.579283 (-0.241771) | 0.390263 / 0.434364 (-0.044101) | 0.385336 / 0.540337 (-0.155001) | 0.566004 / 1.386936 (-0.820932) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006537 / 0.011353 (-0.004816) | 0.003787 / 0.011008 (-0.007221) | 0.062568 / 0.038508 (0.024060) | 0.066672 / 0.023109 (0.043563) | 0.420447 / 0.275898 (0.144549) | 0.457260 / 0.323480 (0.133780) | 0.005005 / 0.007986 (-0.002981) | 0.003037 / 0.004328 (-0.001291) | 0.062095 / 0.004250 (0.057844) | 0.049619 / 0.037052 (0.012567) | 0.429935 / 0.258489 (0.171446) | 0.471566 / 0.293841 (0.177725) | 0.029688 / 0.128546 (-0.098859) | 0.008028 / 0.075646 (-0.067619) | 0.067915 / 0.419271 (-0.351356) | 0.042066 / 0.043533 (-0.001467) | 0.419275 / 0.255139 (0.164136) | 0.444819 / 0.283200 (0.161619) | 0.020100 / 0.141683 (-0.121583) | 1.439057 / 1.452155 (-0.013098) | 1.495657 / 1.492716 (0.002940) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211148 / 0.018006 (0.193142) | 0.423777 / 0.000490 (0.423288) | 0.005892 / 0.000200 (0.005693) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026469 / 0.037411 (-0.010942) | 0.081438 / 0.014526 (0.066912) | 0.092007 / 0.176557 (-0.084550) | 0.143433 / 0.737135 (-0.593703) | 0.093039 / 0.296338 (-0.203300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410468 / 0.215209 (0.195259) | 4.083783 / 2.077655 (2.006128) | 2.234501 / 1.504120 (0.730381) | 2.122323 / 1.541195 (0.581128) | 2.255036 / 1.468490 (0.786546) | 0.497712 / 4.584777 (-4.087065) | 3.231187 / 3.745712 (-0.514525) | 3.005399 / 5.269862 (-2.264463) | 1.909516 / 4.565676 (-2.656161) | 0.057529 / 0.424275 (-0.366746) | 0.006475 / 0.007607 (-0.001132) | 0.477282 / 0.226044 (0.251238) | 4.799566 / 2.268929 (2.530637) | 2.497070 / 55.444624 (-52.947554) | 2.206359 / 6.876477 (-4.670118) | 2.281614 / 2.142072 (0.139541) | 0.581710 / 4.805227 (-4.223518) | 0.121572 / 6.500664 (-6.379092) | 0.058774 / 0.075469 (-0.016695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301880 / 1.841788 (-0.539908) | 18.287330 / 8.074308 (10.213021) | 14.939642 / 10.191392 (4.748250) | 0.153941 / 0.680424 (-0.526483) | 0.018345 / 0.534201 (-0.515856) | 0.335986 / 0.579283 (-0.243297) | 0.384264 / 0.434364 (-0.050099) | 0.393115 / 0.540337 (-0.147223) | 0.573343 / 1.386936 (-0.813594) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d54b6459f4ed0b2519ddec605dd71956d2d1d3e4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004805 / 0.011353 (-0.006548) | 0.003261 / 0.011008 (-0.007747) | 0.061585 / 0.038508 (0.023077) | 0.030236 / 0.023109 (0.007127) | 0.234767 / 0.275898 (-0.041131) | 0.260478 / 0.323480 (-0.063002) | 0.004121 / 0.007986 (-0.003865) | 0.002525 / 0.004328 (-0.001803) | 0.048213 / 0.004250 (0.043962) | 0.045229 / 0.037052 (0.008176) | 0.245143 / 0.258489 (-0.013346) | 0.271818 / 0.293841 (-0.022023) | 0.023594 / 0.128546 (-0.104952) | 0.007335 / 0.075646 (-0.068311) | 0.206246 / 0.419271 (-0.213026) | 0.060783 / 0.043533 (0.017250) | 0.238588 / 0.255139 (-0.016551) | 0.274985 / 0.283200 (-0.008214) | 0.018342 / 0.141683 (-0.123341) | 1.135445 / 1.452155 (-0.316710) | 1.184836 / 1.492716 (-0.307881) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095603 / 0.018006 (0.077597) | 0.290340 / 0.000490 (0.289850) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018804 / 0.037411 (-0.018607) | 0.062525 / 0.014526 (0.047999) | 0.074797 / 0.176557 (-0.101760) | 0.120360 / 0.737135 (-0.616775) | 0.076182 / 0.296338 (-0.220156) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.274981 / 0.215209 (0.059772) | 2.684931 / 2.077655 (0.607276) | 1.453845 / 1.504120 (-0.050275) | 1.348361 / 1.541195 (-0.192834) | 1.402820 / 1.468490 (-0.065670) | 0.396311 / 4.584777 (-4.188466) | 2.396314 / 3.745712 (-1.349398) | 2.744379 / 5.269862 (-2.525482) | 1.615268 / 4.565676 (-2.950409) | 0.045920 / 0.424275 (-0.378355) | 0.004844 / 0.007607 (-0.002763) | 0.331132 / 0.226044 (0.105087) | 3.325484 / 2.268929 (1.056556) | 1.845734 / 55.444624 (-53.598890) | 1.537268 / 6.876477 (-5.339209) | 1.565155 / 2.142072 (-0.576918) | 0.480032 / 4.805227 (-4.325195) | 0.099917 / 6.500664 (-6.400747) | 0.042276 / 0.075469 (-0.033193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973128 / 1.841788 (-0.868660) | 12.643790 / 8.074308 (4.569482) | 10.319586 / 10.191392 (0.128194) | 0.131733 / 0.680424 (-0.548691) | 0.014849 / 0.534201 (-0.519352) | 0.270960 / 0.579283 (-0.308323) | 0.265409 / 0.434364 (-0.168955) | 0.309073 / 0.540337 (-0.231264) | 0.466204 / 1.386936 (-0.920732) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005067 / 0.011353 (-0.006286) | 0.003344 / 0.011008 (-0.007665) | 0.047917 / 0.038508 (0.009409) | 0.059556 / 0.023109 (0.036447) | 0.275777 / 0.275898 (-0.000121) | 0.299703 / 0.323480 (-0.023777) | 0.004185 / 0.007986 (-0.003801) | 0.002602 / 0.004328 (-0.001726) | 0.048723 / 0.004250 (0.044472) | 0.040686 / 0.037052 (0.003634) | 0.281078 / 0.258489 (0.022589) | 0.314725 / 0.293841 (0.020885) | 0.024645 / 0.128546 (-0.103901) | 0.007465 / 0.075646 (-0.068182) | 0.053827 / 0.419271 (-0.365445) | 0.033395 / 0.043533 (-0.010138) | 0.273675 / 0.255139 (0.018536) | 0.291261 / 0.283200 (0.008062) | 0.019733 / 0.141683 (-0.121950) | 1.134084 / 1.452155 (-0.318071) | 1.189186 / 1.492716 (-0.303531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.114960 / 0.018006 (0.096954) | 0.308800 / 0.000490 (0.308311) | 0.000237 / 0.000200 (0.000037) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021633 / 0.037411 (-0.015778) | 0.073192 / 0.014526 (0.058666) | 0.081598 / 0.176557 (-0.094959) | 0.123085 / 0.737135 (-0.614050) | 0.088677 / 0.296338 (-0.207661) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300865 / 0.215209 (0.085656) | 2.956847 / 2.077655 (0.879192) | 1.613890 / 1.504120 (0.109770) | 1.494074 / 1.541195 (-0.047121) | 1.550345 / 1.468490 (0.081855) | 0.408880 / 4.584777 (-4.175897) | 2.422848 / 3.745712 (-1.322865) | 2.690623 / 5.269862 (-2.579239) | 1.546922 / 4.565676 (-3.018755) | 0.047192 / 0.424275 (-0.377083) | 0.004882 / 0.007607 (-0.002725) | 0.360625 / 0.226044 (0.134580) | 3.512678 / 2.268929 (1.243749) | 1.978633 / 55.444624 (-53.465992) | 1.686927 / 6.876477 (-5.189549) | 1.748387 / 2.142072 (-0.393685) | 0.480780 / 4.805227 (-4.324447) | 0.099163 / 6.500664 (-6.401501) | 0.041194 / 0.075469 (-0.034275) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989087 / 1.841788 (-0.852700) | 12.341951 / 8.074308 (4.267643) | 11.109329 / 10.191392 (0.917936) | 0.143329 / 0.680424 (-0.537095) | 0.015565 / 0.534201 (-0.518636) | 0.269532 / 0.579283 (-0.309751) | 0.274899 / 0.434364 (-0.159465) | 0.309308 / 0.540337 (-0.231030) | 0.439651 / 1.386936 (-0.947285) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a3f006a1a88c894ea10610d66dfddd73ad1490 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007880 / 0.011353 (-0.003473) | 0.004386 / 0.011008 (-0.006622) | 0.099067 / 0.038508 (0.060559) | 0.048036 / 0.023109 (0.024927) | 0.368349 / 0.275898 (0.092451) | 0.400052 / 0.323480 (0.076572) | 0.004493 / 0.007986 (-0.003493) | 0.003732 / 0.004328 (-0.000597) | 0.076153 / 0.004250 (0.071902) | 0.071024 / 0.037052 (0.033972) | 0.379771 / 0.258489 (0.121282) | 0.425005 / 0.293841 (0.131164) | 0.036092 / 0.128546 (-0.092454) | 0.009825 / 0.075646 (-0.065822) | 0.340217 / 0.419271 (-0.079055) | 0.089571 / 0.043533 (0.046038) | 0.371426 / 0.255139 (0.116287) | 0.397864 / 0.283200 (0.114664) | 0.029440 / 0.141683 (-0.112243) | 1.778100 / 1.452155 (0.325945) | 1.857202 / 1.492716 (0.364486) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254022 / 0.018006 (0.236015) | 0.549844 / 0.000490 (0.549354) | 0.012824 / 0.000200 (0.012624) | 0.000378 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032334 / 0.037411 (-0.005077) | 0.096101 / 0.014526 (0.081576) | 0.117825 / 0.176557 (-0.058731) | 0.179277 / 0.737135 (-0.557858) | 0.112614 / 0.296338 (-0.183724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455051 / 0.215209 (0.239842) | 4.537086 / 2.077655 (2.459431) | 2.198662 / 1.504120 (0.694542) | 1.982772 / 1.541195 (0.441578) | 2.058673 / 1.468490 (0.590182) | 0.569268 / 4.584777 (-4.015509) | 4.095000 / 3.745712 (0.349288) | 3.891680 / 5.269862 (-1.378182) | 2.345129 / 4.565676 (-2.220548) | 0.066974 / 0.424275 (-0.357301) | 0.008557 / 0.007607 (0.000950) | 0.545290 / 0.226044 (0.319245) | 5.453377 / 2.268929 (3.184448) | 2.858688 / 55.444624 (-52.585936) | 2.502367 / 6.876477 (-4.374109) | 2.515658 / 2.142072 (0.373586) | 0.681423 / 4.805227 (-4.123804) | 0.155975 / 6.500664 (-6.344689) | 0.070872 / 0.075469 (-0.004597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.474674 / 1.841788 (-0.367114) | 21.653619 / 8.074308 (13.579311) | 16.277111 / 10.191392 (6.085719) | 0.166445 / 0.680424 (-0.513979) | 0.021676 / 0.534201 (-0.512525) | 0.466949 / 0.579283 (-0.112334) | 0.500953 / 0.434364 (0.066589) | 0.540413 / 0.540337 (0.000076) | 0.792989 / 1.386936 (-0.593947) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007633 / 0.011353 (-0.003720) | 0.004468 / 0.011008 (-0.006540) | 0.075573 / 0.038508 (0.037065) | 0.081174 / 0.023109 (0.058064) | 0.440741 / 0.275898 (0.164843) | 0.489493 / 0.323480 (0.166013) | 0.006180 / 0.007986 (-0.001805) | 0.003693 / 0.004328 (-0.000636) | 0.074692 / 0.004250 (0.070441) | 0.061732 / 0.037052 (0.024680) | 0.460391 / 0.258489 (0.201902) | 0.505575 / 0.293841 (0.211734) | 0.037692 / 0.128546 (-0.090854) | 0.009870 / 0.075646 (-0.065776) | 0.083830 / 0.419271 (-0.335442) | 0.056255 / 0.043533 (0.012723) | 0.439330 / 0.255139 (0.184191) | 0.475598 / 0.283200 (0.192399) | 0.026626 / 0.141683 (-0.115056) | 1.794410 / 1.452155 (0.342255) | 1.882510 / 1.492716 (0.389794) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236194 / 0.018006 (0.218187) | 0.486109 / 0.000490 (0.485619) | 0.006652 / 0.000200 (0.006453) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037277 / 0.037411 (-0.000134) | 0.108904 / 0.014526 (0.094378) | 0.122699 / 0.176557 (-0.053857) | 0.182388 / 0.737135 (-0.554747) | 0.122826 / 0.296338 (-0.173512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485989 / 0.215209 (0.270780) | 4.913263 / 2.077655 (2.835609) | 2.571618 / 1.504120 (1.067498) | 2.401248 / 1.541195 (0.860054) | 2.501117 / 1.468490 (1.032627) | 0.570989 / 4.584777 (-4.013788) | 4.107420 / 3.745712 (0.361708) | 3.814977 / 5.269862 (-1.454885) | 2.282539 / 4.565676 (-2.283138) | 0.067765 / 0.424275 (-0.356511) | 0.008561 / 0.007607 (0.000954) | 0.584515 / 0.226044 (0.358471) | 5.817821 / 2.268929 (3.548893) | 3.211202 / 55.444624 (-52.233422) | 2.764480 / 6.876477 (-4.111996) | 2.807301 / 2.142072 (0.665229) | 0.676882 / 4.805227 (-4.128346) | 0.150124 / 6.500664 (-6.350540) | 0.067205 / 0.075469 (-0.008265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.594945 / 1.841788 (-0.246843) | 22.533511 / 8.074308 (14.459203) | 17.099693 / 10.191392 (6.908301) | 0.195954 / 0.680424 (-0.484470) | 0.023968 / 0.534201 (-0.510233) | 0.471337 / 0.579283 (-0.107946) | 0.491017 / 0.434364 (0.056653) | 0.561342 / 0.540337 (0.021004) | 0.797116 / 1.386936 (-0.589820) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98871b9ba46e89e75e9d0dddc49f4241373c575d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006235 / 0.011353 (-0.005118) | 0.003688 / 0.011008 (-0.007321) | 0.080801 / 0.038508 (0.042293) | 0.036243 / 0.023109 (0.013134) | 0.312173 / 0.275898 (0.036275) | 0.346239 / 0.323480 (0.022759) | 0.003429 / 0.007986 (-0.004556) | 0.003806 / 0.004328 (-0.000523) | 0.063236 / 0.004250 (0.058986) | 0.053229 / 0.037052 (0.016177) | 0.315184 / 0.258489 (0.056695) | 0.360124 / 0.293841 (0.066283) | 0.027447 / 0.128546 (-0.101099) | 0.008029 / 0.075646 (-0.067618) | 0.262766 / 0.419271 (-0.156505) | 0.068421 / 0.043533 (0.024888) | 0.309028 / 0.255139 (0.053889) | 0.345859 / 0.283200 (0.062659) | 0.021388 / 0.141683 (-0.120295) | 1.452807 / 1.452155 (0.000652) | 1.502803 / 1.492716 (0.010087) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211297 / 0.018006 (0.193291) | 0.423364 / 0.000490 (0.422874) | 0.004574 / 0.000200 (0.004374) | 0.000272 / 0.000054 (0.000218) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023805 / 0.037411 (-0.013606) | 0.072309 / 0.014526 (0.057783) | 0.083274 / 0.176557 (-0.093283) | 0.143594 / 0.737135 (-0.593541) | 0.083777 / 0.296338 (-0.212561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415691 / 0.215209 (0.200482) | 4.128621 / 2.077655 (2.050967) | 1.931128 / 1.504120 (0.427008) | 1.737486 / 1.541195 (0.196292) | 1.806314 / 1.468490 (0.337823) | 0.501405 / 4.584777 (-4.083372) | 3.082042 / 3.745712 (-0.663670) | 2.980224 / 5.269862 (-2.289637) | 1.879780 / 4.565676 (-2.685897) | 0.057546 / 0.424275 (-0.366729) | 0.006422 / 0.007607 (-0.001186) | 0.479813 / 0.226044 (0.253768) | 4.854497 / 2.268929 (2.585568) | 2.529674 / 55.444624 (-52.914950) | 2.283041 / 6.876477 (-4.593436) | 2.377173 / 2.142072 (0.235101) | 0.589654 / 4.805227 (-4.215573) | 0.126190 / 6.500664 (-6.374474) | 0.062391 / 0.075469 (-0.013079) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.232023 / 1.841788 (-0.609764) | 17.576621 / 8.074308 (9.502313) | 13.437075 / 10.191392 (3.245683) | 0.143367 / 0.680424 (-0.537057) | 0.016638 / 0.534201 (-0.517563) | 0.332806 / 0.579283 (-0.246477) | 0.356029 / 0.434364 (-0.078335) | 0.385610 / 0.540337 (-0.154727) | 0.563268 / 1.386936 (-0.823668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006293 / 0.011353 (-0.005060) | 0.003692 / 0.011008 (-0.007317) | 0.062075 / 0.038508 (0.023567) | 0.062104 / 0.023109 (0.038995) | 0.407478 / 0.275898 (0.131580) | 0.434982 / 0.323480 (0.111502) | 0.004889 / 0.007986 (-0.003097) | 0.002915 / 0.004328 (-0.001413) | 0.061426 / 0.004250 (0.057176) | 0.048027 / 0.037052 (0.010974) | 0.410504 / 0.258489 (0.152015) | 0.435383 / 0.293841 (0.141542) | 0.029419 / 0.128546 (-0.099127) | 0.008275 / 0.075646 (-0.067371) | 0.067796 / 0.419271 (-0.351476) | 0.041696 / 0.043533 (-0.001837) | 0.398882 / 0.255139 (0.143743) | 0.419480 / 0.283200 (0.136281) | 0.021519 / 0.141683 (-0.120164) | 1.436961 / 1.452155 (-0.015194) | 1.507961 / 1.492716 (0.015245) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223190 / 0.018006 (0.205184) | 0.416281 / 0.000490 (0.415791) | 0.003370 / 0.000200 (0.003170) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025923 / 0.037411 (-0.011488) | 0.079989 / 0.014526 (0.065463) | 0.091289 / 0.176557 (-0.085268) | 0.141212 / 0.737135 (-0.595923) | 0.091717 / 0.296338 (-0.204622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434640 / 0.215209 (0.219431) | 4.326154 / 2.077655 (2.248500) | 2.364845 / 1.504120 (0.860725) | 2.194040 / 1.541195 (0.652846) | 2.276665 / 1.468490 (0.808175) | 0.501879 / 4.584777 (-4.082898) | 3.073307 / 3.745712 (-0.672405) | 2.893823 / 5.269862 (-2.376039) | 1.820594 / 4.565676 (-2.745083) | 0.057595 / 0.424275 (-0.366680) | 0.006516 / 0.007607 (-0.001091) | 0.513633 / 0.226044 (0.287589) | 5.104799 / 2.268929 (2.835870) | 2.845025 / 55.444624 (-52.599599) | 2.513852 / 6.876477 (-4.362624) | 2.561044 / 2.142072 (0.418972) | 0.582711 / 4.805227 (-4.222516) | 0.120631 / 6.500664 (-6.380034) | 0.056738 / 0.075469 (-0.018731) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303370 / 1.841788 (-0.538418) | 18.023568 / 8.074308 (9.949259) | 14.637973 / 10.191392 (4.446581) | 0.145182 / 0.680424 (-0.535241) | 0.018061 / 0.534201 (-0.516140) | 0.333219 / 0.579283 (-0.246065) | 0.373184 / 0.434364 (-0.061180) | 0.388176 / 0.540337 (-0.152161) | 0.564752 / 1.386936 (-0.822184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aecdc94580d105d4b70c94e8e238ce097f97af90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007230 / 0.011353 (-0.004122) | 0.003727 / 0.011008 (-0.007281) | 0.078893 / 0.038508 (0.040385) | 0.042600 / 0.023109 (0.019491) | 0.301905 / 0.275898 (0.026007) | 0.328478 / 0.323480 (0.004998) | 0.003960 / 0.007986 (-0.004026) | 0.004530 / 0.004328 (0.000201) | 0.059446 / 0.004250 (0.055196) | 0.061241 / 0.037052 (0.024189) | 0.301878 / 0.258489 (0.043389) | 0.340935 / 0.293841 (0.047095) | 0.030559 / 0.128546 (-0.097988) | 0.008016 / 0.075646 (-0.067630) | 0.305174 / 0.419271 (-0.114097) | 0.080374 / 0.043533 (0.036842) | 0.307162 / 0.255139 (0.052023) | 0.342459 / 0.283200 (0.059259) | 0.025881 / 0.141683 (-0.115801) | 1.443311 / 1.452155 (-0.008844) | 1.631060 / 1.492716 (0.138344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242676 / 0.018006 (0.224670) | 0.463941 / 0.000490 (0.463451) | 0.007762 / 0.000200 (0.007562) | 0.000582 / 0.000054 (0.000527) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027334 / 0.037411 (-0.010077) | 0.078910 / 0.014526 (0.064384) | 0.091399 / 0.176557 (-0.085157) | 0.143318 / 0.737135 (-0.593818) | 0.089761 / 0.296338 (-0.206577) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463002 / 0.215209 (0.247793) | 4.627235 / 2.077655 (2.549580) | 2.256699 / 1.504120 (0.752579) | 2.057615 / 1.541195 (0.516421) | 2.126424 / 1.468490 (0.657934) | 0.571969 / 4.584777 (-4.012808) | 4.130260 / 3.745712 (0.384548) | 3.833521 / 5.269862 (-1.436341) | 2.320141 / 4.565676 (-2.245535) | 0.067587 / 0.424275 (-0.356688) | 0.008452 / 0.007607 (0.000845) | 0.546478 / 0.226044 (0.320433) | 5.070678 / 2.268929 (2.801750) | 2.325387 / 55.444624 (-53.119237) | 2.044041 / 6.876477 (-4.832435) | 2.019714 / 2.142072 (-0.122358) | 0.563589 / 4.805227 (-4.241639) | 0.135269 / 6.500664 (-6.365395) | 0.058208 / 0.075469 (-0.017261) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283156 / 1.841788 (-0.558631) | 18.617776 / 8.074308 (10.543468) | 13.360700 / 10.191392 (3.169308) | 0.160001 / 0.680424 (-0.520423) | 0.021538 / 0.534201 (-0.512663) | 0.384169 / 0.579283 (-0.195114) | 0.407517 / 0.434364 (-0.026847) | 0.427295 / 0.540337 (-0.113042) | 0.655288 / 1.386936 (-0.731648) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006854 / 0.011353 (-0.004499) | 0.003442 / 0.011008 (-0.007566) | 0.060622 / 0.038508 (0.022114) | 0.074649 / 0.023109 (0.051540) | 0.341733 / 0.275898 (0.065835) | 0.360096 / 0.323480 (0.036616) | 0.006235 / 0.007986 (-0.001751) | 0.003447 / 0.004328 (-0.000882) | 0.057301 / 0.004250 (0.053051) | 0.059022 / 0.037052 (0.021970) | 0.369523 / 0.258489 (0.111034) | 0.386280 / 0.293841 (0.092439) | 0.034319 / 0.128546 (-0.094228) | 0.008291 / 0.075646 (-0.067355) | 0.070403 / 0.419271 (-0.348868) | 0.050433 / 0.043533 (0.006901) | 0.347262 / 0.255139 (0.092123) | 0.380543 / 0.283200 (0.097343) | 0.024492 / 0.141683 (-0.117191) | 1.446721 / 1.452155 (-0.005433) | 1.541614 / 1.492716 (0.048898) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226148 / 0.018006 (0.208142) | 0.442150 / 0.000490 (0.441660) | 0.004997 / 0.000200 (0.004797) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032866 / 0.037411 (-0.004546) | 0.088097 / 0.014526 (0.073571) | 0.102178 / 0.176557 (-0.074379) | 0.151129 / 0.737135 (-0.586006) | 0.103953 / 0.296338 (-0.192386) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376701 / 0.215209 (0.161492) | 3.886997 / 2.077655 (1.809342) | 2.027143 / 1.504120 (0.523023) | 1.808647 / 1.541195 (0.267453) | 1.867664 / 1.468490 (0.399173) | 0.459487 / 4.584777 (-4.125290) | 3.640801 / 3.745712 (-0.104911) | 3.242512 / 5.269862 (-2.027350) | 1.889174 / 4.565676 (-2.676503) | 0.052415 / 0.424275 (-0.371860) | 0.007479 / 0.007607 (-0.000128) | 0.457706 / 0.226044 (0.231662) | 4.815041 / 2.268929 (2.546112) | 2.542470 / 55.444624 (-52.902154) | 2.137084 / 6.876477 (-4.739392) | 2.122867 / 2.142072 (-0.019205) | 0.553756 / 4.805227 (-4.251471) | 0.118902 / 6.500664 (-6.381763) | 0.058149 / 0.075469 (-0.017320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272615 / 1.841788 (-0.569173) | 19.455709 / 8.074308 (11.381401) | 14.111693 / 10.191392 (3.920301) | 0.165741 / 0.680424 (-0.514683) | 0.023680 / 0.534201 (-0.510521) | 0.431458 / 0.579283 (-0.147825) | 0.433612 / 0.434364 (-0.000752) | 0.465615 / 0.540337 (-0.074722) | 0.678177 / 1.386936 (-0.708759) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#998623fa51991320740b945d0853ee20807304d7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004870 / 0.011353 (-0.006483) | 0.002834 / 0.011008 (-0.008175) | 0.061359 / 0.038508 (0.022851) | 0.031286 / 0.023109 (0.008177) | 0.236701 / 0.275898 (-0.039197) | 0.258139 / 0.323480 (-0.065341) | 0.002943 / 0.007986 (-0.005043) | 0.002989 / 0.004328 (-0.001339) | 0.048046 / 0.004250 (0.043796) | 0.044927 / 0.037052 (0.007874) | 0.241339 / 0.258489 (-0.017151) | 0.273912 / 0.293841 (-0.019929) | 0.023427 / 0.128546 (-0.105119) | 0.007251 / 0.075646 (-0.068395) | 0.202730 / 0.419271 (-0.216542) | 0.056223 / 0.043533 (0.012691) | 0.239908 / 0.255139 (-0.015231) | 0.254723 / 0.283200 (-0.028476) | 0.018223 / 0.141683 (-0.123460) | 1.119691 / 1.452155 (-0.332464) | 1.163802 / 1.492716 (-0.328915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091303 / 0.018006 (0.073297) | 0.302097 / 0.000490 (0.301607) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018201 / 0.037411 (-0.019210) | 0.062092 / 0.014526 (0.047566) | 0.074806 / 0.176557 (-0.101751) | 0.119625 / 0.737135 (-0.617510) | 0.074680 / 0.296338 (-0.221659) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281140 / 0.215209 (0.065931) | 2.752094 / 2.077655 (0.674439) | 1.436813 / 1.504120 (-0.067307) | 1.312947 / 1.541195 (-0.228247) | 1.331022 / 1.468490 (-0.137468) | 0.396579 / 4.584777 (-4.188198) | 2.406181 / 3.745712 (-1.339531) | 2.597180 / 5.269862 (-2.672682) | 1.565879 / 4.565676 (-2.999798) | 0.046330 / 0.424275 (-0.377945) | 0.004776 / 0.007607 (-0.002831) | 0.339681 / 0.226044 (0.113637) | 3.279533 / 2.268929 (1.010605) | 1.793352 / 55.444624 (-53.651272) | 1.493910 / 6.876477 (-5.382567) | 1.514494 / 2.142072 (-0.627579) | 0.467955 / 4.805227 (-4.337272) | 0.097764 / 6.500664 (-6.402900) | 0.041659 / 0.075469 (-0.033810) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943204 / 1.841788 (-0.898583) | 11.350848 / 8.074308 (3.276540) | 10.169944 / 10.191392 (-0.021448) | 0.130882 / 0.680424 (-0.549542) | 0.013804 / 0.534201 (-0.520397) | 0.269107 / 0.579283 (-0.310177) | 0.261685 / 0.434364 (-0.172679) | 0.305610 / 0.540337 (-0.234727) | 0.430586 / 1.386936 (-0.956350) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004835 / 0.011353 (-0.006518) | 0.002530 / 0.011008 (-0.008479) | 0.047383 / 0.038508 (0.008875) | 0.052559 / 0.023109 (0.029450) | 0.265015 / 0.275898 (-0.010883) | 0.286955 / 0.323480 (-0.036525) | 0.003931 / 0.007986 (-0.004054) | 0.002038 / 0.004328 (-0.002290) | 0.047458 / 0.004250 (0.043207) | 0.038257 / 0.037052 (0.001205) | 0.270569 / 0.258489 (0.012080) | 0.298968 / 0.293841 (0.005127) | 0.024615 / 0.128546 (-0.103932) | 0.006969 / 0.075646 (-0.068677) | 0.052361 / 0.419271 (-0.366911) | 0.032701 / 0.043533 (-0.010832) | 0.269126 / 0.255139 (0.013987) | 0.285934 / 0.283200 (0.002735) | 0.018121 / 0.141683 (-0.123562) | 1.129796 / 1.452155 (-0.322359) | 1.272831 / 1.492716 (-0.219885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092058 / 0.018006 (0.074051) | 0.303544 / 0.000490 (0.303054) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020983 / 0.037411 (-0.016428) | 0.069798 / 0.014526 (0.055272) | 0.081410 / 0.176557 (-0.095146) | 0.120403 / 0.737135 (-0.616732) | 0.082813 / 0.296338 (-0.213525) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295943 / 0.215209 (0.080734) | 2.895761 / 2.077655 (0.818106) | 1.583534 / 1.504120 (0.079414) | 1.458397 / 1.541195 (-0.082798) | 1.492113 / 1.468490 (0.023623) | 0.402364 / 4.584777 (-4.182413) | 2.469777 / 3.745712 (-1.275935) | 2.565262 / 5.269862 (-2.704599) | 1.525914 / 4.565676 (-3.039763) | 0.047168 / 0.424275 (-0.377107) | 0.004800 / 0.007607 (-0.002808) | 0.348356 / 0.226044 (0.122311) | 3.463184 / 2.268929 (1.194255) | 1.930240 / 55.444624 (-53.514385) | 1.644312 / 6.876477 (-5.232165) | 1.625477 / 2.142072 (-0.516596) | 0.480781 / 4.805227 (-4.324446) | 0.098431 / 6.500664 (-6.402233) | 0.041071 / 0.075469 (-0.034398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973633 / 1.841788 (-0.868154) | 11.952261 / 8.074308 (3.877953) | 11.038222 / 10.191392 (0.846830) | 0.142755 / 0.680424 (-0.537669) | 0.015389 / 0.534201 (-0.518812) | 0.274144 / 0.579283 (-0.305139) | 0.282319 / 0.434364 (-0.152045) | 0.314330 / 0.540337 (-0.226007) | 0.435315 / 1.386936 (-0.951621) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05200c0a4f8f02c3890ab79a10b44ab0bcf11629 \"CML watermark\")\n", "The red CI job is unrelated to this PR. It appeared 5 days ago. See:\r\n- https://github.com/huggingface/datasets/pull/6390#pullrequestreview-1721070927\r\n- https://github.com/huggingface/datasets/issues/6406", "Let's do a new release once this is merged ? cc @mariosasko as well let us know if the fix sounds good to you", "@lhoestq Yes, this sounds good to me!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004932 / 0.011353 (-0.006421) | 0.002956 / 0.011008 (-0.008052) | 0.061999 / 0.038508 (0.023491) | 0.030174 / 0.023109 (0.007065) | 0.241483 / 0.275898 (-0.034415) | 0.261578 / 0.323480 (-0.061902) | 0.002881 / 0.007986 (-0.005105) | 0.002451 / 0.004328 (-0.001878) | 0.048176 / 0.004250 (0.043925) | 0.045028 / 0.037052 (0.007976) | 0.244304 / 0.258489 (-0.014185) | 0.275834 / 0.293841 (-0.018007) | 0.023312 / 0.128546 (-0.105234) | 0.007361 / 0.075646 (-0.068286) | 0.204433 / 0.419271 (-0.214838) | 0.054561 / 0.043533 (0.011028) | 0.236902 / 0.255139 (-0.018237) | 0.269358 / 0.283200 (-0.013842) | 0.017736 / 0.141683 (-0.123947) | 1.112444 / 1.452155 (-0.339711) | 1.170260 / 1.492716 (-0.322456) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093081 / 0.018006 (0.075074) | 0.311470 / 0.000490 (0.310981) | 0.000212 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018654 / 0.037411 (-0.018757) | 0.063239 / 0.014526 (0.048714) | 0.073759 / 0.176557 (-0.102798) | 0.120279 / 0.737135 (-0.616857) | 0.076214 / 0.296338 (-0.220124) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287219 / 0.215209 (0.072010) | 2.765378 / 2.077655 (0.687723) | 1.459733 / 1.504120 (-0.044387) | 1.325999 / 1.541195 (-0.215196) | 1.349957 / 1.468490 (-0.118533) | 0.413093 / 4.584777 (-4.171684) | 2.394758 / 3.745712 (-1.350954) | 2.633916 / 5.269862 (-2.635945) | 1.621629 / 4.565676 (-2.944047) | 0.046839 / 0.424275 (-0.377436) | 0.004786 / 0.007607 (-0.002822) | 0.336261 / 0.226044 (0.110217) | 3.348196 / 2.268929 (1.079267) | 1.853050 / 55.444624 (-53.591574) | 1.543926 / 6.876477 (-5.332551) | 1.573675 / 2.142072 (-0.568398) | 0.484088 / 4.805227 (-4.321139) | 0.100820 / 6.500664 (-6.399845) | 0.042194 / 0.075469 (-0.033275) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945186 / 1.841788 (-0.896601) | 11.859855 / 8.074308 (3.785547) | 10.459883 / 10.191392 (0.268491) | 0.142024 / 0.680424 (-0.538400) | 0.013882 / 0.534201 (-0.520319) | 0.269584 / 0.579283 (-0.309699) | 0.264353 / 0.434364 (-0.170011) | 0.307988 / 0.540337 (-0.232349) | 0.423655 / 1.386936 (-0.963281) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004891 / 0.011353 (-0.006461) | 0.003087 / 0.011008 (-0.007921) | 0.048206 / 0.038508 (0.009697) | 0.058570 / 0.023109 (0.035461) | 0.268552 / 0.275898 (-0.007346) | 0.287839 / 0.323480 (-0.035641) | 0.004044 / 0.007986 (-0.003942) | 0.002388 / 0.004328 (-0.001940) | 0.048186 / 0.004250 (0.043935) | 0.038719 / 0.037052 (0.001667) | 0.271940 / 0.258489 (0.013451) | 0.299716 / 0.293841 (0.005875) | 0.027166 / 0.128546 (-0.101380) | 0.007388 / 0.075646 (-0.068258) | 0.053885 / 0.419271 (-0.365387) | 0.032804 / 0.043533 (-0.010729) | 0.271664 / 0.255139 (0.016525) | 0.284613 / 0.283200 (0.001414) | 0.018488 / 0.141683 (-0.123195) | 1.125854 / 1.452155 (-0.326301) | 1.195896 / 1.492716 (-0.296820) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092438 / 0.018006 (0.074431) | 0.315265 / 0.000490 (0.314775) | 0.000228 / 0.000200 (0.000028) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021373 / 0.037411 (-0.016038) | 0.070611 / 0.014526 (0.056085) | 0.080391 / 0.176557 (-0.096165) | 0.118749 / 0.737135 (-0.618386) | 0.082340 / 0.296338 (-0.213999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295583 / 0.215209 (0.080374) | 2.882152 / 2.077655 (0.804497) | 1.565088 / 1.504120 (0.060968) | 1.451954 / 1.541195 (-0.089241) | 1.505783 / 1.468490 (0.037293) | 0.404699 / 4.584777 (-4.180078) | 2.451703 / 3.745712 (-1.294009) | 2.596301 / 5.269862 (-2.673560) | 1.547014 / 4.565676 (-3.018662) | 0.047750 / 0.424275 (-0.376525) | 0.004850 / 0.007607 (-0.002757) | 0.346893 / 0.226044 (0.120849) | 3.383355 / 2.268929 (1.114426) | 1.943933 / 55.444624 (-53.500692) | 1.657513 / 6.876477 (-5.218964) | 1.687166 / 2.142072 (-0.454906) | 0.478543 / 4.805227 (-4.326685) | 0.097804 / 6.500664 (-6.402860) | 0.041392 / 0.075469 (-0.034078) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983894 / 1.841788 (-0.857893) | 12.446443 / 8.074308 (4.372135) | 10.973461 / 10.191392 (0.782069) | 0.131630 / 0.680424 (-0.548794) | 0.017196 / 0.534201 (-0.517005) | 0.270873 / 0.579283 (-0.308411) | 0.284379 / 0.434364 (-0.149985) | 0.306103 / 0.540337 (-0.234234) | 0.413762 / 1.386936 (-0.973174) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ad4c6e6e33f0129db8745e84de8c298741aa2 \"CML watermark\")\n", "Note I had to add `pa.ExtensionType.__reduce__` because this is used by `copy.deepcopy` when using `.with_format`. See error below.\r\n\r\nThis method was added in pyarrow-13.0.0: https://github.com/apache/arrow/pull/36170\r\n- We need to re-implement it as long we support lower pyarrow versions\r\n\r\nErrors: https://github.com/huggingface/datasets/actions/runs/6861278161/job/18656665772\r\n```\r\n ____________________________ test_dataset_map[True] ____________________________\r\n[gw1] linux -- Python 3.8.18 /opt/hostedtoolcache/Python/3.8.18/x64/bin/python\r\n\r\n> ???\r\nE KeyError: 'extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>'\r\n\r\npyarrow/types.pxi:3155: KeyError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nwith_none = True\r\n\r\n @pytest.mark.parametrize(\"with_none\", [False, True])\r\n def test_dataset_map(with_none):\r\n ds = datasets.Dataset.from_dict({\"path\": [\"path1\", \"path2\"]})\r\n \r\n def process_data(batch):\r\n batch = {\r\n \"image\": [\r\n np.array(\r\n [\r\n [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\r\n [[10, 20, 30], [40, 50, 60], [70, 80, 90]],\r\n [[100, 200, 300], [400, 500, 600], [700, 800, 900]],\r\n ]\r\n )\r\n for _ in batch[\"path\"]\r\n ]\r\n }\r\n if with_none:\r\n batch[\"image\"][0] = None\r\n return batch\r\n \r\n features = datasets.Features({\"image\": Array3D(dtype=\"int32\", shape=(3, 3, 3))})\r\n processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)\r\n assert processed_ds.shape == (2, 1)\r\n> with processed_ds.with_format(\"numpy\") as pds:\r\n\r\ntests/features/test_array_xd.py:459: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:2669: in with_format\r\n dataset = copy.deepcopy(self)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:270: in _reconstruct\r\n state = deepcopy(state, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:230: in _deepcopy_dict\r\n y[deepcopy(key, memo)] = deepcopy(value, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:153: in deepcopy\r\n y = copier(memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:188: in __deepcopy__\r\n return _deepcopy(self, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:86: in _deepcopy\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:205: in _deepcopy_list\r\n append(deepcopy(a, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> ???\r\nE ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n\r\npyarrow/types.pxi:3157: ValueError\r\n```\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_class_encode_column_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_dummy_dataset_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_csv_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_sql_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[True] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[False] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[mix] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::ArrayXDDynamicTest::test_map_dataset - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[False] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[True] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n===== 15 failed,\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007338 / 0.011353 (-0.004015) | 0.004308 / 0.011008 (-0.006700) | 0.088788 / 0.038508 (0.050280) | 0.039369 / 0.023109 (0.016260) | 0.334527 / 0.275898 (0.058629) | 0.373748 / 0.323480 (0.050268) | 0.005550 / 0.007986 (-0.002435) | 0.003606 / 0.004328 (-0.000723) | 0.072238 / 0.004250 (0.067988) | 0.061271 / 0.037052 (0.024218) | 0.336333 / 0.258489 (0.077844) | 0.398256 / 0.293841 (0.104415) | 0.041941 / 0.128546 (-0.086605) | 0.013372 / 0.075646 (-0.062274) | 0.336221 / 0.419271 (-0.083050) | 0.083013 / 0.043533 (0.039480) | 0.334743 / 0.255139 (0.079604) | 0.362572 / 0.283200 (0.079373) | 0.031161 / 0.141683 (-0.110521) | 1.563441 / 1.452155 (0.111287) | 1.704059 / 1.492716 (0.211343) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252978 / 0.018006 (0.234972) | 0.506348 / 0.000490 (0.505859) | 0.011679 / 0.000200 (0.011479) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026257 / 0.037411 (-0.011154) | 0.085936 / 0.014526 (0.071410) | 0.098542 / 0.176557 (-0.078015) | 0.154507 / 0.737135 (-0.582628) | 0.111493 / 0.296338 (-0.184845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575941 / 0.215209 (0.360732) | 5.590230 / 2.077655 (3.512576) | 2.463330 / 1.504120 (0.959211) | 2.125760 / 1.541195 (0.584565) | 2.095933 / 1.468490 (0.627443) | 0.844768 / 4.584777 (-3.740009) | 4.768995 / 3.745712 (1.023282) | 4.670484 / 5.269862 (-0.599377) | 2.630386 / 4.565676 (-1.935290) | 0.085996 / 0.424275 (-0.338279) | 0.007900 / 0.007607 (0.000293) | 0.685463 / 0.226044 (0.459419) | 6.699310 / 2.268929 (4.430381) | 3.132542 / 55.444624 (-52.312083) | 2.527963 / 6.876477 (-4.348513) | 2.381835 / 2.142072 (0.239763) | 0.909668 / 4.805227 (-3.895559) | 0.209979 / 6.500664 (-6.290685) | 0.079222 / 0.075469 (0.003753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.444895 / 1.841788 (-0.396892) | 20.388140 / 8.074308 (12.313832) | 19.354148 / 10.191392 (9.162756) | 0.222433 / 0.680424 (-0.457991) | 0.029710 / 0.534201 (-0.504491) | 0.427153 / 0.579283 (-0.152130) | 0.537500 / 0.434364 (0.103136) | 0.506917 / 0.540337 (-0.033421) | 0.726088 / 1.386936 (-0.660848) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007652 / 0.011353 (-0.003701) | 0.004320 / 0.011008 (-0.006688) | 0.072721 / 0.038508 (0.034212) | 0.068204 / 0.023109 (0.045095) | 0.392087 / 0.275898 (0.116189) | 0.431638 / 0.323480 (0.108158) | 0.005419 / 0.007986 (-0.002566) | 0.004305 / 0.004328 (-0.000023) | 0.069042 / 0.004250 (0.064791) | 0.051555 / 0.037052 (0.014503) | 0.412141 / 0.258489 (0.153651) | 0.438802 / 0.293841 (0.144961) | 0.043631 / 0.128546 (-0.084915) | 0.014169 / 0.075646 (-0.061478) | 0.079571 / 0.419271 (-0.339701) | 0.056707 / 0.043533 (0.013174) | 0.413698 / 0.255139 (0.158559) | 0.414127 / 0.283200 (0.130928) | 0.031380 / 0.141683 (-0.110303) | 1.677157 / 1.452155 (0.225003) | 1.755155 / 1.492716 (0.262439) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257236 / 0.018006 (0.239230) | 0.521347 / 0.000490 (0.520858) | 0.006282 / 0.000200 (0.006082) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028433 / 0.037411 (-0.008978) | 0.087698 / 0.014526 (0.073172) | 0.108840 / 0.176557 (-0.067716) | 0.157432 / 0.737135 (-0.579704) | 0.103144 / 0.296338 (-0.193195) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598745 / 0.215209 (0.383536) | 5.981460 / 2.077655 (3.903805) | 2.556931 / 1.504120 (1.052811) | 2.179915 / 1.541195 (0.638720) | 2.240841 / 1.468490 (0.772351) | 0.811501 / 4.584777 (-3.773276) | 4.718282 / 3.745712 (0.972570) | 4.365738 / 5.269862 (-0.904124) | 2.669798 / 4.565676 (-1.895878) | 0.099135 / 0.424275 (-0.325140) | 0.007369 / 0.007607 (-0.000238) | 0.669491 / 0.226044 (0.443447) | 6.700389 / 2.268929 (4.431461) | 3.155328 / 55.444624 (-52.289296) | 2.563375 / 6.876477 (-4.313102) | 2.545191 / 2.142072 (0.403119) | 0.961359 / 4.805227 (-3.843868) | 0.189391 / 6.500664 (-6.311273) | 0.061597 / 0.075469 (-0.013873) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.564008 / 1.841788 (-0.277780) | 21.401307 / 8.074308 (13.326999) | 20.693441 / 10.191392 (10.502049) | 0.229340 / 0.680424 (-0.451084) | 0.033637 / 0.534201 (-0.500564) | 0.429394 / 0.579283 (-0.149889) | 0.557202 / 0.434364 (0.122838) | 0.510284 / 0.540337 (-0.030054) | 0.725661 / 1.386936 (-0.661276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45abe297c178b829afcee853f9958b0c5a6469aa \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004820 / 0.011353 (-0.006533) | 0.003152 / 0.011008 (-0.007856) | 0.061842 / 0.038508 (0.023334) | 0.030127 / 0.023109 (0.007018) | 0.257409 / 0.275898 (-0.018489) | 0.269382 / 0.323480 (-0.054097) | 0.004288 / 0.007986 (-0.003698) | 0.002500 / 0.004328 (-0.001829) | 0.048520 / 0.004250 (0.044270) | 0.046815 / 0.037052 (0.009763) | 0.245858 / 0.258489 (-0.012631) | 0.289636 / 0.293841 (-0.004205) | 0.023983 / 0.128546 (-0.104563) | 0.007336 / 0.075646 (-0.068310) | 0.202347 / 0.419271 (-0.216924) | 0.057737 / 0.043533 (0.014204) | 0.245922 / 0.255139 (-0.009217) | 0.268788 / 0.283200 (-0.014412) | 0.017819 / 0.141683 (-0.123864) | 1.149889 / 1.452155 (-0.302265) | 1.227192 / 1.492716 (-0.265524) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092234 / 0.018006 (0.074228) | 0.310259 / 0.000490 (0.309769) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019059 / 0.037411 (-0.018352) | 0.064904 / 0.014526 (0.050378) | 0.073531 / 0.176557 (-0.103026) | 0.120879 / 0.737135 (-0.616257) | 0.075410 / 0.296338 (-0.220929) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275364 / 0.215209 (0.060155) | 2.724379 / 2.077655 (0.646725) | 1.447617 / 1.504120 (-0.056503) | 1.366794 / 1.541195 (-0.174401) | 1.345849 / 1.468490 (-0.122641) | 0.411205 / 4.584777 (-4.173572) | 2.412712 / 3.745712 (-1.333000) | 2.612469 / 5.269862 (-2.657393) | 1.552113 / 4.565676 (-3.013564) | 0.045783 / 0.424275 (-0.378492) | 0.004782 / 0.007607 (-0.002825) | 0.339218 / 0.226044 (0.113174) | 3.359540 / 2.268929 (1.090612) | 1.821369 / 55.444624 (-53.623256) | 1.540742 / 6.876477 (-5.335734) | 1.531845 / 2.142072 (-0.610227) | 0.462009 / 4.805227 (-4.343218) | 0.097794 / 6.500664 (-6.402870) | 0.041222 / 0.075469 (-0.034247) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938319 / 1.841788 (-0.903469) | 11.712003 / 8.074308 (3.637695) | 10.325317 / 10.191392 (0.133925) | 0.126812 / 0.680424 (-0.553612) | 0.013734 / 0.534201 (-0.520467) | 0.279509 / 0.579283 (-0.299774) | 0.269265 / 0.434364 (-0.165099) | 0.322033 / 0.540337 (-0.218304) | 0.441610 / 1.386936 (-0.945326) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004882 / 0.011353 (-0.006471) | 0.002984 / 0.011008 (-0.008024) | 0.048318 / 0.038508 (0.009810) | 0.054642 / 0.023109 (0.031533) | 0.268599 / 0.275898 (-0.007299) | 0.292916 / 0.323480 (-0.030564) | 0.004108 / 0.007986 (-0.003878) | 0.002500 / 0.004328 (-0.001829) | 0.048452 / 0.004250 (0.044202) | 0.038835 / 0.037052 (0.001782) | 0.275410 / 0.258489 (0.016921) | 0.307284 / 0.293841 (0.013443) | 0.024720 / 0.128546 (-0.103826) | 0.007274 / 0.075646 (-0.068372) | 0.054419 / 0.419271 (-0.364853) | 0.032815 / 0.043533 (-0.010718) | 0.273660 / 0.255139 (0.018521) | 0.289183 / 0.283200 (0.005984) | 0.017746 / 0.141683 (-0.123937) | 1.153876 / 1.452155 (-0.298278) | 1.212778 / 1.492716 (-0.279938) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095286 / 0.018006 (0.077280) | 0.305185 / 0.000490 (0.304696) | 0.000230 / 0.000200 (0.000030) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021556 / 0.037411 (-0.015855) | 0.071029 / 0.014526 (0.056503) | 0.081914 / 0.176557 (-0.094643) | 0.120553 / 0.737135 (-0.616582) | 0.086696 / 0.296338 (-0.209642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289750 / 0.215209 (0.074541) | 2.794247 / 2.077655 (0.716592) | 1.577105 / 1.504120 (0.072985) | 1.457706 / 1.541195 (-0.083489) | 1.500481 / 1.468490 (0.031991) | 0.403834 / 4.584777 (-4.180943) | 2.466810 / 3.745712 (-1.278902) | 2.701008 / 5.269862 (-2.568854) | 1.634821 / 4.565676 (-2.930856) | 0.046954 / 0.424275 (-0.377322) | 0.004811 / 0.007607 (-0.002796) | 0.347622 / 0.226044 (0.121578) | 3.407125 / 2.268929 (1.138197) | 1.987121 / 55.444624 (-53.457504) | 1.689978 / 6.876477 (-5.186499) | 1.731801 / 2.142072 (-0.410271) | 0.478926 / 4.805227 (-4.326301) | 0.100730 / 6.500664 (-6.399934) | 0.043078 / 0.075469 (-0.032391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963575 / 1.841788 (-0.878212) | 12.675331 / 8.074308 (4.601023) | 11.167584 / 10.191392 (0.976192) | 0.131199 / 0.680424 (-0.549225) | 0.016030 / 0.534201 (-0.518171) | 0.277783 / 0.579283 (-0.301500) | 0.278693 / 0.434364 (-0.155671) | 0.315141 / 0.540337 (-0.225196) | 0.429104 / 1.386936 (-0.957832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#825c1d25835b64fc3533a63d60bd237f4465f15e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004807 / 0.011353 (-0.006546) | 0.002925 / 0.011008 (-0.008083) | 0.062560 / 0.038508 (0.024052) | 0.029926 / 0.023109 (0.006817) | 0.264708 / 0.275898 (-0.011190) | 0.273464 / 0.323480 (-0.050016) | 0.003197 / 0.007986 (-0.004788) | 0.002544 / 0.004328 (-0.001784) | 0.048230 / 0.004250 (0.043980) | 0.046552 / 0.037052 (0.009500) | 0.249553 / 0.258489 (-0.008936) | 0.282078 / 0.293841 (-0.011762) | 0.023201 / 0.128546 (-0.105346) | 0.007306 / 0.075646 (-0.068340) | 0.241361 / 0.419271 (-0.177910) | 0.058286 / 0.043533 (0.014753) | 0.245854 / 0.255139 (-0.009285) | 0.266053 / 0.283200 (-0.017146) | 0.020294 / 0.141683 (-0.121388) | 1.102215 / 1.452155 (-0.349939) | 1.170733 / 1.492716 (-0.321984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094647 / 0.018006 (0.076641) | 0.303819 / 0.000490 (0.303329) | 0.000250 / 0.000200 (0.000050) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019036 / 0.037411 (-0.018375) | 0.064729 / 0.014526 (0.050203) | 0.074143 / 0.176557 (-0.102414) | 0.120082 / 0.737135 (-0.617054) | 0.076835 / 0.296338 (-0.219503) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283786 / 0.215209 (0.068577) | 2.751446 / 2.077655 (0.673791) | 1.473789 / 1.504120 (-0.030331) | 1.336968 / 1.541195 (-0.204226) | 1.384148 / 1.468490 (-0.084342) | 0.397452 / 4.584777 (-4.187325) | 2.388042 / 3.745712 (-1.357670) | 2.661291 / 5.269862 (-2.608571) | 1.595454 / 4.565676 (-2.970223) | 0.045919 / 0.424275 (-0.378356) | 0.004879 / 0.007607 (-0.002728) | 0.337862 / 0.226044 (0.111818) | 3.355665 / 2.268929 (1.086737) | 1.875261 / 55.444624 (-53.569363) | 1.540874 / 6.876477 (-5.335603) | 1.653632 / 2.142072 (-0.488440) | 0.473090 / 4.805227 (-4.332138) | 0.100151 / 6.500664 (-6.400513) | 0.042357 / 0.075469 (-0.033112) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959550 / 1.841788 (-0.882238) | 12.307145 / 8.074308 (4.232837) | 10.719321 / 10.191392 (0.527929) | 0.128376 / 0.680424 (-0.552048) | 0.014406 / 0.534201 (-0.519795) | 0.295208 / 0.579283 (-0.284075) | 0.268891 / 0.434364 (-0.165473) | 0.305446 / 0.540337 (-0.234892) | 0.429591 / 1.386936 (-0.957345) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005189 / 0.011353 (-0.006164) | 0.003082 / 0.011008 (-0.007926) | 0.048956 / 0.038508 (0.010448) | 0.063403 / 0.023109 (0.040294) | 0.272858 / 0.275898 (-0.003040) | 0.295207 / 0.323480 (-0.028273) | 0.004253 / 0.007986 (-0.003733) | 0.002552 / 0.004328 (-0.001776) | 0.048042 / 0.004250 (0.043792) | 0.040429 / 0.037052 (0.003377) | 0.269614 / 0.258489 (0.011125) | 0.307205 / 0.293841 (0.013364) | 0.027912 / 0.128546 (-0.100634) | 0.007621 / 0.075646 (-0.068026) | 0.054020 / 0.419271 (-0.365251) | 0.036958 / 0.043533 (-0.006574) | 0.272457 / 0.255139 (0.017318) | 0.287966 / 0.283200 (0.004766) | 0.019542 / 0.141683 (-0.122141) | 1.116742 / 1.452155 (-0.335413) | 1.194739 / 1.492716 (-0.297977) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093532 / 0.018006 (0.075526) | 0.303262 / 0.000490 (0.302773) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021984 / 0.037411 (-0.015428) | 0.075024 / 0.014526 (0.060498) | 0.080959 / 0.176557 (-0.095598) | 0.121780 / 0.737135 (-0.615356) | 0.082817 / 0.296338 (-0.213522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292766 / 0.215209 (0.077557) | 2.857457 / 2.077655 (0.779802) | 1.621860 / 1.504120 (0.117740) | 1.473783 / 1.541195 (-0.067412) | 1.535211 / 1.468490 (0.066721) | 0.402212 / 4.584777 (-4.182565) | 2.467143 / 3.745712 (-1.278569) | 2.618162 / 5.269862 (-2.651700) | 1.568682 / 4.565676 (-2.996994) | 0.047123 / 0.424275 (-0.377152) | 0.004780 / 0.007607 (-0.002827) | 0.346959 / 0.226044 (0.120914) | 3.395196 / 2.268929 (1.126268) | 1.957835 / 55.444624 (-53.486789) | 1.674287 / 6.876477 (-5.202190) | 1.715879 / 2.142072 (-0.426193) | 0.479481 / 4.805227 (-4.325746) | 0.100043 / 6.500664 (-6.400621) | 0.041289 / 0.075469 (-0.034180) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965418 / 1.841788 (-0.876370) | 12.703830 / 8.074308 (4.629522) | 11.301401 / 10.191392 (1.110009) | 0.131429 / 0.680424 (-0.548995) | 0.016597 / 0.534201 (-0.517604) | 0.273290 / 0.579283 (-0.305993) | 0.285400 / 0.434364 (-0.148964) | 0.307327 / 0.540337 (-0.233011) | 0.434186 / 1.386936 (-0.952750) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c096bd288d07ed86f340ae090e5d4d9c5351f76f \"CML watermark\")\n" ]
2023-11-13T09:15:39
2023-11-14T10:29:48
2023-11-14T10:23:29
MEMBER
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Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). Fix #6396.
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1,990,098,817
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6,403
Cannot import datasets on google colab (python 3.10.12)
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[ "You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.", "okay, it works! thank you so much! 😄 " ]
2023-11-13T08:14:43
2023-11-16T05:04:22
2023-11-16T05:04:21
NONE
null
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### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12) ![image](https://github.com/huggingface/datasets/assets/15389235/6f7758a2-681d-4436-87d0-5e557838e368) I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free
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1,989,094,542
PR_kwDODunzps5fOBdK
6,402
Update torch_formatter.py
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6402). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005391 / 0.011353 (-0.005962) | 0.003908 / 0.011008 (-0.007100) | 0.064342 / 0.038508 (0.025834) | 0.031385 / 0.023109 (0.008275) | 0.251869 / 0.275898 (-0.024030) | 0.294875 / 0.323480 (-0.028605) | 0.003053 / 0.007986 (-0.004933) | 0.002881 / 0.004328 (-0.001448) | 0.050072 / 0.004250 (0.045822) | 0.044463 / 0.037052 (0.007411) | 0.264646 / 0.258489 (0.006157) | 0.296024 / 0.293841 (0.002183) | 0.027832 / 0.128546 (-0.100714) | 0.010937 / 0.075646 (-0.064710) | 0.207799 / 0.419271 (-0.211472) | 0.036423 / 0.043533 (-0.007110) | 0.251022 / 0.255139 (-0.004117) | 0.271366 / 0.283200 (-0.011833) | 0.019780 / 0.141683 (-0.121903) | 1.149634 / 1.452155 (-0.302521) | 1.196476 / 1.492716 (-0.296240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094857 / 0.018006 (0.076850) | 0.312159 / 0.000490 (0.311669) | 0.000215 / 0.000200 (0.000015) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018858 / 0.037411 (-0.018553) | 0.062022 / 0.014526 (0.047496) | 0.078302 / 0.176557 (-0.098255) | 0.122199 / 0.737135 (-0.614936) | 0.076044 / 0.296338 (-0.220294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286414 / 0.215209 (0.071205) | 2.785446 / 2.077655 (0.707791) | 1.438248 / 1.504120 (-0.065872) | 1.307558 / 1.541195 (-0.233636) | 1.337172 / 1.468490 (-0.131318) | 0.582228 / 4.584777 (-4.002549) | 2.457207 / 3.745712 (-1.288505) | 2.906692 / 5.269862 (-2.363169) | 1.833020 / 4.565676 (-2.732656) | 0.063549 / 0.424275 (-0.360726) | 0.005080 / 0.007607 (-0.002527) | 0.333178 / 0.226044 (0.107133) | 3.332463 / 2.268929 (1.063534) | 1.797209 / 55.444624 (-53.647415) | 1.514446 / 6.876477 (-5.362031) | 1.601252 / 2.142072 (-0.540820) | 0.664933 / 4.805227 (-4.140294) | 0.120140 / 6.500664 (-6.380524) | 0.042769 / 0.075469 (-0.032700) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983738 / 1.841788 (-0.858050) | 11.996685 / 8.074308 (3.922376) | 9.594757 / 10.191392 (-0.596635) | 0.146680 / 0.680424 (-0.533744) | 0.014455 / 0.534201 (-0.519746) | 0.292546 / 0.579283 (-0.286737) | 0.270381 / 0.434364 (-0.163983) | 0.326759 / 0.540337 (-0.213579) | 0.423387 / 1.386936 (-0.963549) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005659 / 0.011353 (-0.005694) | 0.004841 / 0.011008 (-0.006167) | 0.049598 / 0.038508 (0.011090) | 0.031796 / 0.023109 (0.008687) | 0.265967 / 0.275898 (-0.009931) | 0.287648 / 0.323480 (-0.035832) | 0.004264 / 0.007986 (-0.003721) | 0.002759 / 0.004328 (-0.001569) | 0.049249 / 0.004250 (0.044999) | 0.049526 / 0.037052 (0.012474) | 0.357765 / 0.258489 (0.099276) | 0.307563 / 0.293841 (0.013722) | 0.030329 / 0.128546 (-0.098217) | 0.010536 / 0.075646 (-0.065111) | 0.057547 / 0.419271 (-0.361724) | 0.059874 / 0.043533 (0.016341) | 0.264946 / 0.255139 (0.009807) | 0.283666 / 0.283200 (0.000466) | 0.021005 / 0.141683 (-0.120677) | 1.170959 / 1.452155 (-0.281195) | 1.206232 / 1.492716 (-0.286484) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097567 / 0.018006 (0.079561) | 0.308585 / 0.000490 (0.308095) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023267 / 0.037411 (-0.014144) | 0.075896 / 0.014526 (0.061370) | 0.087341 / 0.176557 (-0.089216) | 0.130270 / 0.737135 (-0.606866) | 0.091086 / 0.296338 (-0.205252) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298728 / 0.215209 (0.083519) | 2.876498 / 2.077655 (0.798843) | 1.591584 / 1.504120 (0.087464) | 1.461251 / 1.541195 (-0.079944) | 1.483276 / 1.468490 (0.014786) | 0.572344 / 4.584777 (-4.012433) | 2.474050 / 3.745712 (-1.271662) | 2.707051 / 5.269862 (-2.562811) | 1.759210 / 4.565676 (-2.806466) | 0.063420 / 0.424275 (-0.360855) | 0.005014 / 0.007607 (-0.002593) | 0.344490 / 0.226044 (0.118446) | 3.411065 / 2.268929 (1.142137) | 1.937232 / 55.444624 (-53.507392) | 1.665826 / 6.876477 (-5.210650) | 1.824377 / 2.142072 (-0.317696) | 0.631630 / 4.805227 (-4.173597) | 0.115791 / 6.500664 (-6.384873) | 0.040846 / 0.075469 (-0.034623) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.015248 / 1.841788 (-0.826540) | 12.738696 / 8.074308 (4.664388) | 10.324303 / 10.191392 (0.132911) | 0.153597 / 0.680424 (-0.526827) | 0.015396 / 0.534201 (-0.518805) | 0.287160 / 0.579283 (-0.292123) | 0.279886 / 0.434364 (-0.154478) | 0.324128 / 0.540337 (-0.216210) | 0.456089 / 1.386936 (-0.930847) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a02997de6e990eb78dccd905f05e12404a919156 \"CML watermark\")\n" ]
2023-11-11T19:40:41
2024-03-15T11:31:53
2024-03-15T11:25:37
CONTRIBUTOR
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Ensure PyTorch images are converted to (C, H, W) instead of (H, W, C). See #6394 for motivation.
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dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working
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[ "Seems like it's a problem with the dataset, since in the [README](https://huggingface.co/datasets/Hyperspace-Technologies/scp-wiki-text/blob/main/README.md) the validation is not specified. Try cloning the dataset, removing the README (or validation split), and loading it locally/ ", "@VarunNSrivastava thanks brother, working beautiful now\r\n\r\n```\r\nC:\\_Work\\_datasets>py dataset.py\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████| 3/3 [00:00<?, ?it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 599.90it/s]\r\nGenerating train split: 314294 examples [00:00, 1293222.03 examples/s]\r\nGenerating validation split: 120 examples [00:00, 59053.91 examples/s]\r\nGenerating test split: 34922 examples [00:00, 1343275.84 examples/s]\r\n```" ]
2023-11-11T04:09:07
2023-11-20T17:45:20
2023-11-20T17:45:20
NONE
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### Describe the bug ``` (datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py Downloading readme: 100%|███████████████████████████████████| 360/360 [00:00<00:00, 2.16MB/s] Downloading data: 100%|█████████████████████████████████| 65.1M/65.1M [00:19<00:00, 3.38MB/s] Downloading data: 100%|█████████████████████████████████| 6.35k/6.35k [00:00<00:00, 20.7kB/s] Downloading data: 100%|█████████████████████████████████| 7.29M/7.29M [00:01<00:00, 3.99MB/s] Downloading data files: 100%|██████████████████████████████████| 3/3 [00:21<00:00, 7.14s/it] Extracting data files: 100%|█████████████████████████████████| 3/3 [00:00<00:00, 1624.23it/s] Generating train split: 100%|█████████████| 314294/314294 [00:00<00:00, 668186.58 examples/s] Generating validation split: 120 examples [00:00, 100422.28 examples/s] Generating test split: 100%|████████████████| 34922/34922 [00:00<00:00, 754683.41 examples/s] Traceback (most recent call last): File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module> dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits))) datasets.utils.info_utils.UnexpectedSplits: {'validation'} ``` ### Steps to reproduce the bug Name: `dataset.py` Code: ``` from datasets import load_dataset dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") ``` ### Expected behavior Run without errors ### Environment info ``` name: datasets channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - python=3.10.13=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py310h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py310h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - async-timeout==4.0.3 - attrs==23.1.0 - certifi==2023.7.22 - charset-normalizer==3.3.2 - click==8.1.7 - datasets==2.14.6 - dill==0.3.7 - filelock==3.13.1 - frozenlist==1.4.0 - fsspec==2023.10.0 - huggingface-hub==0.19.0 - idna==3.4 - multidict==6.0.4 - multiprocess==0.70.15 - numpy==1.26.1 - openai==0.27.8 - packaging==23.2 - pandas==2.1.3 - pip==23.3.1 - platformdirs==4.0.0 - pyarrow==14.0.1 - python-dateutil==2.8.2 - pytz==2023.3.post1 - pyyaml==6.0.1 - requests==2.31.0 - six==1.16.0 - tomli==2.0.1 - tqdm==4.66.1 - typer==0.9.0 - typing-extensions==4.8.0 - tzdata==2023.3 - urllib3==2.0.7 - xxhash==3.4.1 - yarl==1.9.2 prefix: /home/mruserbox/miniconda3/envs/datasets ```
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Safely load datasets by disabling execution of dataset loading script
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[ "great idea IMO\r\n\r\nthis could be a `trust_remote_code=True` flag like in transformers. We could also default to loading the Parquet conversion rather than executing code (for dataset repos that have both)", "@julien-c that would be great!", "We added the `trust_remote_code` argument to `load_dataset()` in `datasets` 2.16:\r\n- in the future users will have to pass trust_remote_code=True to use datasets with a script\r\n- for now we just show a warning when a dataset script is used\r\n- we fallback on the Hugging Face Parquet exports when possible (to keep compatibility with old datasets with scripts)\r\n\r\nSo feel free to use `trust_remote_code=False` in the meantime to disable loading from dataset loading scripts :)", "Passing `trust_remote_code=True` explicitly is now mandatory to load a dataset with a script since https://github.com/huggingface/datasets/pull/6954" ]
2023-11-10T23:48:29
2024-06-13T15:56:13
2024-06-13T15:56:13
NONE
null
null
null
### Feature request Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. Any suggested workarounds are welcome as well. ### Motivation This is a security vulnerability that could lead to arbitrary code execution. ### Your contribution n/a
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I_kwDODunzps52hBh3
6,399
TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array
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[ "Seconding encountering this issue." ]
2023-11-10T20:48:46
2024-06-22T00:13:48
null
NONE
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### Describe the bug Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. Thank you! ### Steps to reproduce the bug Traceback (most recent call last): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single writer.write_batch(batch) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 243, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray values = pa.ListArray.from_arrays(offsets, values) File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array ### Expected behavior Type should not be a ChunkedArray ### Environment info datasets v2.14.5 arrow v1.2.3 pyarrow v12.0.1
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Remove redundant condition in builders
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004475 / 0.011353 (-0.006878) | 0.002840 / 0.011008 (-0.008168) | 0.061544 / 0.038508 (0.023036) | 0.031237 / 0.023109 (0.008128) | 0.243270 / 0.275898 (-0.032628) | 0.271903 / 0.323480 (-0.051577) | 0.002906 / 0.007986 (-0.005080) | 0.003118 / 0.004328 (-0.001210) | 0.047362 / 0.004250 (0.043112) | 0.047840 / 0.037052 (0.010788) | 0.244044 / 0.258489 (-0.014445) | 0.279310 / 0.293841 (-0.014531) | 0.023408 / 0.128546 (-0.105138) | 0.007110 / 0.075646 (-0.068536) | 0.207328 / 0.419271 (-0.211943) | 0.058463 / 0.043533 (0.014930) | 0.245631 / 0.255139 (-0.009508) | 0.267755 / 0.283200 (-0.015445) | 0.018147 / 0.141683 (-0.123536) | 1.086877 / 1.452155 (-0.365278) | 1.155380 / 1.492716 (-0.337337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091925 / 0.018006 (0.073919) | 0.299858 / 0.000490 (0.299368) | 0.000232 / 0.000200 (0.000032) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018416 / 0.037411 (-0.018995) | 0.062608 / 0.014526 (0.048082) | 0.073897 / 0.176557 (-0.102660) | 0.120216 / 0.737135 (-0.616919) | 0.075788 / 0.296338 (-0.220550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287823 / 0.215209 (0.072614) | 2.797546 / 2.077655 (0.719891) | 1.470878 / 1.504120 (-0.033242) | 1.347497 / 1.541195 (-0.193698) | 1.363837 / 1.468490 (-0.104653) | 0.400069 / 4.584777 (-4.184708) | 2.338870 / 3.745712 (-1.406842) | 2.564075 / 5.269862 (-2.705787) | 1.568454 / 4.565676 (-2.997222) | 0.047103 / 0.424275 (-0.377172) | 0.004783 / 0.007607 (-0.002824) | 0.345244 / 0.226044 (0.119200) | 3.407752 / 2.268929 (1.138823) | 1.826552 / 55.444624 (-53.618073) | 1.536714 / 6.876477 (-5.339763) | 1.543138 / 2.142072 (-0.598934) | 0.478996 / 4.805227 (-4.326232) | 0.099580 / 6.500664 (-6.401085) | 0.041994 / 0.075469 (-0.033475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947106 / 1.841788 (-0.894682) | 11.391262 / 8.074308 (3.316954) | 10.531141 / 10.191392 (0.339749) | 0.141497 / 0.680424 (-0.538927) | 0.014214 / 0.534201 (-0.519987) | 0.269346 / 0.579283 (-0.309937) | 0.268129 / 0.434364 (-0.166235) | 0.309496 / 0.540337 (-0.230841) | 0.429207 / 1.386936 (-0.957729) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004770 / 0.011353 (-0.006583) | 0.002878 / 0.011008 (-0.008130) | 0.048248 / 0.038508 (0.009740) | 0.051068 / 0.023109 (0.027959) | 0.272076 / 0.275898 (-0.003822) | 0.292423 / 0.323480 (-0.031057) | 0.004016 / 0.007986 (-0.003970) | 0.002522 / 0.004328 (-0.001807) | 0.047617 / 0.004250 (0.043367) | 0.038168 / 0.037052 (0.001115) | 0.275236 / 0.258489 (0.016746) | 0.303811 / 0.293841 (0.009970) | 0.023816 / 0.128546 (-0.104730) | 0.007177 / 0.075646 (-0.068469) | 0.053453 / 0.419271 (-0.365818) | 0.032425 / 0.043533 (-0.011108) | 0.271620 / 0.255139 (0.016481) | 0.289618 / 0.283200 (0.006418) | 0.017986 / 0.141683 (-0.123697) | 1.154225 / 1.452155 (-0.297930) | 1.224244 / 1.492716 (-0.268472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090477 / 0.018006 (0.072471) | 0.299461 / 0.000490 (0.298971) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022043 / 0.037411 (-0.015369) | 0.070327 / 0.014526 (0.055801) | 0.080132 / 0.176557 (-0.096425) | 0.120007 / 0.737135 (-0.617128) | 0.083037 / 0.296338 (-0.213301) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294538 / 0.215209 (0.079329) | 2.882791 / 2.077655 (0.805136) | 1.582923 / 1.504120 (0.078803) | 1.457091 / 1.541195 (-0.084104) | 1.536149 / 1.468490 (0.067659) | 0.401539 / 4.584777 (-4.183238) | 2.440919 / 3.745712 (-1.304793) | 2.503108 / 5.269862 (-2.766753) | 1.509216 / 4.565676 (-3.056460) | 0.046267 / 0.424275 (-0.378008) | 0.004790 / 0.007607 (-0.002817) | 0.336137 / 0.226044 (0.110093) | 3.331655 / 2.268929 (1.062726) | 1.954228 / 55.444624 (-53.490396) | 1.686637 / 6.876477 (-5.189840) | 1.650278 / 2.142072 (-0.491794) | 0.473895 / 4.805227 (-4.331333) | 0.096908 / 6.500664 (-6.403756) | 0.040387 / 0.075469 (-0.035082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972999 / 1.841788 (-0.868789) | 11.978367 / 8.074308 (3.904059) | 10.861092 / 10.191392 (0.669699) | 0.129054 / 0.680424 (-0.551369) | 0.015988 / 0.534201 (-0.518213) | 0.268827 / 0.579283 (-0.310456) | 0.271714 / 0.434364 (-0.162649) | 0.304045 / 0.540337 (-0.236293) | 0.413158 / 1.386936 (-0.973778) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e4348a233a75907c305b3159ac9cb183cf30ea5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005286 / 0.011353 (-0.006067) | 0.002860 / 0.011008 (-0.008149) | 0.062449 / 0.038508 (0.023941) | 0.035346 / 0.023109 (0.012237) | 0.241685 / 0.275898 (-0.034213) | 0.268116 / 0.323480 (-0.055364) | 0.003050 / 0.007986 (-0.004935) | 0.003134 / 0.004328 (-0.001194) | 0.048818 / 0.004250 (0.044567) | 0.049187 / 0.037052 (0.012135) | 0.247395 / 0.258489 (-0.011094) | 0.280301 / 0.293841 (-0.013540) | 0.023801 / 0.128546 (-0.104745) | 0.007653 / 0.075646 (-0.067994) | 0.204185 / 0.419271 (-0.215087) | 0.071251 / 0.043533 (0.027718) | 0.244409 / 0.255139 (-0.010730) | 0.262363 / 0.283200 (-0.020836) | 0.018631 / 0.141683 (-0.123052) | 1.110152 / 1.452155 (-0.342003) | 1.165093 / 1.492716 (-0.327624) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099536 / 0.018006 (0.081530) | 0.309598 / 0.000490 (0.309109) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019213 / 0.037411 (-0.018198) | 0.069296 / 0.014526 (0.054770) | 0.074752 / 0.176557 (-0.101804) | 0.121314 / 0.737135 (-0.615822) | 0.081274 / 0.296338 (-0.215065) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281345 / 0.215209 (0.066136) | 2.755435 / 2.077655 (0.677780) | 1.453358 / 1.504120 (-0.050762) | 1.328222 / 1.541195 (-0.212973) | 1.392281 / 1.468490 (-0.076209) | 0.410539 / 4.584777 (-4.174238) | 2.452072 / 3.745712 (-1.293640) | 2.777757 / 5.269862 (-2.492105) | 1.656719 / 4.565676 (-2.908958) | 0.046844 / 0.424275 (-0.377431) | 0.004785 / 0.007607 (-0.002822) | 0.336567 / 0.226044 (0.110522) | 3.317564 / 2.268929 (1.048635) | 1.830737 / 55.444624 (-53.613888) | 1.528464 / 6.876477 (-5.348013) | 1.620527 / 2.142072 (-0.521545) | 0.480662 / 4.805227 (-4.324565) | 0.100819 / 6.500664 (-6.399845) | 0.042501 / 0.075469 (-0.032968) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962593 / 1.841788 (-0.879195) | 12.508048 / 8.074308 (4.433740) | 11.117398 / 10.191392 (0.926006) | 0.131265 / 0.680424 (-0.549159) | 0.014469 / 0.534201 (-0.519732) | 0.271627 / 0.579283 (-0.307656) | 0.274966 / 0.434364 (-0.159398) | 0.313260 / 0.540337 (-0.227077) | 0.444741 / 1.386936 (-0.942195) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004974 / 0.011353 (-0.006379) | 0.003383 / 0.011008 (-0.007626) | 0.048792 / 0.038508 (0.010284) | 0.052821 / 0.023109 (0.029712) | 0.267123 / 0.275898 (-0.008775) | 0.293604 / 0.323480 (-0.029876) | 0.003968 / 0.007986 (-0.004018) | 0.002594 / 0.004328 (-0.001735) | 0.047690 / 0.004250 (0.043439) | 0.040236 / 0.037052 (0.003183) | 0.267805 / 0.258489 (0.009315) | 0.310543 / 0.293841 (0.016702) | 0.025707 / 0.128546 (-0.102839) | 0.008012 / 0.075646 (-0.067634) | 0.054460 / 0.419271 (-0.364812) | 0.033545 / 0.043533 (-0.009988) | 0.270166 / 0.255139 (0.015027) | 0.285965 / 0.283200 (0.002765) | 0.019391 / 0.141683 (-0.122292) | 1.144991 / 1.452155 (-0.307164) | 1.198491 / 1.492716 (-0.294225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094757 / 0.018006 (0.076751) | 0.306712 / 0.000490 (0.306222) | 0.000218 / 0.000200 (0.000018) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020995 / 0.037411 (-0.016417) | 0.070293 / 0.014526 (0.055767) | 0.081441 / 0.176557 (-0.095116) | 0.119538 / 0.737135 (-0.617597) | 0.081454 / 0.296338 (-0.214885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293451 / 0.215209 (0.078242) | 2.880378 / 2.077655 (0.802723) | 1.572547 / 1.504120 (0.068427) | 1.439172 / 1.541195 (-0.102023) | 1.506343 / 1.468490 (0.037853) | 0.402764 / 4.584777 (-4.182013) | 2.501341 / 3.745712 (-1.244371) | 2.538494 / 5.269862 (-2.731367) | 1.524306 / 4.565676 (-3.041371) | 0.046401 / 0.424275 (-0.377874) | 0.004781 / 0.007607 (-0.002826) | 0.349448 / 0.226044 (0.123404) | 3.416181 / 2.268929 (1.147252) | 1.964204 / 55.444624 (-53.480420) | 1.648564 / 6.876477 (-5.227912) | 1.675977 / 2.142072 (-0.466095) | 0.475717 / 4.805227 (-4.329511) | 0.098416 / 6.500664 (-6.402248) | 0.041212 / 0.075469 (-0.034257) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975928 / 1.841788 (-0.865860) | 12.066648 / 8.074308 (3.992340) | 10.943181 / 10.191392 (0.751789) | 0.149687 / 0.680424 (-0.530736) | 0.015107 / 0.534201 (-0.519094) | 0.268950 / 0.579283 (-0.310333) | 0.280419 / 0.434364 (-0.153945) | 0.305263 / 0.540337 (-0.235074) | 0.408486 / 1.386936 (-0.978450) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#344086a7a1707ef20b57399f813ef64ce679e956 \"CML watermark\")\n" ]
2023-11-10T14:56:43
2023-11-14T10:49:15
2023-11-14T10:43:00
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Minor refactoring to remove redundant condition.
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6,397
Raise a different exception for inexisting dataset vs files without known extension
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2023-11-10T13:22:14
2023-11-22T15:12:34
2023-11-22T15:12:34
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See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557 We have the same error for: - https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist - https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension ``` >>> import datasets >>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. >>> datasets.get_dataset_config_names('severo/test_files_without_extension') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension. ``` To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.
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Issue with pyarrow 14.0.1
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[ "Looks like we should stop using `PyExtensionType` and use `ExtensionType` instead\r\n\r\nsee https://github.com/apache/arrow/commit/f14170976372436ec1d03a724d8d3f3925484ecf", "https://github.com/huggingface/datasets-server/pull/2089#pullrequestreview-1724449532\r\n\r\n> Yes, I understand now: they have disabled their `PyExtensionType` and we use it in `datasets` for arrays... ", "related?\r\n\r\nhttps://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c", "> related?\r\n>\r\n> https://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c\r\n\r\nNo, related to https://github.com/huggingface/datasets/issues/5706", "Running the following is a workaround:\r\n\r\n```\r\nimport pyarrow\r\npyarrow.PyExtensionType.set_auto_load(True)\r\n```" ]
2023-11-10T10:02:12
2023-11-14T10:23:30
2023-11-14T10:23:30
COLLABORATOR
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See https://github.com/huggingface/datasets-server/pull/2089 for reference ``` from datasets import (Array2D, Dataset, Features) feature_type = Array2D(shape=(2, 2), dtype="float32") content = [[0.0, 0.0], [0.0, 0.0]] features = Features({"col": feature_type}) dataset = Dataset.from_dict({"col": [content]}, features=features) ``` generates ``` /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:648: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. pa.PyExtensionType.__init__(self, self.storage_dtype) /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: RuntimeWarning: pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 924, in from_dict return cls(pa_table, info=info, split=split) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 693, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1381, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 111, in _arrow_to_datasets_dtype raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.") ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ```
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6,395
Add ability to set lock type
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[ "We've replaced our filelock implementation with the `filelock` package, so their repo is the right place to request this feature.\r\n\r\nIn the meantime, the following should work: \r\n```python\r\nimport filelock\r\nfilelock.FileLock = filelock.SoftFileLock\r\n\r\nimport datasets\r\n...\r\n```" ]
2023-11-09T22:12:30
2023-11-23T18:50:00
2023-11-23T18:50:00
NONE
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### Feature request Allow setting file lock type, maybe from an environment variable Currently, it only depends on whether fnctl is available: https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16 ### Motivation In my environment, flock isn't supported on a network attached drive ### Your contribution I'll be happy to submit a pr.
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TorchFormatter images (H, W, C) instead of (C, H, W) format
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[ "Here's a PR for that. https://github.com/huggingface/datasets/pull/6402\r\n\r\nIt's not backward compatible, unfortunately. ", "Just ran into this working on data lib that's attempting to achieve common interfaces across hf datasets, webdataset, native torch style datasets. The defacto standards for image tensors are numpy == HWC, torch.Tensor == CHW. \r\n\r\nI had to drop use of 'torch' formatting because as is (H, W, C) makes it incompatible with pretty much all standard torch vision processing (torchvision, etc) including model inputs themselves... not sure what the breakage scope would be, but might be worth considering a breaking change since I'm not aware of many use cases where a torch.Tensor image is expected to be in HWC form. And if I set the format to 'torch', I'd expect to be able to apply torchvision transforms, etc directly to the output...\r\n\r\nEDIT: For 'torch' output to be compatible with torch conventions (namely torchvision for images), should follow this https://pytorch.org/vision/0.17/transforms.html#supported-input-types-and-conventions\r\n\r\nattn @lhoestq \r\n\r\n", "We can define something like `.with_format(\"torch\", image_data_format=\"channels_first\")` and recommend using this in the docs maybe ? also cc @NielsRogge ", "Sounds good to me. I guess it's not allowed to use the channels first format by default for backwards compatibility purposes?", "This works, but am wondering how widespread the use of the function is for image datasets? My hunch would be that it's not used widely enough with image datasets to favour backwards compat (keeping default channels_last) over clumsiness of needing this to be 'correct' for typical use.. but don't have the data to back that up.", "I see. I just checked in the HF libraries and it shouldn't break anything. And to be consistent with them we should actually use C H W. For example `transformers` image processors use C H W by default too.\r\n\r\nSo I'm ok with doing a breaking change to make it consistent with `transformers`, `torchvision`, etc.", "Since it is quite connected, the proposed PR #6402 will not work for monochrome `PIL` images since they only have 2 dimensions as `numpy `arrays. [Torchvision ](https://pytorch.org/vision/stable/_modules/torchvision/transforms/functional.html#pil_to_tensor) adds a channel before permuting. Would that make sense here as well?", "@Modexus yes, indeed that would make sense as torch expects 1, H, W for monochrome, not H,W as you'd often see in numpy (via PIL), OpenCV, etc.\r\n\r\nThe reference should be the torchvision fn https://pytorch.org/vision/main/_modules/torchvision/transforms/functional.html#pil_to_tensor", "My PR now should handle monochrome PIL image. Thanks for the heads up :)" ]
2023-11-09T16:02:15
2024-04-11T12:40:16
2024-04-11T12:40:16
CONTRIBUTOR
null
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### Describe the bug Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy. However, pytorch normally uses (C, H, W) format. Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways. If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor. Is there a reason for this choice? ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([512, 512, 4]) ``` ### Expected behavior ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([4, 512, 512]) ``` ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31 - Python version: 3.11.6 - Huggingface_hub version: 0.18.0 - PyArrow version: 14.0.1 - Pandas version: 2.1.2
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I_kwDODunzps52T19r
6,393
Filter occasionally hangs
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[ "It looks like I may not be the first to encounter this: https://github.com/huggingface/datasets/issues/3172", "Adding some more information, it seems to occur more frequently with large (millions of samples) datasets.", "More information. My code is structured as (1) load (2) map (3) filter (4) filter. It was always the second filter that failed. Combining the two filters into one seems to reliably work.", "@lhoestq it'd be great if someone had a chance to look at this. I suspect it is impacting many users given the other issue that I linked.", "Hi ! Sorry for the late response. Was it happening after the first or the second filter ?\r\n\r\nIt looks like an issue with the garbage collector (which makes it random). Maybe datasets created with `filter` are not always handled properly ? cc @mariosasko", "It was after the second filter (and combining the two filters into one seemingly resolved it). I obviously haven't tried all settings to know that these details are causal, but it did work for me.", "Thanks, that's good to know.\r\n\r\nThe stacktrace suggests an issue when `del self._indices` is called, which happens when a filtered dataset falls out of scope. The indices are a PyArrow table memory mapped from disk, so I'm not quite sure how calling `del` on it can cause this issue. We do `del self._indices` to make sure the file on disk is not used anymore by the current process and avoid e.g. permission errors.\r\n\r\nHopefully we can find a way to reproduce this error, otherwise it will be quite hard to understand what happened", "Yeah, I have a reliable repro, but it is not even close to minimal and uses a dataset I can't share. Perhaps you could try getting close to my setting.\r\n\r\n(1) make a large (~20GB) jsonl with prompt/response pairs\r\n(2) load it on a linux machine (`dataset = load_dataset(...)`)\r\n(3) map a tokenizer to it, with multiprocessing (`tokenized_dataset = dataset.map(...)`)\r\n(4) filter it once based on something, with multiprocessing (`filtered_1 = tokenized_dataset.filter(...)`)\r\n(5) filter it again based on something, with multiprocessing (`filtered_2 = filtered_1.filter(...)`)\r\n\r\nI included the variable names just in case it is relevant that I was creating new datasets each time, not overwriting the same variable.", "@lhoestq I have another version of the repro that seems fairly reliably. I have lots of jsonl files, and I iteratively load each one with `load_dataset('json', data_files='path/to/my/file.jsonl', streaming=False, split='train')` and then `dataset.map(..., num_proc=<int>)`. This iteration hangs in a random place each time. So seems like there is a bug that hits with _some_ frequency.", "With `num_proc=None` it works fine.", "I am also having similar issue to #3172 when trying to tokenize the data. My dataset contains 10M samples. Is there anything that could be done without having to split up the processing into multiple datasets?" ]
2023-11-09T06:18:30
2024-03-05T16:03:12
null
NONE
null
null
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### Describe the bug A call to `.filter` occasionally hangs (after the filter is complete, according to tqdm) There is a trace produced ``` Exception ignored in: <function Dataset.__del__ at 0x7efb48130c10> Traceback (most recent call last): File "/usr/lib/python3/dist-packages/datasets/arrow_dataset.py", line 1366, in __del__ if hasattr(self, "_indices"): File "/usr/lib/python3/dist-packages/composer/core/engine.py", line 123, in sigterm_handler sys.exit(128 + signal) SystemExit: 143 ``` but I'm not sure if the trace is actually from `datasets`, or from surrounding code that is trying to clean up after datasets gets stuck. Unfortunately I can't reproduce this issue anywhere close to reliably. It happens infrequently when using `num_procs > 1`. Anecdotally I started seeing it when using larger datasets (~10M samples). ### Steps to reproduce the bug N/A see description ### Expected behavior map/filter calls always complete sucessfully ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2
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6,392
`push_to_hub` is not robust to hub closing connection
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[ "Hi! We made some improvements to `push_to_hub` to make it more robust a couple of weeks ago but haven't published a release in the meantime, so it would help if you could install `datasets` from `main` (`pip install https://github.com/huggingface/datasets`) and let us know if this improved version of `push_to_hub` resolves the issue (in case the `ConnectionError` happens, re-running `push_to_hub` should be faster now).\r\n\r\nAlso, note that the previous implementation retries the upload, but sometimes this is not enough, so re-running the op is the only option.", "The update helped push more data.\r\nHowever it still crashed a little later:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00206-of-00261.parquet' to the Hub.\r\n```", "I think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n\r\nThe implementation in `main` pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n\r\nBelow is the another error log from another run with `main`. I've reverting back to the current release as it does the job for me.\r\n\r\n```\r\nUploading the dataset shards: 86%|████████▌ | 224/261 [21:46<03:35, 5.83s/it]s]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n p, glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n token = token if token is not None else HfFolder.get_token()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00224-of-00261.parquet' to the Hub.\r\n```", "There's a new error from the hub now:\r\n```\r\nPushing dataset shards to the dataset hub: 49%|████▉ | 128/261 [11:38<12:05, 5.45s/it]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1641, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5308, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 293, in _retry\r\n raise err\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3850, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3237, in create_commit\r\n hf_raise_for_status(commit_resp, endpoint_name=\"commit\")\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main (Request ID: Root=1-654e48e6-598511b14413bb293fa67084;783522b4-66f9-4f8a-8a74-2accf7cabd17)\r\n\r\nYou have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n```\r\n\r\nAt least this is more explicit from the server side.", "> think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n>\r\n>The implementation in main pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n>\r\n>Below is the another error log from another run with main. I've reverting back to the current release as it does the job for me.\r\n\r\nThe `preupload` step is instant for the already uploaded shards, so only the Parquet conversion is repeated without uploading the actual Parquet data (only to check the SHAs). The previous implementation manually checks the Parquet shard's fingerprint to resume uploading, so the current implementation is cleaner.\r\n\r\n> You have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n\r\nThis is the problem with the previous implementation. If the number of shards is large, it creates too many commits for the Hub in a short period.", "But I agree that the `500 Server Error` returned by the Hub is annoying. Earlier today, I also got it on a small 5GB dataset (with 500 MB shards).\r\n\r\n@Wauplin @julien-c Is there something we can do about this?", "@mariosasko can't do much if AWS raises a HTTP 500 unfortunately (we are simply pushing data to a S3 bucket).\r\nWhat we can do is to add a retry mechanism in the multi-part upload logic here: https://github.com/huggingface/huggingface_hub/blob/c972cba1fecb456a7b3325cdd1fdbcc425f21f94/src/huggingface_hub/lfs.py#L370 :confused: ", "@Wauplin That code already retries the request using `http_backoff`, no?", "> That code already retries the request using http_backoff, no?\r\n\r\nCurrently only on HTTP 503 by default. We should add 500 as well (and hope it is a transient error from AWS)", "Opened a PR to retry in case S3 raises HTTP 500. Will also retry on any `ConnectionError` (connection reset by peer, connection lost,...). Hopefully this should make the upload process more robust to transient errors.", "I still get the same error, using `push_to_hub`. Using `git lfs` and pushing the files solved it for me.", "@BEpresent the fix has not been released yet. You can expect a release of `huggingface_hub` (with this fix) today or tomorrow :)" ]
2023-11-08T20:44:53
2023-12-20T07:28:24
2023-12-01T17:51:34
NONE
null
null
null
### Describe the bug Like to #6172, `push_to_hub` will crash if Hub resets the connection and raise the following error: ``` Pushing dataset shards to the dataset hub: 32%|███▏ | 54/171 [06:38<14:23, 7.38s/it] Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" http.client.RemoteDisconnected: Remote end closed connection without response During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 486, in send resp = conn.urlopen( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 799, in urlopen retries = retries.increment( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment raise six.reraise(type(error), error, _stacktrace) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise raise value.with_traceback(tb) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 383, in _wrapped_lfs_upload lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 223, in lfs_upload _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 319, in _upload_multi_part else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 375, in _upload_parts_iteratively part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 258, in http_backoff response = session.request(method=method, url=url, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, **send_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 63, in send return super().send(request, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 501, in send raise ConnectionError(err, request=request) requests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 2bab8c06-b701-4266-aead-fe2e0dc0e3ed)') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "convert_to_hf.py", line 116, in <module> main() File "convert_to_hf.py", line 108, in main audio_dataset.push_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1641, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 5308, in _push_parquet_shards_to_hub _retry( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 290, in _retry return func(*func_args, **func_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 2695, in create_commit upload_lfs_files( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 393, in upload_lfs_files _wrapped_lfs_upload(filtered_actions[0]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 385, in _wrapped_lfs_upload raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc RuntimeError: Error while uploading 'batch_19/train-00054-of-00171-932beb4082c034bf.parquet' to the Hub. ``` The function should retry if the operations fails, or at least offer a way to recover after such a failure. Right now, calling the function again will start sending all the parquets files leading to duplicates in the repository, with no guarantee that it will actually be pushed. Previously, it would crash with an error 400 #4677 . ### Steps to reproduce the bug Any large dataset pushed the hub: ```py audio_dataset.push_to_hub( repo_id="org/dataset", ) ``` ### Expected behavior `push_to_hub` should have an option for max retries or resume. ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.0-1044-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3
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1,984,091,776
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6,391
Webdataset dataset builder
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I added an error message if the first examples don't appear to be in webdataset format\r\n```\r\n\"The TAR archives of the dataset should be in Webdataset format, \"\r\n\"but the files in the archive don't share the same prefix or the same types.\"\r\n```", "@mariosasko could you review this ? I think it's fine to have webdataset as an optional dependency for now, then depending on usage and user feedbacks see if it makes sense to have our own implementation or not", "I just removed the dependency on `webdataset` @mariosasko :)", "took your comments into account, lmk if you see anything else" ]
2023-11-08T17:31:59
2024-05-22T16:51:08
2023-11-28T16:33:10
MEMBER
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Allow `load_dataset` to support the Webdataset format. It allows users to download/stream data from local files or from the Hugging Face Hub. Moreover it will enable the Dataset Viewer for Webdataset datasets on HF. ## Implementation details - I added a new Webdataset builder - dataset with TAR files are now read using the Webdataset builder - Basic decoding from `webdataset` is used by default, except unsafe ones like pickle - HF authentication support is done with `xopen` ## TODOS - [x] tests - [x] docs
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6,390
handle future deprecation argument
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004368 / 0.011353 (-0.006985) | 0.002613 / 0.011008 (-0.008396) | 0.061365 / 0.038508 (0.022856) | 0.029553 / 0.023109 (0.006444) | 0.240535 / 0.275898 (-0.035363) | 0.280634 / 0.323480 (-0.042845) | 0.002923 / 0.007986 (-0.005063) | 0.003696 / 0.004328 (-0.000632) | 0.049824 / 0.004250 (0.045573) | 0.044935 / 0.037052 (0.007882) | 0.246870 / 0.258489 (-0.011619) | 0.317248 / 0.293841 (0.023407) | 0.022717 / 0.128546 (-0.105829) | 0.006933 / 0.075646 (-0.068713) | 0.201118 / 0.419271 (-0.218154) | 0.053422 / 0.043533 (0.009890) | 0.266262 / 0.255139 (0.011123) | 0.269114 / 0.283200 (-0.014086) | 0.016908 / 0.141683 (-0.124775) | 1.154296 / 1.452155 (-0.297859) | 1.218825 / 1.492716 (-0.273892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089908 / 0.018006 (0.071902) | 0.300029 / 0.000490 (0.299539) | 0.000209 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018518 / 0.037411 (-0.018894) | 0.062246 / 0.014526 (0.047720) | 0.073542 / 0.176557 (-0.103014) | 0.119386 / 0.737135 (-0.617749) | 0.075256 / 0.296338 (-0.221082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280812 / 0.215209 (0.065603) | 2.701282 / 2.077655 (0.623628) | 1.455146 / 1.504120 (-0.048974) | 1.310198 / 1.541195 (-0.230996) | 1.335287 / 1.468490 (-0.133203) | 0.388245 / 4.584777 (-4.196532) | 2.357770 / 3.745712 (-1.387942) | 2.534640 / 5.269862 (-2.735222) | 1.541382 / 4.565676 (-3.024295) | 0.045597 / 0.424275 (-0.378678) | 0.004842 / 0.007607 (-0.002765) | 0.325416 / 0.226044 (0.099371) | 3.221873 / 2.268929 (0.952944) | 1.791061 / 55.444624 (-53.653563) | 1.485094 / 6.876477 (-5.391382) | 1.512354 / 2.142072 (-0.629718) | 0.471241 / 4.805227 (-4.333986) | 0.098672 / 6.500664 (-6.401992) | 0.041668 / 0.075469 (-0.033801) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953553 / 1.841788 (-0.888234) | 11.378394 / 8.074308 (3.304086) | 10.355970 / 10.191392 (0.164578) | 0.126891 / 0.680424 (-0.553533) | 0.013808 / 0.534201 (-0.520393) | 0.267800 / 0.579283 (-0.311484) | 0.266436 / 0.434364 (-0.167928) | 0.306668 / 0.540337 (-0.233670) | 0.427666 / 1.386936 (-0.959270) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004908 / 0.011353 (-0.006445) | 0.002698 / 0.011008 (-0.008310) | 0.047492 / 0.038508 (0.008984) | 0.049906 / 0.023109 (0.026797) | 0.271466 / 0.275898 (-0.004432) | 0.291030 / 0.323480 (-0.032449) | 0.003938 / 0.007986 (-0.004047) | 0.002457 / 0.004328 (-0.001871) | 0.047347 / 0.004250 (0.043096) | 0.038599 / 0.037052 (0.001547) | 0.269950 / 0.258489 (0.011461) | 0.303026 / 0.293841 (0.009185) | 0.024196 / 0.128546 (-0.104351) | 0.006889 / 0.075646 (-0.068757) | 0.053357 / 0.419271 (-0.365914) | 0.032249 / 0.043533 (-0.011284) | 0.271660 / 0.255139 (0.016521) | 0.286395 / 0.283200 (0.003196) | 0.017914 / 0.141683 (-0.123769) | 1.128762 / 1.452155 (-0.323393) | 1.206495 / 1.492716 (-0.286221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093384 / 0.018006 (0.075378) | 0.305504 / 0.000490 (0.305014) | 0.000227 / 0.000200 (0.000027) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021183 / 0.037411 (-0.016229) | 0.070113 / 0.014526 (0.055587) | 0.080288 / 0.176557 (-0.096269) | 0.120798 / 0.737135 (-0.616337) | 0.082896 / 0.296338 (-0.213442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292758 / 0.215209 (0.077549) | 2.893975 / 2.077655 (0.816320) | 1.584909 / 1.504120 (0.080789) | 1.455509 / 1.541195 (-0.085686) | 1.501625 / 1.468490 (0.033135) | 0.400772 / 4.584777 (-4.184005) | 2.446319 / 3.745712 (-1.299393) | 2.530690 / 5.269862 (-2.739172) | 1.525957 / 4.565676 (-3.039719) | 0.046070 / 0.424275 (-0.378205) | 0.004756 / 0.007607 (-0.002851) | 0.343039 / 0.226044 (0.116995) | 3.366772 / 2.268929 (1.097844) | 1.948895 / 55.444624 (-53.495729) | 1.666419 / 6.876477 (-5.210058) | 1.658258 / 2.142072 (-0.483814) | 0.470835 / 4.805227 (-4.334392) | 0.098008 / 6.500664 (-6.402656) | 0.040743 / 0.075469 (-0.034726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978025 / 1.841788 (-0.863763) | 11.945229 / 8.074308 (3.870920) | 11.025810 / 10.191392 (0.834418) | 0.129706 / 0.680424 (-0.550717) | 0.015148 / 0.534201 (-0.519053) | 0.269160 / 0.579283 (-0.310123) | 0.284306 / 0.434364 (-0.150058) | 0.307154 / 0.540337 (-0.233183) | 0.409153 / 1.386936 (-0.977783) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c75c104fd79cbf53be25f0fbbeb001e535f7e9b \"CML watermark\")\n" ]
2023-11-08T14:21:25
2023-11-21T02:10:24
2023-11-14T15:15:59
CONTRIBUTOR
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getting this error: ``` /root/miniconda3/envs/py3.10/lib/python3.10/site-packages/datasets/table.py:1387: FutureWarning: promote has been superseded by mode='default'. return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) ``` Since datasets supports arrow greater than 8.0.0, we need to handle both cases. [Arrow v14 docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html) [Arrow v13 docs](https://arrow.apache.org/docs/13.0/python/generated/pyarrow.concat_tables.html)
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1,983,545,744
I_kwDODunzps52OoGQ
6,389
Index 339 out of range for dataset of size 339 <-- save_to_file()
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[ "Hi! Can you make the above reproducer self-contained by adding code that generates the data?", "I managed a workaround eventually but I don't know what it was (I made a lot of changes to seq2seq). I'll try to include generating code in the future. (If I close, I don't know if you see it. Feel free to close; I'll re-open if I encounter it again (if I can))." ]
2023-11-08T12:52:09
2023-11-24T09:14:13
null
NONE
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### Describe the bug When saving out some Audio() data. The data is audio recordings with associated 'sentences'. (They use the audio 'bytes' approach because they're clips within audio files). Code is below the traceback (I can't upload the voice audio/text (it's not even me)). ``` Traceback (most recent call last): File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module> create_dataset(args) File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk for kwargs in kwargs_per_job: File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr> "shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard return self.select( ^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous _check_valid_indices_value(start, len(self)) File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 339 out of range for dataset of size 339. ``` ### Steps to reproduce the bug (I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717) ```python3 #!/usr/bin/env python3 import argparse import os from pathlib import Path import soundfile as sf import datasets datasets.config.DEFAULT_MAX_BATCH_SIZE=35 from datasets import Features, Array2D, Value, Dataset, Sequence, Audio import numpy as np import librosa import sys import soundfile as sf import io import logging logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') # Define the arguments for the command-line interface def parse_args(): parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.") parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True) parser.add_argument("--outds", help="Path to save the dataset file.", required=True) parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None) parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000) parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.") parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0) parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity") return parser.parse_args() # Convert the NumPy arrays to audio bytes in WAV format def numpy_to_bytes(audio_array, sampling_rate=16000): with io.BytesIO() as bytes_io: sf.write(bytes_io, audio_array, samplerate=sampling_rate, format='wav', subtype='FLOAT') # float32 return bytes_io.getvalue() # Function to find audio and label files in a directory def find_audio_label_pairs(indir_labeled): audio_label_pairs = [] for root, _, files in os.walk(indir_labeled): for file in files: if file.endswith(('.mp3', '.wav', '.aac', '.flac')): audio_path = Path(root) / file if args.verbose>1: print(f'File: {audio_path}') label_path = audio_path.with_suffix('.labels.txt') if label_path.exists(): if args.verbose>0: print(f' Pair: {audio_path}') audio_label_pairs.append((audio_path, label_path)) return audio_label_pairs def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs): # Read the label file with open(label_path, 'r') as label_file: labels = label_file.readlines() # Load the full audio file full_audio, current_sr = sf.read(audio_path) if not no_resample and current_sr != sampling_rate: # You can use librosa.resample here if librosa is available full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate) audio_segments = [] sentences = [] # Process each label for label in labels: start_secs, end_secs, label_text = label.strip().split('\t') start_sample = int(float(start_secs) * sampling_rate) end_sample = int(float(end_secs) * sampling_rate) # Extract segment and truncate or pad to max_clip_secs audio_segment = full_audio[start_sample:end_sample] max_samples = int(max_clip_secs * sampling_rate) if len(audio_segment) > max_samples: # Truncate audio_segment = audio_segment[:max_samples] elif len(audio_segment) < max_samples: # Pad padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype) audio_segment = np.concatenate((audio_segment, padding)) audio_segment = numpy_to_bytes(audio_segment) audio_data = { 'path': str(audio_path), 'bytes': audio_segment, } audio_segments.append(audio_data) sentences.append(label_text) return audio_segments, sentences # Main function to create the dataset def create_dataset(args): audio_label_pairs = [] for indir in args.indir_labeled: audio_label_pairs.extend(find_audio_label_pairs(indir)) # Initialize our dataset data dataset_data = { 'path': [], # This will be a list of strings 'audio': [], # This will be a list of dictionaries 'sentence': [], # This will be a list of strings } # Process each audio-label pair and add the data to the dataset for audio_path, label_path in audio_label_pairs[:args.max_clips]: audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs) if audio_segments and sentences: for audio_data, sentence in zip(audio_segments, sentences): if args.verbose>1: print(f'Appending {audio_data["path"]}') dataset_data['path'].append(audio_data['path']) dataset_data['audio'].append({ 'path': audio_data['path'], 'bytes': audio_data['bytes'], }) dataset_data['sentence'].append(sentence) features = Features({ 'path': Value('string'), # Path is redundant in common voice set also 'audio': Audio(sampling_rate=16000), 'sentence': Value('string'), }) hf_dataset = Dataset.from_dict(dataset_data, features=features) for key in dataset_data: for i, item in enumerate(dataset_data[key]): if item is None or (isinstance(item, bytes) and len(item) == 0): logging.error(f"Invalid {key} at index {i}: {item}") import ipdb; ipdb.set_trace(context=16); pass hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') # try: # hf_dataset.save_to_disk(args.outds) # except TypeError as e: # # If there's a TypeError, log the exception and the dataset data that might have caused it # logging.exception("An error occurred while saving the dataset.") # import ipdb; ipdb.set_trace(context=16); pass # for key in dataset_data: # logging.debug(f"{key} length: {len(dataset_data[key])}") # if key == 'audio': # # Log the first 100 bytes of the audio data to avoid huge log files # for i, audio in enumerate(dataset_data[key]): # logging.debug(f"Audio {i}: {audio['bytes'][:100]}") # raise # Run the script if __name__ == "__main__": args = parse_args() create_dataset(args) ``` ### Expected behavior It shouldn't fail. ### Environment info - `datasets` version: 2.14.7.dev0 - Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - `huggingface_hub` version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2
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6,388
How to create 3d medical imgae dataset?
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2023-11-07T11:27:36
2023-11-07T11:28:53
null
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### Feature request I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii') ### Motivation help us to upload 3d medical dataset to huggingface! ### Your contribution I'll submit a PR if I find a way to add this feature
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1,980,224,020
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6,387
How to load existing downloaded dataset ?
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[ "Feel free to use `dataset.save_to_disk(...)`, then scp the directory containing the saved dataset and reload it on your other machine using `dataset = load_from_disk(...)`" ]
2023-11-06T22:51:44
2023-11-16T18:07:01
2023-11-16T18:07:01
NONE
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Hi @mariosasko @lhoestq @katielink Thanks for your contribution and hard work. ### Feature request First, I download a dataset as normal by: ``` from datasets import load_dataset dataset = load_dataset('username/data_name', cache_dir='data') ``` The dataset format in `data` directory will be: ``` -data |-data_name |-test-00000-of-00001-bf4c733542e35fcb.parquet |-train-00000-of-00001-2a1df75c6bce91ab.parquet ``` Then I use SCP to clone this dataset into another machine, and then try: ``` from datasets import load_dataset dataset = load_dataset('data/data_name') # load from local path ``` This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation. How can I just load the dataset without generating and saving these splits again? ### Motivation I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest) ### Your contribution Please refer to the feature
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1,979,878,014
I_kwDODunzps52Aop-
6,386
Formatting overhead
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[ "Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.", "I tracked it down to a quirk of my setup. Apologies." ]
2023-11-06T19:06:38
2023-11-06T23:56:12
2023-11-06T23:56:12
NONE
null
null
null
### Describe the bug Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute. ![image](https://github.com/huggingface/datasets/assets/320321/5e022e0b-0d21-43d0-8e6f-9e641142e96b) ### Steps to reproduce the bug 1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse) 2. Process it using a custom transform, `ds = ds.with_transform(transform_func)` 3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/ 4. Profile with `$ kernprof -l script_to_profile.py` ### Expected behavior Batch formatting should have acceptable overhead. ### Environment info ``` datasets=2.14.6 pyarrow=14.0.0 ```
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1,979,308,338
I_kwDODunzps51-dky
6,385
Get an error when i try to concatenate the squad dataset with my own dataset
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[ "The `answers.text` field in the JSON dataset needs to be a list of strings, not a string.\r\n\r\nSo, here is the fixed code:\r\n```python\r\nfrom huggingface_hub import notebook_login\r\nfrom datasets import load_dataset\r\n\r\n\r\n\r\nnotebook_login(\"mymailadresse\", \"mypassword\")\r\nsquad = load_dataset(\"squad\", split=\"train[:5000]\")\r\nsquad = squad.train_test_split(test_size=0.2)\r\ndataset1 = squad[\"train\"]\r\n\r\n\r\n\r\n\r\nimport json\r\n\r\nmybase = [\r\n {\r\n \"id\": \"1\",\r\n \"context\": \"She lives in Nantes\",\r\n \"question\": \"Where does she live?\",\r\n \"answers\": {\r\n \"text\": [\"Nantes\"],\r\n \"answer_start\": [13],\r\n }\r\n }\r\n]\r\n\r\n\r\n\r\n\r\n# Save the data to a JSON file\r\njson_file_path = r\"data\"\r\nwith open(json_file_path, \"w\", encoding= \"utf-8\") as json_file:\r\n json.dump(mybase, json_file, indent=4)\r\n\r\n\r\n\r\n\r\n# Load the JSON file as a dataset\r\ncustom_dataset = load_dataset(\"json\", data_files=json_file_path, features=dataset1.features)\r\n\r\n\r\n# Access the train split\r\ntrain_dataset = custom_dataset[\"train\"]\r\n\r\n\r\nfrom datasets import concatenate_datasets\r\n\r\n\r\n# Concatenate the datasets\r\nconcatenated_dataset = concatenate_datasets([train_dataset, dataset1])\r\n```", "Thank you @mariosasko for your help ! It works !" ]
2023-11-06T14:29:22
2023-11-06T16:50:45
2023-11-06T16:50:45
NONE
null
null
null
### Describe the bug Hello, I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last): Cell In[9], line 1 concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets _check_if_features_can_be_aligned([dset.features for dset in dsets]) File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned raise ValueError( ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null"). ### Steps to reproduce the bug ```python from huggingface_hub import notebook_login from datasets import load_dataset notebook_login("mymailadresse", "mypassword") squad = load_dataset("squad", split="train[:5000]") squad = squad.train_test_split(test_size=0.2) dataset1 = squad["train"] import json mybase = [ { "id": "1", "context": "She lives in Nantes", "question": "Where does she live?", "answers": { "text": "Nantes", "answer_start": [13], } } ] # Save the data to a JSON file json_file_path = r"C:\Users\mypath\thefile.json" with open(json_file_path, "w", encoding= "utf-8") as json_file: json.dump(mybase, json_file, indent=4) # Load the JSON file as a dataset custom_dataset = load_dataset("json", data_files=json_file_path) # Access the train split train_dataset = custom_dataset["train"] from datasets import concatenate_datasets # Concatenate the datasets concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) ``` ### Expected behavior I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001. ### Environment info Python 3.11.4 and using windows Thank you for your help
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1,979,117,069
I_kwDODunzps519u4N
6,384
Load the local dataset folder from other place
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[ "Solved" ]
2023-11-06T13:07:04
2023-11-19T05:42:06
2023-11-19T05:42:05
NONE
null
null
null
This is from https://github.com/huggingface/diffusers/issues/5573
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6,383
imagenet-1k downloads over and over
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[ "Have you solved this problem?" ]
2023-11-06T02:58:58
2024-06-12T13:15:00
2023-11-06T06:02:39
NONE
null
null
null
### Describe the bug What could be causing this? ``` $ python3 Python 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset("imagenet-1k") Downloading builder script: 100%|██████████| 4.72k/4.72k [00:00<00:00, 7.51MB/s] Downloading readme: 100%|███████████████████| 85.4k/85.4k [00:00<00:00, 510kB/s] Downloading extra modules: 100%|████████████| 46.4k/46.4k [00:00<00:00, 300kB/s] Downloading data: 100%|████████████████████| 29.1G/29.1G [19:36<00:00, 24.8MB/s] Downloading data: 100%|████████████████████| 29.3G/29.3G [08:38<00:00, 56.5MB/s] Downloading data: 100%|████████████████████| 29.0G/29.0G [09:26<00:00, 51.2MB/s] Downloading data: 100%|████████████████████| 29.2G/29.2G [09:38<00:00, 50.6MB/s] Downloading data: 100%|███████████████████▉| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data: 0%| | 106M/29.1G [00:05<23:49, 20.3MB/s] ``` ### Steps to reproduce the bug See above commands/code ### Expected behavior imagenet-1k is downloaded ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.15.1 - PyArrow version: 14.0.0 - Pandas version: 1.5.2
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1,977,400,799
I_kwDODunzps513L3f
6,382
Add CheXpert dataset for vision
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[ "Hey @SauravMaheshkar ! Just responded to your email.\r\n\r\n_For transparency, copying part of my response here:_\r\nI agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.\r\n\r\nI think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. \r\n\r\nI've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week.", "Hey @katielink I would love to try this out. Please guide me.", "Hi @katielink , I would also love to be on board and contribute to this loading script/project if it is still being developed. I'm interested because I personally would like to gain access to the CheXpert dataset and am facing some weird issues, so I'd like to sort it out for me, and potentially others. Please keep me updated and guide me on this as well!!!" ]
2023-11-04T15:36:11
2024-01-10T11:53:52
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### Feature request ### Name **CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison** ### Paper https://arxiv.org/abs/1901.07031 ### Data https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2 ### Motivation CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies. This could also serve as a good pre-training dataset for Kaggle competitions. ### Your contribution Would love to make a PR and pre-process / get this into 🤗
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6,381
Add my dataset
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[ "Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.", "@mariosasko could you provide me proper guide to push data on HF hub ", "You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub)." ]
2023-11-02T20:59:52
2023-11-08T14:37:46
2023-11-06T15:50:14
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## medical data **Description:** This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature. **Citation:** If applicable, please include a citation for this dataset to give credit to the original sources or contributors. **Key Features:** - Language: The text is primarily in English, but it may include content in other languages as well. - Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks. **Usage:** To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task. **Contributors:** - [Keyur Chaudhari] **Contact:** If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com]. Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.
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6,380
Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET
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2023-11-02T17:28:23
2023-11-02T17:31:19
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This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections. The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594. Issue Symptoms & Behaviour: - Download of a large archive file during dataset download via HTTP-GET fails. - An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress. - Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully. - Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded. - Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction. Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour: - Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown - Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`. - Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.
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Avoid redundant warning when encoding NumPy array as `Image`
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008649 / 0.011353 (-0.002704) | 0.005754 / 0.011008 (-0.005254) | 0.101992 / 0.038508 (0.063484) | 0.084932 / 0.023109 (0.061823) | 0.393928 / 0.275898 (0.118030) | 0.414059 / 0.323480 (0.090579) | 0.006564 / 0.007986 (-0.001422) | 0.004746 / 0.004328 (0.000418) | 0.078624 / 0.004250 (0.074373) | 0.060465 / 0.037052 (0.023412) | 0.420767 / 0.258489 (0.162278) | 0.497797 / 0.293841 (0.203956) | 0.047031 / 0.128546 (-0.081516) | 0.014316 / 0.075646 (-0.061330) | 0.340347 / 0.419271 (-0.078925) | 0.067126 / 0.043533 (0.023593) | 0.390806 / 0.255139 (0.135667) | 0.413711 / 0.283200 (0.130512) | 0.037838 / 0.141683 (-0.103845) | 1.713547 / 1.452155 (0.261393) | 1.825591 / 1.492716 (0.332874) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316357 / 0.018006 (0.298350) | 0.594279 / 0.000490 (0.593789) | 0.013659 / 0.000200 (0.013459) | 0.000547 / 0.000054 (0.000492) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031310 / 0.037411 (-0.006101) | 0.090410 / 0.014526 (0.075884) | 0.114620 / 0.176557 (-0.061936) | 0.183036 / 0.737135 (-0.554099) | 0.112700 / 0.296338 (-0.183638) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582424 / 0.215209 (0.367215) | 5.670424 / 2.077655 (3.592769) | 2.444326 / 1.504120 (0.940206) | 2.108555 / 1.541195 (0.567360) | 2.091594 / 1.468490 (0.623104) | 0.839067 / 4.584777 (-3.745710) | 5.280942 / 3.745712 (1.535230) | 4.611059 / 5.269862 (-0.658803) | 2.911145 / 4.565676 (-1.654531) | 0.091929 / 0.424275 (-0.332346) | 0.008774 / 0.007607 (0.001167) | 0.657948 / 0.226044 (0.431904) | 6.816300 / 2.268929 (4.547371) | 3.232260 / 55.444624 (-52.212364) | 2.479626 / 6.876477 (-4.396851) | 2.497886 / 2.142072 (0.355813) | 0.959160 / 4.805227 (-3.846068) | 0.222306 / 6.500664 (-6.278358) | 0.072962 / 0.075469 (-0.002507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.580415 / 1.841788 (-0.261372) | 23.689597 / 8.074308 (15.615289) | 20.430709 / 10.191392 (10.239317) | 0.237891 / 0.680424 (-0.442533) | 0.028194 / 0.534201 (-0.506007) | 0.464915 / 0.579283 (-0.114368) | 0.611512 / 0.434364 (0.177148) | 0.556564 / 0.540337 (0.016227) | 0.811075 / 1.386936 (-0.575861) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008703 / 0.011353 (-0.002649) | 0.005030 / 0.011008 (-0.005978) | 0.079251 / 0.038508 (0.040743) | 0.079054 / 0.023109 (0.055945) | 0.440220 / 0.275898 (0.164322) | 0.479824 / 0.323480 (0.156344) | 0.006312 / 0.007986 (-0.001673) | 0.004506 / 0.004328 (0.000177) | 0.078454 / 0.004250 (0.074203) | 0.061041 / 0.037052 (0.023989) | 0.490104 / 0.258489 (0.231615) | 0.480925 / 0.293841 (0.187084) | 0.049601 / 0.128546 (-0.078945) | 0.013114 / 0.075646 (-0.062532) | 0.092576 / 0.419271 (-0.326696) | 0.059516 / 0.043533 (0.015983) | 0.433728 / 0.255139 (0.178589) | 0.490039 / 0.283200 (0.206839) | 0.035359 / 0.141683 (-0.106324) | 1.823618 / 1.452155 (0.371463) | 1.980894 / 1.492716 (0.488178) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284679 / 0.018006 (0.266673) | 0.606623 / 0.000490 (0.606133) | 0.007531 / 0.000200 (0.007331) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033261 / 0.037411 (-0.004150) | 0.102908 / 0.014526 (0.088382) | 0.123912 / 0.176557 (-0.052644) | 0.169893 / 0.737135 (-0.567242) | 0.115366 / 0.296338 (-0.180973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598239 / 0.215209 (0.383030) | 6.003464 / 2.077655 (3.925809) | 2.828483 / 1.504120 (1.324363) | 2.485996 / 1.541195 (0.944802) | 2.434986 / 1.468490 (0.966496) | 0.832718 / 4.584777 (-3.752058) | 5.327407 / 3.745712 (1.581694) | 4.732271 / 5.269862 (-0.537590) | 3.047555 / 4.565676 (-1.518121) | 0.103576 / 0.424275 (-0.320699) | 0.009795 / 0.007607 (0.002188) | 0.755443 / 0.226044 (0.529399) | 7.465857 / 2.268929 (5.196928) | 3.564923 / 55.444624 (-51.879701) | 2.740483 / 6.876477 (-4.135994) | 3.044993 / 2.142072 (0.902920) | 1.012925 / 4.805227 (-3.792302) | 0.207498 / 6.500664 (-6.293167) | 0.073361 / 0.075469 (-0.002108) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.704988 / 1.841788 (-0.136800) | 24.669992 / 8.074308 (16.595684) | 21.103096 / 10.191392 (10.911704) | 0.253759 / 0.680424 (-0.426665) | 0.040109 / 0.534201 (-0.494092) | 0.465646 / 0.579283 (-0.113637) | 0.619696 / 0.434364 (0.185332) | 0.552228 / 0.540337 (0.011890) | 0.794907 / 1.386936 (-0.592029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85bba8991f6a2d9ed9fd4769d945eeaf318d3aa6 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006347 / 0.011353 (-0.005006) | 0.003725 / 0.011008 (-0.007283) | 0.080233 / 0.038508 (0.041725) | 0.061013 / 0.023109 (0.037904) | 0.390046 / 0.275898 (0.114148) | 0.420526 / 0.323480 (0.097046) | 0.003579 / 0.007986 (-0.004407) | 0.002837 / 0.004328 (-0.001491) | 0.062929 / 0.004250 (0.058678) | 0.048781 / 0.037052 (0.011729) | 0.400722 / 0.258489 (0.142233) | 0.435022 / 0.293841 (0.141182) | 0.027560 / 0.128546 (-0.100986) | 0.007981 / 0.075646 (-0.067666) | 0.262838 / 0.419271 (-0.156433) | 0.045480 / 0.043533 (0.001947) | 0.394443 / 0.255139 (0.139304) | 0.413828 / 0.283200 (0.130628) | 0.023375 / 0.141683 (-0.118307) | 1.412865 / 1.452155 (-0.039290) | 1.495761 / 1.492716 (0.003044) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224876 / 0.018006 (0.206870) | 0.424234 / 0.000490 (0.423745) | 0.007502 / 0.000200 (0.007302) | 0.000220 / 0.000054 (0.000166) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024246 / 0.037411 (-0.013165) | 0.073982 / 0.014526 (0.059456) | 0.082704 / 0.176557 (-0.093852) | 0.143137 / 0.737135 (-0.593998) | 0.083398 / 0.296338 (-0.212941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400220 / 0.215209 (0.185010) | 3.973037 / 2.077655 (1.895382) | 2.025903 / 1.504120 (0.521783) | 1.912888 / 1.541195 (0.371693) | 1.999578 / 1.468490 (0.531088) | 0.499378 / 4.584777 (-4.085399) | 3.025715 / 3.745712 (-0.719997) | 2.992338 / 5.269862 (-2.277524) | 1.851155 / 4.565676 (-2.714522) | 0.057528 / 0.424275 (-0.366747) | 0.006802 / 0.007607 (-0.000805) | 0.469516 / 0.226044 (0.243471) | 4.675630 / 2.268929 (2.406702) | 2.472166 / 55.444624 (-52.972458) | 2.238052 / 6.876477 (-4.638424) | 2.288255 / 2.142072 (0.146183) | 0.584906 / 4.805227 (-4.220321) | 0.125902 / 6.500664 (-6.374762) | 0.060681 / 0.075469 (-0.014788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236383 / 1.841788 (-0.605404) | 17.554238 / 8.074308 (9.479930) | 13.749298 / 10.191392 (3.557906) | 0.144715 / 0.680424 (-0.535708) | 0.017449 / 0.534201 (-0.516752) | 0.334831 / 0.579283 (-0.244452) | 0.362660 / 0.434364 (-0.071704) | 0.385295 / 0.540337 (-0.155043) | 0.541173 / 1.386936 (-0.845763) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006118 / 0.011353 (-0.005235) | 0.003660 / 0.011008 (-0.007348) | 0.062373 / 0.038508 (0.023865) | 0.063404 / 0.023109 (0.040295) | 0.354149 / 0.275898 (0.078251) | 0.410324 / 0.323480 (0.086844) | 0.004826 / 0.007986 (-0.003160) | 0.002881 / 0.004328 (-0.001448) | 0.061631 / 0.004250 (0.057381) | 0.048052 / 0.037052 (0.010999) | 0.352905 / 0.258489 (0.094416) | 0.400096 / 0.293841 (0.106255) | 0.028472 / 0.128546 (-0.100075) | 0.008076 / 0.075646 (-0.067571) | 0.067910 / 0.419271 (-0.351362) | 0.040671 / 0.043533 (-0.002862) | 0.352131 / 0.255139 (0.096992) | 0.402140 / 0.283200 (0.118940) | 0.020065 / 0.141683 (-0.121618) | 1.456938 / 1.452155 (0.004783) | 1.506484 / 1.492716 (0.013767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222295 / 0.018006 (0.204288) | 0.416672 / 0.000490 (0.416183) | 0.003015 / 0.000200 (0.002815) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026428 / 0.037411 (-0.010983) | 0.080072 / 0.014526 (0.065547) | 0.089992 / 0.176557 (-0.086564) | 0.141739 / 0.737135 (-0.595397) | 0.092281 / 0.296338 (-0.204058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417758 / 0.215209 (0.202549) | 4.175673 / 2.077655 (2.098018) | 2.262369 / 1.504120 (0.758249) | 2.100440 / 1.541195 (0.559246) | 2.075827 / 1.468490 (0.607337) | 0.505673 / 4.584777 (-4.079104) | 3.129020 / 3.745712 (-0.616692) | 2.843255 / 5.269862 (-2.426607) | 1.853288 / 4.565676 (-2.712389) | 0.058337 / 0.424275 (-0.365938) | 0.006461 / 0.007607 (-0.001147) | 0.491797 / 0.226044 (0.265753) | 4.933327 / 2.268929 (2.664399) | 2.675374 / 55.444624 (-52.769250) | 2.358103 / 6.876477 (-4.518374) | 2.540436 / 2.142072 (0.398363) | 0.591550 / 4.805227 (-4.213677) | 0.121572 / 6.500664 (-6.379092) | 0.057311 / 0.075469 (-0.018158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365368 / 1.841788 (-0.476419) | 17.763413 / 8.074308 (9.689105) | 14.368754 / 10.191392 (4.177362) | 0.132979 / 0.680424 (-0.547445) | 0.017957 / 0.534201 (-0.516244) | 0.334035 / 0.579283 (-0.245248) | 0.385349 / 0.434364 (-0.049015) | 0.392636 / 0.540337 (-0.147702) | 0.537957 / 1.386936 (-0.848979) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92503c94839b31125b4d5288d0a49d81b9b9b3cc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008053 / 0.011353 (-0.003300) | 0.004966 / 0.011008 (-0.006043) | 0.102219 / 0.038508 (0.063711) | 0.099319 / 0.023109 (0.076210) | 0.418458 / 0.275898 (0.142559) | 0.459344 / 0.323480 (0.135864) | 0.004756 / 0.007986 (-0.003229) | 0.003940 / 0.004328 (-0.000388) | 0.076824 / 0.004250 (0.072573) | 0.068090 / 0.037052 (0.031038) | 0.428689 / 0.258489 (0.170200) | 0.476153 / 0.293841 (0.182312) | 0.036927 / 0.128546 (-0.091619) | 0.010232 / 0.075646 (-0.065414) | 0.345126 / 0.419271 (-0.074145) | 0.063182 / 0.043533 (0.019649) | 0.416633 / 0.255139 (0.161494) | 0.437418 / 0.283200 (0.154218) | 0.028192 / 0.141683 (-0.113491) | 1.768869 / 1.452155 (0.316715) | 1.847022 / 1.492716 (0.354306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269997 / 0.018006 (0.251991) | 0.544246 / 0.000490 (0.543756) | 0.012940 / 0.000200 (0.012740) | 0.000754 / 0.000054 (0.000699) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035570 / 0.037411 (-0.001842) | 0.104318 / 0.014526 (0.089792) | 0.115263 / 0.176557 (-0.061294) | 0.184693 / 0.737135 (-0.552442) | 0.116023 / 0.296338 (-0.180315) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472361 / 0.215209 (0.257152) | 4.714327 / 2.077655 (2.636673) | 2.405434 / 1.504120 (0.901314) | 2.197871 / 1.541195 (0.656677) | 2.312901 / 1.468490 (0.844411) | 0.569736 / 4.584777 (-4.015041) | 4.600008 / 3.745712 (0.854296) | 4.127967 / 5.269862 (-1.141895) | 2.462232 / 4.565676 (-2.103445) | 0.067759 / 0.424275 (-0.356516) | 0.009277 / 0.007607 (0.001670) | 0.569658 / 0.226044 (0.343614) | 5.694050 / 2.268929 (3.425121) | 3.041495 / 55.444624 (-52.403129) | 2.688418 / 6.876477 (-4.188059) | 2.762175 / 2.142072 (0.620102) | 0.683250 / 4.805227 (-4.121977) | 0.158772 / 6.500664 (-6.341892) | 0.073364 / 0.075469 (-0.002105) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.627241 / 1.841788 (-0.214547) | 23.054465 / 8.074308 (14.980157) | 17.122451 / 10.191392 (6.931059) | 0.170272 / 0.680424 (-0.510152) | 0.021678 / 0.534201 (-0.512523) | 0.467301 / 0.579283 (-0.111982) | 0.509480 / 0.434364 (0.075116) | 0.555077 / 0.540337 (0.014740) | 0.816199 / 1.386936 (-0.570737) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 0.011353 (-0.002854) | 0.004724 / 0.011008 (-0.006284) | 0.077519 / 0.038508 (0.039011) | 0.103237 / 0.023109 (0.080127) | 0.447470 / 0.275898 (0.171572) | 0.484778 / 0.323480 (0.161298) | 0.006475 / 0.007986 (-0.001511) | 0.003946 / 0.004328 (-0.000383) | 0.075596 / 0.004250 (0.071346) | 0.069265 / 0.037052 (0.032213) | 0.454185 / 0.258489 (0.195696) | 0.491039 / 0.293841 (0.197198) | 0.038611 / 0.128546 (-0.089935) | 0.009889 / 0.075646 (-0.065758) | 0.084012 / 0.419271 (-0.335260) | 0.057265 / 0.043533 (0.013732) | 0.448622 / 0.255139 (0.193483) | 0.470961 / 0.283200 (0.187762) | 0.029220 / 0.141683 (-0.112463) | 1.773347 / 1.452155 (0.321192) | 1.872669 / 1.492716 (0.379953) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272429 / 0.018006 (0.254423) | 0.569907 / 0.000490 (0.569418) | 0.013359 / 0.000200 (0.013159) | 0.000187 / 0.000054 (0.000133) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038784 / 0.037411 (0.001373) | 0.114958 / 0.014526 (0.100432) | 0.132745 / 0.176557 (-0.043811) | 0.186283 / 0.737135 (-0.550852) | 0.126652 / 0.296338 (-0.169686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482753 / 0.215209 (0.267544) | 4.827287 / 2.077655 (2.749633) | 2.539959 / 1.504120 (1.035839) | 2.348483 / 1.541195 (0.807288) | 2.421739 / 1.468490 (0.953249) | 0.586064 / 4.584777 (-3.998713) | 4.579865 / 3.745712 (0.834152) | 3.950617 / 5.269862 (-1.319244) | 2.528447 / 4.565676 (-2.037229) | 0.070280 / 0.424275 (-0.353995) | 0.008801 / 0.007607 (0.001194) | 0.568857 / 0.226044 (0.342812) | 5.692739 / 2.268929 (3.423810) | 3.192045 / 55.444624 (-52.252579) | 2.768092 / 6.876477 (-4.108384) | 3.002934 / 2.142072 (0.860862) | 0.701887 / 4.805227 (-4.103340) | 0.155563 / 6.500664 (-6.345102) | 0.069397 / 0.075469 (-0.006072) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.607991 / 1.841788 (-0.233796) | 24.658060 / 8.074308 (16.583752) | 17.616229 / 10.191392 (7.424837) | 0.209730 / 0.680424 (-0.470693) | 0.024052 / 0.534201 (-0.510149) | 0.476648 / 0.579283 (-0.102635) | 0.534452 / 0.434364 (0.100089) | 0.567702 / 0.540337 (0.027365) | 0.772933 / 1.386936 (-0.614003) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004684 / 0.011353 (-0.006669) | 0.002944 / 0.011008 (-0.008064) | 0.063065 / 0.038508 (0.024557) | 0.051627 / 0.023109 (0.028518) | 0.243485 / 0.275898 (-0.032413) | 0.275144 / 0.323480 (-0.048336) | 0.002934 / 0.007986 (-0.005052) | 0.002395 / 0.004328 (-0.001934) | 0.048579 / 0.004250 (0.044328) | 0.038940 / 0.037052 (0.001887) | 0.250244 / 0.258489 (-0.008245) | 0.287404 / 0.293841 (-0.006437) | 0.022958 / 0.128546 (-0.105588) | 0.007189 / 0.075646 (-0.068458) | 0.202483 / 0.419271 (-0.216788) | 0.035477 / 0.043533 (-0.008056) | 0.243793 / 0.255139 (-0.011346) | 0.265990 / 0.283200 (-0.017209) | 0.019675 / 0.141683 (-0.122008) | 1.119127 / 1.452155 (-0.333028) | 1.183230 / 1.492716 (-0.309486) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097090 / 0.018006 (0.079084) | 0.305815 / 0.000490 (0.305325) | 0.000228 / 0.000200 (0.000028) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019233 / 0.037411 (-0.018178) | 0.061743 / 0.014526 (0.047217) | 0.077033 / 0.176557 (-0.099524) | 0.119786 / 0.737135 (-0.617349) | 0.074740 / 0.296338 (-0.221598) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284361 / 0.215209 (0.069152) | 2.761501 / 2.077655 (0.683846) | 1.464980 / 1.504120 (-0.039140) | 1.348026 / 1.541195 (-0.193169) | 1.362690 / 1.468490 (-0.105800) | 0.392022 / 4.584777 (-4.192755) | 2.401330 / 3.745712 (-1.344382) | 2.618999 / 5.269862 (-2.650863) | 1.599526 / 4.565676 (-2.966150) | 0.045621 / 0.424275 (-0.378654) | 0.005153 / 0.007607 (-0.002454) | 0.337279 / 0.226044 (0.111234) | 3.330135 / 2.268929 (1.061206) | 1.803544 / 55.444624 (-53.641081) | 1.515545 / 6.876477 (-5.360932) | 1.561745 / 2.142072 (-0.580327) | 0.468735 / 4.805227 (-4.336492) | 0.098882 / 6.500664 (-6.401782) | 0.042923 / 0.075469 (-0.032546) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961106 / 1.841788 (-0.880682) | 12.030489 / 8.074308 (3.956181) | 10.824166 / 10.191392 (0.632774) | 0.132135 / 0.680424 (-0.548289) | 0.015320 / 0.534201 (-0.518881) | 0.269691 / 0.579283 (-0.309592) | 0.270700 / 0.434364 (-0.163664) | 0.308317 / 0.540337 (-0.232020) | 0.397871 / 1.386936 (-0.989065) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004859 / 0.011353 (-0.006494) | 0.003400 / 0.011008 (-0.007609) | 0.048095 / 0.038508 (0.009587) | 0.054885 / 0.023109 (0.031776) | 0.276976 / 0.275898 (0.001078) | 0.302298 / 0.323480 (-0.021182) | 0.004084 / 0.007986 (-0.003902) | 0.002647 / 0.004328 (-0.001681) | 0.048570 / 0.004250 (0.044319) | 0.040683 / 0.037052 (0.003631) | 0.279828 / 0.258489 (0.021339) | 0.306037 / 0.293841 (0.012196) | 0.024263 / 0.128546 (-0.104283) | 0.007336 / 0.075646 (-0.068310) | 0.053768 / 0.419271 (-0.365503) | 0.032284 / 0.043533 (-0.011248) | 0.276706 / 0.255139 (0.021567) | 0.294706 / 0.283200 (0.011506) | 0.018092 / 0.141683 (-0.123591) | 1.153430 / 1.452155 (-0.298725) | 1.208783 / 1.492716 (-0.283933) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096946 / 0.018006 (0.078939) | 0.308118 / 0.000490 (0.307628) | 0.000234 / 0.000200 (0.000034) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021834 / 0.037411 (-0.015577) | 0.070934 / 0.014526 (0.056408) | 0.080310 / 0.176557 (-0.096247) | 0.123299 / 0.737135 (-0.613836) | 0.081591 / 0.296338 (-0.214748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302242 / 0.215209 (0.087033) | 2.934477 / 2.077655 (0.856822) | 1.623768 / 1.504120 (0.119648) | 1.493868 / 1.541195 (-0.047326) | 1.516553 / 1.468490 (0.048063) | 0.410319 / 4.584777 (-4.174458) | 2.471346 / 3.745712 (-1.274366) | 2.667371 / 5.269862 (-2.602491) | 1.625390 / 4.565676 (-2.940286) | 0.046465 / 0.424275 (-0.377810) | 0.004867 / 0.007607 (-0.002740) | 0.355516 / 0.226044 (0.129471) | 3.442294 / 2.268929 (1.173365) | 1.973859 / 55.444624 (-53.470765) | 1.682089 / 6.876477 (-5.194388) | 1.865253 / 2.142072 (-0.276819) | 0.475750 / 4.805227 (-4.329477) | 0.098298 / 6.500664 (-6.402366) | 0.041025 / 0.075469 (-0.034445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969864 / 1.841788 (-0.871924) | 12.437806 / 8.074308 (4.363498) | 10.461262 / 10.191392 (0.269870) | 0.131051 / 0.680424 (-0.549373) | 0.016232 / 0.534201 (-0.517969) | 0.273968 / 0.579283 (-0.305315) | 0.285369 / 0.434364 (-0.148995) | 0.309046 / 0.540337 (-0.231291) | 0.398776 / 1.386936 (-0.988160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n" ]
2023-11-02T16:37:58
2023-11-06T17:53:27
2023-11-02T17:08:07
COLLABORATOR
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false
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Avoid a redundant warning in `encode_np_array` by removing the identity check as NumPy `dtype`s can be equal without having identical `id`s. Additionally, fix "unreachable" checks in `encode_np_array`.
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1,973,942,770
PR_kwDODunzps5eaqhv
6,378
Support pyarrow 14.0.0
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007561 / 0.011353 (-0.003792) | 0.004824 / 0.011008 (-0.006184) | 0.110372 / 0.038508 (0.071864) | 0.076767 / 0.023109 (0.053657) | 0.357094 / 0.275898 (0.081196) | 0.420566 / 0.323480 (0.097086) | 0.004753 / 0.007986 (-0.003232) | 0.004734 / 0.004328 (0.000405) | 0.072926 / 0.004250 (0.068675) | 0.058045 / 0.037052 (0.020992) | 0.401109 / 0.258489 (0.142620) | 0.444585 / 0.293841 (0.150744) | 0.046492 / 0.128546 (-0.082055) | 0.013948 / 0.075646 (-0.061698) | 0.305188 / 0.419271 (-0.114083) | 0.063112 / 0.043533 (0.019579) | 0.384711 / 0.255139 (0.129572) | 0.411375 / 0.283200 (0.128175) | 0.048147 / 0.141683 (-0.093536) | 1.632357 / 1.452155 (0.180202) | 1.661021 / 1.492716 (0.168304) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281104 / 0.018006 (0.263098) | 0.567152 / 0.000490 (0.566662) | 0.007178 / 0.000200 (0.006978) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029337 / 0.037411 (-0.008075) | 0.081644 / 0.014526 (0.067118) | 0.103326 / 0.176557 (-0.073230) | 0.155299 / 0.737135 (-0.581836) | 0.093518 / 0.296338 (-0.202821) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517979 / 0.215209 (0.302769) | 5.250052 / 2.077655 (3.172397) | 2.220543 / 1.504120 (0.716424) | 1.901087 / 1.541195 (0.359892) | 1.920564 / 1.468490 (0.452073) | 0.766289 / 4.584777 (-3.818488) | 5.130968 / 3.745712 (1.385256) | 4.561874 / 5.269862 (-0.707988) | 2.702808 / 4.565676 (-1.862868) | 0.078929 / 0.424275 (-0.345346) | 0.007834 / 0.007607 (0.000226) | 0.636628 / 0.226044 (0.410583) | 6.309391 / 2.268929 (4.040463) | 2.942180 / 55.444624 (-52.502445) | 2.369557 / 6.876477 (-4.506920) | 2.347528 / 2.142072 (0.205456) | 0.911110 / 4.805227 (-3.894117) | 0.189102 / 6.500664 (-6.311562) | 0.068012 / 0.075469 (-0.007457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.494431 / 1.841788 (-0.347356) | 22.161476 / 8.074308 (14.087168) | 19.426403 / 10.191392 (9.235011) | 0.211154 / 0.680424 (-0.469270) | 0.030655 / 0.534201 (-0.503546) | 0.440449 / 0.579283 (-0.138834) | 0.526522 / 0.434364 (0.092158) | 0.517494 / 0.540337 (-0.022844) | 0.727387 / 1.386936 (-0.659549) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008354 / 0.011353 (-0.002999) | 0.006108 / 0.011008 (-0.004900) | 0.069079 / 0.038508 (0.030571) | 0.080402 / 0.023109 (0.057292) | 0.452166 / 0.275898 (0.176268) | 0.440264 / 0.323480 (0.116784) | 0.005942 / 0.007986 (-0.002043) | 0.003397 / 0.004328 (-0.000932) | 0.079856 / 0.004250 (0.075606) | 0.056329 / 0.037052 (0.019276) | 0.424261 / 0.258489 (0.165772) | 0.464362 / 0.293841 (0.170521) | 0.051968 / 0.128546 (-0.076578) | 0.015204 / 0.075646 (-0.060442) | 0.085940 / 0.419271 (-0.333332) | 0.066673 / 0.043533 (0.023140) | 0.436481 / 0.255139 (0.181342) | 0.445285 / 0.283200 (0.162085) | 0.035188 / 0.141683 (-0.106495) | 1.579442 / 1.452155 (0.127288) | 1.686120 / 1.492716 (0.193404) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319039 / 0.018006 (0.301032) | 0.655080 / 0.000490 (0.654591) | 0.005445 / 0.000200 (0.005245) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028566 / 0.037411 (-0.008845) | 0.092131 / 0.014526 (0.077605) | 0.103654 / 0.176557 (-0.072902) | 0.158082 / 0.737135 (-0.579054) | 0.107520 / 0.296338 (-0.188819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573479 / 0.215209 (0.358270) | 5.629751 / 2.077655 (3.552096) | 2.501722 / 1.504120 (0.997602) | 2.156255 / 1.541195 (0.615061) | 2.251296 / 1.468490 (0.782805) | 0.767686 / 4.584777 (-3.817091) | 5.080866 / 3.745712 (1.335154) | 4.353351 / 5.269862 (-0.916510) | 2.818707 / 4.565676 (-1.746970) | 0.082617 / 0.424275 (-0.341658) | 0.008045 / 0.007607 (0.000438) | 0.665462 / 0.226044 (0.439417) | 6.961380 / 2.268929 (4.692452) | 3.308717 / 55.444624 (-52.135907) | 2.664239 / 6.876477 (-4.212238) | 2.782790 / 2.142072 (0.640718) | 0.919567 / 4.805227 (-3.885660) | 0.186731 / 6.500664 (-6.313933) | 0.063437 / 0.075469 (-0.012032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.668076 / 1.841788 (-0.173712) | 22.720187 / 8.074308 (14.645879) | 19.803359 / 10.191392 (9.611967) | 0.237201 / 0.680424 (-0.443223) | 0.041156 / 0.534201 (-0.493045) | 0.458974 / 0.579283 (-0.120309) | 0.620276 / 0.434364 (0.185912) | 0.544079 / 0.540337 (0.003741) | 0.722715 / 1.386936 (-0.664221) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006882 / 0.011353 (-0.004471) | 0.004238 / 0.011008 (-0.006770) | 0.084042 / 0.038508 (0.045534) | 0.074175 / 0.023109 (0.051065) | 0.308771 / 0.275898 (0.032873) | 0.346300 / 0.323480 (0.022820) | 0.005455 / 0.007986 (-0.002530) | 0.003638 / 0.004328 (-0.000690) | 0.065326 / 0.004250 (0.061076) | 0.056080 / 0.037052 (0.019028) | 0.326324 / 0.258489 (0.067834) | 0.360133 / 0.293841 (0.066292) | 0.031577 / 0.128546 (-0.096969) | 0.008675 / 0.075646 (-0.066971) | 0.288051 / 0.419271 (-0.131221) | 0.052769 / 0.043533 (0.009236) | 0.308689 / 0.255139 (0.053550) | 0.328270 / 0.283200 (0.045070) | 0.025028 / 0.141683 (-0.116655) | 1.520670 / 1.452155 (0.068515) | 1.585229 / 1.492716 (0.092513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284078 / 0.018006 (0.266072) | 0.558134 / 0.000490 (0.557644) | 0.015042 / 0.000200 (0.014842) | 0.000429 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028747 / 0.037411 (-0.008664) | 0.083816 / 0.014526 (0.069290) | 0.207467 / 0.176557 (0.030911) | 0.163527 / 0.737135 (-0.573608) | 0.100148 / 0.296338 (-0.196190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376109 / 0.215209 (0.160900) | 3.749639 / 2.077655 (1.671984) | 1.827081 / 1.504120 (0.322961) | 1.662021 / 1.541195 (0.120827) | 1.734655 / 1.468490 (0.266165) | 0.483701 / 4.584777 (-4.101075) | 3.454772 / 3.745712 (-0.290941) | 3.465079 / 5.269862 (-1.804783) | 2.070874 / 4.565676 (-2.494802) | 0.056714 / 0.424275 (-0.367561) | 0.007786 / 0.007607 (0.000179) | 0.455980 / 0.226044 (0.229936) | 4.530612 / 2.268929 (2.261683) | 2.345757 / 55.444624 (-53.098867) | 2.030289 / 6.876477 (-4.846188) | 2.068440 / 2.142072 (-0.073632) | 0.576502 / 4.805227 (-4.228725) | 0.131787 / 6.500664 (-6.368878) | 0.060038 / 0.075469 (-0.015431) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272225 / 1.841788 (-0.569563) | 19.373635 / 8.074308 (11.299327) | 14.167831 / 10.191392 (3.976439) | 0.166336 / 0.680424 (-0.514088) | 0.018420 / 0.534201 (-0.515781) | 0.387878 / 0.579283 (-0.191405) | 0.413105 / 0.434364 (-0.021259) | 0.458618 / 0.540337 (-0.081720) | 0.639031 / 1.386936 (-0.747905) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007122 / 0.011353 (-0.004230) | 0.004193 / 0.011008 (-0.006815) | 0.066194 / 0.038508 (0.027686) | 0.077775 / 0.023109 (0.054666) | 0.349780 / 0.275898 (0.073882) | 0.383417 / 0.323480 (0.059937) | 0.006416 / 0.007986 (-0.001570) | 0.003651 / 0.004328 (-0.000677) | 0.064837 / 0.004250 (0.060587) | 0.058012 / 0.037052 (0.020959) | 0.351085 / 0.258489 (0.092596) | 0.387302 / 0.293841 (0.093462) | 0.032447 / 0.128546 (-0.096099) | 0.008636 / 0.075646 (-0.067011) | 0.071962 / 0.419271 (-0.347309) | 0.047839 / 0.043533 (0.004306) | 0.349508 / 0.255139 (0.094369) | 0.361892 / 0.283200 (0.078693) | 0.024129 / 0.141683 (-0.117554) | 1.523828 / 1.452155 (0.071673) | 1.607371 / 1.492716 (0.114655) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245928 / 0.018006 (0.227922) | 0.567708 / 0.000490 (0.567218) | 0.003789 / 0.000200 (0.003589) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034107 / 0.037411 (-0.003304) | 0.092539 / 0.014526 (0.078014) | 0.110735 / 0.176557 (-0.065821) | 0.163251 / 0.737135 (-0.573884) | 0.110353 / 0.296338 (-0.185985) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399992 / 0.215209 (0.184783) | 3.976526 / 2.077655 (1.898872) | 2.056182 / 1.504120 (0.552062) | 1.856624 / 1.541195 (0.315429) | 1.941540 / 1.468490 (0.473050) | 0.484662 / 4.584777 (-4.100115) | 3.548228 / 3.745712 (-0.197484) | 3.352900 / 5.269862 (-1.916962) | 2.056310 / 4.565676 (-2.509366) | 0.056952 / 0.424275 (-0.367323) | 0.007284 / 0.007607 (-0.000323) | 0.473749 / 0.226044 (0.247704) | 4.736510 / 2.268929 (2.467581) | 2.570711 / 55.444624 (-52.873913) | 2.204237 / 6.876477 (-4.672239) | 2.438512 / 2.142072 (0.296439) | 0.575542 / 4.805227 (-4.229685) | 0.129260 / 6.500664 (-6.371404) | 0.057704 / 0.075469 (-0.017765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316659 / 1.841788 (-0.525128) | 20.103340 / 8.074308 (12.029032) | 14.488385 / 10.191392 (4.296993) | 0.171841 / 0.680424 (-0.508583) | 0.020148 / 0.534201 (-0.514053) | 0.398456 / 0.579283 (-0.180828) | 0.443516 / 0.434364 (0.009152) | 0.479597 / 0.540337 (-0.060741) | 0.643665 / 1.386936 (-0.743271) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n" ]
2023-11-02T10:25:10
2023-11-02T15:24:28
2023-11-02T15:15:44
MEMBER
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false
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Support `pyarrow` 14.0.0. Fix #6377 and fix #6374 (root cause). This fix is analog to a previous one: - #6175
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6,377
Support pyarrow 14.0.0
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2023-11-02T10:22:08
2023-11-02T15:15:45
2023-11-02T15:15:45
MEMBER
null
null
null
Support pyarrow 14.0.0 by fixing the root cause of: - #6374 and revert: - #6375
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1,973,927,468
I_kwDODunzps51p74s
6,376
Caching problem when deleting a dataset
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[ "Thanks for reporting! Can you also share the error message printed in step 5?", "I did not store it at the time but I'll try to re-do a mwe next week to get it again", "I haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sharing) that follows the steps to reproduce the bug. So, I'm closing it.\r\n\r\nBut feel free to re-open it if you have a better reproducer." ]
2023-11-02T10:15:58
2023-12-04T16:53:34
2023-12-04T16:53:33
MEMBER
null
null
null
### Describe the bug Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail. ### Steps to reproduce the bug 1. Create a dataset with n features per row 2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` 3. Go on the hub, delete the repo at `YOUR_PATH` 4. Update your local dataset to have n + m features per row 5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number ### Expected behavior Step 5 should work or display a message to indicate the cache has not been cleared ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.10.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0
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1,973,877,879
PR_kwDODunzps5eacao
6,375
Temporarily pin pyarrow < 14.0.0
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008947 / 0.011353 (-0.002406) | 0.005602 / 0.011008 (-0.005406) | 0.111208 / 0.038508 (0.072700) | 0.082750 / 0.023109 (0.059641) | 0.453277 / 0.275898 (0.177379) | 0.480072 / 0.323480 (0.156592) | 0.005254 / 0.007986 (-0.002731) | 0.005421 / 0.004328 (0.001092) | 0.082899 / 0.004250 (0.078648) | 0.062859 / 0.037052 (0.025807) | 0.466703 / 0.258489 (0.208214) | 0.478241 / 0.293841 (0.184400) | 0.050754 / 0.128546 (-0.077792) | 0.017726 / 0.075646 (-0.057920) | 0.374830 / 0.419271 (-0.044442) | 0.068577 / 0.043533 (0.025044) | 0.453643 / 0.255139 (0.198504) | 0.453736 / 0.283200 (0.170537) | 0.037313 / 0.141683 (-0.104369) | 1.741215 / 1.452155 (0.289060) | 1.862247 / 1.492716 (0.369531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314174 / 0.018006 (0.296168) | 0.644439 / 0.000490 (0.643949) | 0.013914 / 0.000200 (0.013715) | 0.000478 / 0.000054 (0.000424) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030462 / 0.037411 (-0.006949) | 0.096789 / 0.014526 (0.082263) | 0.109999 / 0.176557 (-0.066557) | 0.184610 / 0.737135 (-0.552525) | 0.113846 / 0.296338 (-0.182493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586508 / 0.215209 (0.371299) | 5.785138 / 2.077655 (3.707484) | 2.578512 / 1.504120 (1.074392) | 2.266981 / 1.541195 (0.725786) | 2.442463 / 1.468490 (0.973973) | 0.880973 / 4.584777 (-3.703804) | 5.410327 / 3.745712 (1.664615) | 4.976842 / 5.269862 (-0.293020) | 3.020535 / 4.565676 (-1.545142) | 0.089640 / 0.424275 (-0.334635) | 0.009126 / 0.007607 (0.001519) | 0.682364 / 0.226044 (0.456319) | 6.840507 / 2.268929 (4.571579) | 3.313314 / 55.444624 (-52.131310) | 2.815313 / 6.876477 (-4.061164) | 2.851787 / 2.142072 (0.709715) | 1.044916 / 4.805227 (-3.760312) | 0.218346 / 6.500664 (-6.282318) | 0.075655 / 0.075469 (0.000186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.641767 / 1.841788 (-0.200020) | 24.618096 / 8.074308 (16.543788) | 21.557652 / 10.191392 (11.366260) | 0.211622 / 0.680424 (-0.468801) | 0.028775 / 0.534201 (-0.505426) | 0.480469 / 0.579283 (-0.098814) | 0.593311 / 0.434364 (0.158948) | 0.560620 / 0.540337 (0.020283) | 0.827026 / 1.386936 (-0.559910) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009347 / 0.011353 (-0.002006) | 0.005184 / 0.011008 (-0.005824) | 0.078878 / 0.038508 (0.040370) | 0.083067 / 0.023109 (0.059957) | 0.446591 / 0.275898 (0.170693) | 0.512934 / 0.323480 (0.189454) | 0.006614 / 0.007986 (-0.001372) | 0.004477 / 0.004328 (0.000148) | 0.087403 / 0.004250 (0.083153) | 0.060710 / 0.037052 (0.023658) | 0.451811 / 0.258489 (0.193322) | 0.482031 / 0.293841 (0.188190) | 0.051685 / 0.128546 (-0.076862) | 0.013436 / 0.075646 (-0.062210) | 0.109012 / 0.419271 (-0.310259) | 0.059654 / 0.043533 (0.016121) | 0.439041 / 0.255139 (0.183902) | 0.481708 / 0.283200 (0.198508) | 0.037393 / 0.141683 (-0.104290) | 1.761704 / 1.452155 (0.309549) | 1.946711 / 1.492716 (0.453995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287981 / 0.018006 (0.269975) | 0.610219 / 0.000490 (0.609729) | 0.006733 / 0.000200 (0.006533) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038999 / 0.037411 (0.001588) | 0.100613 / 0.014526 (0.086087) | 0.126445 / 0.176557 (-0.050111) | 0.187596 / 0.737135 (-0.549540) | 0.122130 / 0.296338 (-0.174208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.647686 / 0.215209 (0.432477) | 6.176079 / 2.077655 (4.098424) | 2.800232 / 1.504120 (1.296112) | 2.434625 / 1.541195 (0.893430) | 2.460646 / 1.468490 (0.992155) | 0.923736 / 4.584777 (-3.661041) | 5.480197 / 3.745712 (1.734485) | 4.849250 / 5.269862 (-0.420612) | 3.031576 / 4.565676 (-1.534101) | 0.102525 / 0.424275 (-0.321750) | 0.008688 / 0.007607 (0.001081) | 0.766097 / 0.226044 (0.540052) | 7.626822 / 2.268929 (5.357893) | 3.719155 / 55.444624 (-51.725469) | 2.967121 / 6.876477 (-3.909356) | 3.182464 / 2.142072 (1.040392) | 1.018315 / 4.805227 (-3.786912) | 0.211300 / 6.500664 (-6.289364) | 0.083055 / 0.075469 (0.007586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.731619 / 1.841788 (-0.110168) | 25.315978 / 8.074308 (17.241669) | 22.736306 / 10.191392 (12.544914) | 0.270330 / 0.680424 (-0.410094) | 0.034790 / 0.534201 (-0.499411) | 0.488675 / 0.579283 (-0.090608) | 0.603426 / 0.434364 (0.169062) | 0.572547 / 0.540337 (0.032210) | 0.825719 / 1.386936 (-0.561217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eaa85a4ad79aa0e411218d61a8894cc14a75fa0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008992 / 0.011353 (-0.002360) | 0.005086 / 0.011008 (-0.005923) | 0.107400 / 0.038508 (0.068892) | 0.091894 / 0.023109 (0.068785) | 0.382347 / 0.275898 (0.106449) | 0.446581 / 0.323480 (0.123101) | 0.005179 / 0.007986 (-0.002807) | 0.006356 / 0.004328 (0.002028) | 0.084979 / 0.004250 (0.080729) | 0.060647 / 0.037052 (0.023594) | 0.385940 / 0.258489 (0.127451) | 0.444817 / 0.293841 (0.150976) | 0.049484 / 0.128546 (-0.079062) | 0.014173 / 0.075646 (-0.061473) | 0.345704 / 0.419271 (-0.073567) | 0.068082 / 0.043533 (0.024550) | 0.377170 / 0.255139 (0.122031) | 0.411816 / 0.283200 (0.128616) | 0.043049 / 0.141683 (-0.098633) | 1.681499 / 1.452155 (0.229344) | 1.805428 / 1.492716 (0.312712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.323170 / 0.018006 (0.305164) | 0.693845 / 0.000490 (0.693355) | 0.015499 / 0.000200 (0.015299) | 0.000603 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031629 / 0.037411 (-0.005783) | 0.093511 / 0.014526 (0.078985) | 0.112400 / 0.176557 (-0.064157) | 0.173731 / 0.737135 (-0.563405) | 0.116013 / 0.296338 (-0.180325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.576724 / 0.215209 (0.361515) | 5.775055 / 2.077655 (3.697400) | 2.755869 / 1.504120 (1.251749) | 2.430253 / 1.541195 (0.889058) | 2.479629 / 1.468490 (1.011139) | 0.841472 / 4.584777 (-3.743305) | 5.120536 / 3.745712 (1.374824) | 4.813281 / 5.269862 (-0.456581) | 3.054617 / 4.565676 (-1.511059) | 0.091459 / 0.424275 (-0.332816) | 0.009072 / 0.007607 (0.001465) | 0.742674 / 0.226044 (0.516629) | 7.137861 / 2.268929 (4.868933) | 3.497568 / 55.444624 (-51.947056) | 2.814658 / 6.876477 (-4.061819) | 2.934415 / 2.142072 (0.792343) | 0.970855 / 4.805227 (-3.834372) | 0.213366 / 6.500664 (-6.287299) | 0.078763 / 0.075469 (0.003293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584716 / 1.841788 (-0.257072) | 24.098173 / 8.074308 (16.023865) | 20.746352 / 10.191392 (10.554960) | 0.215313 / 0.680424 (-0.465111) | 0.029538 / 0.534201 (-0.504663) | 0.448672 / 0.579283 (-0.130611) | 0.580023 / 0.434364 (0.145659) | 0.537867 / 0.540337 (-0.002471) | 0.804622 / 1.386936 (-0.582314) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008965 / 0.011353 (-0.002388) | 0.005544 / 0.011008 (-0.005464) | 0.076806 / 0.038508 (0.038298) | 0.085333 / 0.023109 (0.062224) | 0.509974 / 0.275898 (0.234076) | 0.511548 / 0.323480 (0.188068) | 0.007136 / 0.007986 (-0.000849) | 0.004491 / 0.004328 (0.000163) | 0.086687 / 0.004250 (0.082437) | 0.066539 / 0.037052 (0.029486) | 0.483663 / 0.258489 (0.225174) | 0.529480 / 0.293841 (0.235639) | 0.046296 / 0.128546 (-0.082250) | 0.014736 / 0.075646 (-0.060910) | 0.088261 / 0.419271 (-0.331010) | 0.056753 / 0.043533 (0.013220) | 0.511698 / 0.255139 (0.256559) | 0.497956 / 0.283200 (0.214756) | 0.034753 / 0.141683 (-0.106930) | 1.828354 / 1.452155 (0.376199) | 1.799211 / 1.492716 (0.306494) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.389652 / 0.018006 (0.371645) | 0.602522 / 0.000490 (0.602033) | 0.068363 / 0.000200 (0.068163) | 0.000493 / 0.000054 (0.000439) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036431 / 0.037411 (-0.000980) | 0.102162 / 0.014526 (0.087636) | 0.122466 / 0.176557 (-0.054091) | 0.181001 / 0.737135 (-0.556134) | 0.125743 / 0.296338 (-0.170596) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583847 / 0.215209 (0.368638) | 5.913008 / 2.077655 (3.835354) | 2.716088 / 1.504120 (1.211968) | 2.328631 / 1.541195 (0.787437) | 2.459953 / 1.468490 (0.991463) | 0.792829 / 4.584777 (-3.791948) | 5.183965 / 3.745712 (1.438253) | 4.508264 / 5.269862 (-0.761598) | 2.855444 / 4.565676 (-1.710232) | 0.090704 / 0.424275 (-0.333571) | 0.009303 / 0.007607 (0.001696) | 0.694303 / 0.226044 (0.468258) | 6.951876 / 2.268929 (4.682947) | 3.418244 / 55.444624 (-52.026381) | 2.799743 / 6.876477 (-4.076734) | 3.043657 / 2.142072 (0.901584) | 0.921537 / 4.805227 (-3.883691) | 0.191774 / 6.500664 (-6.308890) | 0.068602 / 0.075469 (-0.006867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624842 / 1.841788 (-0.216946) | 24.570622 / 8.074308 (16.496314) | 21.207566 / 10.191392 (11.016174) | 0.217734 / 0.680424 (-0.462689) | 0.033109 / 0.534201 (-0.501091) | 0.451651 / 0.579283 (-0.127632) | 0.590890 / 0.434364 (0.156526) | 0.546195 / 0.540337 (0.005858) | 0.730298 / 1.386936 (-0.656638) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6bdecff73303cf97f279a4e36622faf53133f9c \"CML watermark\")\n" ]
2023-11-02T09:48:58
2023-11-02T10:22:33
2023-11-02T10:11:19
MEMBER
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Temporarily pin `pyarrow` < 14.0.0 until permanent solution is found. Hot fix #6374.
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1,973,857,428
I_kwDODunzps51pqyU
6,374
CI is broken: TypeError: Couldn't cast array
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2023-11-02T09:37:06
2023-11-02T10:11:20
2023-11-02T10:11:20
MEMBER
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See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039 ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ```
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1,973,349,695
PR_kwDODunzps5eYsZc
6,373
Fix typo in `Dataset.map` docstring
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006709 / 0.011353 (-0.004643) | 0.004102 / 0.011008 (-0.006906) | 0.084449 / 0.038508 (0.045941) | 0.076078 / 0.023109 (0.052969) | 0.319831 / 0.275898 (0.043933) | 0.359918 / 0.323480 (0.036438) | 0.006092 / 0.007986 (-0.001894) | 0.003402 / 0.004328 (-0.000926) | 0.064715 / 0.004250 (0.060465) | 0.054541 / 0.037052 (0.017488) | 0.330394 / 0.258489 (0.071905) | 0.366048 / 0.293841 (0.072207) | 0.031594 / 0.128546 (-0.096952) | 0.008591 / 0.075646 (-0.067056) | 0.292983 / 0.419271 (-0.126288) | 0.052986 / 0.043533 (0.009453) | 0.322253 / 0.255139 (0.067114) | 0.340082 / 0.283200 (0.056882) | 0.023390 / 0.141683 (-0.118293) | 1.459038 / 1.452155 (0.006883) | 1.536256 / 1.492716 (0.043540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233527 / 0.018006 (0.215521) | 0.459145 / 0.000490 (0.458655) | 0.007471 / 0.000200 (0.007271) | 0.000281 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028158 / 0.037411 (-0.009253) | 0.083079 / 0.014526 (0.068553) | 0.097159 / 0.176557 (-0.079397) | 0.151927 / 0.737135 (-0.585208) | 0.098024 / 0.296338 (-0.198314) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386882 / 0.215209 (0.171673) | 3.849635 / 2.077655 (1.771981) | 1.832885 / 1.504120 (0.328765) | 1.668356 / 1.541195 (0.127162) | 1.745066 / 1.468490 (0.276576) | 0.484476 / 4.584777 (-4.100301) | 3.547604 / 3.745712 (-0.198108) | 3.480338 / 5.269862 (-1.789523) | 2.066837 / 4.565676 (-2.498840) | 0.056755 / 0.424275 (-0.367520) | 0.007747 / 0.007607 (0.000140) | 0.467999 / 0.226044 (0.241955) | 4.678875 / 2.268929 (2.409946) | 2.341930 / 55.444624 (-53.102695) | 1.985632 / 6.876477 (-4.890844) | 2.046998 / 2.142072 (-0.095074) | 0.579860 / 4.805227 (-4.225367) | 0.131488 / 6.500664 (-6.369176) | 0.060193 / 0.075469 (-0.015276) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249656 / 1.841788 (-0.592132) | 19.079517 / 8.074308 (11.005209) | 14.328827 / 10.191392 (4.137435) | 0.173707 / 0.680424 (-0.506717) | 0.018250 / 0.534201 (-0.515951) | 0.392225 / 0.579283 (-0.187058) | 0.413920 / 0.434364 (-0.020444) | 0.464124 / 0.540337 (-0.076214) | 0.640283 / 1.386936 (-0.746653) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004494) | 0.004068 / 0.011008 (-0.006940) | 0.063936 / 0.038508 (0.025428) | 0.077187 / 0.023109 (0.054078) | 0.365098 / 0.275898 (0.089200) | 0.391003 / 0.323480 (0.067523) | 0.005571 / 0.007986 (-0.002415) | 0.003425 / 0.004328 (-0.000904) | 0.063220 / 0.004250 (0.058970) | 0.056964 / 0.037052 (0.019912) | 0.367793 / 0.258489 (0.109304) | 0.398776 / 0.293841 (0.104935) | 0.033182 / 0.128546 (-0.095364) | 0.008601 / 0.075646 (-0.067045) | 0.070276 / 0.419271 (-0.348996) | 0.048383 / 0.043533 (0.004850) | 0.360414 / 0.255139 (0.105275) | 0.368171 / 0.283200 (0.084971) | 0.023114 / 0.141683 (-0.118569) | 1.503503 / 1.452155 (0.051349) | 1.567279 / 1.492716 (0.074562) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224296 / 0.018006 (0.206290) | 0.455138 / 0.000490 (0.454648) | 0.004014 / 0.000200 (0.003814) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032337 / 0.037411 (-0.005074) | 0.094385 / 0.014526 (0.079859) | 0.109870 / 0.176557 (-0.066687) | 0.156978 / 0.737135 (-0.580157) | 0.107559 / 0.296338 (-0.188780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427409 / 0.215209 (0.212200) | 4.261772 / 2.077655 (2.184117) | 2.276106 / 1.504120 (0.771986) | 2.115232 / 1.541195 (0.574038) | 2.192048 / 1.468490 (0.723558) | 0.488459 / 4.584777 (-4.096318) | 3.675463 / 3.745712 (-0.070249) | 3.322475 / 5.269862 (-1.947387) | 2.072253 / 4.565676 (-2.493424) | 0.058259 / 0.424275 (-0.366017) | 0.007319 / 0.007607 (-0.000288) | 0.499513 / 0.226044 (0.273469) | 4.994774 / 2.268929 (2.725845) | 2.760927 / 55.444624 (-52.683697) | 2.391947 / 6.876477 (-4.484530) | 2.600557 / 2.142072 (0.458484) | 0.587597 / 4.805227 (-4.217630) | 0.131444 / 6.500664 (-6.369220) | 0.057334 / 0.075469 (-0.018135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354636 / 1.841788 (-0.487152) | 19.685735 / 8.074308 (11.611427) | 14.295920 / 10.191392 (4.104528) | 0.171921 / 0.680424 (-0.508503) | 0.019926 / 0.534201 (-0.514274) | 0.395216 / 0.579283 (-0.184068) | 0.432791 / 0.434364 (-0.001573) | 0.473055 / 0.540337 (-0.067282) | 0.638633 / 1.386936 (-0.748303) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n" ]
2023-11-02T01:36:49
2023-11-02T15:18:22
2023-11-02T10:11:38
CONTRIBUTOR
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do not try to download from HF GCS for generator
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007617 / 0.011353 (-0.003735) | 0.005371 / 0.011008 (-0.005638) | 0.092110 / 0.038508 (0.053602) | 0.070654 / 0.023109 (0.047544) | 0.362501 / 0.275898 (0.086603) | 0.412835 / 0.323480 (0.089355) | 0.006752 / 0.007986 (-0.001234) | 0.003752 / 0.004328 (-0.000576) | 0.075644 / 0.004250 (0.071394) | 0.055666 / 0.037052 (0.018614) | 0.355906 / 0.258489 (0.097417) | 0.405078 / 0.293841 (0.111237) | 0.045767 / 0.128546 (-0.082779) | 0.013778 / 0.075646 (-0.061868) | 0.324696 / 0.419271 (-0.094575) | 0.062200 / 0.043533 (0.018667) | 0.359571 / 0.255139 (0.104432) | 0.387274 / 0.283200 (0.104075) | 0.035323 / 0.141683 (-0.106360) | 1.586294 / 1.452155 (0.134139) | 1.707564 / 1.492716 (0.214847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303940 / 0.018006 (0.285934) | 0.583349 / 0.000490 (0.582859) | 0.014845 / 0.000200 (0.014645) | 0.000698 / 0.000054 (0.000643) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028994 / 0.037411 (-0.008417) | 0.085555 / 0.014526 (0.071029) | 0.097856 / 0.176557 (-0.078701) | 0.161480 / 0.737135 (-0.575655) | 0.098573 / 0.296338 (-0.197766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.591294 / 0.215209 (0.376085) | 5.751350 / 2.077655 (3.673695) | 2.241620 / 1.504120 (0.737500) | 1.991083 / 1.541195 (0.449888) | 2.006711 / 1.468490 (0.538221) | 0.832339 / 4.584777 (-3.752438) | 5.213808 / 3.745712 (1.468095) | 4.650355 / 5.269862 (-0.619506) | 2.860494 / 4.565676 (-1.705182) | 0.093090 / 0.424275 (-0.331185) | 0.009740 / 0.007607 (0.002133) | 0.693509 / 0.226044 (0.467464) | 6.828735 / 2.268929 (4.559807) | 2.967763 / 55.444624 (-52.476862) | 2.311461 / 6.876477 (-4.565016) | 2.400051 / 2.142072 (0.257979) | 0.914753 / 4.805227 (-3.890474) | 0.202804 / 6.500664 (-6.297860) | 0.076905 / 0.075469 (0.001436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.576424 / 1.841788 (-0.265363) | 22.963472 / 8.074308 (14.889164) | 19.948105 / 10.191392 (9.756713) | 0.228982 / 0.680424 (-0.451442) | 0.029038 / 0.534201 (-0.505163) | 0.477715 / 0.579283 (-0.101568) | 0.554924 / 0.434364 (0.120560) | 0.532118 / 0.540337 (-0.008219) | 0.775096 / 1.386936 (-0.611840) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009127 / 0.011353 (-0.002226) | 0.004978 / 0.011008 (-0.006030) | 0.084166 / 0.038508 (0.045658) | 0.083391 / 0.023109 (0.060282) | 0.420760 / 0.275898 (0.144862) | 0.459072 / 0.323480 (0.135592) | 0.007102 / 0.007986 (-0.000883) | 0.004175 / 0.004328 (-0.000154) | 0.082922 / 0.004250 (0.078672) | 0.059010 / 0.037052 (0.021957) | 0.416959 / 0.258489 (0.158470) | 0.472220 / 0.293841 (0.178379) | 0.049999 / 0.128546 (-0.078547) | 0.014126 / 0.075646 (-0.061520) | 0.096894 / 0.419271 (-0.322378) | 0.057920 / 0.043533 (0.014387) | 0.405779 / 0.255139 (0.150640) | 0.464286 / 0.283200 (0.181087) | 0.034957 / 0.141683 (-0.106726) | 1.637921 / 1.452155 (0.185767) | 1.768231 / 1.492716 (0.275515) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.354875 / 0.018006 (0.336868) | 0.554667 / 0.000490 (0.554177) | 0.074127 / 0.000200 (0.073927) | 0.000411 / 0.000054 (0.000357) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027681 / 0.037411 (-0.009730) | 0.087746 / 0.014526 (0.073220) | 0.093714 / 0.176557 (-0.082843) | 0.145380 / 0.737135 (-0.591755) | 0.095686 / 0.296338 (-0.200652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522079 / 0.215209 (0.306870) | 5.197366 / 2.077655 (3.119711) | 2.300744 / 1.504120 (0.796624) | 2.056846 / 1.541195 (0.515652) | 2.009897 / 1.468490 (0.541407) | 0.813025 / 4.584777 (-3.771751) | 5.177732 / 3.745712 (1.432020) | 4.076749 / 5.269862 (-1.193112) | 2.545588 / 4.565676 (-2.020088) | 0.083507 / 0.424275 (-0.340769) | 0.007011 / 0.007607 (-0.000596) | 0.598820 / 0.226044 (0.372776) | 6.203730 / 2.268929 (3.934801) | 2.945385 / 55.444624 (-52.499239) | 2.304849 / 6.876477 (-4.571628) | 2.599035 / 2.142072 (0.456962) | 1.002721 / 4.805227 (-3.802506) | 0.191781 / 6.500664 (-6.308883) | 0.064178 / 0.075469 (-0.011292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549560 / 1.841788 (-0.292228) | 22.395727 / 8.074308 (14.321418) | 20.537895 / 10.191392 (10.346503) | 0.246542 / 0.680424 (-0.433882) | 0.031673 / 0.534201 (-0.502528) | 0.442490 / 0.579283 (-0.136793) | 0.589838 / 0.434364 (0.155474) | 0.535201 / 0.540337 (-0.005136) | 0.733660 / 1.386936 (-0.653276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n" ]
2023-11-01T17:57:11
2023-11-02T16:02:52
2023-11-02T15:52:09
CONTRIBUTOR
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attempt to fix https://github.com/huggingface/datasets/issues/6371
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1,972,807,579
I_kwDODunzps51lqeb
6,371
`Dataset.from_generator` should not try to download from HF GCS
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[ "Indeed, setting `try_from_gcs` to `False` makes sense for `from_generator`.\r\n\r\nWe plan to deprecate and remove `try_from_hf_gcs` soon, as we can use Hub for file hosting now, but this is a good temporary fix.\r\n" ]
2023-11-01T17:36:17
2023-11-02T15:52:10
2023-11-02T15:52:10
CONTRIBUTOR
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### Describe the bug When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection. ### Steps to reproduce the bug ```python import datasets def gen(): for _ in range(100): yield {"text": "dummy text"} dataset = datasets.Dataset.from_generator(gen) ``` A minimum example executed on any environment that doesn't have access to HF GCS can result in the error ### Expected behavior `try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51 ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,972,073,909
I_kwDODunzps51i3W1
6,370
TensorDataset format does not work with Trainer from transformers
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[ "I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.", "Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually." ]
2023-11-01T10:09:54
2023-11-29T16:31:08
2023-11-29T16:31:08
NONE
null
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### Describe the bug The model was built to do fine tunning on BERT model for relation extraction. trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset` However, in the document, the required data format is `torch.utils.data.TensorDataset`. ![image](https://github.com/huggingface/datasets/assets/49014051/36fa34ac-3127-4c64-9580-9ab736136d83) Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message *"TypeError: vars() argument must have __dict__ attribute"* ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-5df728c929a2> in <cell line: 1>() ----> 1 trainer.train() 2 trainer.evaluate(test_dataset) 9 frames /usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0) 107 108 if not isinstance(features[0], Mapping): --> 109 features = [vars(f) for f in features] 110 first = features[0] 111 batch = {} TypeError: vars() argument must have __dict__ attribute ``` ### Steps to reproduce the bug Create train_dataset using `torch.utils.data.TensorDataset`, for instance, ```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)``` Feed this `train_dataset` to your trainer and run trainer.train ``` trainer = Trainer(model, training_args, train_dataset=train_dataset, eval_dataset=dev_dataset, compute_metrics=compute_metrics, ) ``` ### Expected behavior Trainer should start training ### Environment info It is running on Google Colab - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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6,369
Multi process map did not load cache file correctly
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[ "The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n![1698841094290](https://github.com/huggingface/datasets/assets/14285786/21fc3c65-e9fd-4a79-b12e-a1d4b9c6cf32)\r\n![1698841117416](https://github.com/huggingface/datasets/assets/14285786/c3e5a530-54d2-4ae6-b902-ce9f85de373b)\r\n\r\n", "The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n", "We now sort `set` and `dict` items to make their hashes deterministic (install from `main` with `pip install git+https://github.com/huggingface/datasets` to test this). Consequently, this should also make the `tokenizer.tokens_trie`'s hash deterministic. Feel free to re-open the issue if this is not the case." ]
2023-11-01T06:36:54
2023-11-30T16:04:46
2023-11-30T16:04:45
NONE
null
null
null
### Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process. ![1698820541284](https://github.com/huggingface/datasets/assets/14285786/0b2fe054-54d8-4e00-96e6-6ca5b69e662b) ![1698820501568](https://github.com/huggingface/datasets/assets/14285786/dd62bf6f-a58f-41bf-9848-ea4fb3b62b9b) Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484) ### Steps to reproduce the bug ``` block_size = data_args.block_size IGNORE_INDEX = -100 Ignore_Input = False def tokenize_function(examples): sources = [] targets = [] for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']): source = instruction + inputs target = f"{output}{tokenizer.eos_token}" sources.append(source) targets.append(target) tokenized_sources = tokenizer(sources, return_attention_mask=False) tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False ) all_input_ids = [] all_labels = [] for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']): if len(s) > block_size and Ignore_Input == False: # print(s) continue input_ids = torch.LongTensor(s + t)[:block_size] if Ignore_Input: labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size] else: labels = input_ids assert len(input_ids) == len(labels) all_input_ids.append(input_ids) all_labels.append(labels) results = { 'input_ids': all_input_ids, 'labels': all_labels, } return results with training_args.main_process_first(desc="dataset map tokenization ", local=False): # print('local_rank',training_args.local_rank) if not data_args.streaming: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset ", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, desc="Running tokenizer on dataset " ) ``` ### Expected behavior This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting ### Environment info transformers == 4.34.1 datasets == 2.14.5
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