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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.", "<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) | 0.007212 / 0.011353 (-0.004141) | 0.005125 / 0.011008 (-0.005883) | 0.098460 / 0.038508 (0.059952) | 0.034040 / 0.023109 (0.010931) | 0.320203 / 0.275898 (0.044305) | 0.357787 / 0.323480 (0.034307) | 0.006000 / 0.007986 (-0.001986) | 0.005644 / 0.004328 (0.001316) | 0.072654 / 0.004250 (0.068403) | 0.049393 / 0.037052 (0.012341) | 0.345686 / 0.258489 (0.087196) | 0.362345 / 0.293841 (0.068504) | 0.036597 / 0.128546 (-0.091949) | 0.012303 / 0.075646 (-0.063343) | 0.334374 / 0.419271 (-0.084897) | 0.062010 / 0.043533 (0.018477) | 0.312547 / 0.255139 (0.057408) | 0.336021 / 0.283200 (0.052821) | 0.112304 / 0.141683 (-0.029378) | 1.446706 / 1.452155 (-0.005449) | 1.523256 / 1.492716 (0.030540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217658 / 0.018006 (0.199652) | 0.449208 / 0.000490 (0.448718) | 0.002878 / 0.000200 (0.002679) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025735 / 0.037411 (-0.011676) | 0.105876 / 0.014526 (0.091350) | 0.114887 / 0.176557 (-0.061669) | 0.170984 / 0.737135 (-0.566152) | 0.121420 / 0.296338 (-0.174918) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419670 / 0.215209 (0.204461) | 4.189453 / 2.077655 (2.111798) | 1.938236 / 1.504120 (0.434116) | 1.769747 / 1.541195 (0.228553) | 1.910919 / 1.468490 (0.442429) | 0.705046 / 4.584777 (-3.879730) | 3.783774 / 3.745712 (0.038062) | 2.096504 / 5.269862 (-3.173358) | 1.339265 / 4.565676 (-3.226412) | 0.086670 / 0.424275 (-0.337605) | 0.012243 / 0.007607 (0.004636) | 0.524701 / 0.226044 (0.298657) | 5.240689 / 2.268929 (2.971760) | 2.473622 / 55.444624 (-52.971003) | 2.170568 / 6.876477 (-4.705909) | 2.289653 / 2.142072 (0.147581) | 0.848913 / 4.805227 (-3.956314) | 0.168332 / 6.500664 (-6.332332) | 0.064926 / 0.075469 (-0.010543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193614 / 1.841788 (-0.648173) | 14.920403 / 8.074308 (6.846095) | 14.475059 / 10.191392 (4.283667) | 0.164458 / 0.680424 (-0.515966) | 0.017613 / 0.534201 (-0.516588) | 0.426311 / 0.579283 (-0.152972) | 0.431478 / 0.434364 (-0.002886) | 0.520280 / 0.540337 (-0.020057) | 0.627738 / 1.386936 (-0.759198) |\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) | 0.007458 / 0.011353 (-0.003895) | 0.005363 / 0.011008 (-0.005645) | 0.076713 / 0.038508 (0.038205) | 0.034189 / 0.023109 (0.011079) | 0.359938 / 0.275898 (0.084040) | 0.395532 / 0.323480 (0.072052) | 0.005977 / 0.007986 (-0.002008) | 0.004263 / 0.004328 (-0.000065) | 0.075971 / 0.004250 (0.071721) | 0.051924 / 0.037052 (0.014871) | 0.362818 / 0.258489 (0.104329) | 0.409897 / 0.293841 (0.116056) | 0.035494 / 0.128546 (-0.093053) | 0.012399 / 0.075646 (-0.063247) | 0.088335 / 0.419271 (-0.330937) | 0.047968 / 0.043533 (0.004435) | 0.355744 / 0.255139 (0.100606) | 0.376339 / 0.283200 (0.093139) | 0.104542 / 0.141683 (-0.037141) | 1.464826 / 1.452155 (0.012672) | 1.600665 / 1.492716 (0.107948) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220841 / 0.018006 (0.202834) | 0.446444 / 0.000490 (0.445954) | 0.000392 / 0.000200 (0.000192) | 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) | 0.029402 / 0.037411 (-0.008009) | 0.116511 / 0.014526 (0.101986) | 0.122959 / 0.176557 (-0.053598) | 0.171674 / 0.737135 (-0.565462) | 0.129871 / 0.296338 (-0.166468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450411 / 0.215209 (0.235202) | 4.471859 / 2.077655 (2.394205) | 2.229439 / 1.504120 (0.725319) | 2.053308 / 1.541195 (0.512114) | 2.142476 / 1.468490 (0.673986) | 0.708299 / 4.584777 (-3.876478) | 3.797830 / 3.745712 (0.052118) | 2.142509 / 5.269862 (-3.127352) | 1.333357 / 4.565676 (-3.232320) | 0.086837 / 0.424275 (-0.337439) | 0.012102 / 0.007607 (0.004495) | 0.548428 / 0.226044 (0.322384) | 5.490611 / 2.268929 (3.221682) | 2.713882 / 55.444624 (-52.730742) | 2.399638 / 6.876477 (-4.476839) | 2.481549 / 2.142072 (0.339477) | 0.839812 / 4.805227 (-3.965415) | 0.168890 / 6.500664 (-6.331774) | 0.065564 / 0.075469 (-0.009906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275507 / 1.841788 (-0.566281) | 14.896343 / 8.074308 (6.822035) | 13.159701 / 10.191392 (2.968309) | 0.172065 / 0.680424 (-0.508359) | 0.017507 / 0.534201 (-0.516694) | 0.420031 / 0.579283 (-0.159252) | 0.438835 / 0.434364 (0.004471) | 0.490597 / 0.540337 (-0.049741) | 0.583952 / 1.386936 (-0.802984) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48c9755d0ae9abe4c4d6cd8c1ce76eff849f0e5c \"CML watermark\")\n" ]

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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) | 0.006567 / 0.011353 (-0.004786) | 0.004479 / 0.011008 (-0.006530) | 0.028286 / 0.038508 (-0.010222) | 0.033137 / 0.023109 (0.010028) | 0.305249 / 0.275898 (0.029351) | 0.330306 / 0.323480 (0.006826) | 0.003747 / 0.007986 (-0.004238) | 0.004409 / 0.004328 (0.000081) | 0.004742 / 0.004250 (0.000491) | 0.040780 / 0.037052 (0.003728) | 0.302879 / 0.258489 (0.044390) | 0.346880 / 0.293841 (0.053039) | 0.032908 / 0.128546 (-0.095638) | 0.010617 / 0.075646 (-0.065029) | 0.257996 / 0.419271 (-0.161275) | 0.051044 / 0.043533 (0.007511) | 0.306113 / 0.255139 (0.050974) | 0.324444 / 0.283200 (0.041244) | 0.100820 / 0.141683 (-0.040863) | 1.478402 / 1.452155 (0.026248) | 1.599398 / 1.492716 (0.106682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216540 / 0.018006 (0.198534) | 0.433480 / 0.000490 (0.432991) | 0.004032 / 0.000200 (0.003832) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027807 / 0.037411 (-0.009604) | 0.107225 / 0.014526 (0.092699) | 0.120157 / 0.176557 (-0.056400) | 0.174130 / 0.737135 (-0.563005) | 0.128902 / 0.296338 (-0.167437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395996 / 0.215209 (0.180787) | 3.936254 / 2.077655 (1.858599) | 1.808864 / 1.504120 (0.304744) | 1.608935 / 1.541195 (0.067741) | 1.646427 / 1.468490 (0.177937) | 0.716026 / 4.584777 (-3.868751) | 3.815045 / 3.745712 (0.069333) | 2.271534 / 5.269862 (-2.998327) | 1.548728 / 4.565676 (-3.016948) | 0.076743 / 0.424275 (-0.347532) | 0.011575 / 0.007607 (0.003968) | 0.499202 / 0.226044 (0.273158) | 4.983754 / 2.268929 (2.714825) | 2.239319 / 55.444624 (-53.205306) | 1.919427 / 6.876477 (-4.957050) | 2.019664 / 2.142072 (-0.122408) | 0.866318 / 4.805227 (-3.938910) | 0.157309 / 6.500664 (-6.343355) | 0.063341 / 0.075469 (-0.012128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180817 / 1.841788 (-0.660971) | 14.579869 / 8.074308 (6.505561) | 14.277848 / 10.191392 (4.086456) | 0.182560 / 0.680424 (-0.497863) | 0.017402 / 0.534201 (-0.516799) | 0.411549 / 0.579283 (-0.167734) | 0.432938 / 0.434364 (-0.001426) | 0.545067 / 0.540337 (0.004730) | 0.642173 / 1.386936 (-0.744763) |\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) | 0.006753 / 0.011353 (-0.004600) | 0.004590 / 0.011008 (-0.006418) | 0.006111 / 0.038508 (-0.032397) | 0.032763 / 0.023109 (0.009654) | 0.401001 / 0.275898 (0.125103) | 0.428063 / 0.323480 (0.104583) | 0.003730 / 0.007986 (-0.004255) | 0.004617 / 0.004328 (0.000289) | 0.004770 / 0.004250 (0.000519) | 0.049718 / 0.037052 (0.012666) | 0.399724 / 0.258489 (0.141235) | 0.440292 / 0.293841 (0.146451) | 0.032846 / 0.128546 (-0.095700) | 0.010842 / 0.075646 (-0.064804) | 0.012642 / 0.419271 (-0.406630) | 0.046043 / 0.043533 (0.002510) | 0.390862 / 0.255139 (0.135723) | 0.407027 / 0.283200 (0.123828) | 0.099349 / 0.141683 (-0.042334) | 1.455739 / 1.452155 (0.003584) | 1.572214 / 1.492716 (0.079497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227186 / 0.018006 (0.209180) | 0.447404 / 0.000490 (0.446914) | 0.000400 / 0.000200 (0.000200) | 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) | 0.029830 / 0.037411 (-0.007581) | 0.112365 / 0.014526 (0.097839) | 0.125736 / 0.176557 (-0.050821) | 0.174781 / 0.737135 (-0.562354) | 0.129439 / 0.296338 (-0.166900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444438 / 0.215209 (0.229229) | 4.459381 / 2.077655 (2.381726) | 2.264541 / 1.504120 (0.760421) | 2.075257 / 1.541195 (0.534062) | 2.181289 / 1.468490 (0.712799) | 0.725279 / 4.584777 (-3.859498) | 3.863253 / 3.745712 (0.117541) | 2.132498 / 5.269862 (-3.137364) | 1.402003 / 4.565676 (-3.163673) | 0.084268 / 0.424275 (-0.340007) | 0.011762 / 0.007607 (0.004155) | 0.556239 / 0.226044 (0.330194) | 5.617998 / 2.268929 (3.349070) | 2.754789 / 55.444624 (-52.689835) | 2.418418 / 6.876477 (-4.458059) | 2.479696 / 2.142072 (0.337624) | 0.870037 / 4.805227 (-3.935190) | 0.160480 / 6.500664 (-6.340184) | 0.064464 / 0.075469 (-0.011005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290916 / 1.841788 (-0.550872) | 14.783173 / 8.074308 (6.708865) | 13.355883 / 10.191392 (3.164491) | 0.169963 / 0.680424 (-0.510461) | 0.017657 / 0.534201 (-0.516544) | 0.409218 / 0.579283 (-0.170065) | 0.422942 / 0.434364 (-0.011422) | 0.494968 / 0.540337 (-0.045369) | 0.587044 / 1.386936 (-0.799892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2051e912d9525bc38a1caf295df0620619c488eb \"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) | 0.007183 / 0.011353 (-0.004169) | 0.004586 / 0.011008 (-0.006423) | 0.032668 / 0.038508 (-0.005840) | 0.040896 / 0.023109 (0.017787) | 0.358225 / 0.275898 (0.082327) | 0.395063 / 0.323480 (0.071583) | 0.004540 / 0.007986 (-0.003446) | 0.003849 / 0.004328 (-0.000480) | 0.005521 / 0.004250 (0.001271) | 0.053314 / 0.037052 (0.016262) | 0.362417 / 0.258489 (0.103928) | 0.414337 / 0.293841 (0.120496) | 0.030698 / 0.128546 (-0.097849) | 0.008823 / 0.075646 (-0.066823) | 0.303583 / 0.419271 (-0.115689) | 0.060277 / 0.043533 (0.016744) | 0.365938 / 0.255139 (0.110799) | 0.379554 / 0.283200 (0.096354) | 0.122545 / 0.141683 (-0.019138) | 1.712098 / 1.452155 (0.259943) | 1.802036 / 1.492716 (0.309319) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239508 / 0.018006 (0.221502) | 0.492194 / 0.000490 (0.491704) | 0.003280 / 0.000200 (0.003081) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033301 / 0.037411 (-0.004110) | 0.125851 / 0.014526 (0.111325) | 0.137757 / 0.176557 (-0.038799) | 0.207603 / 0.737135 (-0.529533) | 0.143507 / 0.296338 (-0.152831) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470662 / 0.215209 (0.255453) | 4.736017 / 2.077655 (2.658363) | 2.154152 / 1.504120 (0.650032) | 1.954243 / 1.541195 (0.413048) | 2.080186 / 1.468490 (0.611696) | 0.622884 / 4.584777 (-3.961893) | 4.385885 / 3.745712 (0.640173) | 2.262085 / 5.269862 (-3.007776) | 1.454215 / 4.565676 (-3.111462) | 0.067342 / 0.424275 (-0.356933) | 0.012913 / 0.007607 (0.005306) | 0.600676 / 0.226044 (0.374631) | 5.915093 / 2.268929 (3.646164) | 2.664915 / 55.444624 (-52.779709) | 2.286986 / 6.876477 (-4.589490) | 2.387776 / 2.142072 (0.245704) | 0.757067 / 4.805227 (-4.048160) | 0.154625 / 6.500664 (-6.346039) | 0.074632 / 0.075469 (-0.000838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.413229 / 1.841788 (-0.428558) | 17.433012 / 8.074308 (9.358704) | 16.980340 / 10.191392 (6.788948) | 0.218943 / 0.680424 (-0.461481) | 0.020525 / 0.534201 (-0.513676) | 0.451847 / 0.579283 (-0.127436) | 0.495587 / 0.434364 (0.061223) | 0.548739 / 0.540337 (0.008402) | 0.662120 / 1.386936 (-0.724816) |\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) | 0.006775 / 0.011353 (-0.004577) | 0.004556 / 0.011008 (-0.006452) | 0.006462 / 0.038508 (-0.032046) | 0.039073 / 0.023109 (0.015964) | 0.429249 / 0.275898 (0.153351) | 0.469946 / 0.323480 (0.146467) | 0.004402 / 0.007986 (-0.003584) | 0.003798 / 0.004328 (-0.000530) | 0.005347 / 0.004250 (0.001097) | 0.053743 / 0.037052 (0.016691) | 0.434635 / 0.258489 (0.176146) | 0.475661 / 0.293841 (0.181820) | 0.029891 / 0.128546 (-0.098656) | 0.009058 / 0.075646 (-0.066588) | 0.010987 / 0.419271 (-0.408284) | 0.053877 / 0.043533 (0.010344) | 0.434428 / 0.255139 (0.179289) | 0.449637 / 0.283200 (0.166437) | 0.124331 / 0.141683 (-0.017352) | 1.736083 / 1.452155 (0.283928) | 1.831632 / 1.492716 (0.338916) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248428 / 0.018006 (0.230422) | 0.493113 / 0.000490 (0.492623) | 0.000429 / 0.000200 (0.000229) | 0.000057 / 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) | 0.031337 / 0.037411 (-0.006074) | 0.132360 / 0.014526 (0.117834) | 0.134734 / 0.176557 (-0.041822) | 0.193811 / 0.737135 (-0.543324) | 0.146883 / 0.296338 (-0.149456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510876 / 0.215209 (0.295666) | 5.170198 / 2.077655 (3.092543) | 2.572105 / 1.504120 (1.067985) | 2.316918 / 1.541195 (0.775723) | 2.449316 / 1.468490 (0.980826) | 0.612219 / 4.584777 (-3.972558) | 4.456740 / 3.745712 (0.711028) | 2.099757 / 5.269862 (-3.170105) | 1.293017 / 4.565676 (-3.272660) | 0.067922 / 0.424275 (-0.356353) | 0.013467 / 0.007607 (0.005860) | 0.634240 / 0.226044 (0.408196) | 6.373111 / 2.268929 (4.104182) | 3.171567 / 55.444624 (-52.273057) | 2.763411 / 6.876477 (-4.113066) | 2.845557 / 2.142072 (0.703485) | 0.763431 / 4.805227 (-4.041797) | 0.155949 / 6.500664 (-6.344715) | 0.076264 / 0.075469 (0.000795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.468075 / 1.841788 (-0.373713) | 17.582354 / 8.074308 (9.508046) | 16.565964 / 10.191392 (6.374572) | 0.163779 / 0.680424 (-0.516644) | 0.020472 / 0.534201 (-0.513728) | 0.444416 / 0.579283 (-0.134867) | 0.488471 / 0.434364 (0.054107) | 0.550661 / 0.540337 (0.010323) | 0.667230 / 1.386936 (-0.719706) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3655cbf1c627c945e393641d35298a166f1e4bf5 \"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) | 0.006160 / 0.011353 (-0.005193) | 0.004093 / 0.011008 (-0.006915) | 0.056485 / 0.038508 (0.017977) | 0.033637 / 0.023109 (0.010528) | 0.296448 / 0.275898 (0.020550) | 0.332532 / 0.323480 (0.009052) | 0.003864 / 0.007986 (-0.004122) | 0.003446 / 0.004328 (-0.000883) | 0.034808 / 0.004250 (0.030558) | 0.048567 / 0.037052 (0.011514) | 0.296090 / 0.258489 (0.037601) | 0.336067 / 0.293841 (0.042226) | 0.026081 / 0.128546 (-0.102465) | 0.007875 / 0.075646 (-0.067771) | 0.286049 / 0.419271 (-0.133222) | 0.050411 / 0.043533 (0.006878) | 0.297016 / 0.255139 (0.041877) | 0.320030 / 0.283200 (0.036830) | 0.110374 / 0.141683 (-0.031308) | 1.432470 / 1.452155 (-0.019684) | 1.492479 / 1.492716 (-0.000238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262352 / 0.018006 (0.244346) | 0.557956 / 0.000490 (0.557467) | 0.010296 / 0.000200 (0.010096) | 0.000315 / 0.000054 (0.000260) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028801 / 0.037411 (-0.008611) | 0.109844 / 0.014526 (0.095318) | 0.122333 / 0.176557 (-0.054224) | 0.180571 / 0.737135 (-0.556564) | 0.125990 / 0.296338 (-0.170348) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401643 / 0.215209 (0.186434) | 4.020993 / 2.077655 (1.943338) | 1.815256 / 1.504120 (0.311136) | 1.619579 / 1.541195 (0.078384) | 1.708889 / 1.468490 (0.240398) | 0.537847 / 4.584777 (-4.046930) | 3.743331 / 3.745712 (-0.002381) | 1.779891 / 5.269862 (-3.489970) | 1.021423 / 4.565676 (-3.544253) | 0.058869 / 0.424275 (-0.365406) | 0.011826 / 0.007607 (0.004218) | 0.499665 / 0.226044 (0.273621) | 4.980928 / 2.268929 (2.712000) | 2.285664 / 55.444624 (-53.158960) | 1.936553 / 6.876477 (-4.939923) | 2.090428 / 2.142072 (-0.051645) | 0.655218 / 4.805227 (-4.150009) | 0.133178 / 6.500664 (-6.367486) | 0.062991 / 0.075469 (-0.012478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168895 / 1.841788 (-0.672892) | 14.656773 / 8.074308 (6.582465) | 13.737921 / 10.191392 (3.546529) | 0.145383 / 0.680424 (-0.535041) | 0.017614 / 0.534201 (-0.516587) | 0.386499 / 0.579283 (-0.192784) | 0.425626 / 0.434364 (-0.008738) | 0.389572 / 0.540337 (-0.150766) | 0.386753 / 1.386936 (-1.000183) |\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) | 0.005998 / 0.011353 (-0.005355) | 0.004265 / 0.011008 (-0.006743) | 0.034743 / 0.038508 (-0.003766) | 0.033929 / 0.023109 (0.010820) | 0.405535 / 0.275898 (0.129636) | 0.407235 / 0.323480 (0.083755) | 0.003972 / 0.007986 (-0.004013) | 0.003616 / 0.004328 (-0.000712) | 0.035278 / 0.004250 (0.031027) | 0.052990 / 0.037052 (0.015937) | 0.405228 / 0.258489 (0.146739) | 0.415007 / 0.293841 (0.121166) | 0.025951 / 0.128546 (-0.102595) | 0.007990 / 0.075646 (-0.067656) | 0.040492 / 0.419271 (-0.378779) | 0.049123 / 0.043533 (0.005591) | 0.399282 / 0.255139 (0.144143) | 0.384303 / 0.283200 (0.101103) | 0.115234 / 0.141683 (-0.026448) | 1.476904 / 1.452155 (0.024749) | 1.627191 / 1.492716 (0.134475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209211 / 0.018006 (0.191205) | 0.566718 / 0.000490 (0.566228) | 0.002094 / 0.000200 (0.001894) | 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) | 0.030885 / 0.037411 (-0.006526) | 0.110777 / 0.014526 (0.096251) | 0.124382 / 0.176557 (-0.052174) | 0.175081 / 0.737135 (-0.562054) | 0.130263 / 0.296338 (-0.166075) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448091 / 0.215209 (0.232882) | 4.484404 / 2.077655 (2.406749) | 2.278438 / 1.504120 (0.774318) | 2.087933 / 1.541195 (0.546738) | 2.186709 / 1.468490 (0.718219) | 0.534822 / 4.584777 (-4.049955) | 3.778229 / 3.745712 (0.032517) | 3.312334 / 5.269862 (-1.957528) | 1.557209 / 4.565676 (-3.008467) | 0.058923 / 0.424275 (-0.365352) | 0.011350 / 0.007607 (0.003743) | 0.550470 / 0.226044 (0.324426) | 5.480347 / 2.268929 (3.211419) | 2.781709 / 55.444624 (-52.662915) | 2.478729 / 6.876477 (-4.397748) | 2.492001 / 2.142072 (0.349929) | 0.652649 / 4.805227 (-4.152578) | 0.131334 / 6.500664 (-6.369330) | 0.065619 / 0.075469 (-0.009850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253998 / 1.841788 (-0.587790) | 15.207433 / 8.074308 (7.133124) | 14.627842 / 10.191392 (4.436450) | 0.146947 / 0.680424 (-0.533477) | 0.017533 / 0.534201 (-0.516668) | 0.391627 / 0.579283 (-0.187656) | 0.431113 / 0.434364 (-0.003251) | 0.413886 / 0.540337 (-0.126451) | 0.414483 / 1.386936 (-0.972453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f4e98701590a4922050051eb0f4d63e6125723d \"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) | 0.007741 / 0.011353 (-0.003612) | 0.004584 / 0.011008 (-0.006424) | 0.067869 / 0.038508 (0.029361) | 0.041612 / 0.023109 (0.018503) | 0.377878 / 0.275898 (0.101980) | 0.421633 / 0.323480 (0.098153) | 0.004614 / 0.007986 (-0.003371) | 0.003824 / 0.004328 (-0.000504) | 0.041479 / 0.004250 (0.037229) | 0.053309 / 0.037052 (0.016256) | 0.390147 / 0.258489 (0.131658) | 0.437706 / 0.293841 (0.143865) | 0.035951 / 0.128546 (-0.092595) | 0.009231 / 0.075646 (-0.066415) | 0.357572 / 0.419271 (-0.061699) | 0.081332 / 0.043533 (0.037799) | 0.370076 / 0.255139 (0.114937) | 0.423653 / 0.283200 (0.140453) | 0.141401 / 0.141683 (-0.000282) | 1.722744 / 1.452155 (0.270589) | 1.914668 / 1.492716 (0.421952) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256568 / 0.018006 (0.238562) | 0.512243 / 0.000490 (0.511753) | 0.019913 / 0.000200 (0.019713) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031742 / 0.037411 (-0.005670) | 0.128537 / 0.014526 (0.114011) | 0.139962 / 0.176557 (-0.036594) | 0.210711 / 0.737135 (-0.526424) | 0.147162 / 0.296338 (-0.149177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509518 / 0.215209 (0.294309) | 5.083788 / 2.077655 (3.006134) | 2.455381 / 1.504120 (0.951262) | 2.208078 / 1.541195 (0.666883) | 2.341807 / 1.468490 (0.873317) | 0.580014 / 4.584777 (-4.004763) | 4.599492 / 3.745712 (0.853780) | 2.403249 / 5.269862 (-2.866612) | 1.559177 / 4.565676 (-3.006500) | 0.072846 / 0.424275 (-0.351429) | 0.017327 / 0.007607 (0.009720) | 0.627747 / 0.226044 (0.401703) | 6.242586 / 2.268929 (3.973657) | 2.982875 / 55.444624 (-52.461750) | 2.588645 / 6.876477 (-4.287832) | 2.765915 / 2.142072 (0.623843) | 0.720455 / 4.805227 (-4.084772) | 0.157474 / 6.500664 (-6.343190) | 0.074295 / 0.075469 (-0.001174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540799 / 1.841788 (-0.300988) | 18.054632 / 8.074308 (9.980324) | 16.544036 / 10.191392 (6.352644) | 0.201423 / 0.680424 (-0.479001) | 0.020497 / 0.534201 (-0.513704) | 0.496275 / 0.579283 (-0.083008) | 0.547380 / 0.434364 (0.113017) | 0.614605 / 0.540337 (0.074267) | 0.749889 / 1.386936 (-0.637047) |\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) | 0.006963 / 0.011353 (-0.004389) | 0.004543 / 0.011008 (-0.006465) | 0.039530 / 0.038508 (0.001022) | 0.038420 / 0.023109 (0.015311) | 0.454885 / 0.275898 (0.178987) | 0.491731 / 0.323480 (0.168251) | 0.004211 / 0.007986 (-0.003775) | 0.003673 / 0.004328 (-0.000655) | 0.038735 / 0.004250 (0.034484) | 0.052085 / 0.037052 (0.015032) | 0.448924 / 0.258489 (0.190435) | 0.499254 / 0.293841 (0.205413) | 0.030069 / 0.128546 (-0.098477) | 0.009082 / 0.075646 (-0.066565) | 0.047181 / 0.419271 (-0.372090) | 0.054758 / 0.043533 (0.011225) | 0.445035 / 0.255139 (0.189896) | 0.475090 / 0.283200 (0.191891) | 0.122641 / 0.141683 (-0.019042) | 1.706514 / 1.452155 (0.254360) | 1.855726 / 1.492716 (0.363010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246028 / 0.018006 (0.228022) | 0.486382 / 0.000490 (0.485892) | 0.003038 / 0.000200 (0.002838) | 0.000107 / 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) | 0.034298 / 0.037411 (-0.003113) | 0.135364 / 0.014526 (0.120838) | 0.146102 / 0.176557 (-0.030455) | 0.207997 / 0.737135 (-0.529139) | 0.153119 / 0.296338 (-0.143219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528758 / 0.215209 (0.313549) | 5.243303 / 2.077655 (3.165648) | 2.617194 / 1.504120 (1.113074) | 2.400740 / 1.541195 (0.859545) | 2.534692 / 1.468490 (1.066202) | 0.585825 / 4.584777 (-3.998952) | 4.879766 / 3.745712 (1.134054) | 2.377419 / 5.269862 (-2.892443) | 1.460711 / 4.565676 (-3.104966) | 0.075572 / 0.424275 (-0.348703) | 0.013650 / 0.007607 (0.006042) | 0.697103 / 0.226044 (0.471058) | 6.444984 / 2.268929 (4.176055) | 3.227662 / 55.444624 (-52.216963) | 2.875163 / 6.876477 (-4.001314) | 2.860953 / 2.142072 (0.718881) | 0.718908 / 4.805227 (-4.086319) | 0.158005 / 6.500664 (-6.342659) | 0.077581 / 0.075469 (0.002112) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.653027 / 1.841788 (-0.188760) | 18.789342 / 8.074308 (10.715034) | 16.762678 / 10.191392 (6.571286) | 0.238920 / 0.680424 (-0.441504) | 0.020698 / 0.534201 (-0.513502) | 0.512634 / 0.579283 (-0.066649) | 0.542235 / 0.434364 (0.107871) | 0.626634 / 0.540337 (0.086297) | 0.753324 / 1.386936 (-0.633612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f978ad8bec6e5e77868c6ffcc6f514354a03901d \"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) | 0.005737 / 0.011353 (-0.005616) | 0.003767 / 0.011008 (-0.007241) | 0.097792 / 0.038508 (0.059284) | 0.028466 / 0.023109 (0.005356) | 0.317703 / 0.275898 (0.041805) | 0.359512 / 0.323480 (0.036032) | 0.003428 / 0.007986 (-0.004558) | 0.002848 / 0.004328 (-0.001481) | 0.075668 / 0.004250 (0.071418) | 0.037165 / 0.037052 (0.000113) | 0.329539 / 0.258489 (0.071050) | 0.361365 / 0.293841 (0.067524) | 0.024777 / 0.128546 (-0.103769) | 0.008324 / 0.075646 (-0.067323) | 0.317346 / 0.419271 (-0.101926) | 0.043296 / 0.043533 (-0.000237) | 0.315318 / 0.255139 (0.060179) | 0.347641 / 0.283200 (0.064441) | 0.089551 / 0.141683 (-0.052132) | 1.506335 / 1.452155 (0.054180) | 1.573931 / 1.492716 (0.081215) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208041 / 0.018006 (0.190034) | 0.428198 / 0.000490 (0.427708) | 0.002568 / 0.000200 (0.002369) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023745 / 0.037411 (-0.013667) | 0.096256 / 0.014526 (0.081730) | 0.104917 / 0.176557 (-0.071639) | 0.164341 / 0.737135 (-0.572794) | 0.107972 / 0.296338 (-0.188367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453995 / 0.215209 (0.238786) | 4.546892 / 2.077655 (2.469238) | 2.185498 / 1.504120 (0.681378) | 1.989156 / 1.541195 (0.447962) | 2.053443 / 1.468490 (0.584953) | 0.559940 / 4.584777 (-4.024837) | 3.420759 / 3.745712 (-0.324954) | 1.771528 / 5.269862 (-3.498333) | 1.139692 / 4.565676 (-3.425984) | 0.067686 / 0.424275 (-0.356589) | 0.011729 / 0.007607 (0.004122) | 0.558001 / 0.226044 (0.331957) | 5.583886 / 2.268929 (3.314957) | 2.678726 / 55.444624 (-52.765899) | 2.324127 / 6.876477 (-4.552350) | 2.472805 / 2.142072 (0.330733) | 0.663163 / 4.805227 (-4.142065) | 0.134892 / 6.500664 (-6.365772) | 0.066722 / 0.075469 (-0.008747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195200 / 1.841788 (-0.646587) | 13.602517 / 8.074308 (5.528209) | 14.036344 / 10.191392 (3.844952) | 0.143759 / 0.680424 (-0.536665) | 0.017215 / 0.534201 (-0.516986) | 0.383749 / 0.579283 (-0.195534) | 0.388229 / 0.434364 (-0.046134) | 0.469366 / 0.540337 (-0.070971) | 0.560408 / 1.386936 (-0.826528) |\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) | 0.005953 / 0.011353 (-0.005400) | 0.003840 / 0.011008 (-0.007168) | 0.077481 / 0.038508 (0.038973) | 0.028318 / 0.023109 (0.005209) | 0.403991 / 0.275898 (0.128093) | 0.433374 / 0.323480 (0.109894) | 0.003572 / 0.007986 (-0.004414) | 0.003033 / 0.004328 (-0.001295) | 0.075873 / 0.004250 (0.071623) | 0.039321 / 0.037052 (0.002269) | 0.416790 / 0.258489 (0.158301) | 0.459368 / 0.293841 (0.165527) | 0.025270 / 0.128546 (-0.103276) | 0.008574 / 0.075646 (-0.067072) | 0.083376 / 0.419271 (-0.335896) | 0.043206 / 0.043533 (-0.000327) | 0.404831 / 0.255139 (0.149692) | 0.418559 / 0.283200 (0.135360) | 0.099135 / 0.141683 (-0.042548) | 1.501315 / 1.452155 (0.049160) | 1.583912 / 1.492716 (0.091195) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241510 / 0.018006 (0.223504) | 0.410473 / 0.000490 (0.409983) | 0.001857 / 0.000200 (0.001657) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025366 / 0.037411 (-0.012045) | 0.103353 / 0.014526 (0.088828) | 0.107934 / 0.176557 (-0.068622) | 0.162388 / 0.737135 (-0.574747) | 0.113550 / 0.296338 (-0.182789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463529 / 0.215209 (0.248320) | 4.657688 / 2.077655 (2.580034) | 2.455088 / 1.504120 (0.950968) | 2.304833 / 1.541195 (0.763638) | 2.317520 / 1.468490 (0.849029) | 0.563395 / 4.584777 (-4.021382) | 3.408489 / 3.745712 (-0.337223) | 2.636379 / 5.269862 (-2.633482) | 1.425355 / 4.565676 (-3.140322) | 0.068335 / 0.424275 (-0.355940) | 0.011713 / 0.007607 (0.004106) | 0.550230 / 0.226044 (0.324186) | 5.519843 / 2.268929 (3.250915) | 2.864986 / 55.444624 (-52.579639) | 2.604821 / 6.876477 (-4.271655) | 2.701501 / 2.142072 (0.559428) | 0.668193 / 4.805227 (-4.137034) | 0.134739 / 6.500664 (-6.365925) | 0.067110 / 0.075469 (-0.008359) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.326358 / 1.841788 (-0.515430) | 14.184172 / 8.074308 (6.109864) | 14.139245 / 10.191392 (3.947853) | 0.151881 / 0.680424 (-0.528542) | 0.016718 / 0.534201 (-0.517483) | 0.367035 / 0.579283 (-0.212248) | 0.393512 / 0.434364 (-0.040852) | 0.441261 / 0.540337 (-0.099076) | 0.533907 / 1.386936 (-0.853029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#54098759d023f0b3e8eccd2dd98d46a1c6d19cce \"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) | 0.006275 / 0.011353 (-0.005078) | 0.003980 / 0.011008 (-0.007028) | 0.097617 / 0.038508 (0.059109) | 0.034089 / 0.023109 (0.010980) | 0.297381 / 0.275898 (0.021483) | 0.330106 / 0.323480 (0.006626) | 0.003838 / 0.007986 (-0.004148) | 0.004042 / 0.004328 (-0.000287) | 0.074305 / 0.004250 (0.070055) | 0.048318 / 0.037052 (0.011265) | 0.295585 / 0.258489 (0.037096) | 0.346924 / 0.293841 (0.053083) | 0.027397 / 0.128546 (-0.101150) | 0.008452 / 0.075646 (-0.067194) | 0.326837 / 0.419271 (-0.092435) | 0.049515 / 0.043533 (0.005982) | 0.303931 / 0.255139 (0.048792) | 0.317647 / 0.283200 (0.034447) | 0.098280 / 0.141683 (-0.043403) | 1.442603 / 1.452155 (-0.009552) | 1.524050 / 1.492716 (0.031334) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215095 / 0.018006 (0.197089) | 0.437662 / 0.000490 (0.437173) | 0.009771 / 0.000200 (0.009571) | 0.000401 / 0.000054 (0.000346) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027169 / 0.037411 (-0.010243) | 0.111383 / 0.014526 (0.096857) | 0.116163 / 0.176557 (-0.060394) | 0.173134 / 0.737135 (-0.564001) | 0.122376 / 0.296338 (-0.173962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398332 / 0.215209 (0.183123) | 3.974166 / 2.077655 (1.896511) | 1.793847 / 1.504120 (0.289727) | 1.615117 / 1.541195 (0.073922) | 1.660288 / 1.468490 (0.191798) | 0.523833 / 4.584777 (-4.060944) | 3.704273 / 3.745712 (-0.041439) | 1.873308 / 5.269862 (-3.396554) | 1.203546 / 4.565676 (-3.362131) | 0.064949 / 0.424275 (-0.359326) | 0.011830 / 0.007607 (0.004223) | 0.497294 / 0.226044 (0.271250) | 4.948663 / 2.268929 (2.679735) | 2.233391 / 55.444624 (-53.211234) | 1.903208 / 6.876477 (-4.973269) | 2.067908 / 2.142072 (-0.074164) | 0.644256 / 4.805227 (-4.160971) | 0.142798 / 6.500664 (-6.357866) | 0.064734 / 0.075469 (-0.010735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.172313 / 1.841788 (-0.669475) | 14.665853 / 8.074308 (6.591545) | 13.147051 / 10.191392 (2.955659) | 0.139338 / 0.680424 (-0.541086) | 0.017452 / 0.534201 (-0.516749) | 0.395660 / 0.579283 (-0.183623) | 0.410138 / 0.434364 (-0.024226) | 0.460357 / 0.540337 (-0.079980) | 0.555670 / 1.386936 (-0.831266) |\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) | 0.006247 / 0.011353 (-0.005106) | 0.004098 / 0.011008 (-0.006910) | 0.075050 / 0.038508 (0.036542) | 0.033232 / 0.023109 (0.010122) | 0.384139 / 0.275898 (0.108241) | 0.420865 / 0.323480 (0.097385) | 0.003889 / 0.007986 (-0.004096) | 0.003336 / 0.004328 (-0.000993) | 0.073837 / 0.004250 (0.069587) | 0.048775 / 0.037052 (0.011723) | 0.386373 / 0.258489 (0.127884) | 0.421718 / 0.293841 (0.127878) | 0.027553 / 0.128546 (-0.100993) | 0.008724 / 0.075646 (-0.066922) | 0.080970 / 0.419271 (-0.338302) | 0.045981 / 0.043533 (0.002448) | 0.364381 / 0.255139 (0.109242) | 0.391203 / 0.283200 (0.108004) | 0.101681 / 0.141683 (-0.040002) | 1.469533 / 1.452155 (0.017378) | 1.562016 / 1.492716 (0.069300) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222318 / 0.018006 (0.204312) | 0.441395 / 0.000490 (0.440905) | 0.000408 / 0.000200 (0.000208) | 0.000057 / 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) | 0.030291 / 0.037411 (-0.007120) | 0.114053 / 0.014526 (0.099527) | 0.123124 / 0.176557 (-0.053433) | 0.173474 / 0.737135 (-0.563661) | 0.129946 / 0.296338 (-0.166393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430342 / 0.215209 (0.215133) | 4.309782 / 2.077655 (2.232128) | 2.110668 / 1.504120 (0.606548) | 1.922881 / 1.541195 (0.381687) | 1.993562 / 1.468490 (0.525072) | 0.523682 / 4.584777 (-4.061095) | 3.774152 / 3.745712 (0.028440) | 3.354783 / 5.269862 (-1.915079) | 1.489793 / 4.565676 (-3.075884) | 0.065169 / 0.424275 (-0.359107) | 0.011626 / 0.007607 (0.004019) | 0.539126 / 0.226044 (0.313081) | 5.372593 / 2.268929 (3.103664) | 2.570652 / 55.444624 (-52.873973) | 2.253353 / 6.876477 (-4.623123) | 2.312876 / 2.142072 (0.170804) | 0.644241 / 4.805227 (-4.160986) | 0.138326 / 6.500664 (-6.362338) | 0.064491 / 0.075469 (-0.010979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344164 / 1.841788 (-0.497624) | 15.124679 / 8.074308 (7.050371) | 14.799310 / 10.191392 (4.607918) | 0.149054 / 0.680424 (-0.531370) | 0.017564 / 0.534201 (-0.516637) | 0.394593 / 0.579283 (-0.184690) | 0.428768 / 0.434364 (-0.005596) | 0.468235 / 0.540337 (-0.072103) | 0.557384 / 1.386936 (-0.829552) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8bfac259e2b5047bb8a0cdcefc8357477ebf93c \"CML watermark\")\n", "@albertvillanova could you take a look at this one ? It directly follows the arrow formatting PR", "I added tests for the `__array__` case which lets you go from any tensor format to any other tensor format.\r\n\r\nI also properly deprecated format_type and added a warning message.", "<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) | 0.007838 / 0.011353 (-0.003515) | 0.005177 / 0.011008 (-0.005831) | 0.131058 / 0.038508 (0.092550) | 0.035959 / 0.023109 (0.012850) | 0.414071 / 0.275898 (0.138173) | 0.429628 / 0.323480 (0.106148) | 0.005151 / 0.007986 (-0.002834) | 0.003979 / 0.004328 (-0.000349) | 0.103209 / 0.004250 (0.098958) | 0.046200 / 0.037052 (0.009148) | 0.414020 / 0.258489 (0.155531) | 0.475748 / 0.293841 (0.181907) | 0.041031 / 0.128546 (-0.087515) | 0.014462 / 0.075646 (-0.061185) | 0.423706 / 0.419271 (0.004434) | 0.063488 / 0.043533 (0.019955) | 0.404937 / 0.255139 (0.149798) | 0.404973 / 0.283200 (0.121773) | 0.114982 / 0.141683 (-0.026701) | 1.911867 / 1.452155 (0.459713) | 1.925274 / 1.492716 (0.432557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284656 / 0.018006 (0.266650) | 0.588329 / 0.000490 (0.587840) | 0.007092 / 0.000200 (0.006892) | 0.000143 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025136 / 0.037411 (-0.012275) | 0.109514 / 0.014526 (0.094988) | 0.117953 / 0.176557 (-0.058603) | 0.195454 / 0.737135 (-0.541682) | 0.134243 / 0.296338 (-0.162096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584045 / 0.215209 (0.368836) | 6.456922 / 2.077655 (4.379267) | 2.759728 / 1.504120 (1.255608) | 2.260913 / 1.541195 (0.719718) | 2.292535 / 1.468490 (0.824045) | 0.906873 / 4.584777 (-3.677904) | 5.554455 / 3.745712 (1.808743) | 4.881557 / 5.269862 (-0.388305) | 2.509121 / 4.565676 (-2.056555) | 0.107191 / 0.424275 (-0.317084) | 0.014684 / 0.007607 (0.007077) | 0.761625 / 0.226044 (0.535580) | 7.582708 / 2.268929 (5.313780) | 3.150160 / 55.444624 (-52.294464) | 2.792284 / 6.876477 (-4.084193) | 2.881321 / 2.142072 (0.739248) | 1.108353 / 4.805227 (-3.696874) | 0.220129 / 6.500664 (-6.280535) | 0.075877 / 0.075469 (0.000408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465743 / 1.841788 (-0.376045) | 17.679219 / 8.074308 (9.604911) | 18.929399 / 10.191392 (8.738007) | 0.219488 / 0.680424 (-0.460935) | 0.028435 / 0.534201 (-0.505766) | 0.512623 / 0.579283 (-0.066660) | 0.619983 / 0.434364 (0.185619) | 0.603430 / 0.540337 (0.063092) | 0.730416 / 1.386936 (-0.656520) |\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) | 0.008285 / 0.011353 (-0.003068) | 0.005771 / 0.011008 (-0.005237) | 0.106444 / 0.038508 (0.067936) | 0.035078 / 0.023109 (0.011969) | 0.441198 / 0.275898 (0.165300) | 0.536279 / 0.323480 (0.212800) | 0.004561 / 0.007986 (-0.003424) | 0.006623 / 0.004328 (0.002294) | 0.102392 / 0.004250 (0.098142) | 0.051736 / 0.037052 (0.014684) | 0.479113 / 0.258489 (0.220624) | 0.535088 / 0.293841 (0.241247) | 0.041805 / 0.128546 (-0.086741) | 0.014031 / 0.075646 (-0.061615) | 0.115795 / 0.419271 (-0.303477) | 0.057913 / 0.043533 (0.014380) | 0.435847 / 0.255139 (0.180708) | 0.524831 / 0.283200 (0.241632) | 0.119419 / 0.141683 (-0.022263) | 1.835577 / 1.452155 (0.383423) | 1.936990 / 1.492716 (0.444273) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288422 / 0.018006 (0.270416) | 0.569776 / 0.000490 (0.569287) | 0.005652 / 0.000200 (0.005452) | 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) | 0.034632 / 0.037411 (-0.002779) | 0.136217 / 0.014526 (0.121691) | 0.139468 / 0.176557 (-0.037089) | 0.206804 / 0.737135 (-0.530331) | 0.148733 / 0.296338 (-0.147606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.667728 / 0.215209 (0.452518) | 6.548972 / 2.077655 (4.471317) | 3.051537 / 1.504120 (1.547417) | 2.581173 / 1.541195 (1.039978) | 2.653443 / 1.468490 (1.184953) | 0.906606 / 4.584777 (-3.678171) | 5.704384 / 3.745712 (1.958672) | 2.848618 / 5.269862 (-2.421244) | 1.821402 / 4.565676 (-2.744274) | 0.118018 / 0.424275 (-0.306257) | 0.014821 / 0.007607 (0.007214) | 0.821967 / 0.226044 (0.595923) | 8.165818 / 2.268929 (5.896889) | 3.744509 / 55.444624 (-51.700116) | 2.901097 / 6.876477 (-3.975380) | 3.018068 / 2.142072 (0.875996) | 1.106155 / 4.805227 (-3.699072) | 0.263118 / 6.500664 (-6.237546) | 0.088508 / 0.075469 (0.013039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.725860 / 1.841788 (-0.115928) | 19.411246 / 8.074308 (11.336938) | 20.807499 / 10.191392 (10.616107) | 0.238417 / 0.680424 (-0.442007) | 0.026550 / 0.534201 (-0.507651) | 0.500715 / 0.579283 (-0.078568) | 0.615547 / 0.434364 (0.181183) | 0.614361 / 0.540337 (0.074023) | 0.720365 / 1.386936 (-0.666571) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae2e77f8344cdcc1c4c876f67936bec33087b19a \"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) | 0.006640 / 0.011353 (-0.004713) | 0.004079 / 0.011008 (-0.006930) | 0.100555 / 0.038508 (0.062046) | 0.037318 / 0.023109 (0.014209) | 0.320050 / 0.275898 (0.044152) | 0.358860 / 0.323480 (0.035380) | 0.003828 / 0.007986 (-0.004158) | 0.003215 / 0.004328 (-0.001113) | 0.076577 / 0.004250 (0.072326) | 0.048080 / 0.037052 (0.011028) | 0.324759 / 0.258489 (0.066270) | 0.361862 / 0.293841 (0.068021) | 0.030759 / 0.128546 (-0.097787) | 0.008998 / 0.075646 (-0.066648) | 0.329105 / 0.419271 (-0.090167) | 0.051407 / 0.043533 (0.007875) | 0.311067 / 0.255139 (0.055928) | 0.334401 / 0.283200 (0.051201) | 0.098307 / 0.141683 (-0.043376) | 1.500931 / 1.452155 (0.048776) | 1.574646 / 1.492716 (0.081930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219080 / 0.018006 (0.201073) | 0.447117 / 0.000490 (0.446627) | 0.009091 / 0.000200 (0.008891) | 0.000396 / 0.000054 (0.000341) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026048 / 0.037411 (-0.011363) | 0.112714 / 0.014526 (0.098188) | 0.116426 / 0.176557 (-0.060131) | 0.172187 / 0.737135 (-0.564948) | 0.121707 / 0.296338 (-0.174632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.358898 / 0.215209 (0.143689) | 3.589212 / 2.077655 (1.511557) | 1.677927 / 1.504120 (0.173807) | 1.515861 / 1.541195 (-0.025334) | 1.598479 / 1.468490 (0.129989) | 0.478265 / 4.584777 (-4.106512) | 3.834982 / 3.745712 (0.089270) | 1.933815 / 5.269862 (-3.336047) | 1.122769 / 4.565676 (-3.442908) | 0.066984 / 0.424275 (-0.357291) | 0.011276 / 0.007607 (0.003669) | 0.512530 / 0.226044 (0.286486) | 5.112667 / 2.268929 (2.843739) | 2.266336 / 55.444624 (-53.178288) | 1.929671 / 6.876477 (-4.946806) | 2.127231 / 2.142072 (-0.014842) | 0.671307 / 4.805227 (-4.133920) | 0.143919 / 6.500664 (-6.356745) | 0.066086 / 0.075469 (-0.009383) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208767 / 1.841788 (-0.633021) | 15.008415 / 8.074308 (6.934106) | 14.085442 / 10.191392 (3.894050) | 0.184164 / 0.680424 (-0.496260) | 0.017619 / 0.534201 (-0.516582) | 0.394443 / 0.579283 (-0.184840) | 0.457653 / 0.434364 (0.023289) | 0.473169 / 0.540337 (-0.067169) | 0.571332 / 1.386936 (-0.815604) |\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) | 0.007009 / 0.011353 (-0.004344) | 0.004330 / 0.011008 (-0.006678) | 0.077462 / 0.038508 (0.038954) | 0.034780 / 0.023109 (0.011671) | 0.395573 / 0.275898 (0.119675) | 0.425444 / 0.323480 (0.101964) | 0.004119 / 0.007986 (-0.003866) | 0.003597 / 0.004328 (-0.000731) | 0.075209 / 0.004250 (0.070958) | 0.050871 / 0.037052 (0.013819) | 0.402990 / 0.258489 (0.144500) | 0.445334 / 0.293841 (0.151493) | 0.032492 / 0.128546 (-0.096054) | 0.009066 / 0.075646 (-0.066581) | 0.083073 / 0.419271 (-0.336198) | 0.051661 / 0.043533 (0.008128) | 0.395207 / 0.255139 (0.140068) | 0.409556 / 0.283200 (0.126356) | 0.106035 / 0.141683 (-0.035648) | 1.506255 / 1.452155 (0.054101) | 1.598724 / 1.492716 (0.106008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194733 / 0.018006 (0.176727) | 0.444920 / 0.000490 (0.444431) | 0.002402 / 0.000200 (0.002202) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030464 / 0.037411 (-0.006947) | 0.119153 / 0.014526 (0.104627) | 0.126081 / 0.176557 (-0.050476) | 0.179692 / 0.737135 (-0.557444) | 0.131834 / 0.296338 (-0.164504) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440153 / 0.215209 (0.224944) | 4.397504 / 2.077655 (2.319850) | 2.138320 / 1.504120 (0.634200) | 1.950596 / 1.541195 (0.409402) | 2.079792 / 1.468490 (0.611302) | 0.537606 / 4.584777 (-4.047171) | 3.689420 / 3.745712 (-0.056292) | 2.960732 / 5.269862 (-2.309129) | 1.585652 / 4.565676 (-2.980024) | 0.066102 / 0.424275 (-0.358173) | 0.011429 / 0.007607 (0.003821) | 0.537011 / 0.226044 (0.310967) | 5.342171 / 2.268929 (3.073242) | 2.624446 / 55.444624 (-52.820179) | 2.313311 / 6.876477 (-4.563166) | 2.389166 / 2.142072 (0.247094) | 0.657547 / 4.805227 (-4.147681) | 0.141640 / 6.500664 (-6.359025) | 0.066102 / 0.075469 (-0.009367) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.130471 / 1.841788 (-0.711317) | 14.824792 / 8.074308 (6.750484) | 13.436463 / 10.191392 (3.245071) | 0.155688 / 0.680424 (-0.524736) | 0.015811 / 0.534201 (-0.518390) | 0.355623 / 0.579283 (-0.223660) | 0.450604 / 0.434364 (0.016241) | 0.472542 / 0.540337 (-0.067796) | 0.563584 / 1.386936 (-0.823352) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#963ff6de6eae80a6de4aabf0092eb3dfbe43096e \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5850). All of your documentation changes will be reflected on that endpoint.", "Good idea. @mariosasko!!!\r\n\r\nPlease note that before this PR, the files are not evenly distributed for archives: `_generate_examples` gets a list of iterators, one for each archive (uncompressed to a directory).", "This change could create silent problems when loading files with extensions that are not listed here. For example\r\n\r\n```python\r\nload_dataset(\"text\", data_files=[\"20230515.log\"])\r\n```\r\n\r\nwouldn't even log anything to say that the file was ignored.\r\n\r\nMaybe it's possible to do this at data files patterns resolution ?\r\n\r\ne.g. in get_data_patterns_in_dataset_repository / get_data_patterns_locally we could return patterns that include the most common extension", "@lhoestq the issue you evoke (.log files skipped by text builder if .log is not added to .txt as supported extension) persists whether you perform the skip at the pattern resolution or in the builder itself.\r\n\r\nThe solution is to add the .log extension (besides the .txt) as supported by text, independently of where we perform the skip (at pattern resolution or in the builder itself).\r\n\r\nAdditionally, at the time we call for pattern resolution, we do not know the builder class yet, so that we cannot pass specific file extensions. First we call data files pattern resolution, and afterwards we call `infer_module_for_data_files` and then know the builder class.", "> @lhoestq the issue you evoke (.log files skipped by text builder if .log is not added to .txt as supported extension) persists whether you perform the skip at the pattern resolution or in the builder itself.\r\n\r\nNo I simply think it's a bad breaking change to not support\r\n\r\n```python\r\nload_dataset(\"<builder_name>\", data_files=[\"path/to/file_with_unknown_or_no_extension\"])\r\n# or\r\nload_dataset(\"<builder_name>\", data_files=[\"https://url.to/file_with_unknown_or_no_extension\"])\r\n```\r\n\r\nIdk if it's the easiest solution, but maybe it's possible to do the change only when inferring the patterns of dataset repositories. This should avoid this breaking change.\r\n\r\nFor example it could do something like that in `get_data_patterns_locally`\r\n\r\n```python\r\n Input:\r\n\r\n my_dataset_repository/\r\n ├── README.md\r\n ├── banner.png\r\n ├── data0.csv\r\n ├── data1.csv\r\n └── data2.csv\r\n\r\n Output:\r\n\r\n {\"train\": [\"**.csv\"]}\r\n```\r\n\r\ninstead of \r\n\r\n```python\r\n Output:\r\n\r\n {\"train\": [\"**\"]}\r\n```", "I agree with @lhoestq - it should still be possible to request parsing a file with a specific builder even if the file's extension is \"invalid\" for the builder, and only ignore non-supported file formats when inferring the patterns.", "Therefore, if I understand correctly, what you suggest is:\r\n- if the user passes a packaged builder to `load_dataset` (e.g. `load_dataset(\"csv\",...`), then the *passed* `data_files` should not be filtered to remove unsupported extensions. No breaking change in this case\r\n- if the user passes a no-script repo/folder to `load_dataset` (e.g. `load_dataset(\"my_dataset_repository\",...`), then the *inferred* data files should be filtered to remove the extensions that are not supported by the inferred module name builder\r\n - if the user passes `data_files` as well, then I guess these should not be filtered, to avoid any breaking change as in the first case above", "Yes that would be ideal imo !", "I think this now fulfills all the requirements.", "I find it a bit confusing to still be able to pass data_files that are going to be silently ignored based on the value of `only_supported_extensions`. My suggestion was to have the right data files pattern, not to filter a posteriori (sorry if my last message was confusing).\r\n\r\nHaving the right data files pattern would also allow users to inspect what's actually being loaded with\r\n```\r\nload_dataset_builder(...).config.data_files\r\n```\r\nand it would list exactly what data files are used." ]

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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) | 0.007565 / 0.011353 (-0.003788) | 0.005361 / 0.011008 (-0.005647) | 0.098963 / 0.038508 (0.060455) | 0.034271 / 0.023109 (0.011162) | 0.323421 / 0.275898 (0.047523) | 0.348495 / 0.323480 (0.025015) | 0.006244 / 0.007986 (-0.001741) | 0.004215 / 0.004328 (-0.000113) | 0.073614 / 0.004250 (0.069364) | 0.049334 / 0.037052 (0.012282) | 0.315277 / 0.258489 (0.056788) | 0.354325 / 0.293841 (0.060484) | 0.035001 / 0.128546 (-0.093545) | 0.012149 / 0.075646 (-0.063497) | 0.335614 / 0.419271 (-0.083657) | 0.050532 / 0.043533 (0.006999) | 0.308500 / 0.255139 (0.053361) | 0.324620 / 0.283200 (0.041421) | 0.110241 / 0.141683 (-0.031442) | 1.443923 / 1.452155 (-0.008232) | 1.559289 / 1.492716 (0.066573) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207629 / 0.018006 (0.189622) | 0.433251 / 0.000490 (0.432762) | 0.003021 / 0.000200 (0.002821) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028312 / 0.037411 (-0.009100) | 0.111829 / 0.014526 (0.097303) | 0.127099 / 0.176557 (-0.049458) | 0.184702 / 0.737135 (-0.552433) | 0.125062 / 0.296338 (-0.171277) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399451 / 0.215209 (0.184242) | 3.966528 / 2.077655 (1.888874) | 1.826004 / 1.504120 (0.321884) | 1.669547 / 1.541195 (0.128353) | 1.751584 / 1.468490 (0.283094) | 0.688308 / 4.584777 (-3.896469) | 3.813275 / 3.745712 (0.067562) | 3.181554 / 5.269862 (-2.088307) | 1.750566 / 4.565676 (-2.815111) | 0.085038 / 0.424275 (-0.339237) | 0.011992 / 0.007607 (0.004385) | 0.502374 / 0.226044 (0.276330) | 4.970614 / 2.268929 (2.701686) | 2.309617 / 55.444624 (-53.135007) | 2.012427 / 6.876477 (-4.864050) | 2.156348 / 2.142072 (0.014276) | 0.834415 / 4.805227 (-3.970812) | 0.167912 / 6.500664 (-6.332752) | 0.065711 / 0.075469 (-0.009758) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223132 / 1.841788 (-0.618656) | 15.126753 / 8.074308 (7.052445) | 14.829184 / 10.191392 (4.637792) | 0.142582 / 0.680424 (-0.537842) | 0.017483 / 0.534201 (-0.516718) | 0.429768 / 0.579283 (-0.149516) | 0.422745 / 0.434364 (-0.011619) | 0.508813 / 0.540337 (-0.031525) | 0.618716 / 1.386936 (-0.768220) |\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) | 0.007749 / 0.011353 (-0.003604) | 0.005433 / 0.011008 (-0.005576) | 0.076223 / 0.038508 (0.037715) | 0.036334 / 0.023109 (0.013225) | 0.375339 / 0.275898 (0.099441) | 0.413674 / 0.323480 (0.090194) | 0.006207 / 0.007986 (-0.001778) | 0.004085 / 0.004328 (-0.000244) | 0.076154 / 0.004250 (0.071904) | 0.050324 / 0.037052 (0.013271) | 0.382919 / 0.258489 (0.124429) | 0.442508 / 0.293841 (0.148667) | 0.035951 / 0.128546 (-0.092595) | 0.012067 / 0.075646 (-0.063580) | 0.087649 / 0.419271 (-0.331623) | 0.048786 / 0.043533 (0.005253) | 0.373541 / 0.255139 (0.118402) | 0.400437 / 0.283200 (0.117237) | 0.102622 / 0.141683 (-0.039061) | 1.472443 / 1.452155 (0.020288) | 1.580178 / 1.492716 (0.087462) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222105 / 0.018006 (0.204098) | 0.445465 / 0.000490 (0.444975) | 0.003671 / 0.000200 (0.003471) | 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) | 0.030808 / 0.037411 (-0.006603) | 0.116687 / 0.014526 (0.102161) | 0.124972 / 0.176557 (-0.051584) | 0.175621 / 0.737135 (-0.561514) | 0.129029 / 0.296338 (-0.167310) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434627 / 0.215209 (0.219418) | 4.330268 / 2.077655 (2.252613) | 2.140266 / 1.504120 (0.636146) | 1.960705 / 1.541195 (0.419510) | 2.035949 / 1.468490 (0.567459) | 0.696830 / 4.584777 (-3.887947) | 3.790468 / 3.745712 (0.044756) | 3.194112 / 5.269862 (-2.075750) | 1.577728 / 4.565676 (-2.987948) | 0.085445 / 0.424275 (-0.338830) | 0.012207 / 0.007607 (0.004600) | 0.555199 / 0.226044 (0.329154) | 5.551539 / 2.268929 (3.282610) | 2.630917 / 55.444624 (-52.813707) | 2.383362 / 6.876477 (-4.493114) | 2.476301 / 2.142072 (0.334229) | 0.845773 / 4.805227 (-3.959455) | 0.169229 / 6.500664 (-6.331435) | 0.066064 / 0.075469 (-0.009405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277543 / 1.841788 (-0.564245) | 15.775637 / 8.074308 (7.701329) | 13.528588 / 10.191392 (3.337196) | 0.167428 / 0.680424 (-0.512996) | 0.017581 / 0.534201 (-0.516620) | 0.454472 / 0.579283 (-0.124811) | 0.427987 / 0.434364 (-0.006377) | 0.551512 / 0.540337 (0.011175) | 0.650811 / 1.386936 (-0.736125) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#96a6f5f526cc90330df597ae0097274742d5b84f \"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) | 0.009800 / 0.011353 (-0.001552) | 0.006443 / 0.011008 (-0.004565) | 0.144137 / 0.038508 (0.105629) | 0.037493 / 0.023109 (0.014383) | 0.482306 / 0.275898 (0.206408) | 0.467625 / 0.323480 (0.144145) | 0.006812 / 0.007986 (-0.001174) | 0.004810 / 0.004328 (0.000481) | 0.109047 / 0.004250 (0.104796) | 0.047169 / 0.037052 (0.010116) | 0.451253 / 0.258489 (0.192764) | 0.511339 / 0.293841 (0.217498) | 0.055583 / 0.128546 (-0.072963) | 0.021810 / 0.075646 (-0.053836) | 0.426522 / 0.419271 (0.007250) | 0.070282 / 0.043533 (0.026749) | 0.469631 / 0.255139 (0.214492) | 0.484951 / 0.283200 (0.201751) | 0.117370 / 0.141683 (-0.024313) | 1.809917 / 1.452155 (0.357763) | 1.882659 / 1.492716 (0.389943) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223843 / 0.018006 (0.205837) | 0.549216 / 0.000490 (0.548726) | 0.007120 / 0.000200 (0.006920) | 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) | 0.033057 / 0.037411 (-0.004354) | 0.128242 / 0.014526 (0.113716) | 0.140906 / 0.176557 (-0.035650) | 0.213122 / 0.737135 (-0.524013) | 0.148115 / 0.296338 (-0.148224) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.638712 / 0.215209 (0.423503) | 6.383684 / 2.077655 (4.306029) | 2.477020 / 1.504120 (0.972900) | 2.129190 / 1.541195 (0.587996) | 2.230503 / 1.468490 (0.762013) | 1.367167 / 4.584777 (-3.217610) | 5.570586 / 3.745712 (1.824873) | 5.462857 / 5.269862 (0.192996) | 2.990604 / 4.565676 (-1.575073) | 0.146543 / 0.424275 (-0.277732) | 0.016060 / 0.007607 (0.008453) | 0.812691 / 0.226044 (0.586646) | 7.928041 / 2.268929 (5.659112) | 3.329494 / 55.444624 (-52.115130) | 2.523452 / 6.876477 (-4.353025) | 2.672374 / 2.142072 (0.530302) | 1.598554 / 4.805227 (-3.206673) | 0.284727 / 6.500664 (-6.215937) | 0.080359 / 0.075469 (0.004889) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501112 / 1.841788 (-0.340675) | 17.553644 / 8.074308 (9.479335) | 22.704062 / 10.191392 (12.512670) | 0.225575 / 0.680424 (-0.454849) | 0.026531 / 0.534201 (-0.507670) | 0.520129 / 0.579283 (-0.059154) | 0.626220 / 0.434364 (0.191856) | 0.631740 / 0.540337 (0.091403) | 0.750611 / 1.386936 (-0.636325) |\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) | 0.009866 / 0.011353 (-0.001487) | 0.005733 / 0.011008 (-0.005275) | 0.111529 / 0.038508 (0.073021) | 0.042001 / 0.023109 (0.018891) | 0.458578 / 0.275898 (0.182680) | 0.507796 / 0.323480 (0.184316) | 0.006547 / 0.007986 (-0.001438) | 0.005611 / 0.004328 (0.001282) | 0.115321 / 0.004250 (0.111070) | 0.048741 / 0.037052 (0.011689) | 0.447611 / 0.258489 (0.189122) | 0.531830 / 0.293841 (0.237989) | 0.052176 / 0.128546 (-0.076370) | 0.022431 / 0.075646 (-0.053216) | 0.120709 / 0.419271 (-0.298562) | 0.067301 / 0.043533 (0.023769) | 0.460577 / 0.255139 (0.205438) | 0.497805 / 0.283200 (0.214605) | 0.121830 / 0.141683 (-0.019853) | 1.876436 / 1.452155 (0.424281) | 1.983491 / 1.492716 (0.490775) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230982 / 0.018006 (0.212976) | 0.540643 / 0.000490 (0.540153) | 0.004646 / 0.000200 (0.004446) | 0.000131 / 0.000054 (0.000077) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034230 / 0.037411 (-0.003181) | 0.136454 / 0.014526 (0.121928) | 0.143370 / 0.176557 (-0.033187) | 0.206752 / 0.737135 (-0.530384) | 0.148722 / 0.296338 (-0.147617) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.704667 / 0.215209 (0.489458) | 7.112079 / 2.077655 (5.034424) | 3.083916 / 1.504120 (1.579797) | 2.606388 / 1.541195 (1.065193) | 2.738505 / 1.468490 (1.270015) | 1.314897 / 4.584777 (-3.269880) | 5.764442 / 3.745712 (2.018729) | 3.491890 / 5.269862 (-1.777972) | 2.299983 / 4.565676 (-2.265693) | 0.169655 / 0.424275 (-0.254620) | 0.015251 / 0.007607 (0.007643) | 0.977230 / 0.226044 (0.751186) | 9.697773 / 2.268929 (7.428844) | 3.826928 / 55.444624 (-51.617697) | 3.108238 / 6.876477 (-3.768239) | 3.103242 / 2.142072 (0.961169) | 1.586645 / 4.805227 (-3.218582) | 0.287181 / 6.500664 (-6.213483) | 0.107332 / 0.075469 (0.031863) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712710 / 1.841788 (-0.129077) | 19.169403 / 8.074308 (11.095095) | 21.777301 / 10.191392 (11.585909) | 0.216918 / 0.680424 (-0.463506) | 0.026551 / 0.534201 (-0.507650) | 0.570383 / 0.579283 (-0.008900) | 0.643885 / 0.434364 (0.209521) | 0.673906 / 0.540337 (0.133568) | 0.824573 / 1.386936 (-0.562363) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ead18b6921c9576a3078d2fb685c38f1e1a4b8a \"CML watermark\")\n" ]

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[ "I wasn't sure to file this against transformers or datasets.", "[`KeyDataset`](https://github.com/huggingface/transformers/blob/7f8b909189547944617741d8d3c6c84504701693/src/transformers/pipelines/pt_utils.py#L296) doesn't support iterable datasets, so you either need to implement a version that does (and also indexing nested (translation) fields):\r\n\r\n```python\r\nfrom torch.utils.data import Dataset, IterableDataset\r\n\r\ndef build_key_fetcher(key: str):\r\n def _key_fetcher(item):\r\n for sub_key in key.split(\".\"):\r\n item = item[sub_key]\r\n return item\r\n return _key_fetcher\r\n\r\nclass KeyDataset(Dataset):\r\n def __new__(cls, dataset: Dataset, key: str):\r\n cls = _KeyIterableDataset if isinstance(dataset, IterableDataset) else _KeyMapDataset\r\n self = object.__new__(cls)\r\n self.dataset = dataset\r\n self.key = key\r\n self._key_fetcher = build_key_fetcher(key)\r\n return self\r\n\r\nclass _KeyMapDataset(KeyDataset):\r\n def __getitem__(self, i):\r\n return self._key_fetcher(self.dataset[i])\r\n \r\n def __len__(self):\r\n return len(self.dataset)\r\n\r\n\r\nclass _KeyIterableDataset(KeyDataset):\r\n def __iter__(self):\r\n for ex in self.dataset:\r\n yield self._key_fetcher(ex)\r\n\r\nks = KeyDataset(ds, \"translation.en\")\r\n```\r\n\r\nor use `IterableDataset`'s `map`:\r\n```python\r\ndef fetch_en_translation(ex):\r\n return {\"en\": ex[\"translation\"][\"en\"]}\r\nks = ds.map(fetch_en_translation, remove_columns=ds.column_names) \r\n```\r\n\r\ncc @sgugger: Perhaps the `KeyDataset` + PyTorch `IterableDataset` case should be supported by Transformers", "@mariosasko The map snippet didn't quite work, but gave me enough of a clue to get it working. The following snippet does work:\r\n```\r\ndef en_translation(x):\r\n return {\"en\":x['translation']['en']}\r\nks = ds.map(en_translation, remove_columns=['translation'])\r\ntest=[]\r\nfor x in iter(ks):\r\n test.append(x['en'])\r\nxx= mt(test)\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nI tried just returning `x['translation']['en`]` in the helper function instead of the dict, but that didn't give me an iterator over strings that pipeline would work with either.\r\n\r\n\r\nThe snippet as is gives the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1704, in main\r\n pdb._runscript(mainpyfile)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/pdb.py\", line 1573, in _runscript\r\n self.run(statement)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/bdb.py\", line 580, in run\r\n exec(cmd, globals, locals)\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/jlquinn/models/hf/ende.t5.pipe.py\", line 1, in <module>\r\n from transformers import pipeline\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 335, in __call__\r\n return super().__call__(*args, **kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 138, in __call__\r\n result = super().__call__(*args, **kwargs)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1027, in __call__\r\n return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/base.py\", line 1033, in run_single\r\n model_inputs = self.preprocess(inputs, **preprocess_params)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 287, in preprocess\r\n return super()._parse_and_tokenize(*args, truncation=truncation)\r\n File \"/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/text2text_generation.py\", line 100, in _parse_and_tokenize\r\n raise ValueError(\r\nValueError: `args[0]`: <datasets.iterable_dataset.IterableDataset object at 0x7f5fd38ef1c0> have the wrong format. The should be either of type `str` or type `list`\r\nUncaught exception. Entering post mortem debugging\r\nRunning 'cont' or 'step' will restart the program\r\n```\r\n", "So perhaps there's no bug exactly, but I would love to see two things: 1) improve the documentation to better understand what's really getting returned. 2) update the example provided of using transformer pipeline with a dataset to include the oddball case that translation appears to be.", "cc @Narsil ", "Hi,\r\n\r\nfor the original snippet, the issue is that `streaming` datasets are not countable (they have no len) and therefore `KeyDataset` cannot work with them ( KeyDataset is a dataset and therefore requires a length).\r\n\r\nI modified slightly the original snippet to make it work:\r\n\r\n```python\r\nfrom transformers import pipeline\r\nfrom transformers.pipelines.pt_utils import KeyDataset\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(path=\"wmt14\", name=\"fr-en\", split=\"test\", streaming=True)\r\nbs = 1\r\nmt = pipeline(\r\n \"translation_en_to_fr\", model=\"hf-internal-testing/tiny-random-T5ForConditionalGeneration\", batch_size=bs\r\n)\r\n\r\n\r\ndef ks(ds):\r\n for item in ds:\r\n yield item[\"translation\"][\"en\"]\r\n\r\n\r\n# print(f\"{ks}\")\r\nxx = mt(ks(ds))\r\nfor x in xx:\r\n print(x)\r\n```\r\n\r\nThis is what the first example in the docs suggests to use (as it's the most flexible): https://huggingface.co/docs/transformers/v4.29.1/en/pipeline_tutorial#using-pipelines-on-a-dataset\r\n\r\n`KeyDataset` really exists only to get a `sized` dataset to work nicer with `tqdm` for instance.\r\n\r\n@sgugger should we update the docs to remove `KeyDataset` entirely ? (We can add a note to pass manually the length of the data to tqdm so that the progress bar option can still be easy to use ?)\r\n", "Maybe moving `KeyDataset` later on in the guide and specify it's mostly for streaming then? Or is it also necessary for batch_size>1 (which is what the current doc implies)?", "Hmm\r\n\r\nIterator (`yield`) :\r\n- Not countable\r\n- Super flexible\r\n- Cannot use `num_workers>1` (threading requires indexing at the correct location, iterators require to iterate in order,so each thread would iterate over the full thing being genuinely a bad idea)\r\n- Can batch\r\n- tqdm doesn't show a nice progress bar (it has no total)\r\n\r\nKeyDataset (Or any PyTorch like Dataset returning the correct object for the pipeline):\r\n- Countable\r\n- Less flexible (not applicable to datasets with streaming), can only work on single keys. But should be easy to read and write your own (like @mariosasko did)\r\n- Works with `num_workers > 1` (Every worker can fetch exactly what's needed)\r\n- Can batch \r\n- tqdm shows a nice progress bar\r\n\r\nIn the docs, if we update all the examples to use iterators, and include an example with\r\n\r\n```\r\nfor item in tqdm.tqdm(pipe(iterator(), total=len(dataset))))\r\n```\r\n\r\nWe can save the biggest feature that doesn't work out of the box with iterators which is the tqdm progress bar.\r\n\r\n`num_workers>1` we can mention it, but it tends to be an issues only on CPU intensive loads, like image (and maybe audio)\r\n" ]

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[ "This is due to the slow resolution of the data files: https://github.com/huggingface/datasets/issues/5537.\r\n\r\nWe plan to switch to `huggingface_hub`'s `HfFileSystem` soon to make the resolution faster (will be up to 20x faster once we merge https://github.com/huggingface/huggingface_hub/pull/1443)\r\n\r\n", "You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.", "> You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.\r\n\r\nThat's unrelated to the problem discussed in this issue. ", "> > You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.\r\n> \r\n> That's unrelated to the problem discussed in this issue.\r\n\r\nSorry, I misunderstood it.", "Closing this issue as it has been addressed in `huggingface_hub`!\r\n\r\n(This now takes 25s to execute on my machine.)", "Thanks for the improvements! 🎉🎉\n\n25 seconds is better but still about 2500x slower than this _should_ be! Loading a tiny 1-2KB metadata file is all that would be necessary with a better design.", "Once we merge https://github.com/huggingface/huggingface_hub/pull/2103, this should only take a few seconds. \r\n\r\nFor the 2500x speed-up (without metadata files with pre-cached results), we wouldn't even be allowed to use `os.path` functions or `requests`/`aiohttp` for HTTP requests, so I don't think this is feasible for us as it would make the code unreadable.\r\n\r\nThe HF Datasets Hub is (almost) platform-agnostic, so you are free to implement your own library (in a faster language than Python) to achieve this kind of performance, and we would be happy to support it 🙂. " ]

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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) | 0.007592 / 0.011353 (-0.003761) | 0.005223 / 0.011008 (-0.005786) | 0.110218 / 0.038508 (0.071710) | 0.027644 / 0.023109 (0.004534) | 0.335063 / 0.275898 (0.059165) | 0.347102 / 0.323480 (0.023623) | 0.005107 / 0.007986 (-0.002878) | 0.003932 / 0.004328 (-0.000396) | 0.086095 / 0.004250 (0.081845) | 0.034735 / 0.037052 (-0.002317) | 0.329029 / 0.258489 (0.070540) | 0.370282 / 0.293841 (0.076441) | 0.043040 / 0.128546 (-0.085507) | 0.019626 / 0.075646 (-0.056021) | 0.336452 / 0.419271 (-0.082819) | 0.070365 / 0.043533 (0.026832) | 0.326881 / 0.255139 (0.071742) | 0.354984 / 0.283200 (0.071785) | 0.102605 / 0.141683 (-0.039077) | 1.459161 / 1.452155 (0.007007) | 1.453599 / 1.492716 (-0.039117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201021 / 0.018006 (0.183015) | 0.456415 / 0.000490 (0.455926) | 0.012349 / 0.000200 (0.012149) | 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) | 0.025199 / 0.037411 (-0.012213) | 0.098536 / 0.014526 (0.084010) | 0.107528 / 0.176557 (-0.069028) | 0.160492 / 0.737135 (-0.576643) | 0.108660 / 0.296338 (-0.187679) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.527020 / 0.215209 (0.311811) | 5.357635 / 2.077655 (3.279980) | 2.062930 / 1.504120 (0.558811) | 1.783009 / 1.541195 (0.241815) | 1.840225 / 1.468490 (0.371735) | 1.074278 / 4.584777 (-3.510499) | 4.710533 / 3.745712 (0.964821) | 2.611202 / 5.269862 (-2.658660) | 1.885487 / 4.565676 (-2.680189) | 0.123201 / 0.424275 (-0.301074) | 0.013880 / 0.007607 (0.006273) | 0.636511 / 0.226044 (0.410467) | 6.516075 / 2.268929 (4.247146) | 2.710138 / 55.444624 (-52.734486) | 2.046606 / 6.876477 (-4.829871) | 2.085907 / 2.142072 (-0.056166) | 1.199489 / 4.805227 (-3.605738) | 0.211668 / 6.500664 (-6.288996) | 0.075436 / 0.075469 (-0.000033) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219771 / 1.841788 (-0.622016) | 14.276215 / 8.074308 (6.201907) | 16.611529 / 10.191392 (6.420137) | 0.221091 / 0.680424 (-0.459333) | 0.024922 / 0.534201 (-0.509279) | 0.431906 / 0.579283 (-0.147377) | 0.518863 / 0.434364 (0.084499) | 0.515366 / 0.540337 (-0.024971) | 0.640411 / 1.386936 (-0.746525) |\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) | 0.007955 / 0.011353 (-0.003398) | 0.004813 / 0.011008 (-0.006196) | 0.076508 / 0.038508 (0.038000) | 0.028137 / 0.023109 (0.005028) | 0.349609 / 0.275898 (0.073711) | 0.403588 / 0.323480 (0.080109) | 0.005456 / 0.007986 (-0.002530) | 0.005677 / 0.004328 (0.001349) | 0.076882 / 0.004250 (0.072632) | 0.039832 / 0.037052 (0.002779) | 0.351930 / 0.258489 (0.093440) | 0.390492 / 0.293841 (0.096651) | 0.045199 / 0.128546 (-0.083347) | 0.023945 / 0.075646 (-0.051701) | 0.091140 / 0.419271 (-0.328132) | 0.057728 / 0.043533 (0.014195) | 0.370663 / 0.255139 (0.115524) | 0.380649 / 0.283200 (0.097449) | 0.097017 / 0.141683 (-0.044666) | 1.362248 / 1.452155 (-0.089907) | 1.445699 / 1.492716 (-0.047018) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204207 / 0.018006 (0.186201) | 0.474471 / 0.000490 (0.473981) | 0.012187 / 0.000200 (0.011987) | 0.000151 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023123 / 0.037411 (-0.014288) | 0.097547 / 0.014526 (0.083021) | 0.113877 / 0.176557 (-0.062679) | 0.158307 / 0.737135 (-0.578828) | 0.113876 / 0.296338 (-0.182462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.519920 / 0.215209 (0.304711) | 5.384371 / 2.077655 (3.306716) | 2.263276 / 1.504120 (0.759156) | 1.960604 / 1.541195 (0.419409) | 2.022864 / 1.468490 (0.554374) | 1.015430 / 4.584777 (-3.569347) | 4.774426 / 3.745712 (1.028714) | 4.549598 / 5.269862 (-0.720264) | 2.412638 / 4.565676 (-2.153039) | 0.117983 / 0.424275 (-0.306292) | 0.013340 / 0.007607 (0.005733) | 0.639826 / 0.226044 (0.413782) | 6.491622 / 2.268929 (4.222693) | 2.946892 / 55.444624 (-52.497732) | 2.376393 / 6.876477 (-4.500084) | 2.285592 / 2.142072 (0.143519) | 1.185049 / 4.805227 (-3.620178) | 0.204127 / 6.500664 (-6.296537) | 0.070285 / 0.075469 (-0.005184) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.439736 / 1.841788 (-0.402052) | 14.852087 / 8.074308 (6.777779) | 15.675742 / 10.191392 (5.484350) | 0.206577 / 0.680424 (-0.473846) | 0.031688 / 0.534201 (-0.502513) | 0.471003 / 0.579283 (-0.108280) | 0.505449 / 0.434364 (0.071085) | 0.506114 / 0.540337 (-0.034224) | 0.583752 / 1.386936 (-0.803184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6fcff8a031db39cb31079bc1fa62ded6e35218c \"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) | 0.012965 / 0.011353 (0.001612) | 0.006660 / 0.011008 (-0.004348) | 0.126060 / 0.038508 (0.087551) | 0.041154 / 0.023109 (0.018045) | 0.413428 / 0.275898 (0.137530) | 0.429035 / 0.323480 (0.105555) | 0.006680 / 0.007986 (-0.001305) | 0.005063 / 0.004328 (0.000734) | 0.092161 / 0.004250 (0.087911) | 0.056092 / 0.037052 (0.019039) | 0.421460 / 0.258489 (0.162971) | 0.450291 / 0.293841 (0.156450) | 0.050820 / 0.128546 (-0.077726) | 0.021392 / 0.075646 (-0.054255) | 0.426915 / 0.419271 (0.007643) | 0.064908 / 0.043533 (0.021375) | 0.406769 / 0.255139 (0.151630) | 0.434344 / 0.283200 (0.151144) | 0.127967 / 0.141683 (-0.013716) | 1.922414 / 1.452155 (0.470260) | 1.940717 / 1.492716 (0.448000) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288024 / 0.018006 (0.270017) | 0.615859 / 0.000490 (0.615369) | 0.007095 / 0.000200 (0.006895) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028182 / 0.037411 (-0.009230) | 0.126277 / 0.014526 (0.111752) | 0.131687 / 0.176557 (-0.044870) | 0.206191 / 0.737135 (-0.530944) | 0.141799 / 0.296338 (-0.154539) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.631580 / 0.215209 (0.416371) | 6.141942 / 2.077655 (4.064287) | 2.476721 / 1.504120 (0.972602) | 2.128850 / 1.541195 (0.587655) | 2.236468 / 1.468490 (0.767978) | 1.188665 / 4.584777 (-3.396112) | 5.481179 / 3.745712 (1.735467) | 3.120333 / 5.269862 (-2.149529) | 2.365889 / 4.565676 (-2.199787) | 0.145081 / 0.424275 (-0.279194) | 0.015866 / 0.007607 (0.008259) | 0.795650 / 0.226044 (0.569605) | 7.595289 / 2.268929 (5.326361) | 3.174418 / 55.444624 (-52.270207) | 2.905207 / 6.876477 (-3.971270) | 2.428263 / 2.142072 (0.286191) | 1.408900 / 4.805227 (-3.396328) | 0.265485 / 6.500664 (-6.235179) | 0.083882 / 0.075469 (0.008413) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517025 / 1.841788 (-0.324762) | 18.110288 / 8.074308 (10.035980) | 20.810003 / 10.191392 (10.618611) | 0.210380 / 0.680424 (-0.470044) | 0.030180 / 0.534201 (-0.504021) | 0.523453 / 0.579283 (-0.055830) | 0.603896 / 0.434364 (0.169532) | 0.622554 / 0.540337 (0.082216) | 0.737973 / 1.386936 (-0.648963) |\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) | 0.009795 / 0.011353 (-0.001558) | 0.006269 / 0.011008 (-0.004739) | 0.099938 / 0.038508 (0.061430) | 0.035162 / 0.023109 (0.012052) | 0.506353 / 0.275898 (0.230455) | 0.527804 / 0.323480 (0.204324) | 0.007211 / 0.007986 (-0.000775) | 0.005498 / 0.004328 (0.001169) | 0.098325 / 0.004250 (0.094075) | 0.054513 / 0.037052 (0.017461) | 0.525764 / 0.258489 (0.267274) | 0.576699 / 0.293841 (0.282858) | 0.052800 / 0.128546 (-0.075747) | 0.021192 / 0.075646 (-0.054454) | 0.117676 / 0.419271 (-0.301596) | 0.055415 / 0.043533 (0.011882) | 0.516746 / 0.255139 (0.261607) | 0.528417 / 0.283200 (0.245217) | 0.116947 / 0.141683 (-0.024735) | 1.757864 / 1.452155 (0.305709) | 2.043632 / 1.492716 (0.550916) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284018 / 0.018006 (0.266011) | 0.595086 / 0.000490 (0.594596) | 0.001945 / 0.000200 (0.001745) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032255 / 0.037411 (-0.005157) | 0.128201 / 0.014526 (0.113676) | 0.139189 / 0.176557 (-0.037367) | 0.199750 / 0.737135 (-0.537385) | 0.149406 / 0.296338 (-0.146933) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.652184 / 0.215209 (0.436975) | 6.453319 / 2.077655 (4.375664) | 2.831566 / 1.504120 (1.327446) | 2.453064 / 1.541195 (0.911869) | 2.622056 / 1.468490 (1.153566) | 1.191279 / 4.584777 (-3.393498) | 5.504720 / 3.745712 (1.759007) | 5.916900 / 5.269862 (0.647038) | 2.974400 / 4.565676 (-1.591277) | 0.142851 / 0.424275 (-0.281424) | 0.015241 / 0.007607 (0.007634) | 0.917537 / 0.226044 (0.691493) | 8.277645 / 2.268929 (6.008717) | 3.700495 / 55.444624 (-51.744130) | 3.047127 / 6.876477 (-3.829350) | 3.093216 / 2.142072 (0.951143) | 1.413529 / 4.805227 (-3.391698) | 0.259395 / 6.500664 (-6.241270) | 0.083144 / 0.075469 (0.007675) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632240 / 1.841788 (-0.209548) | 18.687403 / 8.074308 (10.613095) | 20.134091 / 10.191392 (9.942699) | 0.238792 / 0.680424 (-0.441632) | 0.027645 / 0.534201 (-0.506556) | 0.518200 / 0.579283 (-0.061083) | 0.613535 / 0.434364 (0.179171) | 0.631414 / 0.540337 (0.091076) | 0.724658 / 1.386936 (-0.662278) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac7caa5e195ad76c7e8ef98914813383f4f668cf \"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) | 0.006228 / 0.011353 (-0.005125) | 0.004517 / 0.011008 (-0.006492) | 0.097998 / 0.038508 (0.059490) | 0.027903 / 0.023109 (0.004793) | 0.309789 / 0.275898 (0.033891) | 0.332784 / 0.323480 (0.009304) | 0.004757 / 0.007986 (-0.003228) | 0.003348 / 0.004328 (-0.000981) | 0.075193 / 0.004250 (0.070942) | 0.037382 / 0.037052 (0.000330) | 0.306929 / 0.258489 (0.048440) | 0.347304 / 0.293841 (0.053463) | 0.030235 / 0.128546 (-0.098312) | 0.011516 / 0.075646 (-0.064131) | 0.322249 / 0.419271 (-0.097023) | 0.044125 / 0.043533 (0.000592) | 0.303874 / 0.255139 (0.048735) | 0.326808 / 0.283200 (0.043608) | 0.088137 / 0.141683 (-0.053546) | 1.521426 / 1.452155 (0.069272) | 1.573823 / 1.492716 (0.081107) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203204 / 0.018006 (0.185197) | 0.402247 / 0.000490 (0.401757) | 0.003146 / 0.000200 (0.002946) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022955 / 0.037411 (-0.014456) | 0.096059 / 0.014526 (0.081533) | 0.105552 / 0.176557 (-0.071004) | 0.167459 / 0.737135 (-0.569676) | 0.106723 / 0.296338 (-0.189615) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454626 / 0.215209 (0.239417) | 4.556346 / 2.077655 (2.478691) | 2.220349 / 1.504120 (0.716229) | 2.011820 / 1.541195 (0.470625) | 2.048149 / 1.468490 (0.579659) | 0.697583 / 4.584777 (-3.887194) | 3.428394 / 3.745712 (-0.317318) | 1.863872 / 5.269862 (-3.405989) | 1.159691 / 4.565676 (-3.405985) | 0.082598 / 0.424275 (-0.341677) | 0.012202 / 0.007607 (0.004594) | 0.555617 / 0.226044 (0.329572) | 5.545481 / 2.268929 (3.276553) | 2.650850 / 55.444624 (-52.793775) | 2.305864 / 6.876477 (-4.570613) | 2.392252 / 2.142072 (0.250179) | 0.808512 / 4.805227 (-3.996716) | 0.152086 / 6.500664 (-6.348578) | 0.066440 / 0.075469 (-0.009029) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.211789 / 1.841788 (-0.629999) | 13.515546 / 8.074308 (5.441238) | 13.859870 / 10.191392 (3.668478) | 0.150335 / 0.680424 (-0.530088) | 0.016578 / 0.534201 (-0.517623) | 0.379145 / 0.579283 (-0.200138) | 0.393735 / 0.434364 (-0.040628) | 0.460219 / 0.540337 (-0.080118) | 0.555896 / 1.386936 (-0.831040) |\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) | 0.006402 / 0.011353 (-0.004950) | 0.004558 / 0.011008 (-0.006450) | 0.077332 / 0.038508 (0.038824) | 0.027955 / 0.023109 (0.004846) | 0.407877 / 0.275898 (0.131979) | 0.432552 / 0.323480 (0.109072) | 0.004850 / 0.007986 (-0.003135) | 0.003329 / 0.004328 (-0.000999) | 0.075767 / 0.004250 (0.071517) | 0.035940 / 0.037052 (-0.001112) | 0.419544 / 0.258489 (0.161055) | 0.454672 / 0.293841 (0.160831) | 0.030461 / 0.128546 (-0.098085) | 0.011536 / 0.075646 (-0.064111) | 0.085774 / 0.419271 (-0.333498) | 0.039408 / 0.043533 (-0.004125) | 0.389909 / 0.255139 (0.134770) | 0.403287 / 0.283200 (0.120088) | 0.088385 / 0.141683 (-0.053298) | 1.596840 / 1.452155 (0.144686) | 1.659296 / 1.492716 (0.166580) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216349 / 0.018006 (0.198342) | 0.394969 / 0.000490 (0.394479) | 0.000408 / 0.000200 (0.000208) | 0.000059 / 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) | 0.024346 / 0.037411 (-0.013066) | 0.099609 / 0.014526 (0.085084) | 0.106779 / 0.176557 (-0.069778) | 0.156889 / 0.737135 (-0.580247) | 0.110625 / 0.296338 (-0.185714) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443809 / 0.215209 (0.228600) | 4.450524 / 2.077655 (2.372870) | 2.151694 / 1.504120 (0.647574) | 1.952521 / 1.541195 (0.411326) | 1.963320 / 1.468490 (0.494830) | 0.709291 / 4.584777 (-3.875486) | 3.415708 / 3.745712 (-0.330005) | 1.850498 / 5.269862 (-3.419363) | 1.164355 / 4.565676 (-3.401321) | 0.084977 / 0.424275 (-0.339298) | 0.013284 / 0.007607 (0.005677) | 0.555103 / 0.226044 (0.329059) | 5.583587 / 2.268929 (3.314658) | 2.608754 / 55.444624 (-52.835870) | 2.264079 / 6.876477 (-4.612398) | 2.272455 / 2.142072 (0.130382) | 0.820849 / 4.805227 (-3.984379) | 0.155063 / 6.500664 (-6.345601) | 0.069709 / 0.075469 (-0.005760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293285 / 1.841788 (-0.548503) | 14.181867 / 8.074308 (6.107559) | 13.021280 / 10.191392 (2.829888) | 0.130101 / 0.680424 (-0.550323) | 0.016461 / 0.534201 (-0.517740) | 0.383651 / 0.579283 (-0.195632) | 0.387353 / 0.434364 (-0.047011) | 0.443351 / 0.540337 (-0.096986) | 0.529448 / 1.386936 (-0.857488) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05145d50b5bb1b7b42b76516cd6492d4868c46ba \"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) | 0.007513 / 0.011353 (-0.003840) | 0.005328 / 0.011008 (-0.005680) | 0.096937 / 0.038508 (0.058429) | 0.036230 / 0.023109 (0.013121) | 0.325808 / 0.275898 (0.049910) | 0.363601 / 0.323480 (0.040121) | 0.006130 / 0.007986 (-0.001855) | 0.004352 / 0.004328 (0.000023) | 0.073543 / 0.004250 (0.069293) | 0.054114 / 0.037052 (0.017062) | 0.328952 / 0.258489 (0.070463) | 0.366943 / 0.293841 (0.073102) | 0.035768 / 0.128546 (-0.092778) | 0.012505 / 0.075646 (-0.063142) | 0.332260 / 0.419271 (-0.087012) | 0.066673 / 0.043533 (0.023140) | 0.323866 / 0.255139 (0.068727) | 0.341311 / 0.283200 (0.058112) | 0.129898 / 0.141683 (-0.011785) | 1.456890 / 1.452155 (0.004735) | 1.546933 / 1.492716 (0.054217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299236 / 0.018006 (0.281229) | 0.496134 / 0.000490 (0.495645) | 0.004233 / 0.000200 (0.004033) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028089 / 0.037411 (-0.009322) | 0.104723 / 0.014526 (0.090197) | 0.121032 / 0.176557 (-0.055525) | 0.179916 / 0.737135 (-0.557220) | 0.126628 / 0.296338 (-0.169711) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403497 / 0.215209 (0.188288) | 4.052481 / 2.077655 (1.974827) | 1.804419 / 1.504120 (0.300299) | 1.619833 / 1.541195 (0.078638) | 1.732438 / 1.468490 (0.263948) | 0.702474 / 4.584777 (-3.882303) | 3.808973 / 3.745712 (0.063261) | 3.682764 / 5.269862 (-1.587098) | 1.919184 / 4.565676 (-2.646493) | 0.086638 / 0.424275 (-0.337637) | 0.012265 / 0.007607 (0.004658) | 0.501273 / 0.226044 (0.275229) | 5.010918 / 2.268929 (2.741989) | 2.278114 / 55.444624 (-53.166510) | 1.942266 / 6.876477 (-4.934211) | 2.101982 / 2.142072 (-0.040091) | 0.847622 / 4.805227 (-3.957606) | 0.172973 / 6.500664 (-6.327691) | 0.066884 / 0.075469 (-0.008586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187609 / 1.841788 (-0.654179) | 15.089485 / 8.074308 (7.015177) | 14.787398 / 10.191392 (4.596006) | 0.168254 / 0.680424 (-0.512170) | 0.018266 / 0.534201 (-0.515935) | 0.423204 / 0.579283 (-0.156079) | 0.435238 / 0.434364 (0.000874) | 0.512473 / 0.540337 (-0.027864) | 0.618091 / 1.386936 (-0.768845) |\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) | 0.007249 / 0.011353 (-0.004104) | 0.005297 / 0.011008 (-0.005711) | 0.076428 / 0.038508 (0.037920) | 0.033565 / 0.023109 (0.010456) | 0.373756 / 0.275898 (0.097858) | 0.407405 / 0.323480 (0.083925) | 0.006100 / 0.007986 (-0.001886) | 0.006482 / 0.004328 (0.002153) | 0.075884 / 0.004250 (0.071633) | 0.055338 / 0.037052 (0.018286) | 0.378721 / 0.258489 (0.120232) | 0.427065 / 0.293841 (0.133224) | 0.036285 / 0.128546 (-0.092261) | 0.012460 / 0.075646 (-0.063186) | 0.087641 / 0.419271 (-0.331630) | 0.048199 / 0.043533 (0.004666) | 0.386785 / 0.255139 (0.131646) | 0.386702 / 0.283200 (0.103503) | 0.110087 / 0.141683 (-0.031596) | 1.511204 / 1.452155 (0.059050) | 1.585671 / 1.492716 (0.092954) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313558 / 0.018006 (0.295552) | 0.496991 / 0.000490 (0.496501) | 0.001492 / 0.000200 (0.001292) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031814 / 0.037411 (-0.005597) | 0.113486 / 0.014526 (0.098960) | 0.125208 / 0.176557 (-0.051348) | 0.174469 / 0.737135 (-0.562666) | 0.131095 / 0.296338 (-0.165244) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439282 / 0.215209 (0.224073) | 4.362286 / 2.077655 (2.284631) | 2.153271 / 1.504120 (0.649151) | 1.990482 / 1.541195 (0.449288) | 2.103322 / 1.468490 (0.634831) | 0.692522 / 4.584777 (-3.892254) | 3.861931 / 3.745712 (0.116219) | 3.686294 / 5.269862 (-1.583567) | 1.734525 / 4.565676 (-2.831152) | 0.085057 / 0.424275 (-0.339218) | 0.012116 / 0.007607 (0.004509) | 0.547996 / 0.226044 (0.321952) | 5.513835 / 2.268929 (3.244906) | 2.723829 / 55.444624 (-52.720795) | 2.404715 / 6.876477 (-4.471761) | 2.514768 / 2.142072 (0.372696) | 0.834972 / 4.805227 (-3.970255) | 0.168261 / 6.500664 (-6.332403) | 0.066464 / 0.075469 (-0.009005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259923 / 1.841788 (-0.581865) | 15.646277 / 8.074308 (7.571969) | 13.097598 / 10.191392 (2.906206) | 0.187991 / 0.680424 (-0.492433) | 0.017358 / 0.534201 (-0.516843) | 0.427979 / 0.579283 (-0.151304) | 0.425747 / 0.434364 (-0.008617) | 0.501907 / 0.540337 (-0.038431) | 0.595106 / 1.386936 (-0.791830) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db56f7f0d2f0b99af4da17d388c205152504c7d9 \"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) | 0.009378 / 0.011353 (-0.001975) | 0.006434 / 0.011008 (-0.004574) | 0.120603 / 0.038508 (0.082095) | 0.042929 / 0.023109 (0.019820) | 0.366853 / 0.275898 (0.090955) | 0.436795 / 0.323480 (0.113315) | 0.007730 / 0.007986 (-0.000256) | 0.004842 / 0.004328 (0.000513) | 0.091058 / 0.004250 (0.086808) | 0.058256 / 0.037052 (0.021203) | 0.378692 / 0.258489 (0.120203) | 0.467384 / 0.293841 (0.173543) | 0.042948 / 0.128546 (-0.085598) | 0.015172 / 0.075646 (-0.060475) | 0.409225 / 0.419271 (-0.010046) | 0.083672 / 0.043533 (0.040140) | 0.390088 / 0.255139 (0.134949) | 0.406965 / 0.283200 (0.123765) | 0.142132 / 0.141683 (0.000449) | 1.765737 / 1.452155 (0.313582) | 1.895419 / 1.492716 (0.402703) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244052 / 0.018006 (0.226046) | 0.553383 / 0.000490 (0.552893) | 0.006798 / 0.000200 (0.006598) | 0.000227 / 0.000054 (0.000173) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032032 / 0.037411 (-0.005380) | 0.129990 / 0.014526 (0.115464) | 0.140338 / 0.176557 (-0.036219) | 0.212155 / 0.737135 (-0.524980) | 0.147395 / 0.296338 (-0.148943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478760 / 0.215209 (0.263551) | 4.751335 / 2.077655 (2.673680) | 2.164755 / 1.504120 (0.660635) | 1.944288 / 1.541195 (0.403094) | 2.077657 / 1.468490 (0.609167) | 0.818519 / 4.584777 (-3.766258) | 4.689013 / 3.745712 (0.943301) | 2.484079 / 5.269862 (-2.785782) | 1.788632 / 4.565676 (-2.777044) | 0.100484 / 0.424275 (-0.323791) | 0.013838 / 0.007607 (0.006231) | 0.589650 / 0.226044 (0.363605) | 5.859461 / 2.268929 (3.590533) | 2.670025 / 55.444624 (-52.774599) | 2.688709 / 6.876477 (-4.187768) | 2.408060 / 2.142072 (0.265988) | 0.972107 / 4.805227 (-3.833120) | 0.194425 / 6.500664 (-6.306239) | 0.076077 / 0.075469 (0.000608) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430150 / 1.841788 (-0.411638) | 17.710507 / 8.074308 (9.636199) | 16.210789 / 10.191392 (6.019397) | 0.163940 / 0.680424 (-0.516484) | 0.020295 / 0.534201 (-0.513906) | 0.472596 / 0.579283 (-0.106687) | 0.483107 / 0.434364 (0.048743) | 0.585269 / 0.540337 (0.044931) | 0.705526 / 1.386936 (-0.681410) |\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) | 0.008864 / 0.011353 (-0.002489) | 0.006095 / 0.011008 (-0.004913) | 0.088702 / 0.038508 (0.050194) | 0.041596 / 0.023109 (0.018486) | 0.453515 / 0.275898 (0.177617) | 0.476217 / 0.323480 (0.152737) | 0.007574 / 0.007986 (-0.000412) | 0.004727 / 0.004328 (0.000398) | 0.087271 / 0.004250 (0.083021) | 0.059631 / 0.037052 (0.022578) | 0.449379 / 0.258489 (0.190890) | 0.494436 / 0.293841 (0.200595) | 0.043448 / 0.128546 (-0.085098) | 0.014580 / 0.075646 (-0.061067) | 0.103836 / 0.419271 (-0.315435) | 0.057537 / 0.043533 (0.014004) | 0.449359 / 0.255139 (0.194220) | 0.447577 / 0.283200 (0.164377) | 0.123600 / 0.141683 (-0.018083) | 1.748448 / 1.452155 (0.296294) | 1.902116 / 1.492716 (0.409399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237214 / 0.018006 (0.219207) | 0.497648 / 0.000490 (0.497158) | 0.003519 / 0.000200 (0.003319) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034477 / 0.037411 (-0.002934) | 0.132627 / 0.014526 (0.118101) | 0.139721 / 0.176557 (-0.036836) | 0.195705 / 0.737135 (-0.541430) | 0.150762 / 0.296338 (-0.145577) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521306 / 0.215209 (0.306097) | 5.184982 / 2.077655 (3.107328) | 2.503979 / 1.504120 (0.999859) | 2.301054 / 1.541195 (0.759860) | 2.352713 / 1.468490 (0.884222) | 0.819804 / 4.584777 (-3.764973) | 4.584011 / 3.745712 (0.838299) | 2.497311 / 5.269862 (-2.772550) | 1.561262 / 4.565676 (-3.004414) | 0.101814 / 0.424275 (-0.322461) | 0.014078 / 0.007607 (0.006471) | 0.666564 / 0.226044 (0.440520) | 6.616379 / 2.268929 (4.347450) | 3.263892 / 55.444624 (-52.180732) | 2.891774 / 6.876477 (-3.984703) | 2.945260 / 2.142072 (0.803188) | 1.014379 / 4.805227 (-3.790848) | 0.201762 / 6.500664 (-6.298902) | 0.078012 / 0.075469 (0.002543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567808 / 1.841788 (-0.273980) | 19.096552 / 8.074308 (11.022244) | 15.522285 / 10.191392 (5.330893) | 0.226568 / 0.680424 (-0.453856) | 0.021078 / 0.534201 (-0.513123) | 0.501686 / 0.579283 (-0.077597) | 0.517575 / 0.434364 (0.083211) | 0.589685 / 0.540337 (0.049348) | 0.705053 / 1.386936 (-0.681883) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db56f7f0d2f0b99af4da17d388c205152504c7d9 \"CML watermark\")\n" ]

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[ "Hi ! You can try to use the [Image](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Image) type which [decodes images on-the-fly](https://huggingface.co/docs/datasets/v2.12.0/en/about_dataset_features#image-feature) into pytorch tensors :)\r\n\r\n```python\r\nds = Dataset.from_dict({\"tensor\":a}).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 5.04 s, sys: 96.5 ms, total: 5.14 s\r\n# Wall time: 5.14 s\r\n# 10000\r\n```\r\n\r\n```python\r\nfeatures = Features({\"tensor\": Image()})\r\nds = Dataset.from_dict({\"tensor\":a}, features=features).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 1.86 s, sys: 49 ms, total: 1.91 s\r\n# Wall time: 1.9 s\r\n# 10000\r\n```\r\n\r\n-> Speed x2.7\r\n\r\nAnd if you want to keep using arrays of integers, consider using the [Array2D](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Array2D) or [Array3D](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Array3D) types which are even faster (since it doesn't decode images):\r\n\r\n```python\r\nfeatures = Features({\"tensor\": Array2D(shape=(100, 224), dtype=\"float32\")})\r\nds = Dataset.from_dict({\"tensor\":a}, features=features).with_format(\"torch\")\r\n%time sum(1 for _ in ds)\r\n# CPU times: user 828 ms, sys: 68.4 ms, total: 896 ms\r\n# Wall time: 897 ms\r\n# 10000\r\n```\r\n\r\n-> Speed x5.7\r\n\r\nBatching also speeds up a lot\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\ndl = DataLoader(ds, batch_size=100)\r\n%time sum(1 for _ in dl)\r\n# CPU times: user 564 ms, sys: 83.5 ms, total: 648 ms\r\n# Wall time: 579 ms\r\n# 100\r\n```\r\n\r\n-> Speed x8.9\r\n\r\n```python\r\n%time sum(1 for _ in ds.iter(batch_size=100))\r\n# CPU times: user 119 ms, sys: 96.8 ms, total: 215 ms\r\n# Wall time: 117 ms\r\n# 100\r\n```\r\n\r\n-> Speed x46", "Anyway, regarding the speed difference between numpy and pytorch, I think the issue is that we first convert numpy sub-arrays to pytorch and then consolidate into one tensor, while we should to the opposite. Indeed converting a numpy array to pytorch has a fix cost that seems to cause a slow down. The current pipeline is\r\n\r\n```\r\narrow -> nested numpy arrays -> lists of torch tensors -> one torch tensor\r\n```\r\n\r\nand we should do\r\n\r\n```\r\narrow -> nested numpy arrays -> one numpy array -> one torch tensor\r\n```", "I have a similar issue: iterating over a dataset takes 5s without applying any transform, but takes ~30s after applying a transform.\r\nHere is the minimum code to reproduce the problem\r\n\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport torchvision \r\nfrom torchvision.transforms import ToTensor, Normalize\r\n\r\n\r\n#################################\r\n# Without transform\r\n#################################\r\n \r\ntrain_dataset = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\n\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"img\", \"fine_label\"])\r\n\r\ntrain_loader= DataLoader(\r\n train_dataset,\r\n batch_size=100,\r\n pin_memory=False,\r\n shuffle=True,\r\n num_workers=8,\r\n)\r\n\r\nfor batch in tqdm(train_loader, desc=\"Loading data, no transform\"):\r\n pass\r\n\r\n\r\n#################################\r\n# With transform\r\n#################################\r\n\r\ntransform_func = torchvision.transforms.Compose([\r\n ToTensor(), \r\n Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] \r\n)\r\n \r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"img\": transform_func(x[\"img\"])},\r\n)\r\n\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"img\", \"fine_label\"])\r\n\r\n\r\ntrain_loader= DataLoader(\r\n train_dataset,\r\n batch_size=100,\r\n pin_memory=False,\r\n shuffle=True,\r\n num_workers=8,\r\n)\r\n\r\n\r\nfor batch in tqdm(train_loader, desc=\"Loading data after transform\"):\r\n pass \r\n```\r\n\r\nI have also tried converting the Image column to an Array3D\r\n```python\r\nimg_shape = train_dataset[0][\"img\"].shape\r\n\r\nfeatures = train_dataset.features.copy()\r\nfeatures[\"x\"] = Array3D(shape=img_shape, dtype=\"float32\")\r\n\r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"x\": np.array(x[\"img\"], dtype=np.uint8)},\r\n features=features,\r\n)\r\ntrain_dataset.cast_column(\"x\", Array3D(shape=img_shape, dtype=\"float32\"))\r\ntrain_dataset.set_format(type=\"numpy\", columns=[\"x\", \"fine_label\"])\r\n```\r\nbut to no avail. Any clue?", "Thanks! I convert my dataset feature to Array3D and this speed became awesome!" ]

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[ "Please report this in the `transformers` repo, as it's not related to `datasets`" ]

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[ "Transferring this issue as it's related to the 🤗 Datasets library ", "Hi @surya-narayanan! Could you provide some code snippet?", "This method has been recently added to the `IterableDataset`, so you need to update the `datasets`' installation (`pip install -U datasets`) to use it." ]

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[ "Transferring as this is relating to the 🤗 Datasets library", "You need to use `IterableDatasetDict` instead of `DatasetDict` for iterable datasets." ]

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[ "As the name says, `load_from_disk` load the data from your disk. If the data is hosted on S3, it is first downloaded locally and then loaded from your disk.\r\n\r\nThere is a discussion on streaming data from S3 here though: #5281 ", "@lhoestq \r\nThanks for your comment. I have checked out the discussion before and attempted at replicating the mentioned changes in the main branch (#5580). What I found was that if a dataset is saved using `save_to_disk`, it cannot be read by `load_dataset`. The error message asks me to to use `load_from_disk` instead. What would be the correct way of saving the data in this scenario?", "Using `push_to_hub` you can save the dataset on the HF Hub as parquet files, and reload it / stream it using `load_dataset` :)\r\n\r\nIf you want to save your dataset somewhere else you can use `.to_parquet` to get a parquet file. If your dataset is big it's usually recommended to shard it into multi parquet files (around 1GB each).", "@lhoestq \r\nThanks for the explanation. Appreciate it. I'll try this out.", "@lhoestq\r\nI tried the method you mentioned. This the current scenario I'm facing:\r\n\r\n- The parquet file can be read from disk and streaming can be enabled.\r\n- The parquet file can be read from `s3` (local MinIO).\r\n- When `streaming=True` is enabled for `s3`, I get the error mentioned below:\r\n\r\n```\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:502, in S3FileSystem.set_session(self, refresh, kwargs)\r\n 500 conf = AioConfig(**config_kwargs)\r\n 501 if self.session is None:\r\n--> 502 self.session = aiobotocore.session.AioSession(**self.kwargs)\r\n 504 for parameters in (config_kwargs, self.kwargs, init_kwargs, client_kwargs):\r\n 505 for option in (\"region_name\", \"endpoint_url\"):\r\n\r\nTypeError: __init__() got an unexpected keyword argument 'headers'\r\n```\r\n\r\nDoes this mean there is a bug in the main branch?", "Streaming from S3 is still experimental, there might be a few bugs unfortunately.\r\n\r\nCan you share the full stack trace ?", "@lhoestq \r\nSure, here you go:\r\n\r\n```python\r\nTypeError Traceback (most recent call last)\r\nCell In[8], line 1\r\n----> 1 dataset = load_dataset(\"parquet\", data_files=[\"s3://<bucket name>/<data folder>/data-parquet\"], storage_options=fs.storage_options, streaming=True)\r\n\r\nFile ~/.../datasets/src/datasets/load.py:1790, 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, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile ~/.../datasets/src/datasets/builder.py:1264, in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1257 dl_manager = StreamingDownloadManager(\r\n 1258 base_path=base_path or self.base_path,\r\n 1259 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1260 dataset_name=self.name,\r\n 1261 data_dir=self.config.data_dir,\r\n 1262 )\r\n 1263 self._check_manual_download(dl_manager)\r\n-> 1264 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1265 # By default, return all splits\r\n 1266 if split is None:\r\n\r\nFile ~/.../datasets/src/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager)\r\n 32 if not self.config.data_files:\r\n 33 raise ValueError(f\"At least one data file must be specified, but got data_files={self.config.data_files}\")\r\n---> 34 data_files = dl_manager.download_and_extract(self.config.data_files)\r\n 35 if isinstance(data_files, (str, list, tuple)):\r\n 36 files = data_files\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1087, in StreamingDownloadManager.download_and_extract(self, url_or_urls)\r\n 1069 def download_and_extract(self, url_or_urls):\r\n 1070 \"\"\"Prepare given `url_or_urls` for streaming (add extraction protocol).\r\n 1071 \r\n 1072 This is the lazy version of `DownloadManager.download_and_extract` for streaming.\r\n (...)\r\n 1085 url(s): (`str` or `list` or `dict`), URL(s) to stream data from matching the given input `url_or_urls`.\r\n 1086 \"\"\"\r\n-> 1087 return self.extract(self.download(url_or_urls))\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1039, in StreamingDownloadManager.extract(self, url_or_urls)\r\n 1020 def extract(self, url_or_urls):\r\n 1021 \"\"\"Add extraction protocol for given url(s) for streaming.\r\n 1022 \r\n 1023 This is the lazy version of `DownloadManager.extract` for streaming.\r\n (...)\r\n 1037 ```\r\n 1038 \"\"\"\r\n-> 1039 urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)\r\n 1040 return urlpaths\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:443, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 441 num_proc = 1\r\n 442 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 443 mapped = [\r\n 444 _single_map_nested((function, obj, types, None, True, None))\r\n 445 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 446 ]\r\n 447 else:\r\n 448 num_proc = num_proc if num_proc <= len(iterable) else len(iterable)\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:444, in <listcomp>(.0)\r\n 441 num_proc = 1\r\n 442 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 443 mapped = [\r\n--> 444 _single_map_nested((function, obj, types, None, True, None))\r\n 445 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 446 ]\r\n 447 else:\r\n 448 num_proc = num_proc if num_proc <= len(iterable) else len(iterable)\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:363, in _single_map_nested(args)\r\n 361 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n 362 else:\r\n--> 363 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n 364 if isinstance(data_struct, list):\r\n 365 return mapped\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:363, in <listcomp>(.0)\r\n 361 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n 362 else:\r\n--> 363 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n 364 if isinstance(data_struct, list):\r\n 365 return mapped\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:346, in _single_map_nested(args)\r\n 344 # Singleton first to spare some computation\r\n 345 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 346 return function(data_struct)\r\n 348 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 349 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1044, in StreamingDownloadManager._extract(self, urlpath)\r\n 1042 def _extract(self, urlpath: str) -> str:\r\n 1043 urlpath = str(urlpath)\r\n-> 1044 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n 1045 # get inner file: zip://train-00000.json.gz::https://foo.bar/data.zip -> zip://train-00000.json.gz\r\n 1046 path = urlpath.split(\"::\")[0]\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:433, in _get_extraction_protocol(urlpath, use_auth_token)\r\n 431 else:\r\n 432 urlpath, kwargs = urlpath, {}\r\n--> 433 with fsspec.open(urlpath, **kwargs) as f:\r\n 434 return _get_extraction_protocol_with_magic_number(f)\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/core.py:102, in OpenFile.__enter__(self)\r\n 99 def __enter__(self):\r\n 100 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 102 f = self.fs.open(self.path, mode=mode)\r\n 104 self.fobjects = [f]\r\n 106 if self.compression is not None:\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1199, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1197 else:\r\n 1198 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1199 f = self._open(\r\n 1200 path,\r\n 1201 mode=mode,\r\n 1202 block_size=block_size,\r\n 1203 autocommit=ac,\r\n 1204 cache_options=cache_options,\r\n 1205 **kwargs,\r\n 1206 )\r\n 1207 if compression is not None:\r\n 1208 from fsspec.compression import compr\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:659, in S3FileSystem._open(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, cache_options, **kwargs)\r\n 656 if cache_type is None:\r\n 657 cache_type = self.default_cache_type\r\n--> 659 return S3File(\r\n 660 self,\r\n 661 path,\r\n 662 mode,\r\n 663 block_size=block_size,\r\n 664 acl=acl,\r\n 665 version_id=version_id,\r\n 666 fill_cache=fill_cache,\r\n 667 s3_additional_kwargs=kw,\r\n 668 cache_type=cache_type,\r\n 669 autocommit=autocommit,\r\n 670 requester_pays=requester_pays,\r\n 671 cache_options=cache_options,\r\n 672 )\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:2043, in S3File.__init__(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays, cache_options)\r\n 2041 self.details = s3.info(path)\r\n 2042 self.version_id = self.details.get(\"VersionId\")\r\n-> 2043 super().__init__(\r\n 2044 s3,\r\n 2045 path,\r\n 2046 mode,\r\n 2047 block_size,\r\n 2048 autocommit=autocommit,\r\n 2049 cache_type=cache_type,\r\n 2050 cache_options=cache_options,\r\n 2051 )\r\n 2052 self.s3 = self.fs # compatibility\r\n 2054 # when not using autocommit we want to have transactional state to manage\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1555, in AbstractBufferedFile.__init__(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 1553 self.size = size\r\n 1554 else:\r\n-> 1555 self.size = self.details[\"size\"]\r\n 1556 self.cache = caches[cache_type](\r\n 1557 self.blocksize, self._fetch_range, self.size, **cache_options\r\n 1558 )\r\n 1559 else:\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1568, in AbstractBufferedFile.details(self)\r\n 1565 @property\r\n 1566 def details(self):\r\n 1567 if self._details is None:\r\n-> 1568 self._details = self.fs.info(self.path)\r\n 1569 return self._details\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:115, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 112 @functools.wraps(func)\r\n 113 def wrapper(*args, **kwargs):\r\n 114 self = obj or args[0]\r\n--> 115 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:100, in sync(loop, func, timeout, *args, **kwargs)\r\n 98 raise FSTimeoutError from return_result\r\n 99 elif isinstance(return_result, BaseException):\r\n--> 100 raise return_result\r\n 101 else:\r\n 102 return return_result\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:55, in _runner(event, coro, result, timeout)\r\n 53 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 54 try:\r\n---> 55 result[0] = await coro\r\n 56 except Exception as ex:\r\n 57 result[0] = ex\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:1248, in S3FileSystem._info(self, path, bucket, key, refresh, version_id)\r\n 1246 if key:\r\n 1247 try:\r\n-> 1248 out = await self._call_s3(\r\n 1249 \"head_object\",\r\n 1250 self.kwargs,\r\n 1251 Bucket=bucket,\r\n 1252 Key=key,\r\n 1253 **version_id_kw(version_id),\r\n 1254 **self.req_kw,\r\n 1255 )\r\n 1256 return {\r\n 1257 \"ETag\": out.get(\"ETag\", \"\"),\r\n 1258 \"LastModified\": out[\"LastModified\"],\r\n (...)\r\n 1264 \"ContentType\": out.get(\"ContentType\"),\r\n 1265 }\r\n 1266 except FileNotFoundError:\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:341, in S3FileSystem._call_s3(self, method, *akwarglist, **kwargs)\r\n 340 async def _call_s3(self, method, *akwarglist, **kwargs):\r\n--> 341 await self.set_session()\r\n 342 s3 = await self.get_s3(kwargs.get(\"Bucket\"))\r\n 343 method = getattr(s3, method)\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:502, in S3FileSystem.set_session(self, refresh, kwargs)\r\n 500 conf = AioConfig(**config_kwargs)\r\n 501 if self.session is None:\r\n--> 502 self.session = aiobotocore.session.AioSession(**self.kwargs)\r\n 504 for parameters in (config_kwargs, self.kwargs, init_kwargs, client_kwargs):\r\n 505 for option in (\"region_name\", \"endpoint_url\"):\r\n\r\nTypeError: __init__() got an unexpected keyword argument 'headers'\r\n```", "Is `\"data-parquet\"` a file ? In `data_files` you should pass the paths to the parquet files (not to a directory). Glob patterns are not supported yet for S3 URLs.\r\n\r\nThe bug seems to happen because your provided data file has no extension. Because of that it tries to infer it from the file content, but fails because `_get_extraction_protocol` doesn't support S3 URLs yet.\r\n\r\n", "@lhoestq \r\nThank you for your answer. Saving the file with `.parquet` extension solved the issue! This is really great! Really appreciate all the help! \r\n\r\nLet me know if I should close the issue or feel free to close it if you want.", "Cool ! I'm glad it worked out :)\r\n\r\nSure feel free to close the issue, since the original question about streaming with load_from_disk has been answered anyway" ]

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[ "Hi ! Doing `load_dataset(\"path/to/data.csv\")` is not supported yet, but you can do\r\n\r\n```python\r\nds = load_dataset(\"csv\", data_files=[\"path/to/data.csv\"])\r\n```", "@lhoestq thank you.", "The other question: \r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1767, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1498, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1127, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 708, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/data_files.py\", line 796, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/data_files.py\", line 362, in resolve_patterns_locally_or_by_urls\r\n for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/data_files.py\", line 306, in _resolve_single_pattern_locally\r\n raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '/home/fm001/hzl/Data/qa/' at /\r\n>>> mydata = load_dataset(\"/home/fm001/hzl/Data/qa/\")\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1767, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1508, in load_dataset_builder\r\n builder_cls = import_main_class(dataset_module.module_path)\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 115, in import_main_class\r\n module = importlib.import_module(module_path)\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/importlib/__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\n File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n File \"/home/fm001/.cache/huggingface/modules/datasets_modules/datasets/qa/b8b9f481eff9d17b769b4b50f30a51da32b47c94d1af4d2bdffb9fc2c589513a/qa.py\", line 2, in <module>\r\n mydata = load_dataset(\"/home/fm001/hzl/Data/qa/\")\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1767, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py\", line 1524, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\nTypeError: 'NoneType' object is not callable\r\n\r\nAnd I follow the setting with https://huggingface.co/docs/datasets/dataset_script" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5836). All of your documentation changes will be reflected on that endpoint.", "The error seems unrelated to the changes, so feel free to merge.", "<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) | 0.006562 / 0.011353 (-0.004791) | 0.004568 / 0.011008 (-0.006440) | 0.098151 / 0.038508 (0.059643) | 0.028117 / 0.023109 (0.005008) | 0.305442 / 0.275898 (0.029544) | 0.338288 / 0.323480 (0.014808) | 0.005012 / 0.007986 (-0.002973) | 0.003415 / 0.004328 (-0.000913) | 0.075022 / 0.004250 (0.070771) | 0.036869 / 0.037052 (-0.000183) | 0.301427 / 0.258489 (0.042937) | 0.348485 / 0.293841 (0.054644) | 0.030761 / 0.128546 (-0.097785) | 0.011461 / 0.075646 (-0.064185) | 0.321987 / 0.419271 (-0.097285) | 0.042885 / 0.043533 (-0.000648) | 0.300691 / 0.255139 (0.045552) | 0.333208 / 0.283200 (0.050008) | 0.090203 / 0.141683 (-0.051480) | 1.459744 / 1.452155 (0.007590) | 1.522960 / 1.492716 (0.030243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213219 / 0.018006 (0.195213) | 0.408118 / 0.000490 (0.407629) | 0.003716 / 0.000200 (0.003516) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023060 / 0.037411 (-0.014351) | 0.097423 / 0.014526 (0.082897) | 0.103988 / 0.176557 (-0.072568) | 0.162793 / 0.737135 (-0.574343) | 0.108282 / 0.296338 (-0.188056) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431628 / 0.215209 (0.216419) | 4.300881 / 2.077655 (2.223226) | 2.058853 / 1.504120 (0.554733) | 1.897910 / 1.541195 (0.356715) | 1.991723 / 1.468490 (0.523233) | 0.699686 / 4.584777 (-3.885091) | 3.395004 / 3.745712 (-0.350708) | 1.841613 / 5.269862 (-3.428248) | 1.152347 / 4.565676 (-3.413330) | 0.082517 / 0.424275 (-0.341758) | 0.012323 / 0.007607 (0.004715) | 0.535812 / 0.226044 (0.309767) | 5.374103 / 2.268929 (3.105174) | 2.429662 / 55.444624 (-53.014962) | 2.097199 / 6.876477 (-4.779277) | 2.172625 / 2.142072 (0.030552) | 0.810156 / 4.805227 (-3.995071) | 0.151629 / 6.500664 (-6.349035) | 0.066528 / 0.075469 (-0.008941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220667 / 1.841788 (-0.621121) | 13.696976 / 8.074308 (5.622668) | 14.042916 / 10.191392 (3.851524) | 0.129626 / 0.680424 (-0.550798) | 0.016593 / 0.534201 (-0.517607) | 0.383747 / 0.579283 (-0.195536) | 0.386872 / 0.434364 (-0.047492) | 0.456524 / 0.540337 (-0.083813) | 0.545033 / 1.386936 (-0.841903) |\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) | 0.006361 / 0.011353 (-0.004992) | 0.004516 / 0.011008 (-0.006493) | 0.077155 / 0.038508 (0.038647) | 0.027239 / 0.023109 (0.004130) | 0.359892 / 0.275898 (0.083994) | 0.391994 / 0.323480 (0.068514) | 0.004950 / 0.007986 (-0.003036) | 0.003379 / 0.004328 (-0.000949) | 0.077057 / 0.004250 (0.072806) | 0.039562 / 0.037052 (0.002509) | 0.364244 / 0.258489 (0.105755) | 0.416033 / 0.293841 (0.122192) | 0.031049 / 0.128546 (-0.097497) | 0.011479 / 0.075646 (-0.064167) | 0.086479 / 0.419271 (-0.332793) | 0.039381 / 0.043533 (-0.004151) | 0.372143 / 0.255139 (0.117004) | 0.388569 / 0.283200 (0.105369) | 0.090954 / 0.141683 (-0.050728) | 1.540957 / 1.452155 (0.088802) | 1.596841 / 1.492716 (0.104125) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221130 / 0.018006 (0.203123) | 0.403728 / 0.000490 (0.403238) | 0.003172 / 0.000200 (0.002972) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024963 / 0.037411 (-0.012449) | 0.101065 / 0.014526 (0.086539) | 0.110846 / 0.176557 (-0.065710) | 0.158578 / 0.737135 (-0.578557) | 0.112235 / 0.296338 (-0.184104) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457320 / 0.215209 (0.242111) | 4.548094 / 2.077655 (2.470439) | 2.175376 / 1.504120 (0.671256) | 1.964755 / 1.541195 (0.423561) | 2.008128 / 1.468490 (0.539638) | 0.702448 / 4.584777 (-3.882329) | 3.437595 / 3.745712 (-0.308117) | 3.009871 / 5.269862 (-2.259990) | 1.558181 / 4.565676 (-3.007496) | 0.082568 / 0.424275 (-0.341707) | 0.012371 / 0.007607 (0.004764) | 0.550688 / 0.226044 (0.324644) | 5.534210 / 2.268929 (3.265282) | 2.649605 / 55.444624 (-52.795020) | 2.317293 / 6.876477 (-4.559184) | 2.351525 / 2.142072 (0.209453) | 0.808971 / 4.805227 (-3.996256) | 0.152737 / 6.500664 (-6.347927) | 0.068416 / 0.075469 (-0.007053) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340219 / 1.841788 (-0.501569) | 13.903388 / 8.074308 (5.829080) | 13.063477 / 10.191392 (2.872085) | 0.130216 / 0.680424 (-0.550208) | 0.016522 / 0.534201 (-0.517679) | 0.398946 / 0.579283 (-0.180337) | 0.382450 / 0.434364 (-0.051914) | 0.491007 / 0.540337 (-0.049330) | 0.577747 / 1.386936 (-0.809189) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#15c37ed142e4fbcb8c00ae62d4c71c84ce41959a \"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) | 0.007812 / 0.011353 (-0.003541) | 0.005563 / 0.011008 (-0.005446) | 0.099372 / 0.038508 (0.060864) | 0.035629 / 0.023109 (0.012520) | 0.301457 / 0.275898 (0.025559) | 0.339136 / 0.323480 (0.015656) | 0.006152 / 0.007986 (-0.001834) | 0.005843 / 0.004328 (0.001515) | 0.075280 / 0.004250 (0.071030) | 0.052789 / 0.037052 (0.015736) | 0.301805 / 0.258489 (0.043316) | 0.347918 / 0.293841 (0.054078) | 0.036182 / 0.128546 (-0.092364) | 0.012655 / 0.075646 (-0.062991) | 0.334428 / 0.419271 (-0.084844) | 0.062746 / 0.043533 (0.019213) | 0.296932 / 0.255139 (0.041793) | 0.314115 / 0.283200 (0.030916) | 0.121291 / 0.141683 (-0.020392) | 1.453252 / 1.452155 (0.001097) | 1.564714 / 1.492716 (0.071997) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243810 / 0.018006 (0.225804) | 0.547129 / 0.000490 (0.546640) | 0.004666 / 0.000200 (0.004466) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028214 / 0.037411 (-0.009197) | 0.108878 / 0.014526 (0.094352) | 0.122313 / 0.176557 (-0.054243) | 0.182412 / 0.737135 (-0.554723) | 0.127014 / 0.296338 (-0.169324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423946 / 0.215209 (0.208737) | 4.207112 / 2.077655 (2.129457) | 2.048658 / 1.504120 (0.544538) | 1.843593 / 1.541195 (0.302398) | 1.952426 / 1.468490 (0.483936) | 0.712098 / 4.584777 (-3.872679) | 3.824971 / 3.745712 (0.079258) | 3.507141 / 5.269862 (-1.762721) | 1.868866 / 4.565676 (-2.696810) | 0.087895 / 0.424275 (-0.336380) | 0.012783 / 0.007607 (0.005176) | 0.524087 / 0.226044 (0.298042) | 5.246498 / 2.268929 (2.977570) | 2.495944 / 55.444624 (-52.948680) | 2.126779 / 6.876477 (-4.749698) | 2.315545 / 2.142072 (0.173472) | 0.859546 / 4.805227 (-3.945681) | 0.173457 / 6.500664 (-6.327208) | 0.067483 / 0.075469 (-0.007986) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.173851 / 1.841788 (-0.667937) | 15.091913 / 8.074308 (7.017605) | 14.640035 / 10.191392 (4.448643) | 0.168498 / 0.680424 (-0.511926) | 0.017513 / 0.534201 (-0.516688) | 0.425770 / 0.579283 (-0.153513) | 0.434248 / 0.434364 (-0.000116) | 0.504204 / 0.540337 (-0.036134) | 0.616885 / 1.386936 (-0.770051) |\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) | 0.007775 / 0.011353 (-0.003578) | 0.005153 / 0.011008 (-0.005855) | 0.075461 / 0.038508 (0.036953) | 0.034994 / 0.023109 (0.011885) | 0.372389 / 0.275898 (0.096491) | 0.397911 / 0.323480 (0.074431) | 0.006572 / 0.007986 (-0.001413) | 0.005549 / 0.004328 (0.001220) | 0.075101 / 0.004250 (0.070851) | 0.054014 / 0.037052 (0.016962) | 0.368964 / 0.258489 (0.110475) | 0.425353 / 0.293841 (0.131512) | 0.035546 / 0.128546 (-0.093001) | 0.012707 / 0.075646 (-0.062939) | 0.087418 / 0.419271 (-0.331853) | 0.046425 / 0.043533 (0.002893) | 0.363982 / 0.255139 (0.108843) | 0.376421 / 0.283200 (0.093221) | 0.105369 / 0.141683 (-0.036314) | 1.494408 / 1.452155 (0.042253) | 1.596783 / 1.492716 (0.104067) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258780 / 0.018006 (0.240773) | 0.533373 / 0.000490 (0.532883) | 0.000432 / 0.000200 (0.000232) | 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) | 0.030687 / 0.037411 (-0.006725) | 0.110231 / 0.014526 (0.095705) | 0.123738 / 0.176557 (-0.052819) | 0.171999 / 0.737135 (-0.565137) | 0.127673 / 0.296338 (-0.168665) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448058 / 0.215209 (0.232849) | 4.459381 / 2.077655 (2.381726) | 2.234020 / 1.504120 (0.729900) | 2.038616 / 1.541195 (0.497421) | 2.123795 / 1.468490 (0.655305) | 0.702664 / 4.584777 (-3.882113) | 3.837133 / 3.745712 (0.091420) | 2.138574 / 5.269862 (-3.131287) | 1.375955 / 4.565676 (-3.189722) | 0.086996 / 0.424275 (-0.337280) | 0.012461 / 0.007607 (0.004854) | 0.557978 / 0.226044 (0.331934) | 5.648613 / 2.268929 (3.379685) | 2.777829 / 55.444624 (-52.666796) | 2.392424 / 6.876477 (-4.484052) | 2.482823 / 2.142072 (0.340750) | 0.851891 / 4.805227 (-3.953336) | 0.171335 / 6.500664 (-6.329329) | 0.065041 / 0.075469 (-0.010428) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.319697 / 1.841788 (-0.522091) | 15.748688 / 8.074308 (7.674380) | 13.397042 / 10.191392 (3.205650) | 0.166424 / 0.680424 (-0.514000) | 0.017755 / 0.534201 (-0.516446) | 0.424989 / 0.579283 (-0.154294) | 0.424705 / 0.434364 (-0.009659) | 0.494190 / 0.540337 (-0.046147) | 0.588315 / 1.386936 (-0.798622) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#15c37ed142e4fbcb8c00ae62d4c71c84ce41959a \"CML watermark\")\n" ]

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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) | 0.006640 / 0.011353 (-0.004713) | 0.004606 / 0.011008 (-0.006402) | 0.098870 / 0.038508 (0.060362) | 0.028201 / 0.023109 (0.005092) | 0.304396 / 0.275898 (0.028498) | 0.339804 / 0.323480 (0.016324) | 0.005011 / 0.007986 (-0.002974) | 0.003530 / 0.004328 (-0.000799) | 0.075223 / 0.004250 (0.070973) | 0.037922 / 0.037052 (0.000870) | 0.310273 / 0.258489 (0.051784) | 0.348324 / 0.293841 (0.054483) | 0.030181 / 0.128546 (-0.098365) | 0.011584 / 0.075646 (-0.064062) | 0.322637 / 0.419271 (-0.096635) | 0.043119 / 0.043533 (-0.000414) | 0.314514 / 0.255139 (0.059375) | 0.334384 / 0.283200 (0.051185) | 0.092551 / 0.141683 (-0.049132) | 1.496694 / 1.452155 (0.044539) | 1.555426 / 1.492716 (0.062710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205078 / 0.018006 (0.187072) | 0.399200 / 0.000490 (0.398710) | 0.004881 / 0.000200 (0.004681) | 0.000200 / 0.000054 (0.000146) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025042 / 0.037411 (-0.012369) | 0.101501 / 0.014526 (0.086975) | 0.107430 / 0.176557 (-0.069127) | 0.170107 / 0.737135 (-0.567028) | 0.111253 / 0.296338 (-0.185086) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460358 / 0.215209 (0.245149) | 4.592037 / 2.077655 (2.514383) | 2.222612 / 1.504120 (0.718493) | 2.022804 / 1.541195 (0.481610) | 2.040824 / 1.468490 (0.572334) | 0.700485 / 4.584777 (-3.884292) | 3.427847 / 3.745712 (-0.317866) | 2.836916 / 5.269862 (-2.432946) | 1.505055 / 4.565676 (-3.060621) | 0.083206 / 0.424275 (-0.341069) | 0.046492 / 0.007607 (0.038885) | 0.555562 / 0.226044 (0.329518) | 5.563574 / 2.268929 (3.294645) | 2.635273 / 55.444624 (-52.809351) | 2.299377 / 6.876477 (-4.577100) | 2.394512 / 2.142072 (0.252440) | 0.809541 / 4.805227 (-3.995686) | 0.151814 / 6.500664 (-6.348850) | 0.067241 / 0.075469 (-0.008228) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188396 / 1.841788 (-0.653392) | 13.714596 / 8.074308 (5.640288) | 14.076906 / 10.191392 (3.885514) | 0.143447 / 0.680424 (-0.536977) | 0.016514 / 0.534201 (-0.517687) | 0.383075 / 0.579283 (-0.196209) | 0.386997 / 0.434364 (-0.047367) | 0.441941 / 0.540337 (-0.098396) | 0.522145 / 1.386936 (-0.864791) |\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) | 0.006266 / 0.011353 (-0.005086) | 0.004562 / 0.011008 (-0.006446) | 0.077472 / 0.038508 (0.038964) | 0.027596 / 0.023109 (0.004486) | 0.400498 / 0.275898 (0.124600) | 0.406728 / 0.323480 (0.083248) | 0.004745 / 0.007986 (-0.003241) | 0.003375 / 0.004328 (-0.000954) | 0.076645 / 0.004250 (0.072394) | 0.037756 / 0.037052 (0.000703) | 0.415183 / 0.258489 (0.156694) | 0.413758 / 0.293841 (0.119917) | 0.030624 / 0.128546 (-0.097922) | 0.011525 / 0.075646 (-0.064121) | 0.086033 / 0.419271 (-0.333238) | 0.039307 / 0.043533 (-0.004226) | 0.418192 / 0.255139 (0.163053) | 0.403152 / 0.283200 (0.119952) | 0.094141 / 0.141683 (-0.047542) | 1.459012 / 1.452155 (0.006857) | 1.546493 / 1.492716 (0.053777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239494 / 0.018006 (0.221488) | 0.420918 / 0.000490 (0.420428) | 0.000411 / 0.000200 (0.000211) | 0.000057 / 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) | 0.024525 / 0.037411 (-0.012886) | 0.099793 / 0.014526 (0.085267) | 0.105888 / 0.176557 (-0.070669) | 0.155912 / 0.737135 (-0.581223) | 0.109937 / 0.296338 (-0.186401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470108 / 0.215209 (0.254899) | 4.696390 / 2.077655 (2.618735) | 2.467841 / 1.504120 (0.963721) | 2.275012 / 1.541195 (0.733818) | 2.430736 / 1.468490 (0.962245) | 0.700442 / 4.584777 (-3.884335) | 3.458451 / 3.745712 (-0.287261) | 1.921120 / 5.269862 (-3.348742) | 1.183292 / 4.565676 (-3.382384) | 0.083985 / 0.424275 (-0.340290) | 0.012510 / 0.007607 (0.004903) | 0.589066 / 0.226044 (0.363022) | 5.896070 / 2.268929 (3.627141) | 2.935379 / 55.444624 (-52.509245) | 2.599524 / 6.876477 (-4.276953) | 2.663426 / 2.142072 (0.521354) | 0.812096 / 4.805227 (-3.993131) | 0.152559 / 6.500664 (-6.348105) | 0.066906 / 0.075469 (-0.008563) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333341 / 1.841788 (-0.508446) | 14.441667 / 8.074308 (6.367359) | 14.754069 / 10.191392 (4.562677) | 0.155707 / 0.680424 (-0.524716) | 0.016983 / 0.534201 (-0.517218) | 0.389386 / 0.579283 (-0.189897) | 0.394106 / 0.434364 (-0.040258) | 0.447355 / 0.540337 (-0.092982) | 0.533142 / 1.386936 (-0.853794) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99ee4467ce77f8f718159a535e237dd8790b5bed \"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) | 0.007801 / 0.011353 (-0.003552) | 0.004884 / 0.011008 (-0.006124) | 0.114754 / 0.038508 (0.076245) | 0.040427 / 0.023109 (0.017318) | 0.402064 / 0.275898 (0.126166) | 0.428830 / 0.323480 (0.105350) | 0.006429 / 0.007986 (-0.001556) | 0.004394 / 0.004328 (0.000066) | 0.087681 / 0.004250 (0.083431) | 0.053684 / 0.037052 (0.016632) | 0.399967 / 0.258489 (0.141478) | 0.445298 / 0.293841 (0.151457) | 0.033194 / 0.128546 (-0.095352) | 0.010288 / 0.075646 (-0.065359) | 0.390719 / 0.419271 (-0.028552) | 0.059311 / 0.043533 (0.015778) | 0.393651 / 0.255139 (0.138512) | 0.418395 / 0.283200 (0.135196) | 0.121494 / 0.141683 (-0.020189) | 1.735470 / 1.452155 (0.283315) | 1.820485 / 1.492716 (0.327769) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012887 / 0.018006 (-0.005119) | 0.491652 / 0.000490 (0.491162) | 0.005481 / 0.000200 (0.005281) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030931 / 0.037411 (-0.006480) | 0.125212 / 0.014526 (0.110686) | 0.136004 / 0.176557 (-0.040552) | 0.201686 / 0.737135 (-0.535449) | 0.140181 / 0.296338 (-0.156157) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475003 / 0.215209 (0.259794) | 4.743918 / 2.077655 (2.666263) | 2.149422 / 1.504120 (0.645302) | 1.925016 / 1.541195 (0.383821) | 2.061441 / 1.468490 (0.592951) | 0.619845 / 4.584777 (-3.964932) | 4.534691 / 3.745712 (0.788979) | 2.248198 / 5.269862 (-3.021664) | 1.409868 / 4.565676 (-3.155808) | 0.080265 / 0.424275 (-0.344010) | 0.014455 / 0.007607 (0.006848) | 0.597810 / 0.226044 (0.371765) | 5.845492 / 2.268929 (3.576564) | 2.729139 / 55.444624 (-52.715486) | 2.313879 / 6.876477 (-4.562598) | 2.418763 / 2.142072 (0.276690) | 0.748687 / 4.805227 (-4.056540) | 0.165278 / 6.500664 (-6.335387) | 0.076848 / 0.075469 (0.001379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.416349 / 1.841788 (-0.425439) | 17.440903 / 8.074308 (9.366595) | 17.025733 / 10.191392 (6.834341) | 0.167428 / 0.680424 (-0.512995) | 0.020484 / 0.534201 (-0.513717) | 0.470273 / 0.579283 (-0.109010) | 0.494380 / 0.434364 (0.060016) | 0.566131 / 0.540337 (0.025794) | 0.690444 / 1.386936 (-0.696492) |\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) | 0.007695 / 0.011353 (-0.003657) | 0.005551 / 0.011008 (-0.005457) | 0.087812 / 0.038508 (0.049304) | 0.039107 / 0.023109 (0.015998) | 0.436461 / 0.275898 (0.160563) | 0.465116 / 0.323480 (0.141636) | 0.006590 / 0.007986 (-0.001396) | 0.004672 / 0.004328 (0.000343) | 0.087109 / 0.004250 (0.082858) | 0.054227 / 0.037052 (0.017175) | 0.442660 / 0.258489 (0.184171) | 0.484296 / 0.293841 (0.190455) | 0.033308 / 0.128546 (-0.095238) | 0.010780 / 0.075646 (-0.064866) | 0.095255 / 0.419271 (-0.324016) | 0.054399 / 0.043533 (0.010866) | 0.431734 / 0.255139 (0.176595) | 0.453583 / 0.283200 (0.170383) | 0.116067 / 0.141683 (-0.025616) | 1.780701 / 1.452155 (0.328546) | 1.851077 / 1.492716 (0.358360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228000 / 0.018006 (0.209994) | 0.485733 / 0.000490 (0.485243) | 0.003955 / 0.000200 (0.003755) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033974 / 0.037411 (-0.003437) | 0.134504 / 0.014526 (0.119978) | 0.144421 / 0.176557 (-0.032135) | 0.202171 / 0.737135 (-0.534964) | 0.152015 / 0.296338 (-0.144323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520462 / 0.215209 (0.305253) | 5.233339 / 2.077655 (3.155684) | 2.575013 / 1.504120 (1.070893) | 2.384119 / 1.541195 (0.842924) | 2.403856 / 1.468490 (0.935366) | 0.618656 / 4.584777 (-3.966121) | 4.663582 / 3.745712 (0.917870) | 3.738594 / 5.269862 (-1.531268) | 1.794903 / 4.565676 (-2.770773) | 0.077903 / 0.424275 (-0.346372) | 0.014681 / 0.007607 (0.007074) | 0.648615 / 0.226044 (0.422570) | 6.503721 / 2.268929 (4.234792) | 3.326239 / 55.444624 (-52.118386) | 2.989791 / 6.876477 (-3.886685) | 2.995479 / 2.142072 (0.853407) | 0.765483 / 4.805227 (-4.039744) | 0.169783 / 6.500664 (-6.330882) | 0.077533 / 0.075469 (0.002064) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.518736 / 1.841788 (-0.323051) | 17.989119 / 8.074308 (9.914811) | 15.484365 / 10.191392 (5.292973) | 0.168507 / 0.680424 (-0.511917) | 0.020289 / 0.534201 (-0.513912) | 0.467491 / 0.579283 (-0.111793) | 0.501714 / 0.434364 (0.067350) | 0.553418 / 0.540337 (0.013081) | 0.662199 / 1.386936 (-0.724737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ebda17e4362bd5f6123543a14fa526a3b54481a \"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) | 0.007044 / 0.011353 (-0.004309) | 0.004750 / 0.011008 (-0.006258) | 0.096694 / 0.038508 (0.058186) | 0.035682 / 0.023109 (0.012573) | 0.300613 / 0.275898 (0.024715) | 0.334831 / 0.323480 (0.011351) | 0.006428 / 0.007986 (-0.001558) | 0.004456 / 0.004328 (0.000128) | 0.075060 / 0.004250 (0.070810) | 0.053166 / 0.037052 (0.016114) | 0.299601 / 0.258489 (0.041112) | 0.359521 / 0.293841 (0.065680) | 0.028072 / 0.128546 (-0.100474) | 0.009216 / 0.075646 (-0.066430) | 0.328895 / 0.419271 (-0.090377) | 0.050881 / 0.043533 (0.007349) | 0.298265 / 0.255139 (0.043126) | 0.318095 / 0.283200 (0.034896) | 0.116046 / 0.141683 (-0.025637) | 1.491312 / 1.452155 (0.039157) | 1.556053 / 1.492716 (0.063337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014248 / 0.018006 (-0.003758) | 0.551455 / 0.000490 (0.550965) | 0.006096 / 0.000200 (0.005897) | 0.000145 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030598 / 0.037411 (-0.006813) | 0.109549 / 0.014526 (0.095023) | 0.123207 / 0.176557 (-0.053350) | 0.181940 / 0.737135 (-0.555195) | 0.128965 / 0.296338 (-0.167374) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404552 / 0.215209 (0.189343) | 4.030674 / 2.077655 (1.953020) | 1.841819 / 1.504120 (0.337699) | 1.650055 / 1.541195 (0.108860) | 1.763208 / 1.468490 (0.294718) | 0.532715 / 4.584777 (-4.052062) | 3.774810 / 3.745712 (0.029098) | 3.221927 / 5.269862 (-2.047934) | 1.607974 / 4.565676 (-2.957702) | 0.067160 / 0.424275 (-0.357116) | 0.012479 / 0.007607 (0.004872) | 0.498801 / 0.226044 (0.272757) | 4.980567 / 2.268929 (2.711638) | 2.356017 / 55.444624 (-53.088608) | 2.018975 / 6.876477 (-4.857502) | 2.218343 / 2.142072 (0.076270) | 0.645714 / 4.805227 (-4.159514) | 0.145470 / 6.500664 (-6.355195) | 0.065666 / 0.075469 (-0.009803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205756 / 1.841788 (-0.636031) | 15.682779 / 8.074308 (7.608470) | 14.748987 / 10.191392 (4.557595) | 0.167105 / 0.680424 (-0.513319) | 0.017554 / 0.534201 (-0.516647) | 0.393924 / 0.579283 (-0.185359) | 0.432659 / 0.434364 (-0.001705) | 0.502033 / 0.540337 (-0.038304) | 0.602244 / 1.386936 (-0.784692) |\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) | 0.007077 / 0.011353 (-0.004276) | 0.004911 / 0.011008 (-0.006097) | 0.075120 / 0.038508 (0.036612) | 0.035460 / 0.023109 (0.012351) | 0.362569 / 0.275898 (0.086671) | 0.398995 / 0.323480 (0.075515) | 0.006587 / 0.007986 (-0.001398) | 0.004571 / 0.004328 (0.000242) | 0.074647 / 0.004250 (0.070397) | 0.057331 / 0.037052 (0.020279) | 0.365123 / 0.258489 (0.106634) | 0.408617 / 0.293841 (0.114776) | 0.028911 / 0.128546 (-0.099635) | 0.009533 / 0.075646 (-0.066113) | 0.081566 / 0.419271 (-0.337705) | 0.048841 / 0.043533 (0.005308) | 0.367245 / 0.255139 (0.112106) | 0.375975 / 0.283200 (0.092776) | 0.123211 / 0.141683 (-0.018472) | 1.471588 / 1.452155 (0.019433) | 1.569342 / 1.492716 (0.076625) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.328443 / 0.018006 (0.310436) | 0.541402 / 0.000490 (0.540912) | 0.000440 / 0.000200 (0.000240) | 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) | 0.030772 / 0.037411 (-0.006639) | 0.115833 / 0.014526 (0.101307) | 0.127837 / 0.176557 (-0.048719) | 0.180897 / 0.737135 (-0.556238) | 0.132458 / 0.296338 (-0.163881) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445979 / 0.215209 (0.230770) | 4.453101 / 2.077655 (2.375447) | 2.276625 / 1.504120 (0.772505) | 2.102167 / 1.541195 (0.560972) | 2.181583 / 1.468490 (0.713093) | 0.525069 / 4.584777 (-4.059708) | 3.803446 / 3.745712 (0.057734) | 1.954173 / 5.269862 (-3.315688) | 1.088734 / 4.565676 (-3.476942) | 0.066020 / 0.424275 (-0.358255) | 0.012158 / 0.007607 (0.004551) | 0.546828 / 0.226044 (0.320783) | 5.454060 / 2.268929 (3.185132) | 2.756154 / 55.444624 (-52.688470) | 2.476501 / 6.876477 (-4.399976) | 2.525875 / 2.142072 (0.383803) | 0.647515 / 4.805227 (-4.157712) | 0.144511 / 6.500664 (-6.356153) | 0.067060 / 0.075469 (-0.008409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306456 / 1.841788 (-0.535332) | 15.822623 / 8.074308 (7.748315) | 14.929114 / 10.191392 (4.737721) | 0.168650 / 0.680424 (-0.511773) | 0.018043 / 0.534201 (-0.516158) | 0.396712 / 0.579283 (-0.182572) | 0.425800 / 0.434364 (-0.008564) | 0.466452 / 0.540337 (-0.073885) | 0.564370 / 1.386936 (-0.822566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ebda17e4362bd5f6123543a14fa526a3b54481a \"CML watermark\")\n" ]

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[ "Hi ! The numpy formatting detaults to int64 and float32 - but you can use uint8 using\r\n```python\r\nds = ds.with_format(\"numpy\", dtype=np.uint8)\r\n```", "Related to https://github.com/huggingface/datasets/issues/5517.", "Thank you!\r\nBy setting `ds.with_format(\"numpy\", dtype=np.uint8)`, the dataset returns the data in `uint8`.\r\n\r\nHowever, `with_format` and `set_format` seem to cast the data on-the-fly.\r\nI want to reduce the dataset size by using `uint8` instead of `int64` and I observe no difference between using `int64` and `uint8` for the vector.\r\nIs there any way to actually store the data in `uint8` and save the disk space and the downloading time when loaded from the hub?\r\n", "If the feature type is `Value(\"uint8\")` then it's written an uint8 on disk using the uint8 Arrow dtype.\r\n\r\ne.g.\r\n```python\r\nds = Dataset.from_dict({\"a\": range(10)}, features=Features({\"a\": Value(\"uint8\")}))\r\nds.data.nbytes\r\n# 10\r\n```", "Oh, I understand now.\r\nThe data was stored in `uint8` from the beginning (when the dataset returns `int64`).\r\n\r\nThank you for your time!\r\nMy question is fully resolved." ]

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[ "Thanks for reporting, @agombert.\r\n\r\nIn this case, I think the root issue is authentication: before pushing to Hub, you should authenticate. See our docs: https://huggingface.co/docs/datasets/upload_dataset#upload-with-python\r\n> 2. To upload a dataset on the Hub in Python, you need to log in to your Hugging Face account:\r\n ```\r\n huggingface-cli login\r\n ```", "Hey @albertvillanova well I actually did :D \r\n\r\n<img width=\"1079\" alt=\"Capture d’écran 2023-05-09 à 18 02 58\" src=\"https://github.com/huggingface/datasets/assets/17645711/e091aa20-06b1-4dd3-bfdb-35e832c66f8d\">\r\n", "That is weird that you get a Forbidden error if you are properly authenticated...\r\n\r\nToday we had a big outage issue affecting the Hugging Face Hub. Could you please retry to push_to_hub your dataset? Maybe that was the cause...", "Yes I've just tried again and same error 403 :/", "Login successful but also got this error \"Forbidden: pass `create_pr=1` as a query parameter to create a Pull Request\"", "Make sure your API token has a `write` role. I had the same issue as you with the `read` token. Creating a `write` token and using that solved the issue.", "> Make sure your API token has a `write` role. I had the same issue as you with the `read` token. Creating a `write` token and using that solved the issue.\r\n\r\nI generate a token with write role. It works! thank you so much.", "@dmitrijsk amazing thanks so much ! \r\nThe error should be clearer when the token is read-only – I wasted a lot of time there..", "Based on the number of reactions (https://github.com/huggingface/datasets/issues/5833#issuecomment-1586521001), many users have issues debugging this. @Wauplin Maybe a more informative error can be thrown in `hfh` if a token's role is insufficient for an op. WDYT?", "Yes indeed. I created an issue some time ago about it: https://github.com/huggingface/huggingface_hub/issues/1653. I'll prioritize it more and let you know. Thanks for the ping.", "Hey everyone :wave: The error message has been fixed to be more informative. As mentioned in https://github.com/huggingface/datasets/issues/5833#issuecomment-1586521001, the n°1 reason why this is happening is that a `read` token has been used instead of `write`. The fix has being shipped on the server meaning that you don't need to update any client library. The new error message looks like this: \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/models/Wauplin/test_recovered/preupload/main (Request ID: Root=1-6532752e-1ee492b070d9e3020e68bddc;25ca3387-44bc-433e-b49f-6e290305ed10)\r\n\r\nForbidden: you must use a write token to upload to a repository.\r\n```\r\n\r\n---\r\n\r\ncc @mariosasko I let you close this issue if you feel it's completely solved", "@Wauplin Reopening it. Indeed, the above error message is thrown if pushing to an **existing** repo with a `read` token. However, if the repo does not exist and needs to be created (by calling `create_repo` in `push_to_hub`), then passing a `read` token will raise the following:\r\n```python\r\nHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nHfHubHTTPError Traceback (most recent call last)\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py](https://localhost:8080/#) in hf_raise_for_status(response, endpoint_name)\r\n 318 # Convert `HTTPError` into a `HfHubHTTPError` to display request information\r\n 319 # as well (request id and/or server error message)\r\n--> 320 raise HfHubHTTPError(str(e), response=response) from e\r\n 321 \r\n 322 \r\n\r\nHfHubHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create (Request ID: Root=1-653291a1-0e9cc2b16c049ff510834d47;7edeb8a3-4bf1-4d9b-8f7f-06d430da9ee7)\r\n\r\nYou don't have the rights to create a dataset under this namespace\r\n```\r\n\r\nI think this error message should also be more informative!", "> However, if the repo does not exist and needs to be created (by calling create_repo in push_to_hub), then passing a read token will raise the following:\r\n\r\nThis seems to be a different issue than the one reported above right? Still agree that a more informative message would be nice. Can you open an issue on moon-landing for it please? (not sure I can open a PR myself for this one :grimacing: )", "@Wauplin Done :)" ]

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[ "moved to https://github.com/huggingface/transformers/issues/23233" ]

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[ "I am experiencing the same problem with the following environment:\r\n\r\n* `datasets` version: 2.11.0\r\n* Platform: `Linux 5.19.0-41-generic x86_64 GNU/Linux`\r\n* Python version: `3.8.5`\r\n* Huggingface_hub version: 0.13.3\r\n* PyArrow version: `11.0.0`\r\n* Pandas version: `1.5.3`\r\n\r\nTrying to get some diagnostics, I got the following: \r\n\r\n```python\r\n>>> from huggingface_hub import scan_cache_dir\r\n>>> sd = scan_cache_dir()\r\n>>> sd\r\nHFCacheInfo(size_on_disk=0, repos=frozenset(), warnings=[CorruptedCacheException('Repo path is not a directory: /home/myname/.cache/huggingface/hub/version_diffusers_cache.txt')])\r\n\r\n```\r\nHowever, that might also be because I had tried to manually specify the `cache_dir` and that resulted in trying to download the dataset again ... but into a folder one level higher up than it should have.\r\n\r\nNote that my issue is with the `huggan/wikiart` dataset, so it is not a dataset-specific issue.", "same problem with a private dataset repo, seems the huggingface hub server got some connection problem?", "Yes, dataset server seems down for now", "@SingL3 You can avoid this error by setting the [`HF_DATASETS_OFFLINE`](https://huggingface.co/docs/datasets/v2.12.0/en/loading#offline) env variable to 1. By default, if an internet connection is available, we check whether the cache of a cached dataset is up-to-date.\r\n\r\n@lucidBrot `datasets`' cache is still not aligned with `huggigface_hub`'s. We plan to align it eventually.", "Today we had a big issue affecting the Hugging Face Hub, thus all the `504 Server Error: Gateway Time-out` errors.\r\n\r\nIt is fixed now and loading your datasets should work as expected.", "Hi, @albertvillanova.\r\nIf there is a locally cached version of datasets or something cache using huggingface_hub, when a network problem(either client or server) occurs, is it a better way to fallback to use the current cached version rather than raise a exception and exit?" ]

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[ "Can you paste the error stack trace?", "That is weird. I can't reproduce it again after reboot.\r\n```python\r\nIn [2]: import platform\r\n\r\nIn [3]: platform.platform()\r\nOut[3]: 'macOS-13.2-arm64-arm-64bit'\r\n\r\nIn [4]: from datasets import load_dataset\r\n ...:\r\n ...: jazzy = load_dataset(\"nomic-ai/gpt4all-j-prompt-generations\", revision='v1.2-jazzy')\r\nFound cached dataset parquet (/Users/sarit/.cache/huggingface/datasets/nomic-ai___parquet/nomic-ai--gpt4all-j-prompt-generations-a3b62015e2e52043/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 63.25it/s]\r\n```" ]

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[ "Hi! \r\n\r\nYou can call `map` as follows to avoid the error:\r\n```python\r\naugmented_dataset_cln = dataset_cln['train'].map(augment_dataset, features=dataset_cln['train'].features)\r\n```", "Thanks it is solved", "Hi! \r\nI have run into the same problem with you. Could you please let me know how you solve it? Thanks!" ]

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[ "Indeed the JSON dataset builder raises an error when it encounters an unexpected type.\r\n\r\nThere's an old PR open to add away to ignore such elements though, if it can help: https://github.com/huggingface/datasets/pull/2838" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Added env var", "@lhoestq would you or another maintainer be able to review please? :)", "I removed the env var", "<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) | 0.005771 / 0.011353 (-0.005582) | 0.004086 / 0.011008 (-0.006922) | 0.097170 / 0.038508 (0.058661) | 0.027464 / 0.023109 (0.004355) | 0.305425 / 0.275898 (0.029527) | 0.343869 / 0.323480 (0.020389) | 0.004899 / 0.007986 (-0.003087) | 0.003294 / 0.004328 (-0.001034) | 0.074710 / 0.004250 (0.070459) | 0.034982 / 0.037052 (-0.002070) | 0.306063 / 0.258489 (0.047574) | 0.343115 / 0.293841 (0.049274) | 0.025155 / 0.128546 (-0.103392) | 0.008429 / 0.075646 (-0.067217) | 0.318680 / 0.419271 (-0.100591) | 0.043304 / 0.043533 (-0.000229) | 0.306703 / 0.255139 (0.051564) | 0.335535 / 0.283200 (0.052335) | 0.087428 / 0.141683 (-0.054255) | 1.483769 / 1.452155 (0.031614) | 1.538753 / 1.492716 (0.046037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203313 / 0.018006 (0.185307) | 0.413864 / 0.000490 (0.413375) | 0.003186 / 0.000200 (0.002986) | 0.000068 / 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) | 0.022862 / 0.037411 (-0.014550) | 0.097306 / 0.014526 (0.082780) | 0.102823 / 0.176557 (-0.073733) | 0.162803 / 0.737135 (-0.574333) | 0.106311 / 0.296338 (-0.190028) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451710 / 0.215209 (0.236501) | 4.508520 / 2.077655 (2.430865) | 2.181118 / 1.504120 (0.676998) | 1.977607 / 1.541195 (0.436412) | 2.008366 / 1.468490 (0.539876) | 0.565388 / 4.584777 (-4.019389) | 3.439318 / 3.745712 (-0.306394) | 1.747512 / 5.269862 (-3.522349) | 1.102124 / 4.565676 (-3.463553) | 0.069212 / 0.424275 (-0.355063) | 0.011926 / 0.007607 (0.004318) | 0.553414 / 0.226044 (0.327370) | 5.548959 / 2.268929 (3.280031) | 2.628769 / 55.444624 (-52.815856) | 2.301003 / 6.876477 (-4.575473) | 2.341744 / 2.142072 (0.199672) | 0.673092 / 4.805227 (-4.132135) | 0.137722 / 6.500664 (-6.362942) | 0.066909 / 0.075469 (-0.008560) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196854 / 1.841788 (-0.644934) | 13.421776 / 8.074308 (5.347468) | 13.839760 / 10.191392 (3.648368) | 0.140557 / 0.680424 (-0.539867) | 0.016619 / 0.534201 (-0.517582) | 0.357985 / 0.579283 (-0.221298) | 0.387018 / 0.434364 (-0.047346) | 0.452798 / 0.540337 (-0.087540) | 0.542085 / 1.386936 (-0.844851) |\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) | 0.005868 / 0.011353 (-0.005484) | 0.004103 / 0.011008 (-0.006905) | 0.076126 / 0.038508 (0.037618) | 0.027744 / 0.023109 (0.004635) | 0.357257 / 0.275898 (0.081359) | 0.387981 / 0.323480 (0.064501) | 0.004807 / 0.007986 (-0.003178) | 0.003337 / 0.004328 (-0.000991) | 0.075486 / 0.004250 (0.071236) | 0.035121 / 0.037052 (-0.001931) | 0.361385 / 0.258489 (0.102896) | 0.399346 / 0.293841 (0.105505) | 0.025263 / 0.128546 (-0.103284) | 0.008571 / 0.075646 (-0.067075) | 0.081815 / 0.419271 (-0.337457) | 0.041114 / 0.043533 (-0.002418) | 0.362840 / 0.255139 (0.107701) | 0.380926 / 0.283200 (0.097727) | 0.092728 / 0.141683 (-0.048955) | 1.517647 / 1.452155 (0.065492) | 1.534914 / 1.492716 (0.042198) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199669 / 0.018006 (0.181663) | 0.399070 / 0.000490 (0.398580) | 0.002014 / 0.000200 (0.001814) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024541 / 0.037411 (-0.012870) | 0.099676 / 0.014526 (0.085151) | 0.106503 / 0.176557 (-0.070054) | 0.153755 / 0.737135 (-0.583380) | 0.108564 / 0.296338 (-0.187775) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443842 / 0.215209 (0.228633) | 4.441158 / 2.077655 (2.363503) | 2.159496 / 1.504120 (0.655376) | 1.955358 / 1.541195 (0.414163) | 1.973864 / 1.468490 (0.505374) | 0.550467 / 4.584777 (-4.034310) | 3.381831 / 3.745712 (-0.363881) | 2.561192 / 5.269862 (-2.708670) | 1.361684 / 4.565676 (-3.203992) | 0.068140 / 0.424275 (-0.356135) | 0.012005 / 0.007607 (0.004398) | 0.551921 / 0.226044 (0.325877) | 5.503591 / 2.268929 (3.234662) | 2.591609 / 55.444624 (-52.853015) | 2.246681 / 6.876477 (-4.629796) | 2.290941 / 2.142072 (0.148868) | 0.655212 / 4.805227 (-4.150015) | 0.136013 / 6.500664 (-6.364651) | 0.066995 / 0.075469 (-0.008474) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300438 / 1.841788 (-0.541350) | 13.866224 / 8.074308 (5.791916) | 13.932624 / 10.191392 (3.741232) | 0.144345 / 0.680424 (-0.536079) | 0.016623 / 0.534201 (-0.517578) | 0.357629 / 0.579283 (-0.221654) | 0.389759 / 0.434364 (-0.044605) | 0.417704 / 0.540337 (-0.122633) | 0.501358 / 1.386936 (-0.885578) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89f775226321ba94e5bf4670a323c0fb44f5f65c \"CML watermark\")\n", "Thank you!" ]

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[ "Hi! \r\n\r\nThis would only work if `bigscience/xP3` was a no-code dataset, but it isn't (it has a Python builder script).\r\n\r\nBut this should work: \r\n```python\r\nload_dataset(\"json\", data_files=\"https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl\")\r\n```\r\n\r\n", "I see, it's not compatible w/ regex right?\r\ne.g.\r\n`load_dataset(\"json\", data_files=\"https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/*\")`", "> I see, it's not compatible w/ regex right? e.g. `load_dataset(\"json\", data_files=\"https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/*\")`\r\n\r\nIt should work for patterns that \"reference\" the local filesystem, but to make this work with the Hub, we must implement https://github.com/huggingface/datasets/issues/5281 first.\r\n\r\nIn the meantime, you can fetch these glob files with `HfFileSystem` and pass them as a list to `load_dataset`:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import HfFileSystem, hf_hub_url # `HfFileSystem` requires the latest version of `huggingface_hub`\r\n\r\nfs = HfFileSystem()\r\nglob_files = fs.glob(\"datasets/bigscience/xP3/ur/*\")\r\n# convert fsspec URLs to HTTP URLs\r\nresolved_paths = [fs.resolve_path(file) for file in glob_files]\r\ndata_files = [hf_hub_url(resolved_path.repo_id, resolved_path.path_in_repo, repo_type=resolved_path.repo_type) for resolved_path in resolved_paths]\r\n\r\nds = load_dataset(\"json\", data_files=data_files)\r\n```", "This works using `load_dataset(\"json\", data_files=\"hf://datasets/bigscience/xP3/ur/*\")` now, closing" ]

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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) | 0.007658 / 0.011353 (-0.003695) | 0.005497 / 0.011008 (-0.005511) | 0.097142 / 0.038508 (0.058633) | 0.034602 / 0.023109 (0.011493) | 0.304191 / 0.275898 (0.028293) | 0.329103 / 0.323480 (0.005624) | 0.005936 / 0.007986 (-0.002049) | 0.004324 / 0.004328 (-0.000004) | 0.073387 / 0.004250 (0.069137) | 0.049657 / 0.037052 (0.012604) | 0.301352 / 0.258489 (0.042863) | 0.343095 / 0.293841 (0.049254) | 0.036767 / 0.128546 (-0.091779) | 0.012438 / 0.075646 (-0.063208) | 0.333804 / 0.419271 (-0.085468) | 0.064557 / 0.043533 (0.021024) | 0.302397 / 0.255139 (0.047258) | 0.319739 / 0.283200 (0.036540) | 0.119264 / 0.141683 (-0.022418) | 1.465309 / 1.452155 (0.013155) | 1.578194 / 1.492716 (0.085478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256552 / 0.018006 (0.238545) | 0.555344 / 0.000490 (0.554854) | 0.004845 / 0.000200 (0.004645) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027215 / 0.037411 (-0.010197) | 0.107071 / 0.014526 (0.092545) | 0.116343 / 0.176557 (-0.060213) | 0.172646 / 0.737135 (-0.564490) | 0.123366 / 0.296338 (-0.172973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411421 / 0.215209 (0.196212) | 4.126028 / 2.077655 (2.048373) | 1.975826 / 1.504120 (0.471706) | 1.784404 / 1.541195 (0.243210) | 1.848697 / 1.468490 (0.380207) | 0.686400 / 4.584777 (-3.898377) | 3.677649 / 3.745712 (-0.068063) | 2.077787 / 5.269862 (-3.192075) | 1.310912 / 4.565676 (-3.254764) | 0.083980 / 0.424275 (-0.340295) | 0.012183 / 0.007607 (0.004575) | 0.506969 / 0.226044 (0.280924) | 5.094730 / 2.268929 (2.825802) | 2.419790 / 55.444624 (-53.024834) | 2.106592 / 6.876477 (-4.769884) | 2.244309 / 2.142072 (0.102237) | 0.814312 / 4.805227 (-3.990915) | 0.167872 / 6.500664 (-6.332792) | 0.065339 / 0.075469 (-0.010130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193314 / 1.841788 (-0.648474) | 14.980621 / 8.074308 (6.906313) | 14.352452 / 10.191392 (4.161060) | 0.164531 / 0.680424 (-0.515893) | 0.017432 / 0.534201 (-0.516769) | 0.422193 / 0.579283 (-0.157090) | 0.410047 / 0.434364 (-0.024317) | 0.497011 / 0.540337 (-0.043326) | 0.581395 / 1.386936 (-0.805541) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.005449 / 0.011008 (-0.005559) | 0.074320 / 0.038508 (0.035812) | 0.034261 / 0.023109 (0.011152) | 0.378265 / 0.275898 (0.102367) | 0.414419 / 0.323480 (0.090939) | 0.005804 / 0.007986 (-0.002182) | 0.004205 / 0.004328 (-0.000124) | 0.073266 / 0.004250 (0.069015) | 0.050444 / 0.037052 (0.013392) | 0.372999 / 0.258489 (0.114510) | 0.436032 / 0.293841 (0.142191) | 0.035432 / 0.128546 (-0.093114) | 0.012581 / 0.075646 (-0.063065) | 0.085777 / 0.419271 (-0.333495) | 0.046902 / 0.043533 (0.003369) | 0.378732 / 0.255139 (0.123593) | 0.401746 / 0.283200 (0.118547) | 0.113398 / 0.141683 (-0.028285) | 1.463851 / 1.452155 (0.011696) | 1.566387 / 1.492716 (0.073670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261246 / 0.018006 (0.243240) | 0.546730 / 0.000490 (0.546241) | 0.005245 / 0.000200 (0.005045) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029441 / 0.037411 (-0.007970) | 0.111834 / 0.014526 (0.097308) | 0.122411 / 0.176557 (-0.054145) | 0.171288 / 0.737135 (-0.565847) | 0.130338 / 0.296338 (-0.166001) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433405 / 0.215209 (0.218196) | 4.315790 / 2.077655 (2.238135) | 2.121934 / 1.504120 (0.617814) | 1.924123 / 1.541195 (0.382928) | 2.029077 / 1.468490 (0.560587) | 0.710245 / 4.584777 (-3.874532) | 3.844393 / 3.745712 (0.098681) | 3.576580 / 5.269862 (-1.693281) | 1.930985 / 4.565676 (-2.634691) | 0.092186 / 0.424275 (-0.332090) | 0.012307 / 0.007607 (0.004700) | 0.533722 / 0.226044 (0.307677) | 5.324447 / 2.268929 (3.055519) | 2.615451 / 55.444624 (-52.829174) | 2.282310 / 6.876477 (-4.594167) | 2.319847 / 2.142072 (0.177774) | 0.849364 / 4.805227 (-3.955864) | 0.172722 / 6.500664 (-6.327942) | 0.064721 / 0.075469 (-0.010748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289942 / 1.841788 (-0.551846) | 15.875062 / 8.074308 (7.800754) | 14.784682 / 10.191392 (4.593290) | 0.144432 / 0.680424 (-0.535991) | 0.017703 / 0.534201 (-0.516498) | 0.424357 / 0.579283 (-0.154926) | 0.419078 / 0.434364 (-0.015286) | 0.489331 / 0.540337 (-0.051006) | 0.585284 / 1.386936 (-0.801652) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e3f4f124a1b118a5bfff5bae76b25a68aedbebbc \"CML watermark\")\n" ]

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[ "Hi ! Can you try adding the `s3://` prefix ?\r\n```python\r\nf\"s3://{s3_bucket}/{s3_dir}/{dataset_name}\"\r\n```", "Ugh, yeah that was it. Thank you!" ]

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[ "Hi ! Can you try with a more recent version of `datasets` ?", "Ok, yes it worked with the most recent version. Thanks" ]

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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) | 0.007593 / 0.011353 (-0.003760) | 0.005554 / 0.011008 (-0.005454) | 0.097663 / 0.038508 (0.059155) | 0.034915 / 0.023109 (0.011806) | 0.303116 / 0.275898 (0.027218) | 0.342376 / 0.323480 (0.018897) | 0.006044 / 0.007986 (-0.001942) | 0.004239 / 0.004328 (-0.000090) | 0.074561 / 0.004250 (0.070310) | 0.049109 / 0.037052 (0.012057) | 0.311302 / 0.258489 (0.052813) | 0.360717 / 0.293841 (0.066876) | 0.035119 / 0.128546 (-0.093428) | 0.012465 / 0.075646 (-0.063181) | 0.333648 / 0.419271 (-0.085624) | 0.051294 / 0.043533 (0.007762) | 0.297298 / 0.255139 (0.042159) | 0.321957 / 0.283200 (0.038757) | 0.108206 / 0.141683 (-0.033477) | 1.425023 / 1.452155 (-0.027132) | 1.526395 / 1.492716 (0.033678) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300694 / 0.018006 (0.282688) | 0.515141 / 0.000490 (0.514651) | 0.003965 / 0.000200 (0.003765) | 0.000260 / 0.000054 (0.000206) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029428 / 0.037411 (-0.007983) | 0.107634 / 0.014526 (0.093108) | 0.123662 / 0.176557 (-0.052895) | 0.182886 / 0.737135 (-0.554249) | 0.128361 / 0.296338 (-0.167977) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398809 / 0.215209 (0.183600) | 3.984428 / 2.077655 (1.906773) | 1.795337 / 1.504120 (0.291217) | 1.609235 / 1.541195 (0.068040) | 1.724825 / 1.468490 (0.256335) | 0.698413 / 4.584777 (-3.886364) | 3.857479 / 3.745712 (0.111767) | 2.135203 / 5.269862 (-3.134659) | 1.348458 / 4.565676 (-3.217218) | 0.086445 / 0.424275 (-0.337830) | 0.012717 / 0.007607 (0.005110) | 0.498713 / 0.226044 (0.272668) | 4.988685 / 2.268929 (2.719757) | 2.284764 / 55.444624 (-53.159860) | 1.961162 / 6.876477 (-4.915315) | 2.147514 / 2.142072 (0.005441) | 0.850334 / 4.805227 (-3.954894) | 0.171664 / 6.500664 (-6.329000) | 0.065526 / 0.075469 (-0.009943) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204398 / 1.841788 (-0.637390) | 15.625790 / 8.074308 (7.551482) | 14.614980 / 10.191392 (4.423588) | 0.167135 / 0.680424 (-0.513289) | 0.017631 / 0.534201 (-0.516570) | 0.427337 / 0.579283 (-0.151946) | 0.439203 / 0.434364 (0.004839) | 0.499670 / 0.540337 (-0.040668) | 0.587577 / 1.386936 (-0.799359) |\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) | 0.007866 / 0.011353 (-0.003486) | 0.005798 / 0.011008 (-0.005210) | 0.075803 / 0.038508 (0.037295) | 0.035773 / 0.023109 (0.012664) | 0.361965 / 0.275898 (0.086067) | 0.402780 / 0.323480 (0.079300) | 0.006521 / 0.007986 (-0.001465) | 0.004613 / 0.004328 (0.000284) | 0.075196 / 0.004250 (0.070946) | 0.055324 / 0.037052 (0.018272) | 0.363468 / 0.258489 (0.104979) | 0.410344 / 0.293841 (0.116503) | 0.036324 / 0.128546 (-0.092222) | 0.012891 / 0.075646 (-0.062755) | 0.086991 / 0.419271 (-0.332280) | 0.048082 / 0.043533 (0.004549) | 0.357238 / 0.255139 (0.102099) | 0.377065 / 0.283200 (0.093865) | 0.118586 / 0.141683 (-0.023097) | 1.463161 / 1.452155 (0.011007) | 1.582686 / 1.492716 (0.089969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267916 / 0.018006 (0.249909) | 0.540862 / 0.000490 (0.540373) | 0.003148 / 0.000200 (0.002948) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032290 / 0.037411 (-0.005122) | 0.115468 / 0.014526 (0.100943) | 0.125743 / 0.176557 (-0.050814) | 0.177469 / 0.737135 (-0.559667) | 0.133579 / 0.296338 (-0.162759) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446727 / 0.215209 (0.231518) | 4.467938 / 2.077655 (2.390284) | 2.330171 / 1.504120 (0.826052) | 2.165624 / 1.541195 (0.624429) | 2.298063 / 1.468490 (0.829573) | 0.702241 / 4.584777 (-3.882536) | 3.845302 / 3.745712 (0.099590) | 2.169278 / 5.269862 (-3.100584) | 1.401392 / 4.565676 (-3.164285) | 0.086672 / 0.424275 (-0.337603) | 0.012355 / 0.007607 (0.004748) | 0.543639 / 0.226044 (0.317595) | 5.425876 / 2.268929 (3.156947) | 2.781794 / 55.444624 (-52.662831) | 2.503724 / 6.876477 (-4.372752) | 2.622580 / 2.142072 (0.480507) | 0.847143 / 4.805227 (-3.958084) | 0.171721 / 6.500664 (-6.328943) | 0.067894 / 0.075469 (-0.007575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292194 / 1.841788 (-0.549594) | 15.497311 / 8.074308 (7.423003) | 15.002463 / 10.191392 (4.811071) | 0.152244 / 0.680424 (-0.528180) | 0.018085 / 0.534201 (-0.516116) | 0.445787 / 0.579283 (-0.133496) | 0.448960 / 0.434364 (0.014596) | 0.515319 / 0.540337 (-0.025019) | 0.623840 / 1.386936 (-0.763096) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8417a41547ce0c939bd342398be621f5ce3e340 \"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) | 0.006938 / 0.011353 (-0.004415) | 0.005100 / 0.011008 (-0.005909) | 0.096525 / 0.038508 (0.058017) | 0.033764 / 0.023109 (0.010655) | 0.301107 / 0.275898 (0.025209) | 0.333140 / 0.323480 (0.009660) | 0.005719 / 0.007986 (-0.002266) | 0.005192 / 0.004328 (0.000864) | 0.073685 / 0.004250 (0.069434) | 0.048149 / 0.037052 (0.011096) | 0.299244 / 0.258489 (0.040754) | 0.347518 / 0.293841 (0.053677) | 0.034810 / 0.128546 (-0.093736) | 0.012284 / 0.075646 (-0.063363) | 0.333600 / 0.419271 (-0.085672) | 0.050750 / 0.043533 (0.007217) | 0.299782 / 0.255139 (0.044643) | 0.322712 / 0.283200 (0.039512) | 0.105659 / 0.141683 (-0.036024) | 1.457536 / 1.452155 (0.005381) | 1.571604 / 1.492716 (0.078887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207190 / 0.018006 (0.189184) | 0.439230 / 0.000490 (0.438740) | 0.006403 / 0.000200 (0.006203) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027424 / 0.037411 (-0.009987) | 0.107180 / 0.014526 (0.092655) | 0.118356 / 0.176557 (-0.058201) | 0.175557 / 0.737135 (-0.561579) | 0.125671 / 0.296338 (-0.170668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411249 / 0.215209 (0.196039) | 4.094494 / 2.077655 (2.016839) | 1.946843 / 1.504120 (0.442723) | 1.766503 / 1.541195 (0.225308) | 1.831406 / 1.468490 (0.362916) | 0.704637 / 4.584777 (-3.880140) | 3.819204 / 3.745712 (0.073492) | 3.412598 / 5.269862 (-1.857263) | 1.796385 / 4.565676 (-2.769291) | 0.084591 / 0.424275 (-0.339684) | 0.012568 / 0.007607 (0.004961) | 0.506372 / 0.226044 (0.280327) | 5.049461 / 2.268929 (2.780532) | 2.409860 / 55.444624 (-53.034765) | 2.064514 / 6.876477 (-4.811963) | 2.192808 / 2.142072 (0.050735) | 0.833773 / 4.805227 (-3.971455) | 0.167948 / 6.500664 (-6.332716) | 0.064617 / 0.075469 (-0.010852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.174739 / 1.841788 (-0.667048) | 14.605634 / 8.074308 (6.531326) | 14.321043 / 10.191392 (4.129651) | 0.145892 / 0.680424 (-0.534532) | 0.017413 / 0.534201 (-0.516788) | 0.444940 / 0.579283 (-0.134343) | 0.430792 / 0.434364 (-0.003572) | 0.539699 / 0.540337 (-0.000638) | 0.640279 / 1.386936 (-0.746657) |\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) | 0.007159 / 0.011353 (-0.004194) | 0.005313 / 0.011008 (-0.005695) | 0.073630 / 0.038508 (0.035122) | 0.033459 / 0.023109 (0.010350) | 0.356959 / 0.275898 (0.081061) | 0.385918 / 0.323480 (0.062438) | 0.005714 / 0.007986 (-0.002272) | 0.004074 / 0.004328 (-0.000254) | 0.073278 / 0.004250 (0.069028) | 0.047193 / 0.037052 (0.010140) | 0.360300 / 0.258489 (0.101811) | 0.398052 / 0.293841 (0.104212) | 0.035670 / 0.128546 (-0.092876) | 0.012499 / 0.075646 (-0.063147) | 0.086677 / 0.419271 (-0.332595) | 0.046534 / 0.043533 (0.003001) | 0.370029 / 0.255139 (0.114890) | 0.376040 / 0.283200 (0.092841) | 0.105184 / 0.141683 (-0.036499) | 1.419779 / 1.452155 (-0.032375) | 1.538925 / 1.492716 (0.046209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220465 / 0.018006 (0.202459) | 0.438836 / 0.000490 (0.438346) | 0.000428 / 0.000200 (0.000228) | 0.000060 / 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) | 0.029114 / 0.037411 (-0.008298) | 0.111871 / 0.014526 (0.097345) | 0.124367 / 0.176557 (-0.052189) | 0.173737 / 0.737135 (-0.563398) | 0.128435 / 0.296338 (-0.167904) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440706 / 0.215209 (0.225497) | 4.414826 / 2.077655 (2.337171) | 2.128899 / 1.504120 (0.624780) | 1.929551 / 1.541195 (0.388357) | 2.013130 / 1.468490 (0.544640) | 0.708566 / 4.584777 (-3.876211) | 3.846459 / 3.745712 (0.100747) | 2.158829 / 5.269862 (-3.111032) | 1.339454 / 4.565676 (-3.226223) | 0.086345 / 0.424275 (-0.337930) | 0.012085 / 0.007607 (0.004478) | 0.546360 / 0.226044 (0.320316) | 5.461612 / 2.268929 (3.192683) | 2.657388 / 55.444624 (-52.787237) | 2.298403 / 6.876477 (-4.578074) | 2.344572 / 2.142072 (0.202499) | 0.844276 / 4.805227 (-3.960951) | 0.170225 / 6.500664 (-6.330439) | 0.064684 / 0.075469 (-0.010785) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265114 / 1.841788 (-0.576674) | 15.058156 / 8.074308 (6.983848) | 14.485182 / 10.191392 (4.293790) | 0.165960 / 0.680424 (-0.514464) | 0.017481 / 0.534201 (-0.516719) | 0.425141 / 0.579283 (-0.154142) | 0.434883 / 0.434364 (0.000519) | 0.506701 / 0.540337 (-0.033637) | 0.613240 / 1.386936 (-0.773697) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f019dffffb214b44b30dd9ac56fdea12259e148 \"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) | 0.007651 / 0.011353 (-0.003702) | 0.005503 / 0.011008 (-0.005505) | 0.098751 / 0.038508 (0.060243) | 0.036822 / 0.023109 (0.013713) | 0.340754 / 0.275898 (0.064856) | 0.387247 / 0.323480 (0.063767) | 0.006513 / 0.007986 (-0.001473) | 0.006135 / 0.004328 (0.001807) | 0.073656 / 0.004250 (0.069406) | 0.055508 / 0.037052 (0.018456) | 0.352493 / 0.258489 (0.094004) | 0.408003 / 0.293841 (0.114162) | 0.036346 / 0.128546 (-0.092201) | 0.012562 / 0.075646 (-0.063085) | 0.335111 / 0.419271 (-0.084160) | 0.051928 / 0.043533 (0.008395) | 0.339405 / 0.255139 (0.084266) | 0.366840 / 0.283200 (0.083640) | 0.114353 / 0.141683 (-0.027330) | 1.449062 / 1.452155 (-0.003092) | 1.567310 / 1.492716 (0.074594) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262975 / 0.018006 (0.244968) | 0.570302 / 0.000490 (0.569813) | 0.003419 / 0.000200 (0.003219) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027363 / 0.037411 (-0.010049) | 0.109033 / 0.014526 (0.094507) | 0.119048 / 0.176557 (-0.057509) | 0.175891 / 0.737135 (-0.561244) | 0.124577 / 0.296338 (-0.171762) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397988 / 0.215209 (0.182779) | 3.993210 / 2.077655 (1.915555) | 1.809275 / 1.504120 (0.305155) | 1.614664 / 1.541195 (0.073469) | 1.723650 / 1.468490 (0.255159) | 0.698484 / 4.584777 (-3.886293) | 3.914135 / 3.745712 (0.168423) | 2.142622 / 5.269862 (-3.127239) | 1.360215 / 4.565676 (-3.205461) | 0.086340 / 0.424275 (-0.337935) | 0.012836 / 0.007607 (0.005229) | 0.500728 / 0.226044 (0.274684) | 5.006744 / 2.268929 (2.737815) | 2.350668 / 55.444624 (-53.093956) | 1.979816 / 6.876477 (-4.896660) | 2.190159 / 2.142072 (0.048087) | 0.854063 / 4.805227 (-3.951164) | 0.170203 / 6.500664 (-6.330461) | 0.066903 / 0.075469 (-0.008566) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.184012 / 1.841788 (-0.657775) | 15.407350 / 8.074308 (7.333042) | 14.758180 / 10.191392 (4.566788) | 0.169280 / 0.680424 (-0.511144) | 0.017419 / 0.534201 (-0.516781) | 0.434359 / 0.579283 (-0.144925) | 0.442515 / 0.434364 (0.008151) | 0.503132 / 0.540337 (-0.037205) | 0.602589 / 1.386936 (-0.784347) |\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) | 0.008022 / 0.011353 (-0.003331) | 0.005473 / 0.011008 (-0.005535) | 0.076106 / 0.038508 (0.037598) | 0.037065 / 0.023109 (0.013956) | 0.380039 / 0.275898 (0.104141) | 0.394205 / 0.323480 (0.070725) | 0.006447 / 0.007986 (-0.001539) | 0.006011 / 0.004328 (0.001682) | 0.075236 / 0.004250 (0.070985) | 0.054425 / 0.037052 (0.017372) | 0.381707 / 0.258489 (0.123218) | 0.411237 / 0.293841 (0.117396) | 0.037222 / 0.128546 (-0.091324) | 0.012627 / 0.075646 (-0.063020) | 0.086733 / 0.419271 (-0.332538) | 0.053857 / 0.043533 (0.010324) | 0.373374 / 0.255139 (0.118235) | 0.381680 / 0.283200 (0.098480) | 0.121962 / 0.141683 (-0.019721) | 1.430804 / 1.452155 (-0.021351) | 1.562517 / 1.492716 (0.069801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262034 / 0.018006 (0.244028) | 0.563497 / 0.000490 (0.563007) | 0.002726 / 0.000200 (0.002526) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031071 / 0.037411 (-0.006341) | 0.111983 / 0.014526 (0.097457) | 0.126634 / 0.176557 (-0.049923) | 0.177511 / 0.737135 (-0.559625) | 0.132599 / 0.296338 (-0.163739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436148 / 0.215209 (0.220939) | 4.344850 / 2.077655 (2.267195) | 2.105877 / 1.504120 (0.601757) | 1.920934 / 1.541195 (0.379739) | 2.072930 / 1.468490 (0.604440) | 0.701793 / 4.584777 (-3.882984) | 3.841621 / 3.745712 (0.095909) | 3.602550 / 5.269862 (-1.667311) | 1.775999 / 4.565676 (-2.789677) | 0.086024 / 0.424275 (-0.338251) | 0.012275 / 0.007607 (0.004668) | 0.532815 / 0.226044 (0.306770) | 5.336273 / 2.268929 (3.067344) | 2.638842 / 55.444624 (-52.805782) | 2.301842 / 6.876477 (-4.574635) | 2.407448 / 2.142072 (0.265376) | 0.855836 / 4.805227 (-3.949392) | 0.170348 / 6.500664 (-6.330317) | 0.066926 / 0.075469 (-0.008543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291515 / 1.841788 (-0.550272) | 15.869825 / 8.074308 (7.795517) | 15.068227 / 10.191392 (4.876835) | 0.156953 / 0.680424 (-0.523471) | 0.017761 / 0.534201 (-0.516440) | 0.429515 / 0.579283 (-0.149768) | 0.432758 / 0.434364 (-0.001605) | 0.500080 / 0.540337 (-0.040258) | 0.601451 / 1.386936 (-0.785485) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b148b09da2074fcaba0538a23c7f46d28d387c \"CML watermark\")\n", "Will need to take https://github.com/huggingface/datasets/pull/5810 into account if it gets merged before this one", "<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) | 0.006914 / 0.011353 (-0.004439) | 0.004727 / 0.011008 (-0.006281) | 0.098880 / 0.038508 (0.060372) | 0.036663 / 0.023109 (0.013554) | 0.317575 / 0.275898 (0.041677) | 0.360301 / 0.323480 (0.036821) | 0.006084 / 0.007986 (-0.001901) | 0.004118 / 0.004328 (-0.000210) | 0.074330 / 0.004250 (0.070079) | 0.042422 / 0.037052 (0.005369) | 0.335625 / 0.258489 (0.077136) | 0.366616 / 0.293841 (0.072775) | 0.028523 / 0.128546 (-0.100023) | 0.008883 / 0.075646 (-0.066763) | 0.332475 / 0.419271 (-0.086797) | 0.051746 / 0.043533 (0.008214) | 0.324952 / 0.255139 (0.069813) | 0.339660 / 0.283200 (0.056460) | 0.103714 / 0.141683 (-0.037969) | 1.472130 / 1.452155 (0.019976) | 1.516548 / 1.492716 (0.023831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229538 / 0.018006 (0.211532) | 0.449077 / 0.000490 (0.448588) | 0.003707 / 0.000200 (0.003507) | 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) | 0.027897 / 0.037411 (-0.009514) | 0.115452 / 0.014526 (0.100926) | 0.118830 / 0.176557 (-0.057726) | 0.176228 / 0.737135 (-0.560907) | 0.125966 / 0.296338 (-0.170372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436947 / 0.215209 (0.221738) | 4.355687 / 2.077655 (2.278033) | 2.195857 / 1.504120 (0.691737) | 2.028133 / 1.541195 (0.486938) | 2.119872 / 1.468490 (0.651382) | 0.524256 / 4.584777 (-4.060521) | 3.864064 / 3.745712 (0.118352) | 3.446181 / 5.269862 (-1.823680) | 1.610307 / 4.565676 (-2.955370) | 0.065981 / 0.424275 (-0.358294) | 0.012172 / 0.007607 (0.004565) | 0.545341 / 0.226044 (0.319297) | 5.451728 / 2.268929 (3.182800) | 2.690734 / 55.444624 (-52.753890) | 2.368203 / 6.876477 (-4.508274) | 2.549533 / 2.142072 (0.407460) | 0.651296 / 4.805227 (-4.153931) | 0.143697 / 6.500664 (-6.356968) | 0.065170 / 0.075469 (-0.010299) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198898 / 1.841788 (-0.642890) | 15.349348 / 8.074308 (7.275040) | 15.314467 / 10.191392 (5.123075) | 0.177219 / 0.680424 (-0.503205) | 0.018223 / 0.534201 (-0.515978) | 0.396209 / 0.579283 (-0.183074) | 0.427810 / 0.434364 (-0.006554) | 0.475107 / 0.540337 (-0.065230) | 0.561224 / 1.386936 (-0.825712) |\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) | 0.007024 / 0.011353 (-0.004329) | 0.004851 / 0.011008 (-0.006157) | 0.075031 / 0.038508 (0.036523) | 0.036411 / 0.023109 (0.013302) | 0.375999 / 0.275898 (0.100101) | 0.433033 / 0.323480 (0.109553) | 0.006089 / 0.007986 (-0.001897) | 0.005638 / 0.004328 (0.001309) | 0.072599 / 0.004250 (0.068348) | 0.048489 / 0.037052 (0.011436) | 0.381807 / 0.258489 (0.123318) | 0.441531 / 0.293841 (0.147691) | 0.029044 / 0.128546 (-0.099503) | 0.009052 / 0.075646 (-0.066595) | 0.080086 / 0.419271 (-0.339186) | 0.046919 / 0.043533 (0.003386) | 0.360399 / 0.255139 (0.105260) | 0.405445 / 0.283200 (0.122245) | 0.108815 / 0.141683 (-0.032868) | 1.415168 / 1.452155 (-0.036987) | 1.511756 / 1.492716 (0.019040) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210287 / 0.018006 (0.192281) | 0.445139 / 0.000490 (0.444650) | 0.000386 / 0.000200 (0.000186) | 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) | 0.030457 / 0.037411 (-0.006954) | 0.117225 / 0.014526 (0.102699) | 0.122833 / 0.176557 (-0.053724) | 0.170441 / 0.737135 (-0.566694) | 0.131589 / 0.296338 (-0.164750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446541 / 0.215209 (0.231332) | 4.471214 / 2.077655 (2.393560) | 2.145894 / 1.504120 (0.641774) | 1.958113 / 1.541195 (0.416919) | 2.069623 / 1.468490 (0.601132) | 0.527562 / 4.584777 (-4.057215) | 3.838285 / 3.745712 (0.092573) | 1.884780 / 5.269862 (-3.385081) | 1.088124 / 4.565676 (-3.477553) | 0.066099 / 0.424275 (-0.358176) | 0.011973 / 0.007607 (0.004366) | 0.540369 / 0.226044 (0.314325) | 5.403554 / 2.268929 (3.134626) | 2.749920 / 55.444624 (-52.694704) | 2.543169 / 6.876477 (-4.333308) | 2.403116 / 2.142072 (0.261043) | 0.638723 / 4.805227 (-4.166505) | 0.142232 / 6.500664 (-6.358432) | 0.065551 / 0.075469 (-0.009918) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.298307 / 1.841788 (-0.543481) | 15.986177 / 8.074308 (7.911869) | 15.530453 / 10.191392 (5.339061) | 0.160138 / 0.680424 (-0.520286) | 0.017988 / 0.534201 (-0.516213) | 0.397857 / 0.579283 (-0.181427) | 0.435071 / 0.434364 (0.000707) | 0.480096 / 0.540337 (-0.060241) | 0.589139 / 1.386936 (-0.797797) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5bd9c974e08e059ce36dc0843256747016e843c5 \"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) | 0.006976 / 0.011353 (-0.004377) | 0.005068 / 0.011008 (-0.005940) | 0.098178 / 0.038508 (0.059670) | 0.035167 / 0.023109 (0.012057) | 0.324093 / 0.275898 (0.048195) | 0.350749 / 0.323480 (0.027269) | 0.006128 / 0.007986 (-0.001858) | 0.004361 / 0.004328 (0.000033) | 0.075412 / 0.004250 (0.071161) | 0.052083 / 0.037052 (0.015031) | 0.326726 / 0.258489 (0.068237) | 0.371450 / 0.293841 (0.077609) | 0.028522 / 0.128546 (-0.100025) | 0.009210 / 0.075646 (-0.066436) | 0.329296 / 0.419271 (-0.089976) | 0.051182 / 0.043533 (0.007649) | 0.319863 / 0.255139 (0.064724) | 0.329140 / 0.283200 (0.045941) | 0.111653 / 0.141683 (-0.030030) | 1.464205 / 1.452155 (0.012050) | 1.555779 / 1.492716 (0.063062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282372 / 0.018006 (0.264366) | 0.569227 / 0.000490 (0.568737) | 0.005289 / 0.000200 (0.005089) | 0.000095 / 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) | 0.029875 / 0.037411 (-0.007537) | 0.111889 / 0.014526 (0.097364) | 0.125678 / 0.176557 (-0.050878) | 0.184695 / 0.737135 (-0.552441) | 0.129737 / 0.296338 (-0.166602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417404 / 0.215209 (0.202195) | 4.172367 / 2.077655 (2.094712) | 2.008088 / 1.504120 (0.503968) | 1.813182 / 1.541195 (0.271988) | 1.882727 / 1.468490 (0.414237) | 0.525764 / 4.584777 (-4.059013) | 3.815202 / 3.745712 (0.069490) | 1.884197 / 5.269862 (-3.385664) | 1.073779 / 4.565676 (-3.491897) | 0.066125 / 0.424275 (-0.358150) | 0.012473 / 0.007607 (0.004866) | 0.522197 / 0.226044 (0.296153) | 5.218486 / 2.268929 (2.949557) | 2.413846 / 55.444624 (-53.030779) | 2.093298 / 6.876477 (-4.783179) | 2.320583 / 2.142072 (0.178511) | 0.648832 / 4.805227 (-4.156395) | 0.146168 / 6.500664 (-6.354496) | 0.065869 / 0.075469 (-0.009600) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.181859 / 1.841788 (-0.659929) | 15.369517 / 8.074308 (7.295209) | 14.896270 / 10.191392 (4.704878) | 0.146793 / 0.680424 (-0.533630) | 0.017960 / 0.534201 (-0.516241) | 0.421801 / 0.579283 (-0.157482) | 0.438357 / 0.434364 (0.003993) | 0.524554 / 0.540337 (-0.015783) | 0.621041 / 1.386936 (-0.765895) |\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) | 0.007104 / 0.011353 (-0.004249) | 0.004895 / 0.011008 (-0.006113) | 0.075641 / 0.038508 (0.037133) | 0.034821 / 0.023109 (0.011712) | 0.363875 / 0.275898 (0.087977) | 0.403042 / 0.323480 (0.079562) | 0.006747 / 0.007986 (-0.001238) | 0.005793 / 0.004328 (0.001465) | 0.074709 / 0.004250 (0.070458) | 0.058801 / 0.037052 (0.021749) | 0.366900 / 0.258489 (0.108411) | 0.414442 / 0.293841 (0.120601) | 0.029099 / 0.128546 (-0.099448) | 0.009394 / 0.075646 (-0.066253) | 0.082612 / 0.419271 (-0.336659) | 0.049076 / 0.043533 (0.005543) | 0.358828 / 0.255139 (0.103689) | 0.378261 / 0.283200 (0.095061) | 0.122147 / 0.141683 (-0.019535) | 1.454155 / 1.452155 (0.002000) | 1.572437 / 1.492716 (0.079720) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293133 / 0.018006 (0.275127) | 0.536785 / 0.000490 (0.536295) | 0.000457 / 0.000200 (0.000257) | 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) | 0.031046 / 0.037411 (-0.006366) | 0.113929 / 0.014526 (0.099403) | 0.126222 / 0.176557 (-0.050335) | 0.173992 / 0.737135 (-0.563143) | 0.129635 / 0.296338 (-0.166704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441984 / 0.215209 (0.226775) | 4.406002 / 2.077655 (2.328348) | 2.173912 / 1.504120 (0.669792) | 2.000507 / 1.541195 (0.459312) | 2.172766 / 1.468490 (0.704276) | 0.524530 / 4.584777 (-4.060247) | 3.758827 / 3.745712 (0.013115) | 1.886701 / 5.269862 (-3.383160) | 1.073601 / 4.565676 (-3.492075) | 0.066137 / 0.424275 (-0.358139) | 0.011926 / 0.007607 (0.004319) | 0.541103 / 0.226044 (0.315059) | 5.404162 / 2.268929 (3.135233) | 2.634271 / 55.444624 (-52.810354) | 2.366156 / 6.876477 (-4.510321) | 2.566877 / 2.142072 (0.424804) | 0.639088 / 4.805227 (-4.166139) | 0.141810 / 6.500664 (-6.358854) | 0.065446 / 0.075469 (-0.010023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.288173 / 1.841788 (-0.553614) | 15.897051 / 8.074308 (7.822743) | 15.243404 / 10.191392 (5.052012) | 0.162380 / 0.680424 (-0.518043) | 0.017716 / 0.534201 (-0.516485) | 0.396400 / 0.579283 (-0.182883) | 0.420479 / 0.434364 (-0.013885) | 0.476238 / 0.540337 (-0.064099) | 0.583039 / 1.386936 (-0.803897) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bd373f69f12e926f4e2a489c14df36c38ce07bcc \"CML watermark\")\n", "I fixed the docstring and type hint", "<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) | 0.006310 / 0.011353 (-0.005043) | 0.004297 / 0.011008 (-0.006711) | 0.098288 / 0.038508 (0.059780) | 0.029295 / 0.023109 (0.006185) | 0.386804 / 0.275898 (0.110906) | 0.425717 / 0.323480 (0.102237) | 0.005516 / 0.007986 (-0.002470) | 0.005058 / 0.004328 (0.000730) | 0.074318 / 0.004250 (0.070068) | 0.040609 / 0.037052 (0.003557) | 0.388159 / 0.258489 (0.129670) | 0.428683 / 0.293841 (0.134842) | 0.026207 / 0.128546 (-0.102340) | 0.008655 / 0.075646 (-0.066991) | 0.321601 / 0.419271 (-0.097671) | 0.055329 / 0.043533 (0.011796) | 0.390452 / 0.255139 (0.135313) | 0.409084 / 0.283200 (0.125884) | 0.099555 / 0.141683 (-0.042128) | 1.484289 / 1.452155 (0.032134) | 1.549892 / 1.492716 (0.057176) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219466 / 0.018006 (0.201460) | 0.437288 / 0.000490 (0.436798) | 0.003556 / 0.000200 (0.003356) | 0.000080 / 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) | 0.023876 / 0.037411 (-0.013535) | 0.100205 / 0.014526 (0.085679) | 0.106365 / 0.176557 (-0.070191) | 0.164353 / 0.737135 (-0.572782) | 0.109987 / 0.296338 (-0.186352) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418819 / 0.215209 (0.203610) | 4.168558 / 2.077655 (2.090903) | 1.862883 / 1.504120 (0.358764) | 1.673308 / 1.541195 (0.132114) | 1.742338 / 1.468490 (0.273848) | 0.550113 / 4.584777 (-4.034664) | 3.492085 / 3.745712 (-0.253627) | 1.734579 / 5.269862 (-3.535283) | 1.006876 / 4.565676 (-3.558801) | 0.068014 / 0.424275 (-0.356261) | 0.012242 / 0.007607 (0.004634) | 0.520633 / 0.226044 (0.294588) | 5.214095 / 2.268929 (2.945167) | 2.319282 / 55.444624 (-53.125343) | 1.979521 / 6.876477 (-4.896956) | 2.099595 / 2.142072 (-0.042477) | 0.659306 / 4.805227 (-4.145921) | 0.135282 / 6.500664 (-6.365382) | 0.067417 / 0.075469 (-0.008052) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.232099 / 1.841788 (-0.609689) | 13.967219 / 8.074308 (5.892910) | 14.347105 / 10.191392 (4.155713) | 0.146360 / 0.680424 (-0.534063) | 0.017021 / 0.534201 (-0.517180) | 0.363254 / 0.579283 (-0.216030) | 0.404391 / 0.434364 (-0.029973) | 0.428670 / 0.540337 (-0.111668) | 0.514942 / 1.386936 (-0.871994) |\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) | 0.006360 / 0.011353 (-0.004993) | 0.004160 / 0.011008 (-0.006848) | 0.074856 / 0.038508 (0.036347) | 0.028624 / 0.023109 (0.005515) | 0.355624 / 0.275898 (0.079726) | 0.403678 / 0.323480 (0.080198) | 0.005253 / 0.007986 (-0.002732) | 0.004808 / 0.004328 (0.000480) | 0.074215 / 0.004250 (0.069964) | 0.040641 / 0.037052 (0.003588) | 0.358473 / 0.258489 (0.099984) | 0.414442 / 0.293841 (0.120601) | 0.025595 / 0.128546 (-0.102951) | 0.008506 / 0.075646 (-0.067140) | 0.081547 / 0.419271 (-0.337725) | 0.039719 / 0.043533 (-0.003814) | 0.355420 / 0.255139 (0.100281) | 0.380953 / 0.283200 (0.097753) | 0.100064 / 0.141683 (-0.041618) | 1.459639 / 1.452155 (0.007484) | 1.557288 / 1.492716 (0.064572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232837 / 0.018006 (0.214831) | 0.424788 / 0.000490 (0.424298) | 0.000397 / 0.000200 (0.000197) | 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) | 0.026156 / 0.037411 (-0.011256) | 0.103633 / 0.014526 (0.089107) | 0.109633 / 0.176557 (-0.066923) | 0.159407 / 0.737135 (-0.577728) | 0.113874 / 0.296338 (-0.182465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471980 / 0.215209 (0.256771) | 4.724424 / 2.077655 (2.646769) | 2.459950 / 1.504120 (0.955830) | 2.280926 / 1.541195 (0.739731) | 2.368478 / 1.468490 (0.899987) | 0.552809 / 4.584777 (-4.031968) | 3.461985 / 3.745712 (-0.283728) | 1.757060 / 5.269862 (-3.512802) | 1.009599 / 4.565676 (-3.556077) | 0.068407 / 0.424275 (-0.355868) | 0.012341 / 0.007607 (0.004734) | 0.576287 / 0.226044 (0.350242) | 5.767331 / 2.268929 (3.498402) | 2.965743 / 55.444624 (-52.478882) | 2.644935 / 6.876477 (-4.231542) | 2.699663 / 2.142072 (0.557591) | 0.656005 / 4.805227 (-4.149222) | 0.136315 / 6.500664 (-6.364349) | 0.068355 / 0.075469 (-0.007114) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308301 / 1.841788 (-0.533486) | 14.587268 / 8.074308 (6.512960) | 14.385670 / 10.191392 (4.194278) | 0.148154 / 0.680424 (-0.532270) | 0.016798 / 0.534201 (-0.517402) | 0.360761 / 0.579283 (-0.218523) | 0.392566 / 0.434364 (-0.041798) | 0.431604 / 0.540337 (-0.108734) | 0.528463 / 1.386936 (-0.858473) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2778e1ab255545cb2171379fd2276c85768a2ad \"CML watermark\")\n", "let me know if it sounds good for you now @albertvillanova :)", "<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) | 0.008414 / 0.011353 (-0.002939) | 0.005320 / 0.011008 (-0.005688) | 0.115585 / 0.038508 (0.077077) | 0.040815 / 0.023109 (0.017706) | 0.363453 / 0.275898 (0.087555) | 0.385954 / 0.323480 (0.062474) | 0.006463 / 0.007986 (-0.001523) | 0.005571 / 0.004328 (0.001242) | 0.084831 / 0.004250 (0.080581) | 0.050294 / 0.037052 (0.013242) | 0.375684 / 0.258489 (0.117195) | 0.394672 / 0.293841 (0.100831) | 0.033618 / 0.128546 (-0.094928) | 0.010451 / 0.075646 (-0.065195) | 0.388937 / 0.419271 (-0.030334) | 0.059974 / 0.043533 (0.016441) | 0.360437 / 0.255139 (0.105298) | 0.375149 / 0.283200 (0.091950) | 0.118397 / 0.141683 (-0.023286) | 1.726759 / 1.452155 (0.274604) | 1.811928 / 1.492716 (0.319212) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239186 / 0.018006 (0.221180) | 0.483728 / 0.000490 (0.483238) | 0.003285 / 0.000200 (0.003085) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030514 / 0.037411 (-0.006898) | 0.127111 / 0.014526 (0.112585) | 0.136185 / 0.176557 (-0.040371) | 0.204541 / 0.737135 (-0.532594) | 0.143228 / 0.296338 (-0.153111) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465840 / 0.215209 (0.250631) | 4.611160 / 2.077655 (2.533506) | 2.119307 / 1.504120 (0.615187) | 1.882463 / 1.541195 (0.341268) | 1.946067 / 1.468490 (0.477577) | 0.602352 / 4.584777 (-3.982425) | 4.576313 / 3.745712 (0.830601) | 2.112860 / 5.269862 (-3.157001) | 1.224388 / 4.565676 (-3.341289) | 0.073808 / 0.424275 (-0.350467) | 0.013157 / 0.007607 (0.005550) | 0.592208 / 0.226044 (0.366163) | 5.948971 / 2.268929 (3.680042) | 2.690144 / 55.444624 (-52.754480) | 2.236489 / 6.876477 (-4.639987) | 2.423617 / 2.142072 (0.281545) | 0.752053 / 4.805227 (-4.053175) | 0.168185 / 6.500664 (-6.332480) | 0.075454 / 0.075469 (-0.000015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.407432 / 1.841788 (-0.434356) | 17.054545 / 8.074308 (8.980236) | 15.661362 / 10.191392 (5.469970) | 0.175027 / 0.680424 (-0.505397) | 0.020262 / 0.534201 (-0.513939) | 0.479052 / 0.579283 (-0.100231) | 0.509829 / 0.434364 (0.075465) | 0.601935 / 0.540337 (0.061598) | 0.726754 / 1.386936 (-0.660182) |\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) | 0.007698 / 0.011353 (-0.003655) | 0.005267 / 0.011008 (-0.005741) | 0.085832 / 0.038508 (0.047324) | 0.041974 / 0.023109 (0.018865) | 0.418966 / 0.275898 (0.143068) | 0.466314 / 0.323480 (0.142834) | 0.006580 / 0.007986 (-0.001406) | 0.007063 / 0.004328 (0.002735) | 0.087120 / 0.004250 (0.082870) | 0.054908 / 0.037052 (0.017856) | 0.423813 / 0.258489 (0.165323) | 0.489878 / 0.293841 (0.196037) | 0.032823 / 0.128546 (-0.095723) | 0.010471 / 0.075646 (-0.065175) | 0.095839 / 0.419271 (-0.323432) | 0.056421 / 0.043533 (0.012888) | 0.420526 / 0.255139 (0.165387) | 0.447975 / 0.283200 (0.164775) | 0.126604 / 0.141683 (-0.015079) | 1.723097 / 1.452155 (0.270942) | 1.819539 / 1.492716 (0.326822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279604 / 0.018006 (0.261598) | 0.496129 / 0.000490 (0.495639) | 0.005419 / 0.000200 (0.005219) | 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) | 0.035069 / 0.037411 (-0.002343) | 0.133064 / 0.014526 (0.118538) | 0.145404 / 0.176557 (-0.031152) | 0.205237 / 0.737135 (-0.531898) | 0.150684 / 0.296338 (-0.145654) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513596 / 0.215209 (0.298387) | 5.104861 / 2.077655 (3.027206) | 2.487908 / 1.504120 (0.983788) | 2.271383 / 1.541195 (0.730188) | 2.421043 / 1.468490 (0.952553) | 0.625204 / 4.584777 (-3.959573) | 4.555389 / 3.745712 (0.809677) | 4.181518 / 5.269862 (-1.088344) | 1.676059 / 4.565676 (-2.889617) | 0.078786 / 0.424275 (-0.345489) | 0.014186 / 0.007607 (0.006579) | 0.638360 / 0.226044 (0.412315) | 6.367915 / 2.268929 (4.098986) | 3.095175 / 55.444624 (-52.349449) | 2.706707 / 6.876477 (-4.169769) | 2.735907 / 2.142072 (0.593835) | 0.756323 / 4.805227 (-4.048905) | 0.164783 / 6.500664 (-6.335881) | 0.076291 / 0.075469 (0.000822) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.667058 / 1.841788 (-0.174730) | 18.687459 / 8.074308 (10.613151) | 17.111596 / 10.191392 (6.920204) | 0.167218 / 0.680424 (-0.513206) | 0.020995 / 0.534201 (-0.513206) | 0.463985 / 0.579283 (-0.115298) | 0.502705 / 0.434364 (0.068341) | 0.562877 / 0.540337 (0.022540) | 0.682249 / 1.386936 (-0.704687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#028822a5d657f6c1251f61b56a701c4d7d2ab0a7 \"CML watermark\")\n", "> Maybe we should fix all the tests in test_iterable_dataset.py that contain .with_format(\"torch\")?\r\n\r\nthey're updated in https://github.com/huggingface/datasets/pull/5852", "<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) | 0.005931 / 0.011353 (-0.005421) | 0.004004 / 0.011008 (-0.007004) | 0.098632 / 0.038508 (0.060124) | 0.027820 / 0.023109 (0.004711) | 0.302944 / 0.275898 (0.027046) | 0.332684 / 0.323480 (0.009204) | 0.005529 / 0.007986 (-0.002457) | 0.004814 / 0.004328 (0.000485) | 0.074477 / 0.004250 (0.070227) | 0.034875 / 0.037052 (-0.002178) | 0.304542 / 0.258489 (0.046053) | 0.342853 / 0.293841 (0.049012) | 0.025263 / 0.128546 (-0.103283) | 0.008558 / 0.075646 (-0.067089) | 0.322522 / 0.419271 (-0.096750) | 0.043980 / 0.043533 (0.000447) | 0.306618 / 0.255139 (0.051479) | 0.331692 / 0.283200 (0.048492) | 0.087434 / 0.141683 (-0.054248) | 1.464686 / 1.452155 (0.012531) | 1.575038 / 1.492716 (0.082322) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221920 / 0.018006 (0.203914) | 0.417108 / 0.000490 (0.416619) | 0.004625 / 0.000200 (0.004425) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023493 / 0.037411 (-0.013918) | 0.096684 / 0.014526 (0.082158) | 0.102035 / 0.176557 (-0.074522) | 0.166609 / 0.737135 (-0.570526) | 0.107456 / 0.296338 (-0.188883) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418713 / 0.215209 (0.203504) | 4.156913 / 2.077655 (2.079258) | 1.869064 / 1.504120 (0.364944) | 1.666219 / 1.541195 (0.125024) | 1.676491 / 1.468490 (0.208001) | 0.553843 / 4.584777 (-4.030934) | 3.380471 / 3.745712 (-0.365241) | 2.970370 / 5.269862 (-2.299491) | 1.421597 / 4.565676 (-3.144080) | 0.068019 / 0.424275 (-0.356256) | 0.012995 / 0.007607 (0.005387) | 0.519410 / 0.226044 (0.293365) | 5.198251 / 2.268929 (2.929323) | 2.352969 / 55.444624 (-53.091655) | 2.008981 / 6.876477 (-4.867496) | 2.066519 / 2.142072 (-0.075553) | 0.658982 / 4.805227 (-4.146245) | 0.134341 / 6.500664 (-6.366323) | 0.065893 / 0.075469 (-0.009576) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.207509 / 1.841788 (-0.634279) | 13.863838 / 8.074308 (5.789530) | 13.363359 / 10.191392 (3.171967) | 0.129076 / 0.680424 (-0.551348) | 0.016818 / 0.534201 (-0.517383) | 0.357956 / 0.579283 (-0.221327) | 0.386174 / 0.434364 (-0.048189) | 0.418663 / 0.540337 (-0.121674) | 0.498708 / 1.386936 (-0.888228) |\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) | 0.006132 / 0.011353 (-0.005220) | 0.004335 / 0.011008 (-0.006673) | 0.078517 / 0.038508 (0.040009) | 0.027685 / 0.023109 (0.004576) | 0.357956 / 0.275898 (0.082058) | 0.392397 / 0.323480 (0.068918) | 0.005364 / 0.007986 (-0.002622) | 0.004922 / 0.004328 (0.000593) | 0.078061 / 0.004250 (0.073810) | 0.038889 / 0.037052 (0.001837) | 0.360952 / 0.258489 (0.102463) | 0.402790 / 0.293841 (0.108949) | 0.025542 / 0.128546 (-0.103004) | 0.008718 / 0.075646 (-0.066929) | 0.085799 / 0.419271 (-0.333472) | 0.044256 / 0.043533 (0.000723) | 0.358366 / 0.255139 (0.103227) | 0.393500 / 0.283200 (0.110300) | 0.096382 / 0.141683 (-0.045301) | 1.530889 / 1.452155 (0.078735) | 1.621007 / 1.492716 (0.128291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180572 / 0.018006 (0.162566) | 0.429478 / 0.000490 (0.428988) | 0.002966 / 0.000200 (0.002766) | 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) | 0.024530 / 0.037411 (-0.012881) | 0.101401 / 0.014526 (0.086875) | 0.108208 / 0.176557 (-0.068349) | 0.159582 / 0.737135 (-0.577554) | 0.111170 / 0.296338 (-0.185168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465768 / 0.215209 (0.250559) | 4.706311 / 2.077655 (2.628656) | 2.437756 / 1.504120 (0.933636) | 2.245694 / 1.541195 (0.704499) | 2.282637 / 1.468490 (0.814147) | 0.552752 / 4.584777 (-4.032025) | 3.432992 / 3.745712 (-0.312720) | 1.800054 / 5.269862 (-3.469808) | 1.037852 / 4.565676 (-3.527824) | 0.068240 / 0.424275 (-0.356035) | 0.012433 / 0.007607 (0.004826) | 0.574867 / 0.226044 (0.348822) | 5.707623 / 2.268929 (3.438695) | 2.909746 / 55.444624 (-52.534878) | 2.585423 / 6.876477 (-4.291054) | 2.636801 / 2.142072 (0.494729) | 0.686593 / 4.805227 (-4.118634) | 0.136633 / 6.500664 (-6.364031) | 0.068598 / 0.075469 (-0.006871) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286628 / 1.841788 (-0.555159) | 14.333258 / 8.074308 (6.258949) | 14.355793 / 10.191392 (4.164401) | 0.133459 / 0.680424 (-0.546965) | 0.017090 / 0.534201 (-0.517111) | 0.358852 / 0.579283 (-0.220431) | 0.399929 / 0.434364 (-0.034435) | 0.422838 / 0.540337 (-0.117500) | 0.515199 / 1.386936 (-0.871737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7437d0f676da8634b5655a227cb8c3508c7372a2 \"CML watermark\")\n" ]

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[ "Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.\r\n\r\nThis class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core/dataset_builder.py#L81), so feel free to re-use parts of it." ]

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[ "Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions). ", "> Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions).\r\n\r\nHi! Thank you for your reply! Everything works perfectly with your suggestion!\r\n\r\nClosing the issue.\r\n" ]

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[ "This [reply](https://discuss.huggingface.co/t/how-do-i-add-things-rows-to-an-already-saved-dataset/27423) from @mariosasko on the forums may be useful :)", "In this case, I think we can avoid the `PermissionError` by unpacking the underlying `ConcatenationTable` and saving only the newly added data blocks (in new files).", "Thanks @stevhliu and @mariosasko , so saving to individual files then loading them later, concatenating again and saving again is the recommended way. Good to know.\r\n\r\nQuestion that I hope doesn't sound rude: is this sort of thing (processing a dataset that doesn't fit in memory) outside of `datasets`'s core area of focus? Are there other tools you would recommend to do this sort of thing that play nice with `datasets`? Or is it just that I've found myself in a niche situation that hasn't specifically been catered for?" ]

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[ "Hi ! Unfortunately I couldn't reproduce on my side locally and with datasets 2.11 and python 3.10.11 on colab.\r\nWhat version of `multiprocess` are you using ?", "I've got `multiprocess` version `0.70.14`.\r\n\r\nI've done some more testing and the error only occurs in PyCharm's Python Console. It seems to be [this PyCharm bug](https://youtrack.jetbrains.com/issue/PY-51922/Multiprocessing-bug.-Can-only-run-in-debugger.), I'll close this.", "For other users facing this, my workaround is to conditionally set `num_proc` so I can work interactively in the PyCharm Python Console while developing, then when I'm ready to run on the whole dataset, run it as a script and use multiprocessing.\r\n\r\n```py\r\nmapped_ds = ds.map(\r\n my_map_function,\r\n batched=True,\r\n remove_columns=ds.column_names,\r\n num_proc=1 if \"PYCHARM_HOSTED\" in os.environ else 8,\r\n)\r\n```" ]

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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) | 0.007862 / 0.011353 (-0.003491) | 0.005747 / 0.011008 (-0.005261) | 0.106818 / 0.038508 (0.068310) | 0.036630 / 0.023109 (0.013521) | 0.344218 / 0.275898 (0.068320) | 0.398803 / 0.323480 (0.075324) | 0.006187 / 0.007986 (-0.001799) | 0.005686 / 0.004328 (0.001358) | 0.078568 / 0.004250 (0.074318) | 0.051786 / 0.037052 (0.014734) | 0.361736 / 0.258489 (0.103247) | 0.396323 / 0.293841 (0.102482) | 0.037943 / 0.128546 (-0.090603) | 0.013957 / 0.075646 (-0.061689) | 0.366782 / 0.419271 (-0.052490) | 0.054700 / 0.043533 (0.011167) | 0.349692 / 0.255139 (0.094553) | 0.366481 / 0.283200 (0.083281) | 0.117394 / 0.141683 (-0.024289) | 1.593156 / 1.452155 (0.141001) | 1.708864 / 1.492716 (0.216148) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229529 / 0.018006 (0.211523) | 0.490531 / 0.000490 (0.490042) | 0.002934 / 0.000200 (0.002734) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028074 / 0.037411 (-0.009337) | 0.122321 / 0.014526 (0.107795) | 0.129120 / 0.176557 (-0.047436) | 0.188413 / 0.737135 (-0.548722) | 0.138983 / 0.296338 (-0.157355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479350 / 0.215209 (0.264141) | 4.926201 / 2.077655 (2.848546) | 2.265557 / 1.504120 (0.761437) | 2.014580 / 1.541195 (0.473386) | 2.120517 / 1.468490 (0.652027) | 0.795334 / 4.584777 (-3.789443) | 4.509754 / 3.745712 (0.764042) | 4.328313 / 5.269862 (-0.941548) | 2.153304 / 4.565676 (-2.412373) | 0.102942 / 0.424275 (-0.321333) | 0.053504 / 0.007607 (0.045896) | 0.609392 / 0.226044 (0.383347) | 6.114048 / 2.268929 (3.845119) | 2.773306 / 55.444624 (-52.671318) | 2.443434 / 6.876477 (-4.433042) | 2.612005 / 2.142072 (0.469932) | 0.950435 / 4.805227 (-3.854792) | 0.194081 / 6.500664 (-6.306583) | 0.074513 / 0.075469 (-0.000956) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.402897 / 1.841788 (-0.438891) | 18.263033 / 8.074308 (10.188724) | 16.579809 / 10.191392 (6.388417) | 0.212319 / 0.680424 (-0.468104) | 0.020468 / 0.534201 (-0.513733) | 0.494850 / 0.579283 (-0.084433) | 0.483790 / 0.434364 (0.049426) | 0.572073 / 0.540337 (0.031735) | 0.684353 / 1.386936 (-0.702583) |\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) | 0.009732 / 0.011353 (-0.001621) | 0.005901 / 0.011008 (-0.005107) | 0.084568 / 0.038508 (0.046060) | 0.038743 / 0.023109 (0.015634) | 0.431323 / 0.275898 (0.155425) | 0.472124 / 0.323480 (0.148644) | 0.006255 / 0.007986 (-0.001731) | 0.005892 / 0.004328 (0.001563) | 0.081913 / 0.004250 (0.077662) | 0.055560 / 0.037052 (0.018507) | 0.442857 / 0.258489 (0.184368) | 0.481887 / 0.293841 (0.188046) | 0.040730 / 0.128546 (-0.087816) | 0.014339 / 0.075646 (-0.061307) | 0.099258 / 0.419271 (-0.320013) | 0.054692 / 0.043533 (0.011159) | 0.436323 / 0.255139 (0.181184) | 0.461046 / 0.283200 (0.177846) | 0.125972 / 0.141683 (-0.015710) | 1.673173 / 1.452155 (0.221018) | 1.781364 / 1.492716 (0.288648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271450 / 0.018006 (0.253444) | 0.514484 / 0.000490 (0.513994) | 0.000455 / 0.000200 (0.000255) | 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) | 0.036104 / 0.037411 (-0.001308) | 0.143306 / 0.014526 (0.128780) | 0.151105 / 0.176557 (-0.025451) | 0.210737 / 0.737135 (-0.526399) | 0.151404 / 0.296338 (-0.144934) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573613 / 0.215209 (0.358404) | 5.828222 / 2.077655 (3.750567) | 2.993028 / 1.504120 (1.488908) | 2.617900 / 1.541195 (1.076706) | 2.754673 / 1.468490 (1.286183) | 1.010624 / 4.584777 (-3.574152) | 4.971261 / 3.745712 (1.225549) | 4.382017 / 5.269862 (-0.887845) | 1.971894 / 4.565676 (-2.593782) | 0.104404 / 0.424275 (-0.319871) | 0.014595 / 0.007607 (0.006988) | 0.657684 / 0.226044 (0.431639) | 6.566151 / 2.268929 (4.297222) | 3.221378 / 55.444624 (-52.223246) | 2.809402 / 6.876477 (-4.067075) | 2.882426 / 2.142072 (0.740354) | 1.006134 / 4.805227 (-3.799093) | 0.204469 / 6.500664 (-6.296196) | 0.078147 / 0.075469 (0.002678) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.574768 / 1.841788 (-0.267020) | 18.193335 / 8.074308 (10.119027) | 17.275353 / 10.191392 (7.083961) | 0.166890 / 0.680424 (-0.513534) | 0.020612 / 0.534201 (-0.513589) | 0.496179 / 0.579283 (-0.083104) | 0.507824 / 0.434364 (0.073460) | 0.620984 / 0.540337 (0.080647) | 0.749727 / 1.386936 (-0.637209) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06988d3e01820b93ebcdc76158339fd6f67329dc \"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) | 0.006534 / 0.011353 (-0.004819) | 0.004456 / 0.011008 (-0.006553) | 0.097978 / 0.038508 (0.059470) | 0.027614 / 0.023109 (0.004505) | 0.309833 / 0.275898 (0.033935) | 0.337006 / 0.323480 (0.013526) | 0.004986 / 0.007986 (-0.002999) | 0.004521 / 0.004328 (0.000193) | 0.075053 / 0.004250 (0.070803) | 0.037095 / 0.037052 (0.000043) | 0.305430 / 0.258489 (0.046941) | 0.345298 / 0.293841 (0.051457) | 0.029784 / 0.128546 (-0.098762) | 0.011449 / 0.075646 (-0.064197) | 0.323346 / 0.419271 (-0.095925) | 0.042188 / 0.043533 (-0.001345) | 0.318653 / 0.255139 (0.063514) | 0.333799 / 0.283200 (0.050599) | 0.088194 / 0.141683 (-0.053488) | 1.511012 / 1.452155 (0.058857) | 1.578205 / 1.492716 (0.085489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229695 / 0.018006 (0.211689) | 0.413276 / 0.000490 (0.412786) | 0.009142 / 0.000200 (0.008942) | 0.000537 / 0.000054 (0.000482) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024327 / 0.037411 (-0.013084) | 0.097953 / 0.014526 (0.083427) | 0.105551 / 0.176557 (-0.071005) | 0.169397 / 0.737135 (-0.567738) | 0.109784 / 0.296338 (-0.186554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417713 / 0.215209 (0.202504) | 4.190703 / 2.077655 (2.113048) | 1.873504 / 1.504120 (0.369384) | 1.664540 / 1.541195 (0.123346) | 1.704539 / 1.468490 (0.236049) | 0.699840 / 4.584777 (-3.884937) | 3.480605 / 3.745712 (-0.265107) | 1.844229 / 5.269862 (-3.425633) | 1.155793 / 4.565676 (-3.409883) | 0.083013 / 0.424275 (-0.341262) | 0.012414 / 0.007607 (0.004807) | 0.518357 / 0.226044 (0.292313) | 5.186136 / 2.268929 (2.917207) | 2.329263 / 55.444624 (-53.115361) | 1.991395 / 6.876477 (-4.885081) | 2.074563 / 2.142072 (-0.067509) | 0.801388 / 4.805227 (-4.003839) | 0.152236 / 6.500664 (-6.348428) | 0.067414 / 0.075469 (-0.008055) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197290 / 1.841788 (-0.644497) | 13.666537 / 8.074308 (5.592229) | 13.017190 / 10.191392 (2.825798) | 0.142109 / 0.680424 (-0.538314) | 0.016321 / 0.534201 (-0.517880) | 0.378434 / 0.579283 (-0.200849) | 0.381101 / 0.434364 (-0.053263) | 0.444113 / 0.540337 (-0.096225) | 0.521448 / 1.386936 (-0.865488) |\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) | 0.006273 / 0.011353 (-0.005080) | 0.004408 / 0.011008 (-0.006600) | 0.077100 / 0.038508 (0.038592) | 0.027361 / 0.023109 (0.004251) | 0.358170 / 0.275898 (0.082272) | 0.390125 / 0.323480 (0.066646) | 0.004736 / 0.007986 (-0.003250) | 0.004663 / 0.004328 (0.000334) | 0.077626 / 0.004250 (0.073376) | 0.037103 / 0.037052 (0.000051) | 0.360044 / 0.258489 (0.101555) | 0.411539 / 0.293841 (0.117698) | 0.030173 / 0.128546 (-0.098373) | 0.011618 / 0.075646 (-0.064028) | 0.086036 / 0.419271 (-0.333235) | 0.039077 / 0.043533 (-0.004456) | 0.382223 / 0.255139 (0.127084) | 0.384817 / 0.283200 (0.101618) | 0.094591 / 0.141683 (-0.047092) | 1.494961 / 1.452155 (0.042807) | 1.583769 / 1.492716 (0.091053) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227467 / 0.018006 (0.209460) | 0.396648 / 0.000490 (0.396159) | 0.000382 / 0.000200 (0.000182) | 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) | 0.025346 / 0.037411 (-0.012065) | 0.102086 / 0.014526 (0.087560) | 0.108570 / 0.176557 (-0.067986) | 0.158777 / 0.737135 (-0.578359) | 0.112885 / 0.296338 (-0.183453) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460731 / 0.215209 (0.245522) | 4.556450 / 2.077655 (2.478795) | 2.258185 / 1.504120 (0.754065) | 2.122584 / 1.541195 (0.581389) | 2.224638 / 1.468490 (0.756148) | 0.691909 / 4.584777 (-3.892868) | 3.482634 / 3.745712 (-0.263078) | 2.772837 / 5.269862 (-2.497024) | 1.533897 / 4.565676 (-3.031780) | 0.083025 / 0.424275 (-0.341250) | 0.012629 / 0.007607 (0.005022) | 0.548397 / 0.226044 (0.322352) | 5.492005 / 2.268929 (3.223077) | 2.669841 / 55.444624 (-52.774784) | 2.366947 / 6.876477 (-4.509529) | 2.496795 / 2.142072 (0.354722) | 0.804868 / 4.805227 (-4.000359) | 0.151686 / 6.500664 (-6.348978) | 0.068333 / 0.075469 (-0.007136) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.320414 / 1.841788 (-0.521374) | 14.367567 / 8.074308 (6.293258) | 14.047702 / 10.191392 (3.856310) | 0.129087 / 0.680424 (-0.551337) | 0.016658 / 0.534201 (-0.517543) | 0.381949 / 0.579283 (-0.197335) | 0.390105 / 0.434364 (-0.044258) | 0.445947 / 0.540337 (-0.094390) | 0.531074 / 1.386936 (-0.855862) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c67c9f3797ecc231b34d87ddef489c1238ec4046 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5814). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @hrbigelow , I'm no expert for such a question so I'll ping @lhoestq from the `datasets` library (also this issue could be moved there if someone with permission can do it :) )", "Hi ! Many datasets are made of several files, and how they are parsed often requires a python script. Because of that, datasets like wmt14 are not available as a single file on HF. Though you can create this file using `datasets`:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"wmt14\", \"de-en\", split=\"train\")\r\n\r\nds.to_json(\"wmt14-train.json\")\r\n# OR to parquet, which is compressed:\r\n# ds.to_parquet(\"wmt14-train.parquet\")\r\n```\r\n\r\nWe are also working on providing parquet exports for all datasets, but wmt14 is not supported yet (we're rolling it out for datasets <1GB first). They're usually available in the `refs/convert/parquet` branch (empty for wmt14):\r\n\r\n<img width=\"267\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/235878909-7339f5a4-be19-4ada-85d8-8a50d23acf35.png\">\r\n", "also cc @nateraw for visibility on this (and cc @osanseviero too)", "I've requested support for creating a Kaggle dataset from an imported HF dataset repo on their \"forum\" here: https://www.kaggle.com/discussions/product-feedback/427142 (upvotes appreciated 🙂)" ]

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[ "This error means a `DatasetBuilder` subclass that generates the dataset could not be found inside the script, so make sure `dushowxa-characters/dushowxa-characters.py `is a valid dataset script (assuming `path_or_dataset` is `dushowxa-characters`)\r\n\r\nAlso, we should improve the error to make it more obvious what the problem is." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Sorry, the local test passed because it was inadvertently testing the main branch. I am currently fixing where the test failed.", "- I have fixed the bug and addressed the above two points.\r\n- I have tested locally and confirmed that the test passes.\r\n\r\nPlease check the contents. @lhoestq \r\n\r\n5715a7e64bdd2951e6705aee58d592392e1538d6", "Cool ! You can run `make style` to fix code formatting to fix the ci", "I had forgotten about it. I did it. @lhoestq \r\n00248926a37c6f1387614aa388c36fdc105a59f5", "Thanks for putting this together @yuukicammy ! Looking forward to using this new addition ASAP. \r\n@lhoestq - sorry to bother you with this, but if this looks good to you, any chance we could get this merged in? \r\n\r\nThanks again to you both! ", "Yup there's just one test to remove and we can merge", "Sorry for my understanding wrong! Correspondence has been addressed. @lhoestq \r\n ca511b7b29fdde51ffd69b58bda79220472e9e94\r\n\r\nThanks for your comment! @brianhill11 ", "<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) | 0.006788 / 0.011353 (-0.004564) | 0.004372 / 0.011008 (-0.006636) | 0.097746 / 0.038508 (0.059238) | 0.034858 / 0.023109 (0.011749) | 0.298122 / 0.275898 (0.022224) | 0.335272 / 0.323480 (0.011792) | 0.005810 / 0.007986 (-0.002175) | 0.004944 / 0.004328 (0.000616) | 0.072352 / 0.004250 (0.068101) | 0.041730 / 0.037052 (0.004678) | 0.316482 / 0.258489 (0.057992) | 0.338710 / 0.293841 (0.044869) | 0.027975 / 0.128546 (-0.100571) | 0.008746 / 0.075646 (-0.066901) | 0.329336 / 0.419271 (-0.089935) | 0.051327 / 0.043533 (0.007794) | 0.300695 / 0.255139 (0.045556) | 0.322813 / 0.283200 (0.039613) | 0.101133 / 0.141683 (-0.040550) | 1.422767 / 1.452155 (-0.029388) | 1.538364 / 1.492716 (0.045648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.016698 / 0.018006 (-0.001308) | 0.447042 / 0.000490 (0.446552) | 0.007609 / 0.000200 (0.007409) | 0.000277 / 0.000054 (0.000223) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026732 / 0.037411 (-0.010679) | 0.108295 / 0.014526 (0.093769) | 0.116905 / 0.176557 (-0.059652) | 0.173166 / 0.737135 (-0.563969) | 0.122560 / 0.296338 (-0.173779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394893 / 0.215209 (0.179683) | 3.950314 / 2.077655 (1.872659) | 1.780576 / 1.504120 (0.276456) | 1.579855 / 1.541195 (0.038660) | 1.711197 / 1.468490 (0.242707) | 0.521469 / 4.584777 (-4.063308) | 3.838850 / 3.745712 (0.093138) | 3.101095 / 5.269862 (-2.168767) | 1.531574 / 4.565676 (-3.034102) | 0.065291 / 0.424275 (-0.358984) | 0.011979 / 0.007607 (0.004372) | 0.496543 / 0.226044 (0.270498) | 4.965446 / 2.268929 (2.696517) | 2.250788 / 55.444624 (-53.193837) | 1.923231 / 6.876477 (-4.953245) | 2.075372 / 2.142072 (-0.066700) | 0.638708 / 4.805227 (-4.166519) | 0.142048 / 6.500664 (-6.358616) | 0.064225 / 0.075469 (-0.011244) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.211799 / 1.841788 (-0.629989) | 14.791822 / 8.074308 (6.717514) | 14.274993 / 10.191392 (4.083601) | 0.163942 / 0.680424 (-0.516482) | 0.017541 / 0.534201 (-0.516660) | 0.396440 / 0.579283 (-0.182843) | 0.427502 / 0.434364 (-0.006861) | 0.494273 / 0.540337 (-0.046064) | 0.586877 / 1.386936 (-0.800059) |\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) | 0.006846 / 0.011353 (-0.004506) | 0.004854 / 0.011008 (-0.006154) | 0.075654 / 0.038508 (0.037146) | 0.034295 / 0.023109 (0.011186) | 0.378095 / 0.275898 (0.102197) | 0.407833 / 0.323480 (0.084353) | 0.006155 / 0.007986 (-0.001830) | 0.004259 / 0.004328 (-0.000070) | 0.076195 / 0.004250 (0.071944) | 0.051901 / 0.037052 (0.014849) | 0.375027 / 0.258489 (0.116538) | 0.428189 / 0.293841 (0.134348) | 0.028814 / 0.128546 (-0.099733) | 0.009209 / 0.075646 (-0.066438) | 0.083681 / 0.419271 (-0.335591) | 0.049158 / 0.043533 (0.005625) | 0.366669 / 0.255139 (0.111530) | 0.388767 / 0.283200 (0.105568) | 0.107837 / 0.141683 (-0.033845) | 1.476354 / 1.452155 (0.024199) | 1.580160 / 1.492716 (0.087443) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218900 / 0.018006 (0.200894) | 0.445475 / 0.000490 (0.444985) | 0.000423 / 0.000200 (0.000223) | 0.000063 / 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) | 0.029740 / 0.037411 (-0.007671) | 0.115192 / 0.014526 (0.100666) | 0.122439 / 0.176557 (-0.054118) | 0.170639 / 0.737135 (-0.566496) | 0.128085 / 0.296338 (-0.168254) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437745 / 0.215209 (0.222536) | 4.385695 / 2.077655 (2.308040) | 2.189893 / 1.504120 (0.685773) | 2.023160 / 1.541195 (0.481965) | 2.112798 / 1.468490 (0.644308) | 0.522497 / 4.584777 (-4.062280) | 3.881356 / 3.745712 (0.135644) | 3.206090 / 5.269862 (-2.063772) | 1.308241 / 4.565676 (-3.257435) | 0.065635 / 0.424275 (-0.358640) | 0.012288 / 0.007607 (0.004681) | 0.537265 / 0.226044 (0.311220) | 5.361641 / 2.268929 (3.092712) | 2.638941 / 55.444624 (-52.805684) | 2.344717 / 6.876477 (-4.531759) | 2.437619 / 2.142072 (0.295546) | 0.645079 / 4.805227 (-4.160149) | 0.143852 / 6.500664 (-6.356812) | 0.065796 / 0.075469 (-0.009673) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276588 / 1.841788 (-0.565200) | 15.239396 / 8.074308 (7.165088) | 13.150591 / 10.191392 (2.959199) | 0.163635 / 0.680424 (-0.516789) | 0.017533 / 0.534201 (-0.516668) | 0.397659 / 0.579283 (-0.181624) | 0.425589 / 0.434364 (-0.008774) | 0.466570 / 0.540337 (-0.073768) | 0.563953 / 1.386936 (-0.822983) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#807d5c5ed4f8db7761b92bed498b2193acce8fb7 \"CML watermark\")\n" ]

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[ "Hi ! I don't remember exactly how it was done, but maybe you have to embed `f\"{title}<sep>{text}\"` ?\r\n\r\nUsing a HF tokenizer it corresponds to doing\r\n```python\r\ntokenized = tokenizer(titles, texts)\r\n```" ]

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[ "Hi @lhoestq or other maintainers, this is ready for review, could you please take a look?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5807). All of your documentation changes will be reflected on that endpoint.", "Per the discussion in #5798, will implement with `joblibspark` instead." ]

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[ "Implementing this makes sense (e.g., `tensorflow_datasets`' imagefolder returns image filenames). Also, in Datasets 3.0, we plan only to store the bytes of an image/audio, not its path, so this feature would be useful when the path info is still needed.", "Hey @mariosasko, Can I work on this issue, this one seems interesting to implement. I have contributed to jupyterlab recently, and would love to contribute here as well. ", "@tsabbir96 if you are planning to start working on this, you can take on this issue by writing a comment with only the keyword: #self-assign", "#self-assign", "@albertvillanova thank you for letting me contribute here. \r\n@albertvillanova @mariosasko As I am totally new to this repo, could you tell me something more about this issue or perhaps give me some idea on how I can proceed with it? Thanks!", "Hello there, is this issue resolved? @tsabbir96 are you still working on it? Otherwise I would love to give it a try", "@EduardoPach This issue is still relevant, so feel free to work on it.", "Hey @mariosasko, I've taken the time to take a look at how we load the datasets usually. My main question now is about the final solution.\r\n\r\nSo the idea is that whenever we load the datasets we also add a new column in the _generate_tables() method from the builders called filename (or file_name) that should be related files contained in each split, right?\r\n\r\nDo you have any suggestions of where I could add that? ", "Is this issue still open? If yes, I'd like to work upon on it. Thanks", "> Is this issue still open? If yes, I'd like to work upon on it. Thanks\n\nDefinitely still open. I gave it a try, but then didn't get any feedback on my last question so I stopped . Feel free to work on it.", "It's still open, so feel free to work on it. This can be implemented by adding a param to the packaged builders' configs that inserts a column with file names (in `_generate_tables`) when `True`. Naming this column `file_name` sounds good to me.", "Hi is the issues still open, is see no activity since September but it shows that it is still assigned to tsabbir96. If \r\ntsabbir96 is not planning to continue, can i get it assigned to me.", "Looking forward to your implementation. I also really need this feature. \r\nThanks" ]

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[ "I can work on this. The link to the tutorial seems to be broken though @polinaeterna. ", "@isunitha98selvan would be great, thank you! which link are you talking about? I think it should work: https://huggingface.co/docs/datasets/create_dataset" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). 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) | 0.006448 / 0.011353 (-0.004905) | 0.004440 / 0.011008 (-0.006568) | 0.097837 / 0.038508 (0.059328) | 0.027754 / 0.023109 (0.004645) | 0.306462 / 0.275898 (0.030564) | 0.332454 / 0.323480 (0.008975) | 0.004984 / 0.007986 (-0.003001) | 0.004703 / 0.004328 (0.000375) | 0.075213 / 0.004250 (0.070962) | 0.036524 / 0.037052 (-0.000529) | 0.310149 / 0.258489 (0.051659) | 0.346392 / 0.293841 (0.052552) | 0.031012 / 0.128546 (-0.097534) | 0.011598 / 0.075646 (-0.064049) | 0.323066 / 0.419271 (-0.096206) | 0.042945 / 0.043533 (-0.000588) | 0.302286 / 0.255139 (0.047147) | 0.327813 / 0.283200 (0.044614) | 0.092540 / 0.141683 (-0.049143) | 1.532893 / 1.452155 (0.080739) | 1.556676 / 1.492716 (0.063960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195126 / 0.018006 (0.177120) | 0.399623 / 0.000490 (0.399133) | 0.003176 / 0.000200 (0.002976) | 0.000068 / 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) | 0.023612 / 0.037411 (-0.013799) | 0.097794 / 0.014526 (0.083268) | 0.104665 / 0.176557 (-0.071891) | 0.167145 / 0.737135 (-0.569990) | 0.108769 / 0.296338 (-0.187570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437818 / 0.215209 (0.222608) | 4.354896 / 2.077655 (2.277242) | 2.092832 / 1.504120 (0.588712) | 1.957630 / 1.541195 (0.416435) | 2.033135 / 1.468490 (0.564645) | 0.702316 / 4.584777 (-3.882461) | 3.448035 / 3.745712 (-0.297678) | 1.906762 / 5.269862 (-3.363100) | 1.253274 / 4.565676 (-3.312402) | 0.082486 / 0.424275 (-0.341789) | 0.012442 / 0.007607 (0.004835) | 0.532096 / 0.226044 (0.306052) | 5.366580 / 2.268929 (3.097652) | 2.441904 / 55.444624 (-53.002720) | 2.112116 / 6.876477 (-4.764361) | 2.185471 / 2.142072 (0.043398) | 0.797905 / 4.805227 (-4.007322) | 0.149811 / 6.500664 (-6.350853) | 0.066507 / 0.075469 (-0.008962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206300 / 1.841788 (-0.635487) | 13.620851 / 8.074308 (5.546543) | 14.190666 / 10.191392 (3.999274) | 0.142343 / 0.680424 (-0.538081) | 0.016867 / 0.534201 (-0.517334) | 0.381557 / 0.579283 (-0.197726) | 0.373935 / 0.434364 (-0.060429) | 0.437856 / 0.540337 (-0.102481) | 0.525235 / 1.386936 (-0.861701) |\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) | 0.006598 / 0.011353 (-0.004755) | 0.004487 / 0.011008 (-0.006522) | 0.077582 / 0.038508 (0.039073) | 0.028008 / 0.023109 (0.004899) | 0.341602 / 0.275898 (0.065704) | 0.377105 / 0.323480 (0.053625) | 0.004999 / 0.007986 (-0.002986) | 0.004791 / 0.004328 (0.000462) | 0.076418 / 0.004250 (0.072167) | 0.038347 / 0.037052 (0.001295) | 0.343196 / 0.258489 (0.084707) | 0.382459 / 0.293841 (0.088618) | 0.030597 / 0.128546 (-0.097950) | 0.011579 / 0.075646 (-0.064067) | 0.085876 / 0.419271 (-0.333396) | 0.043241 / 0.043533 (-0.000292) | 0.343754 / 0.255139 (0.088615) | 0.380689 / 0.283200 (0.097489) | 0.096015 / 0.141683 (-0.045668) | 1.464419 / 1.452155 (0.012264) | 1.574010 / 1.492716 (0.081294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.156433 / 0.018006 (0.138427) | 0.403179 / 0.000490 (0.402690) | 0.002415 / 0.000200 (0.002215) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024946 / 0.037411 (-0.012465) | 0.100568 / 0.014526 (0.086042) | 0.106440 / 0.176557 (-0.070117) | 0.158457 / 0.737135 (-0.578678) | 0.110774 / 0.296338 (-0.185564) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434734 / 0.215209 (0.219525) | 4.343874 / 2.077655 (2.266220) | 2.059759 / 1.504120 (0.555639) | 1.855124 / 1.541195 (0.313930) | 1.908567 / 1.468490 (0.440077) | 0.695283 / 4.584777 (-3.889494) | 3.347724 / 3.745712 (-0.397988) | 2.979498 / 5.269862 (-2.290364) | 1.532040 / 4.565676 (-3.033636) | 0.083021 / 0.424275 (-0.341254) | 0.012522 / 0.007607 (0.004915) | 0.540934 / 0.226044 (0.314890) | 5.385690 / 2.268929 (3.116762) | 2.507409 / 55.444624 (-52.937216) | 2.160537 / 6.876477 (-4.715939) | 2.269195 / 2.142072 (0.127123) | 0.804718 / 4.805227 (-4.000509) | 0.152432 / 6.500664 (-6.348232) | 0.068783 / 0.075469 (-0.006686) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294698 / 1.841788 (-0.547090) | 14.152792 / 8.074308 (6.078484) | 14.233132 / 10.191392 (4.041740) | 0.143655 / 0.680424 (-0.536768) | 0.016844 / 0.534201 (-0.517357) | 0.380246 / 0.579283 (-0.199037) | 0.381730 / 0.434364 (-0.052633) | 0.456838 / 0.540337 (-0.083499) | 0.543677 / 1.386936 (-0.843259) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b28d5610887f2e107765f5f1557679184db08214 \"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) | 0.008586 / 0.011353 (-0.002767) | 0.005886 / 0.011008 (-0.005122) | 0.114522 / 0.038508 (0.076014) | 0.040966 / 0.023109 (0.017857) | 0.366655 / 0.275898 (0.090757) | 0.408765 / 0.323480 (0.085285) | 0.006822 / 0.007986 (-0.001164) | 0.004508 / 0.004328 (0.000180) | 0.084715 / 0.004250 (0.080465) | 0.054007 / 0.037052 (0.016954) | 0.380500 / 0.258489 (0.122011) | 0.410377 / 0.293841 (0.116536) | 0.041040 / 0.128546 (-0.087507) | 0.013940 / 0.075646 (-0.061707) | 0.398456 / 0.419271 (-0.020816) | 0.059315 / 0.043533 (0.015782) | 0.353640 / 0.255139 (0.098501) | 0.388682 / 0.283200 (0.105482) | 0.121744 / 0.141683 (-0.019939) | 1.729306 / 1.452155 (0.277151) | 1.824768 / 1.492716 (0.332052) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228806 / 0.018006 (0.210800) | 0.492790 / 0.000490 (0.492300) | 0.010815 / 0.000200 (0.010615) | 0.000372 / 0.000054 (0.000318) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031750 / 0.037411 (-0.005662) | 0.127160 / 0.014526 (0.112635) | 0.136717 / 0.176557 (-0.039839) | 0.205590 / 0.737135 (-0.531545) | 0.142596 / 0.296338 (-0.153742) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486419 / 0.215209 (0.271210) | 4.858572 / 2.077655 (2.780918) | 2.173867 / 1.504120 (0.669747) | 1.934619 / 1.541195 (0.393424) | 2.104185 / 1.468490 (0.635695) | 0.837913 / 4.584777 (-3.746864) | 4.552192 / 3.745712 (0.806480) | 2.565040 / 5.269862 (-2.704822) | 1.808499 / 4.565676 (-2.757178) | 0.103283 / 0.424275 (-0.320993) | 0.015040 / 0.007607 (0.007433) | 0.602325 / 0.226044 (0.376281) | 6.038655 / 2.268929 (3.769727) | 2.759789 / 55.444624 (-52.684835) | 2.330990 / 6.876477 (-4.545487) | 2.404111 / 2.142072 (0.262038) | 1.011637 / 4.805227 (-3.793590) | 0.202142 / 6.500664 (-6.298522) | 0.079496 / 0.075469 (0.004026) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.429543 / 1.841788 (-0.412245) | 18.052409 / 8.074308 (9.978101) | 16.989154 / 10.191392 (6.797762) | 0.208981 / 0.680424 (-0.471443) | 0.020490 / 0.534201 (-0.513711) | 0.502746 / 0.579283 (-0.076537) | 0.491769 / 0.434364 (0.057405) | 0.581970 / 0.540337 (0.041632) | 0.695816 / 1.386936 (-0.691120) |\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) | 0.008449 / 0.011353 (-0.002904) | 0.006633 / 0.011008 (-0.004375) | 0.088638 / 0.038508 (0.050130) | 0.040013 / 0.023109 (0.016904) | 0.413108 / 0.275898 (0.137210) | 0.446310 / 0.323480 (0.122830) | 0.006515 / 0.007986 (-0.001471) | 0.006223 / 0.004328 (0.001894) | 0.089823 / 0.004250 (0.085573) | 0.052029 / 0.037052 (0.014977) | 0.407263 / 0.258489 (0.148774) | 0.449416 / 0.293841 (0.155576) | 0.041810 / 0.128546 (-0.086736) | 0.014604 / 0.075646 (-0.061042) | 0.103728 / 0.419271 (-0.315543) | 0.058212 / 0.043533 (0.014679) | 0.408936 / 0.255139 (0.153797) | 0.436727 / 0.283200 (0.153528) | 0.124344 / 0.141683 (-0.017339) | 1.752112 / 1.452155 (0.299957) | 1.859104 / 1.492716 (0.366387) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231172 / 0.018006 (0.213166) | 0.502974 / 0.000490 (0.502485) | 0.005586 / 0.000200 (0.005386) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034097 / 0.037411 (-0.003314) | 0.133780 / 0.014526 (0.119254) | 0.142321 / 0.176557 (-0.034236) | 0.199807 / 0.737135 (-0.537329) | 0.150073 / 0.296338 (-0.146266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515658 / 0.215209 (0.300449) | 5.129783 / 2.077655 (3.052129) | 2.534767 / 1.504120 (1.030648) | 2.352468 / 1.541195 (0.811274) | 2.430708 / 1.468490 (0.962218) | 0.850087 / 4.584777 (-3.734690) | 4.529622 / 3.745712 (0.783910) | 2.451986 / 5.269862 (-2.817876) | 1.569568 / 4.565676 (-2.996109) | 0.102907 / 0.424275 (-0.321368) | 0.014420 / 0.007607 (0.006813) | 0.635124 / 0.226044 (0.409080) | 6.260496 / 2.268929 (3.991568) | 3.094984 / 55.444624 (-52.349640) | 2.780629 / 6.876477 (-4.095847) | 2.947620 / 2.142072 (0.805548) | 1.002397 / 4.805227 (-3.802830) | 0.200502 / 6.500664 (-6.300162) | 0.076577 / 0.075469 (0.001107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.505958 / 1.841788 (-0.335829) | 18.364986 / 8.074308 (10.290678) | 16.707214 / 10.191392 (6.515822) | 0.210976 / 0.680424 (-0.469447) | 0.022077 / 0.534201 (-0.512124) | 0.516174 / 0.579283 (-0.063109) | 0.502469 / 0.434364 (0.068105) | 0.626790 / 0.540337 (0.086453) | 0.747230 / 1.386936 (-0.639706) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc5fef5b6d91f009e4101684adcb374df2c170f6 \"CML watermark\")\n" ]

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[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5803). 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) | 0.008303 / 0.011353 (-0.003050) | 0.005681 / 0.011008 (-0.005327) | 0.111830 / 0.038508 (0.073322) | 0.039222 / 0.023109 (0.016112) | 0.336773 / 0.275898 (0.060875) | 0.376673 / 0.323480 (0.053193) | 0.006756 / 0.007986 (-0.001230) | 0.006078 / 0.004328 (0.001749) | 0.083552 / 0.004250 (0.079301) | 0.054430 / 0.037052 (0.017377) | 0.337310 / 0.258489 (0.078821) | 0.386138 / 0.293841 (0.092297) | 0.040068 / 0.128546 (-0.088478) | 0.013895 / 0.075646 (-0.061751) | 0.384174 / 0.419271 (-0.035097) | 0.058244 / 0.043533 (0.014711) | 0.342410 / 0.255139 (0.087271) | 0.362417 / 0.283200 (0.079217) | 0.123470 / 0.141683 (-0.018213) | 1.662938 / 1.452155 (0.210784) | 1.786488 / 1.492716 (0.293771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232629 / 0.018006 (0.214622) | 0.478252 / 0.000490 (0.477762) | 0.008519 / 0.000200 (0.008319) | 0.000111 / 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) | 0.031222 / 0.037411 (-0.006190) | 0.125875 / 0.014526 (0.111350) | 0.138995 / 0.176557 (-0.037562) | 0.213073 / 0.737135 (-0.524062) | 0.141848 / 0.296338 (-0.154490) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463648 / 0.215209 (0.248439) | 4.582969 / 2.077655 (2.505314) | 2.104622 / 1.504120 (0.600502) | 1.887697 / 1.541195 (0.346502) | 1.946096 / 1.468490 (0.477606) | 0.809008 / 4.584777 (-3.775769) | 4.527871 / 3.745712 (0.782159) | 4.862721 / 5.269862 (-0.407141) | 2.423257 / 4.565676 (-2.142419) | 0.101080 / 0.424275 (-0.323196) | 0.014767 / 0.007607 (0.007160) | 0.574471 / 0.226044 (0.348427) | 5.746445 / 2.268929 (3.477516) | 2.682584 / 55.444624 (-52.762040) | 2.320113 / 6.876477 (-4.556364) | 2.474530 / 2.142072 (0.332458) | 0.992979 / 4.805227 (-3.812249) | 0.200812 / 6.500664 (-6.299852) | 0.076291 / 0.075469 (0.000822) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.395533 / 1.841788 (-0.446254) | 17.418803 / 8.074308 (9.344495) | 16.584875 / 10.191392 (6.393483) | 0.167739 / 0.680424 (-0.512685) | 0.020923 / 0.534201 (-0.513278) | 0.500788 / 0.579283 (-0.078496) | 0.510270 / 0.434364 (0.075906) | 0.589608 / 0.540337 (0.049270) | 0.694233 / 1.386936 (-0.692703) |\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) | 0.008440 / 0.011353 (-0.002913) | 0.005871 / 0.011008 (-0.005137) | 0.085805 / 0.038508 (0.047297) | 0.039324 / 0.023109 (0.016215) | 0.400587 / 0.275898 (0.124689) | 0.431729 / 0.323480 (0.108249) | 0.006557 / 0.007986 (-0.001429) | 0.005778 / 0.004328 (0.001450) | 0.084394 / 0.004250 (0.080144) | 0.055274 / 0.037052 (0.018222) | 0.410568 / 0.258489 (0.152079) | 0.439952 / 0.293841 (0.146111) | 0.040335 / 0.128546 (-0.088211) | 0.013968 / 0.075646 (-0.061679) | 0.098765 / 0.419271 (-0.320507) | 0.055897 / 0.043533 (0.012364) | 0.387584 / 0.255139 (0.132445) | 0.412568 / 0.283200 (0.129368) | 0.120393 / 0.141683 (-0.021290) | 1.730996 / 1.452155 (0.278841) | 1.821538 / 1.492716 (0.328822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245688 / 0.018006 (0.227682) | 0.484888 / 0.000490 (0.484398) | 0.000485 / 0.000200 (0.000285) | 0.000068 / 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) | 0.032340 / 0.037411 (-0.005072) | 0.130819 / 0.014526 (0.116293) | 0.138491 / 0.176557 (-0.038065) | 0.196902 / 0.737135 (-0.540233) | 0.145404 / 0.296338 (-0.150935) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.487643 / 0.215209 (0.272434) | 4.818956 / 2.077655 (2.741301) | 2.332316 / 1.504120 (0.828196) | 2.102018 / 1.541195 (0.560823) | 2.156743 / 1.468490 (0.688253) | 0.803365 / 4.584777 (-3.781412) | 4.308561 / 3.745712 (0.562849) | 2.373331 / 5.269862 (-2.896530) | 1.539474 / 4.565676 (-3.026202) | 0.099081 / 0.424275 (-0.325194) | 0.014627 / 0.007607 (0.007020) | 0.609883 / 0.226044 (0.383838) | 6.092402 / 2.268929 (3.823474) | 2.858137 / 55.444624 (-52.586488) | 2.463256 / 6.876477 (-4.413220) | 2.637048 / 2.142072 (0.494976) | 0.959552 / 4.805227 (-3.845676) | 0.194170 / 6.500664 (-6.306495) | 0.075231 / 0.075469 (-0.000238) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.516502 / 1.841788 (-0.325285) | 18.077893 / 8.074308 (10.003585) | 16.507961 / 10.191392 (6.316569) | 0.171643 / 0.680424 (-0.508780) | 0.020378 / 0.534201 (-0.513823) | 0.491508 / 0.579283 (-0.087775) | 0.492136 / 0.434364 (0.057772) | 0.602258 / 0.540337 (0.061920) | 0.719882 / 1.386936 (-0.667054) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#330ac3e95fd3f2d61bac31b5b9c24399a5b54723 \"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) | 0.006572 / 0.011353 (-0.004781) | 0.004647 / 0.011008 (-0.006362) | 0.098277 / 0.038508 (0.059769) | 0.027937 / 0.023109 (0.004828) | 0.339833 / 0.275898 (0.063935) | 0.398305 / 0.323480 (0.074825) | 0.005093 / 0.007986 (-0.002893) | 0.003374 / 0.004328 (-0.000954) | 0.075287 / 0.004250 (0.071037) | 0.037355 / 0.037052 (0.000303) | 0.339779 / 0.258489 (0.081290) | 0.403756 / 0.293841 (0.109915) | 0.030705 / 0.128546 (-0.097841) | 0.011596 / 0.075646 (-0.064050) | 0.323809 / 0.419271 (-0.095463) | 0.043357 / 0.043533 (-0.000176) | 0.342817 / 0.255139 (0.087678) | 0.386330 / 0.283200 (0.103130) | 0.088229 / 0.141683 (-0.053454) | 1.466017 / 1.452155 (0.013862) | 1.566551 / 1.492716 (0.073835) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196276 / 0.018006 (0.178269) | 0.420321 / 0.000490 (0.419831) | 0.002234 / 0.000200 (0.002034) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023999 / 0.037411 (-0.013412) | 0.095117 / 0.014526 (0.080592) | 0.102544 / 0.176557 (-0.074013) | 0.164796 / 0.737135 (-0.572340) | 0.107030 / 0.296338 (-0.189309) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429299 / 0.215209 (0.214089) | 4.272503 / 2.077655 (2.194849) | 2.101890 / 1.504120 (0.597771) | 1.978907 / 1.541195 (0.437713) | 2.008993 / 1.468490 (0.540503) | 0.695171 / 4.584777 (-3.889606) | 3.427050 / 3.745712 (-0.318662) | 1.892945 / 5.269862 (-3.376917) | 1.247156 / 4.565676 (-3.318521) | 0.082576 / 0.424275 (-0.341699) | 0.012526 / 0.007607 (0.004918) | 0.526338 / 0.226044 (0.300293) | 5.313855 / 2.268929 (3.044927) | 2.421134 / 55.444624 (-53.023490) | 2.072026 / 6.876477 (-4.804451) | 2.159846 / 2.142072 (0.017773) | 0.800753 / 4.805227 (-4.004474) | 0.150507 / 6.500664 (-6.350157) | 0.066378 / 0.075469 (-0.009091) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218709 / 1.841788 (-0.623079) | 13.649239 / 8.074308 (5.574931) | 13.952762 / 10.191392 (3.761370) | 0.141967 / 0.680424 (-0.538457) | 0.016443 / 0.534201 (-0.517758) | 0.380408 / 0.579283 (-0.198875) | 0.377693 / 0.434364 (-0.056671) | 0.439819 / 0.540337 (-0.100518) | 0.529667 / 1.386936 (-0.857269) |\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) | 0.006722 / 0.011353 (-0.004630) | 0.004495 / 0.011008 (-0.006513) | 0.075459 / 0.038508 (0.036951) | 0.028135 / 0.023109 (0.005026) | 0.349904 / 0.275898 (0.074006) | 0.390620 / 0.323480 (0.067140) | 0.005175 / 0.007986 (-0.002810) | 0.004720 / 0.004328 (0.000392) | 0.074243 / 0.004250 (0.069993) | 0.039084 / 0.037052 (0.002032) | 0.352486 / 0.258489 (0.093997) | 0.397549 / 0.293841 (0.103708) | 0.030596 / 0.128546 (-0.097950) | 0.011627 / 0.075646 (-0.064020) | 0.083394 / 0.419271 (-0.335878) | 0.042155 / 0.043533 (-0.001378) | 0.345668 / 0.255139 (0.090529) | 0.383474 / 0.283200 (0.100275) | 0.096530 / 0.141683 (-0.045153) | 1.493360 / 1.452155 (0.041206) | 1.572259 / 1.492716 (0.079543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.162605 / 0.018006 (0.144599) | 0.409513 / 0.000490 (0.409023) | 0.002029 / 0.000200 (0.001829) | 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) | 0.025824 / 0.037411 (-0.011588) | 0.102439 / 0.014526 (0.087913) | 0.109515 / 0.176557 (-0.067041) | 0.160650 / 0.737135 (-0.576486) | 0.112971 / 0.296338 (-0.183367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433293 / 0.215209 (0.218084) | 4.340286 / 2.077655 (2.262631) | 2.055857 / 1.504120 (0.551737) | 1.854451 / 1.541195 (0.313256) | 1.912752 / 1.468490 (0.444261) | 0.700076 / 4.584777 (-3.884701) | 3.361542 / 3.745712 (-0.384170) | 2.760204 / 5.269862 (-2.509658) | 1.477395 / 4.565676 (-3.088282) | 0.082868 / 0.424275 (-0.341407) | 0.012479 / 0.007607 (0.004872) | 0.532749 / 0.226044 (0.306704) | 5.323701 / 2.268929 (3.054772) | 2.509524 / 55.444624 (-52.935100) | 2.168668 / 6.876477 (-4.707809) | 2.259112 / 2.142072 (0.117040) | 0.806686 / 4.805227 (-3.998542) | 0.154620 / 6.500664 (-6.346044) | 0.068348 / 0.075469 (-0.007121) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316512 / 1.841788 (-0.525276) | 14.158143 / 8.074308 (6.083835) | 14.110643 / 10.191392 (3.919251) | 0.143760 / 0.680424 (-0.536664) | 0.016851 / 0.534201 (-0.517350) | 0.376594 / 0.579283 (-0.202689) | 0.386957 / 0.434364 (-0.047407) | 0.466185 / 0.540337 (-0.074152) | 0.550269 / 1.386936 (-0.836667) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8e1af7b30c94ce77abd9de732f19198e197d900c \"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) | 0.009457 / 0.011353 (-0.001896) | 0.006453 / 0.011008 (-0.004555) | 0.136392 / 0.038508 (0.097884) | 0.038378 / 0.023109 (0.015269) | 0.413171 / 0.275898 (0.137273) | 0.451605 / 0.323480 (0.128126) | 0.007123 / 0.007986 (-0.000863) | 0.006316 / 0.004328 (0.001987) | 0.103009 / 0.004250 (0.098758) | 0.049182 / 0.037052 (0.012130) | 0.398635 / 0.258489 (0.140146) | 0.463146 / 0.293841 (0.169305) | 0.056247 / 0.128546 (-0.072299) | 0.019589 / 0.075646 (-0.056058) | 0.475882 / 0.419271 (0.056610) | 0.094918 / 0.043533 (0.051385) | 0.416502 / 0.255139 (0.161363) | 0.447129 / 0.283200 (0.163929) | 0.133314 / 0.141683 (-0.008369) | 2.132888 / 1.452155 (0.680733) | 2.073383 / 1.492716 (0.580667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273037 / 0.018006 (0.255030) | 0.625675 / 0.000490 (0.625185) | 0.003449 / 0.000200 (0.003249) | 0.000185 / 0.000054 (0.000130) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031889 / 0.037411 (-0.005523) | 0.131673 / 0.014526 (0.117148) | 0.141575 / 0.176557 (-0.034982) | 0.214978 / 0.737135 (-0.522158) | 0.145586 / 0.296338 (-0.150752) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.711135 / 0.215209 (0.495926) | 7.162492 / 2.077655 (5.084837) | 2.906028 / 1.504120 (1.401908) | 2.488855 / 1.541195 (0.947660) | 2.574628 / 1.468490 (1.106138) | 1.587824 / 4.584777 (-2.996953) | 6.332962 / 3.745712 (2.587250) | 5.419578 / 5.269862 (0.149717) | 2.935413 / 4.565676 (-1.630263) | 0.169159 / 0.424275 (-0.255116) | 0.015358 / 0.007607 (0.007751) | 0.862036 / 0.226044 (0.635992) | 8.559256 / 2.268929 (6.290328) | 3.530756 / 55.444624 (-51.913868) | 2.626288 / 6.876477 (-4.250188) | 2.770063 / 2.142072 (0.627990) | 1.500116 / 4.805227 (-3.305112) | 0.265109 / 6.500664 (-6.235555) | 0.084944 / 0.075469 (0.009475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.631060 / 1.841788 (-0.210728) | 19.022827 / 8.074308 (10.948519) | 22.973632 / 10.191392 (12.782240) | 0.296265 / 0.680424 (-0.384158) | 0.032317 / 0.534201 (-0.501884) | 0.624171 / 0.579283 (0.044888) | 0.690643 / 0.434364 (0.256279) | 0.691206 / 0.540337 (0.150869) | 0.758855 / 1.386936 (-0.628081) |\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) | 0.009441 / 0.011353 (-0.001912) | 0.006270 / 0.011008 (-0.004739) | 0.110284 / 0.038508 (0.071776) | 0.035952 / 0.023109 (0.012842) | 0.521894 / 0.275898 (0.245996) | 0.582624 / 0.323480 (0.259144) | 0.011400 / 0.007986 (0.003414) | 0.004677 / 0.004328 (0.000348) | 0.115721 / 0.004250 (0.111470) | 0.048521 / 0.037052 (0.011469) | 0.497142 / 0.258489 (0.238653) | 0.573733 / 0.293841 (0.279892) | 0.055788 / 0.128546 (-0.072759) | 0.020949 / 0.075646 (-0.054697) | 0.132968 / 0.419271 (-0.286303) | 0.063045 / 0.043533 (0.019512) | 0.537769 / 0.255139 (0.282630) | 0.527560 / 0.283200 (0.244361) | 0.123756 / 0.141683 (-0.017927) | 1.994111 / 1.452155 (0.541956) | 2.104623 / 1.492716 (0.611907) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279057 / 0.018006 (0.261051) | 0.537342 / 0.000490 (0.536852) | 0.007782 / 0.000200 (0.007582) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032018 / 0.037411 (-0.005394) | 0.133456 / 0.014526 (0.118930) | 0.142039 / 0.176557 (-0.034517) | 0.213769 / 0.737135 (-0.523366) | 0.143811 / 0.296338 (-0.152527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.680142 / 0.215209 (0.464933) | 6.450439 / 2.077655 (4.372784) | 2.820724 / 1.504120 (1.316604) | 2.520407 / 1.541195 (0.979212) | 2.568972 / 1.468490 (1.100482) | 1.250584 / 4.584777 (-3.334193) | 6.108222 / 3.745712 (2.362509) | 3.065965 / 5.269862 (-2.203897) | 2.108675 / 4.565676 (-2.457002) | 0.167870 / 0.424275 (-0.256405) | 0.015127 / 0.007607 (0.007520) | 0.849645 / 0.226044 (0.623600) | 8.508727 / 2.268929 (6.239799) | 3.707897 / 55.444624 (-51.736727) | 3.009279 / 6.876477 (-3.867198) | 3.067179 / 2.142072 (0.925106) | 1.516370 / 4.805227 (-3.288858) | 0.264845 / 6.500664 (-6.235819) | 0.095137 / 0.075469 (0.019668) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.826306 / 1.841788 (-0.015481) | 20.119641 / 8.074308 (12.045333) | 21.532158 / 10.191392 (11.340766) | 0.278631 / 0.680424 (-0.401793) | 0.029494 / 0.534201 (-0.504707) | 0.621887 / 0.579283 (0.042604) | 0.686864 / 0.434364 (0.252500) | 0.695412 / 0.540337 (0.155074) | 0.864829 / 1.386936 (-0.522108) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8e1af7b30c94ce77abd9de732f19198e197d900c \"CML watermark\")\n" ]

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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) | 0.007818 / 0.011353 (-0.003535) | 0.005456 / 0.011008 (-0.005552) | 0.114685 / 0.038508 (0.076177) | 0.038398 / 0.023109 (0.015289) | 0.351289 / 0.275898 (0.075391) | 0.389170 / 0.323480 (0.065690) | 0.006213 / 0.007986 (-0.001773) | 0.005796 / 0.004328 (0.001467) | 0.085315 / 0.004250 (0.081065) | 0.049251 / 0.037052 (0.012198) | 0.368119 / 0.258489 (0.109630) | 0.394725 / 0.293841 (0.100884) | 0.040390 / 0.128546 (-0.088157) | 0.014076 / 0.075646 (-0.061570) | 0.393771 / 0.419271 (-0.025500) | 0.058929 / 0.043533 (0.015397) | 0.349526 / 0.255139 (0.094387) | 0.378409 / 0.283200 (0.095210) | 0.114354 / 0.141683 (-0.027329) | 1.749244 / 1.452155 (0.297089) | 1.847946 / 1.492716 (0.355229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241648 / 0.018006 (0.223641) | 0.468419 / 0.000490 (0.467929) | 0.004311 / 0.000200 (0.004111) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029978 / 0.037411 (-0.007433) | 0.121832 / 0.014526 (0.107306) | 0.133516 / 0.176557 (-0.043041) | 0.199174 / 0.737135 (-0.537961) | 0.138181 / 0.296338 (-0.158158) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478346 / 0.215209 (0.263137) | 4.723967 / 2.077655 (2.646312) | 2.107724 / 1.504120 (0.603604) | 1.874810 / 1.541195 (0.333615) | 1.911568 / 1.468490 (0.443078) | 0.800966 / 4.584777 (-3.783811) | 4.399032 / 3.745712 (0.653320) | 2.346160 / 5.269862 (-2.923702) | 1.506673 / 4.565676 (-3.059004) | 0.099119 / 0.424275 (-0.325156) | 0.014055 / 0.007607 (0.006448) | 0.582419 / 0.226044 (0.356375) | 5.789147 / 2.268929 (3.520218) | 2.632443 / 55.444624 (-52.812182) | 2.217630 / 6.876477 (-4.658846) | 2.337709 / 2.142072 (0.195637) | 0.995345 / 4.805227 (-3.809882) | 0.200040 / 6.500664 (-6.300624) | 0.076855 / 0.075469 (0.001386) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.386104 / 1.841788 (-0.455683) | 17.109772 / 8.074308 (9.035464) | 16.147612 / 10.191392 (5.956220) | 0.162846 / 0.680424 (-0.517577) | 0.020692 / 0.534201 (-0.513509) | 0.495752 / 0.579283 (-0.083531) | 0.475715 / 0.434364 (0.041351) | 0.619826 / 0.540337 (0.079488) | 0.720745 / 1.386936 (-0.666191) |\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) | 0.008255 / 0.011353 (-0.003098) | 0.006118 / 0.011008 (-0.004890) | 0.088004 / 0.038508 (0.049496) | 0.039225 / 0.023109 (0.016116) | 0.399290 / 0.275898 (0.123392) | 0.432272 / 0.323480 (0.108792) | 0.007382 / 0.007986 (-0.000603) | 0.004576 / 0.004328 (0.000248) | 0.086511 / 0.004250 (0.082260) | 0.050472 / 0.037052 (0.013420) | 0.404160 / 0.258489 (0.145671) | 0.445356 / 0.293841 (0.151515) | 0.041549 / 0.128546 (-0.086997) | 0.014148 / 0.075646 (-0.061498) | 0.101697 / 0.419271 (-0.317574) | 0.057474 / 0.043533 (0.013941) | 0.395093 / 0.255139 (0.139954) | 0.418613 / 0.283200 (0.135414) | 0.123217 / 0.141683 (-0.018466) | 1.726146 / 1.452155 (0.273991) | 1.852746 / 1.492716 (0.360029) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256876 / 0.018006 (0.238870) | 0.476336 / 0.000490 (0.475846) | 0.000465 / 0.000200 (0.000265) | 0.000068 / 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) | 0.034304 / 0.037411 (-0.003107) | 0.132617 / 0.014526 (0.118091) | 0.141712 / 0.176557 (-0.034845) | 0.198101 / 0.737135 (-0.539034) | 0.150877 / 0.296338 (-0.145461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504717 / 0.215209 (0.289508) | 5.035060 / 2.077655 (2.957405) | 2.494812 / 1.504120 (0.990692) | 2.306601 / 1.541195 (0.765406) | 2.481860 / 1.468490 (1.013370) | 0.826041 / 4.584777 (-3.758736) | 4.414748 / 3.745712 (0.669036) | 2.417899 / 5.269862 (-2.851963) | 1.574548 / 4.565676 (-2.991128) | 0.101712 / 0.424275 (-0.322563) | 0.014388 / 0.007607 (0.006781) | 0.616674 / 0.226044 (0.390630) | 6.180382 / 2.268929 (3.911453) | 2.969110 / 55.444624 (-52.475514) | 2.574383 / 6.876477 (-4.302094) | 2.711008 / 2.142072 (0.568935) | 0.997679 / 4.805227 (-3.807548) | 0.201241 / 6.500664 (-6.299423) | 0.076132 / 0.075469 (0.000663) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.542704 / 1.841788 (-0.299084) | 17.610700 / 8.074308 (9.536392) | 16.152973 / 10.191392 (5.961581) | 0.166040 / 0.680424 (-0.514384) | 0.020286 / 0.534201 (-0.513915) | 0.506724 / 0.579283 (-0.072559) | 0.484348 / 0.434364 (0.049984) | 0.606524 / 0.540337 (0.066187) | 0.734997 / 1.386936 (-0.651939) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a200ec9126a0879f3d38d4e9e3787633a23af42e \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi ! We're using process pools for parallelism right now. I was wondering if there's a package that implements the same API as a process pool but runs with Spark under the hood ? That or something similar would be cool because users could use whatever distributed framework they want this way.\r\n\r\nFeel free to ping us when you'd like to open PRs for this kind of things, so that we can discuss this before you start working on it ^^", "Hi, thanks for taking a look and providing your input! I don't know of such packages, and even it exists, I don't think with the process pool API it's possible to run Spark as backend properly; otherwise I understand a unified API would be preferable.\r\n\r\nThe process pool API requires splitting the workload to a fixed number parts for multiprocessing; meanwhile distributed framework such as Spark has sophisticated scheduler to distribute the workload to the processes on multiple machines in a cluster, so the way of splitting things for `multiprocessing.pool` would not suit / be as flexible as directly calling the `sparkContext.parallelize` API.\r\n\r\nI think this could be a good addition to scale the `datasets` implementation to distributed workers, and from my benchmark results so far it looks promising compared with multiprocessing.", "I see ! I think we only need an equivalent of `pool.map`. We use it to run download and conversion of data files on disk. That would require less changes in the internal code - and therefore less tests to write ;)\r\n\r\nWe also use `pool.apply_async` in some places with a `Queue` to get progress updates of the running jobs. I'm mentioning this in case there's a way to get a python generator from a running spark job ? This is less important though", "For Spark, `rdd.map` (where `rdd` can be created by `sparkContext.parallelize`) is the most similar as `pool.map`, but it requires creating a Spark RDD first that is used for distributing the `iterable` and the actual parallelization is managed by the Spark framework; `pool.map` takes the splits of `iterable` that are split into `num_proc` parts by the Python code. You can also check my PR #5807 in the `src/datasets/utils/py_utils.py` file to compare the differences of the APIs, it might make more sense than the the above description.\r\n\r\nGiven the different inputs and mechanisms of calling the `map` functions, this is why I think it's not that feasible to reuse most of the `multiprocessing` code.\r\n\r\nProgress bar updating might be challenging with Spark, I'll consider it as a followup work.", "Indeed I think the current use of multiprocessing.Pool in `map_nested` can be rewritten to work like `sparkContext.parallelize` - without splitting the iterable.\r\n\r\nMaybe from the user's perspective it's ok to let multiprocessing.Pool or spark distribute the load on their own, as long as it takes a list and runs jobs in parallel in the end :)\r\n", "From your feedback, seems to me there are two paths to consider now for supporting spark's `map` function in `map_nested` now:\r\n1. Keep the current `pool.map` implementation, and add an if statement for the spark's `map` code (which is what I did in my current PR) -- the code change is just a few lines in the `map_nested` function, and it has been tested by unit tests + manual testing on real Spark clusters; if you have other concerns I'd also be happy to address them.\r\n2. Rewrite the current `pool.map` implementation to remove splitting the iterable, and we will still need to add an if statement to use either\r\n```python\r\nwith Pool(...) as pool:\r\n mapped = pool.map(_single_map_nested, iterable)\r\n```\r\nor\r\n```python\r\nrdd = spark.sparkContext.parallelize(iterable)\r\nmapped = rdd.map(lambda obj: _single_map_nested((function, obj, types, None, True, None))).collect()\r\n```\r\nbecause there is no unified API that supports both `pool.map` and `rdd.map`. This can be more unified and flexible in the long run, but might require more work, and it will change the existing multiprocessing behavior, which is why I'm not leaning towards this option.\r\n\r\nAm I understanding correctly?", "Yup correct ! I think it's a nice path because it would be possible for users to define whatever parallel processing backend they want. I think we still need to discuss how that would look like in the `datasets` API : how to specify it has to use the \"spark\" parallel backend ? And how to specify the spark session parameters (number of executors etc.) ? Maybe there is something more practical than `use_spark=True`\r\n\r\nI'll check with the team internally if they have some ideas, but feel free to share your thoughts here !", "Sure, please let me know if you have more updates regarding the API and implementation from the team.\r\n\r\nFor parameters we don't need to worry about setting them for Spark, because Spark will figure out the environment / number of worker nodes by itself, so it's preferable to just provide some parameter such as `use_spark` to use the RDD `map` function.", "Hi! I wanted to check in to see if there is any update from the team.\r\n\r\nA potential change of API I can think of is change the argument to `distributed_backend=...`, which accepts `str`, such as `load_dataset(..., distributed_backend=\"spark\")`.\r\n\r\nImplementation wise, we can add a class / function to abstract away the details of using multiprocessing vs. spark vs. other parallel processing frameworks in `map_nested` and `_prepare_split`.", "I found this quite interesting: https://github.com/joblib/joblib-spark with this syntax:\r\n\r\n```python\r\nwith parallel_backend('spark', n_jobs=3):\r\n ...\r\n```\r\n\r\ncc @lu-wang-dl who might know better", "Joblib spark is providing Spark backend for joblib. We can implement a general parallel backend like\r\n```\r\nwith parallel_backend(\"<parallel-backedn>\", n_jobs=..):\r\n```\r\n\r\nIt can support multiprocessing , spark, ray, and etc. https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend", "Thank you @lhoestq for finding this repo. I validated that it can distribute downloading jobs with Spark to arbitrary cluster worker nodes evenly with `n_jobs=-1`.\r\n\r\nFor the API, I think it makes sense to define it as\r\n```python\r\nload_dataset(..., parallel_backend=<str>)\r\n```\r\nwhere `parallel_backend` can be `spark`, `multiprocessing`, and potentially other supported joblib backends including `ray` and `dask`.\r\n\r\nImplementation-wise, do you think it is better to just use `joblib` for `spark` backend in `map_nested`, or also migrate the `multiprocessing.Pool` code to use `joblib`?", "Hello @lhoestq, I wanted to follow up on my previous comment with some prototyping code that demonstrates how `map_nested` would be like if we unify `multiprocessing` and `spark` with `joblib`. The snippet hasn't hashed out the details such as dealing with `tqdm` yet.\r\n\r\nIn terms of API, the way of using multiprocessing is still the same; for Spark, the user sets `parallel_backend='spark'` can reuse the `num_proc` argument to pass in the number of executors, or preferably, just set `num_proc=-1` and joblib is able to decide it (I've validated it by running it on a Spark cluster).\r\n\r\n```python\r\ndef map_nested(\r\n # ... same args\r\n parallel_backend: Optional[str] = None, # proposed new argument\r\n):\r\n\r\n # ... same code\r\n\r\n # allow user to specify num_proc=-1, so that joblib will optimize it\r\n if (num_proc <= 1 and num_proc != -1) or len(iterable) < parallel_min_length:\r\n # same code\r\n mapped = [\r\n _single_map_nested((function, obj, types, None, True, None))\r\n for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n ]\r\n else:\r\n if not parallel_backend:\r\n parallel_backend = 'loky' # 'loky' is joblib's own implementation of robust multiprocessing\r\n \r\n n_jobs = min(num_proc, len(iterable))\r\n\r\n if parallel_backend == 'spark':\r\n n_jobs = -1 # 'loky' is joblib's own implementation of robust multiprocessing\r\n from joblibspark import register_spark\r\n register_spark()\r\n\r\n # parallelized with the same API\r\n with joblib.parallel_backend(parallel_backend, n_jobs=n_jobs):\r\n mapped = joblib.Parallel()(\r\n joblib.delayed(\r\n _single_map_nested((function, obj, types, None, True, None))\r\n )(obj) for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n )\r\n \r\n # ... same code\r\n```\r\nWe can always `joblib` for Spark and other distributed backends such as Ray if people want to support them later. It's worth noting that some distributed backends do not currently have `joblib` implementations.\r\n\r\nI would appreciate your thoughts on this proposed new API. We can also discuss the pros and cons of migrating the `multiprocessing` code to `joblib` later.", "Nice ! It should be quite easy to make the change then :)\r\n\r\nI think adding spark support can actually be less than 20 lines of code and would roughly require one line of code to change in map_nested:\r\n\r\nMaybe we can define a new `datasets.parallel` submodule that has the `parallel_backend()` context manager and a `parallel_map()` function that uses `Pool.map` by default and `joblib` otherwise.\r\n\r\n`joblib` would be an optional dependency, and `joblib-spark` as well.\r\n\r\nThen whenever someone wants to use Spark, they can do something like this (similar to scikit-learn parallel_backend):\r\n\r\n```python\r\nfrom datasets.parallel import parallel_backend\r\n\r\nwith parallel_backend(\"spark\"):\r\n ds = load_dataset(...)\r\n```\r\n\r\nWhat do you think ?", "Although until we've switched to all the steps in `load_dataset` to use `datasets.parallel`, I would require the user to explicitly say which step should use Spark. Maybe something like this, but I'm not sure yet:\r\n\r\n```python\r\nfrom datasets.parallel import parallel_backend\r\n\r\nwith parallel_backend(\"spark\", steps=[\"download\"]):\r\n ds = load_dataset(...)\r\n```\r\nfor now some steps can be NotImplemented:\r\n```python\r\nfrom datasets.parallel import parallel_backend\r\n\r\nwith parallel_backend(\"spark\", steps=[\"download\", \"prepare\"]):\r\n# NotImplementedError: the \"prepare\" step that converts the raw data files to Arrow is not compatible with the \"spark\" backend yet\r\n```\r\n\r\nThis way we can progressively roll out Spark support for the other data loading/processing steps without breaking changes between `datasets` versions", "Sounds good! I like the partial rollout idea.\r\nSo for example `map_nested` would call `parallel_map` under the hood if `num_proc != 1` or `parallel_backend` is specified right?\r\nI would be happy to start a PR next week to explore this path.", "Awesome ! I think map_nested can call `parallel_map()` if num_proc > 1, and `parallel_map` can be responsible to use Pool.map by default or joblib." ]

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[ "Hi @haonan-li , thank you for the report! It seems to be a bug on the [`huggingface_hub`](https://github.com/huggingface/huggingface_hub) site, there is even no such dataset as `mbzuai/bactrian-x` on the Hub. I opened and [issue](https://github.com/huggingface/huggingface_hub/issues/1453) there.", "I think `load_dataset(\"mbzuai/bactrian-x\")` shouldn't be loaded at all and raise an error but because of [this fallback](https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L1194) to packaged loaders when no other options are applicable, it loads the dataset with standard `json` loader instead of the custom loading script." ]

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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) | 0.010480 / 0.011353 (-0.000872) | 0.006743 / 0.011008 (-0.004265) | 0.126503 / 0.038508 (0.087995) | 0.036918 / 0.023109 (0.013808) | 0.387372 / 0.275898 (0.111474) | 0.456930 / 0.323480 (0.133450) | 0.008038 / 0.007986 (0.000052) | 0.005082 / 0.004328 (0.000753) | 0.093312 / 0.004250 (0.089062) | 0.065440 / 0.037052 (0.028387) | 0.378172 / 0.258489 (0.119683) | 0.430049 / 0.293841 (0.136208) | 0.054372 / 0.128546 (-0.074174) | 0.021875 / 0.075646 (-0.053772) | 0.441722 / 0.419271 (0.022450) | 0.063716 / 0.043533 (0.020183) | 0.375718 / 0.255139 (0.120579) | 0.413688 / 0.283200 (0.130488) | 0.122583 / 0.141683 (-0.019100) | 1.835992 / 1.452155 (0.383838) | 1.915862 / 1.492716 (0.423145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275305 / 0.018006 (0.257299) | 0.617170 / 0.000490 (0.616680) | 0.006467 / 0.000200 (0.006267) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031057 / 0.037411 (-0.006354) | 0.135178 / 0.014526 (0.120653) | 0.139265 / 0.176557 (-0.037292) | 0.221597 / 0.737135 (-0.515538) | 0.147632 / 0.296338 (-0.148706) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.640621 / 0.215209 (0.425411) | 6.354359 / 2.077655 (4.276704) | 2.748945 / 1.504120 (1.244825) | 2.396637 / 1.541195 (0.855442) | 2.395193 / 1.468490 (0.926703) | 1.209604 / 4.584777 (-3.375173) | 5.626901 / 3.745712 (1.881189) | 3.300941 / 5.269862 (-1.968920) | 2.123598 / 4.565676 (-2.442078) | 0.144270 / 0.424275 (-0.280005) | 0.015114 / 0.007607 (0.007507) | 0.812352 / 0.226044 (0.586307) | 8.024250 / 2.268929 (5.755322) | 3.557589 / 55.444624 (-51.887036) | 2.840632 / 6.876477 (-4.035845) | 3.152319 / 2.142072 (1.010246) | 1.447232 / 4.805227 (-3.357995) | 0.251740 / 6.500664 (-6.248924) | 0.083725 / 0.075469 (0.008256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568032 / 1.841788 (-0.273755) | 18.463860 / 8.074308 (10.389552) | 21.217395 / 10.191392 (11.026003) | 0.228457 / 0.680424 (-0.451967) | 0.031398 / 0.534201 (-0.502803) | 0.547627 / 0.579283 (-0.031656) | 0.642921 / 0.434364 (0.208557) | 0.687857 / 0.540337 (0.147520) | 0.800940 / 1.386936 (-0.585996) |\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) | 0.009933 / 0.011353 (-0.001420) | 0.006065 / 0.011008 (-0.004943) | 0.102556 / 0.038508 (0.064048) | 0.034646 / 0.023109 (0.011537) | 0.437951 / 0.275898 (0.162053) | 0.482439 / 0.323480 (0.158959) | 0.007715 / 0.007986 (-0.000271) | 0.007426 / 0.004328 (0.003098) | 0.096427 / 0.004250 (0.092177) | 0.052983 / 0.037052 (0.015930) | 0.464533 / 0.258489 (0.206044) | 0.484848 / 0.293841 (0.191007) | 0.050415 / 0.128546 (-0.078131) | 0.021001 / 0.075646 (-0.054645) | 0.121214 / 0.419271 (-0.298058) | 0.061658 / 0.043533 (0.018125) | 0.431898 / 0.255139 (0.176759) | 0.482106 / 0.283200 (0.198907) | 0.128524 / 0.141683 (-0.013159) | 1.775714 / 1.452155 (0.323559) | 1.904738 / 1.492716 (0.412021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287641 / 0.018006 (0.269635) | 0.600667 / 0.000490 (0.600178) | 0.005097 / 0.000200 (0.004897) | 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) | 0.032836 / 0.037411 (-0.004575) | 0.133114 / 0.014526 (0.118588) | 0.150874 / 0.176557 (-0.025683) | 0.217069 / 0.737135 (-0.520066) | 0.160387 / 0.296338 (-0.135951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668444 / 0.215209 (0.453235) | 6.240015 / 2.077655 (4.162360) | 2.808661 / 1.504120 (1.304542) | 2.336550 / 1.541195 (0.795356) | 2.538973 / 1.468490 (1.070483) | 1.189292 / 4.584777 (-3.395485) | 5.781028 / 3.745712 (2.035315) | 3.149895 / 5.269862 (-2.119967) | 2.130646 / 4.565676 (-2.435030) | 0.144944 / 0.424275 (-0.279331) | 0.014650 / 0.007607 (0.007043) | 0.792313 / 0.226044 (0.566269) | 7.933108 / 2.268929 (5.664180) | 3.527527 / 55.444624 (-51.917098) | 2.864271 / 6.876477 (-4.012205) | 3.098330 / 2.142072 (0.956258) | 1.421208 / 4.805227 (-3.384019) | 0.255638 / 6.500664 (-6.245026) | 0.086971 / 0.075469 (0.011502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585317 / 1.841788 (-0.256471) | 18.643133 / 8.074308 (10.568825) | 21.921256 / 10.191392 (11.729864) | 0.215493 / 0.680424 (-0.464931) | 0.028348 / 0.534201 (-0.505853) | 0.556925 / 0.579283 (-0.022358) | 0.631480 / 0.434364 (0.197116) | 0.654026 / 0.540337 (0.113689) | 0.799727 / 1.386936 (-0.587209) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#62520514b524b5904c7e4f0beddab1971212a96a \"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) | 0.006516 / 0.011353 (-0.004837) | 0.004500 / 0.011008 (-0.006509) | 0.097639 / 0.038508 (0.059131) | 0.028336 / 0.023109 (0.005227) | 0.377263 / 0.275898 (0.101365) | 0.409209 / 0.323480 (0.085729) | 0.004832 / 0.007986 (-0.003154) | 0.004629 / 0.004328 (0.000301) | 0.075046 / 0.004250 (0.070795) | 0.034080 / 0.037052 (-0.002972) | 0.377565 / 0.258489 (0.119076) | 0.419204 / 0.293841 (0.125363) | 0.030343 / 0.128546 (-0.098203) | 0.011465 / 0.075646 (-0.064182) | 0.322777 / 0.419271 (-0.096494) | 0.043774 / 0.043533 (0.000241) | 0.375808 / 0.255139 (0.120669) | 0.402665 / 0.283200 (0.119465) | 0.086811 / 0.141683 (-0.054872) | 1.518686 / 1.452155 (0.066531) | 1.540381 / 1.492716 (0.047664) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197730 / 0.018006 (0.179724) | 0.409285 / 0.000490 (0.408795) | 0.004739 / 0.000200 (0.004539) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022974 / 0.037411 (-0.014437) | 0.096843 / 0.014526 (0.082317) | 0.103241 / 0.176557 (-0.073316) | 0.163691 / 0.737135 (-0.573444) | 0.107905 / 0.296338 (-0.188433) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449408 / 0.215209 (0.234199) | 4.501375 / 2.077655 (2.423720) | 2.181491 / 1.504120 (0.677371) | 1.986153 / 1.541195 (0.444958) | 2.024735 / 1.468490 (0.556245) | 0.695368 / 4.584777 (-3.889409) | 3.416912 / 3.745712 (-0.328800) | 1.893343 / 5.269862 (-3.376519) | 1.275535 / 4.565676 (-3.290142) | 0.082772 / 0.424275 (-0.341503) | 0.012365 / 0.007607 (0.004758) | 0.553859 / 0.226044 (0.327814) | 5.540014 / 2.268929 (3.271085) | 2.634298 / 55.444624 (-52.810326) | 2.286686 / 6.876477 (-4.589790) | 2.384402 / 2.142072 (0.242330) | 0.806413 / 4.805227 (-3.998814) | 0.151757 / 6.500664 (-6.348907) | 0.067155 / 0.075469 (-0.008314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198776 / 1.841788 (-0.643012) | 13.517434 / 8.074308 (5.443126) | 13.926300 / 10.191392 (3.734908) | 0.141887 / 0.680424 (-0.538537) | 0.016571 / 0.534201 (-0.517630) | 0.383179 / 0.579283 (-0.196104) | 0.395189 / 0.434364 (-0.039175) | 0.479635 / 0.540337 (-0.060702) | 0.570576 / 1.386936 (-0.816360) |\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) | 0.006691 / 0.011353 (-0.004662) | 0.004634 / 0.011008 (-0.006375) | 0.077087 / 0.038508 (0.038579) | 0.028281 / 0.023109 (0.005172) | 0.340108 / 0.275898 (0.064210) | 0.370611 / 0.323480 (0.047131) | 0.004997 / 0.007986 (-0.002988) | 0.003336 / 0.004328 (-0.000992) | 0.074814 / 0.004250 (0.070563) | 0.039001 / 0.037052 (0.001948) | 0.344225 / 0.258489 (0.085736) | 0.380621 / 0.293841 (0.086780) | 0.030858 / 0.128546 (-0.097689) | 0.011623 / 0.075646 (-0.064023) | 0.085016 / 0.419271 (-0.334256) | 0.042378 / 0.043533 (-0.001155) | 0.341428 / 0.255139 (0.086289) | 0.364823 / 0.283200 (0.081624) | 0.096695 / 0.141683 (-0.044988) | 1.527683 / 1.452155 (0.075528) | 1.585361 / 1.492716 (0.092645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184280 / 0.018006 (0.166274) | 0.397845 / 0.000490 (0.397355) | 0.004415 / 0.000200 (0.004215) | 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) | 0.024296 / 0.037411 (-0.013115) | 0.101053 / 0.014526 (0.086527) | 0.108968 / 0.176557 (-0.067589) | 0.155732 / 0.737135 (-0.581403) | 0.112604 / 0.296338 (-0.183735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440819 / 0.215209 (0.225609) | 4.394017 / 2.077655 (2.316363) | 2.092456 / 1.504120 (0.588336) | 1.880186 / 1.541195 (0.338991) | 1.918035 / 1.468490 (0.449545) | 0.698059 / 4.584777 (-3.886718) | 3.422598 / 3.745712 (-0.323114) | 1.860465 / 5.269862 (-3.409396) | 1.157788 / 4.565676 (-3.407889) | 0.083566 / 0.424275 (-0.340709) | 0.012440 / 0.007607 (0.004832) | 0.549526 / 0.226044 (0.323481) | 5.500623 / 2.268929 (3.231694) | 2.546980 / 55.444624 (-52.897644) | 2.199527 / 6.876477 (-4.676949) | 2.297276 / 2.142072 (0.155203) | 0.801580 / 4.805227 (-4.003648) | 0.151842 / 6.500664 (-6.348822) | 0.067165 / 0.075469 (-0.008305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329097 / 1.841788 (-0.512691) | 13.830354 / 8.074308 (5.756046) | 14.155250 / 10.191392 (3.963858) | 0.144517 / 0.680424 (-0.535907) | 0.016738 / 0.534201 (-0.517463) | 0.379337 / 0.579283 (-0.199946) | 0.391382 / 0.434364 (-0.042982) | 0.459153 / 0.540337 (-0.081184) | 0.547287 / 1.386936 (-0.839649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2efb0289c887ec60d54e0715cd85c111cb45f9ee \"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) | 0.007176 / 0.011353 (-0.004177) | 0.005125 / 0.011008 (-0.005883) | 0.096060 / 0.038508 (0.057552) | 0.033262 / 0.023109 (0.010152) | 0.311461 / 0.275898 (0.035563) | 0.340673 / 0.323480 (0.017193) | 0.005700 / 0.007986 (-0.002286) | 0.005223 / 0.004328 (0.000894) | 0.072812 / 0.004250 (0.068561) | 0.042078 / 0.037052 (0.005025) | 0.320042 / 0.258489 (0.061553) | 0.346539 / 0.293841 (0.052698) | 0.035284 / 0.128546 (-0.093262) | 0.012021 / 0.075646 (-0.063625) | 0.331555 / 0.419271 (-0.087717) | 0.051058 / 0.043533 (0.007525) | 0.303001 / 0.255139 (0.047862) | 0.328431 / 0.283200 (0.045231) | 0.100954 / 0.141683 (-0.040729) | 1.407445 / 1.452155 (-0.044710) | 1.512826 / 1.492716 (0.020110) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216442 / 0.018006 (0.198436) | 0.446298 / 0.000490 (0.445809) | 0.004701 / 0.000200 (0.004501) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028088 / 0.037411 (-0.009324) | 0.108669 / 0.014526 (0.094144) | 0.119597 / 0.176557 (-0.056960) | 0.178249 / 0.737135 (-0.558886) | 0.123914 / 0.296338 (-0.172424) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413437 / 0.215209 (0.198228) | 4.136602 / 2.077655 (2.058947) | 1.875872 / 1.504120 (0.371752) | 1.680783 / 1.541195 (0.139588) | 1.757059 / 1.468490 (0.288569) | 0.711080 / 4.584777 (-3.873697) | 3.791701 / 3.745712 (0.045989) | 2.111612 / 5.269862 (-3.158250) | 1.351204 / 4.565676 (-3.214473) | 0.086477 / 0.424275 (-0.337798) | 0.012359 / 0.007607 (0.004752) | 0.504984 / 0.226044 (0.278940) | 5.040456 / 2.268929 (2.771527) | 2.266946 / 55.444624 (-53.177679) | 1.957827 / 6.876477 (-4.918650) | 2.120490 / 2.142072 (-0.021583) | 0.856148 / 4.805227 (-3.949079) | 0.172414 / 6.500664 (-6.328250) | 0.066833 / 0.075469 (-0.008636) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198163 / 1.841788 (-0.643625) | 14.944930 / 8.074308 (6.870622) | 14.317196 / 10.191392 (4.125804) | 0.166104 / 0.680424 (-0.514320) | 0.017443 / 0.534201 (-0.516758) | 0.423025 / 0.579283 (-0.156258) | 0.437476 / 0.434364 (0.003112) | 0.500156 / 0.540337 (-0.040181) | 0.606226 / 1.386936 (-0.780710) |\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) | 0.007417 / 0.011353 (-0.003936) | 0.005143 / 0.011008 (-0.005865) | 0.076401 / 0.038508 (0.037893) | 0.034818 / 0.023109 (0.011709) | 0.339633 / 0.275898 (0.063735) | 0.373839 / 0.323480 (0.050359) | 0.006004 / 0.007986 (-0.001982) | 0.005403 / 0.004328 (0.001075) | 0.074150 / 0.004250 (0.069899) | 0.050489 / 0.037052 (0.013436) | 0.343357 / 0.258489 (0.084868) | 0.377009 / 0.293841 (0.083168) | 0.035921 / 0.128546 (-0.092625) | 0.012197 / 0.075646 (-0.063449) | 0.087992 / 0.419271 (-0.331279) | 0.049452 / 0.043533 (0.005919) | 0.340495 / 0.255139 (0.085356) | 0.360277 / 0.283200 (0.077077) | 0.111114 / 0.141683 (-0.030569) | 1.463888 / 1.452155 (0.011734) | 1.548320 / 1.492716 (0.055604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228437 / 0.018006 (0.210431) | 0.445120 / 0.000490 (0.444631) | 0.000392 / 0.000200 (0.000192) | 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) | 0.029965 / 0.037411 (-0.007446) | 0.113484 / 0.014526 (0.098958) | 0.125249 / 0.176557 (-0.051308) | 0.177201 / 0.737135 (-0.559934) | 0.128750 / 0.296338 (-0.167589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420089 / 0.215209 (0.204880) | 4.195772 / 2.077655 (2.118117) | 2.021539 / 1.504120 (0.517419) | 1.825118 / 1.541195 (0.283924) | 1.904090 / 1.468490 (0.435600) | 0.716276 / 4.584777 (-3.868501) | 3.742257 / 3.745712 (-0.003455) | 3.368880 / 5.269862 (-1.900981) | 1.728285 / 4.565676 (-2.837392) | 0.087656 / 0.424275 (-0.336619) | 0.012263 / 0.007607 (0.004656) | 0.524321 / 0.226044 (0.298277) | 5.217610 / 2.268929 (2.948682) | 2.474670 / 55.444624 (-52.969955) | 2.135452 / 6.876477 (-4.741025) | 2.292578 / 2.142072 (0.150505) | 0.852109 / 4.805227 (-3.953119) | 0.172031 / 6.500664 (-6.328633) | 0.065230 / 0.075469 (-0.010240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260494 / 1.841788 (-0.581293) | 15.019167 / 8.074308 (6.944859) | 14.647586 / 10.191392 (4.456193) | 0.170578 / 0.680424 (-0.509846) | 0.017619 / 0.534201 (-0.516582) | 0.423116 / 0.579283 (-0.156167) | 0.426680 / 0.434364 (-0.007684) | 0.519563 / 0.540337 (-0.020775) | 0.619335 / 1.386936 (-0.767601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e210dc20c19b5e6af05df9ca6e82984dfb42465f \"CML watermark\")\n" ]

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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) | 0.010844 / 0.011353 (-0.000509) | 0.007329 / 0.011008 (-0.003680) | 0.133764 / 0.038508 (0.095256) | 0.040213 / 0.023109 (0.017103) | 0.413466 / 0.275898 (0.137568) | 0.452860 / 0.323480 (0.129380) | 0.008109 / 0.007986 (0.000123) | 0.005773 / 0.004328 (0.001444) | 0.109969 / 0.004250 (0.105718) | 0.053001 / 0.037052 (0.015949) | 0.416377 / 0.258489 (0.157888) | 0.477486 / 0.293841 (0.183645) | 0.056556 / 0.128546 (-0.071990) | 0.024322 / 0.075646 (-0.051324) | 0.437750 / 0.419271 (0.018479) | 0.087732 / 0.043533 (0.044199) | 0.421540 / 0.255139 (0.166401) | 0.429143 / 0.283200 (0.145944) | 0.144864 / 0.141683 (0.003181) | 1.882785 / 1.452155 (0.430631) | 1.980721 / 1.492716 (0.488005) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285497 / 0.018006 (0.267491) | 0.601820 / 0.000490 (0.601331) | 0.005003 / 0.000200 (0.004804) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030673 / 0.037411 (-0.006739) | 0.126883 / 0.014526 (0.112357) | 0.137677 / 0.176557 (-0.038880) | 0.211504 / 0.737135 (-0.525632) | 0.144752 / 0.296338 (-0.151587) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665845 / 0.215209 (0.450636) | 6.369040 / 2.077655 (4.291385) | 2.708979 / 1.504120 (1.204859) | 2.370842 / 1.541195 (0.829647) | 2.445987 / 1.468490 (0.977497) | 1.260806 / 4.584777 (-3.323971) | 5.979216 / 3.745712 (2.233504) | 3.334350 / 5.269862 (-1.935512) | 2.187298 / 4.565676 (-2.378379) | 0.155494 / 0.424275 (-0.268781) | 0.017351 / 0.007607 (0.009744) | 0.853626 / 0.226044 (0.627581) | 8.375001 / 2.268929 (6.106072) | 3.528312 / 55.444624 (-51.916313) | 2.890509 / 6.876477 (-3.985968) | 3.051016 / 2.142072 (0.908944) | 1.529811 / 4.805227 (-3.275416) | 0.273883 / 6.500664 (-6.226781) | 0.086617 / 0.075469 (0.011148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648231 / 1.841788 (-0.193557) | 19.487109 / 8.074308 (11.412801) | 23.474621 / 10.191392 (13.283229) | 0.221392 / 0.680424 (-0.459032) | 0.028878 / 0.534201 (-0.505323) | 0.582302 / 0.579283 (0.003019) | 0.615059 / 0.434364 (0.180695) | 0.656082 / 0.540337 (0.115745) | 0.740544 / 1.386936 (-0.646392) |\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) | 0.010687 / 0.011353 (-0.000665) | 0.007114 / 0.011008 (-0.003894) | 0.135426 / 0.038508 (0.096918) | 0.041027 / 0.023109 (0.017918) | 0.466441 / 0.275898 (0.190543) | 0.503545 / 0.323480 (0.180065) | 0.009418 / 0.007986 (0.001432) | 0.004976 / 0.004328 (0.000647) | 0.101342 / 0.004250 (0.097092) | 0.058289 / 0.037052 (0.021237) | 0.473715 / 0.258489 (0.215226) | 0.539556 / 0.293841 (0.245715) | 0.063138 / 0.128546 (-0.065408) | 0.020429 / 0.075646 (-0.055217) | 0.124179 / 0.419271 (-0.295093) | 0.066400 / 0.043533 (0.022867) | 0.450793 / 0.255139 (0.195654) | 0.494163 / 0.283200 (0.210964) | 0.131179 / 0.141683 (-0.010504) | 1.876396 / 1.452155 (0.424241) | 1.974148 / 1.492716 (0.481432) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313362 / 0.018006 (0.295356) | 0.602618 / 0.000490 (0.602129) | 0.008279 / 0.000200 (0.008079) | 0.000155 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037250 / 0.037411 (-0.000161) | 0.144151 / 0.014526 (0.129625) | 0.155733 / 0.176557 (-0.020824) | 0.214334 / 0.737135 (-0.522801) | 0.167124 / 0.296338 (-0.129214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686471 / 0.215209 (0.471262) | 6.749174 / 2.077655 (4.671520) | 3.024941 / 1.504120 (1.520821) | 2.553363 / 1.541195 (1.012168) | 2.679107 / 1.468490 (1.210617) | 1.317212 / 4.584777 (-3.267565) | 5.917575 / 3.745712 (2.171862) | 3.412715 / 5.269862 (-1.857146) | 2.203478 / 4.565676 (-2.362198) | 0.150387 / 0.424275 (-0.273888) | 0.015977 / 0.007607 (0.008370) | 0.862999 / 0.226044 (0.636954) | 8.706459 / 2.268929 (6.437530) | 3.762648 / 55.444624 (-51.681977) | 2.992544 / 6.876477 (-3.883933) | 3.135796 / 2.142072 (0.993724) | 1.504140 / 4.805227 (-3.301088) | 0.268265 / 6.500664 (-6.232399) | 0.083297 / 0.075469 (0.007828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.690193 / 1.841788 (-0.151594) | 19.912854 / 8.074308 (11.838546) | 23.568217 / 10.191392 (13.376825) | 0.285125 / 0.680424 (-0.395299) | 0.030593 / 0.534201 (-0.503608) | 0.565305 / 0.579283 (-0.013978) | 0.659283 / 0.434364 (0.224919) | 0.678864 / 0.540337 (0.138527) | 0.793634 / 1.386936 (-0.593302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d0edbe3f3258b7e580d1b58c0eea6637b5e22b2 \"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) | 0.011615 / 0.011353 (0.000262) | 0.006716 / 0.011008 (-0.004292) | 0.146868 / 0.038508 (0.108360) | 0.037621 / 0.023109 (0.014512) | 0.425563 / 0.275898 (0.149664) | 0.483217 / 0.323480 (0.159737) | 0.007830 / 0.007986 (-0.000156) | 0.005940 / 0.004328 (0.001612) | 0.100771 / 0.004250 (0.096521) | 0.063907 / 0.037052 (0.026854) | 0.422993 / 0.258489 (0.164503) | 0.496514 / 0.293841 (0.202673) | 0.056004 / 0.128546 (-0.072542) | 0.021441 / 0.075646 (-0.054206) | 0.453589 / 0.419271 (0.034317) | 0.067555 / 0.043533 (0.024022) | 0.442490 / 0.255139 (0.187351) | 0.503941 / 0.283200 (0.220742) | 0.134023 / 0.141683 (-0.007660) | 1.886329 / 1.452155 (0.434175) | 2.030867 / 1.492716 (0.538150) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288063 / 0.018006 (0.270057) | 0.627177 / 0.000490 (0.626687) | 0.006335 / 0.000200 (0.006135) | 0.000171 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032424 / 0.037411 (-0.004987) | 0.132749 / 0.014526 (0.118223) | 0.144727 / 0.176557 (-0.031829) | 0.232577 / 0.737135 (-0.504558) | 0.157315 / 0.296338 (-0.139024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623058 / 0.215209 (0.407849) | 6.272447 / 2.077655 (4.194792) | 2.506778 / 1.504120 (1.002658) | 2.203094 / 1.541195 (0.661899) | 2.346972 / 1.468490 (0.878482) | 1.358498 / 4.584777 (-3.226279) | 5.879670 / 3.745712 (2.133958) | 5.818406 / 5.269862 (0.548545) | 3.231936 / 4.565676 (-1.333741) | 0.154013 / 0.424275 (-0.270263) | 0.021541 / 0.007607 (0.013934) | 0.823746 / 0.226044 (0.597702) | 8.140304 / 2.268929 (5.871375) | 3.366911 / 55.444624 (-52.077714) | 2.696856 / 6.876477 (-4.179621) | 2.845743 / 2.142072 (0.703671) | 1.522363 / 4.805227 (-3.282864) | 0.278938 / 6.500664 (-6.221726) | 0.085044 / 0.075469 (0.009575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.681348 / 1.841788 (-0.160440) | 19.686703 / 8.074308 (11.612395) | 22.995655 / 10.191392 (12.804263) | 0.218876 / 0.680424 (-0.461548) | 0.029334 / 0.534201 (-0.504867) | 0.560846 / 0.579283 (-0.018438) | 0.645210 / 0.434364 (0.210846) | 0.697842 / 0.540337 (0.157505) | 0.832875 / 1.386936 (-0.554061) |\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) | 0.009509 / 0.011353 (-0.001844) | 0.006471 / 0.011008 (-0.004537) | 0.101477 / 0.038508 (0.062969) | 0.035281 / 0.023109 (0.012171) | 0.470032 / 0.275898 (0.194134) | 0.501475 / 0.323480 (0.177995) | 0.007641 / 0.007986 (-0.000344) | 0.006784 / 0.004328 (0.002455) | 0.096111 / 0.004250 (0.091861) | 0.055199 / 0.037052 (0.018146) | 0.470095 / 0.258489 (0.211606) | 0.530955 / 0.293841 (0.237114) | 0.056161 / 0.128546 (-0.072385) | 0.022055 / 0.075646 (-0.053591) | 0.121585 / 0.419271 (-0.297686) | 0.063736 / 0.043533 (0.020203) | 0.470771 / 0.255139 (0.215632) | 0.490546 / 0.283200 (0.207346) | 0.128825 / 0.141683 (-0.012858) | 1.898639 / 1.452155 (0.446484) | 2.052305 / 1.492716 (0.559589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322526 / 0.018006 (0.304520) | 0.628096 / 0.000490 (0.627607) | 0.006837 / 0.000200 (0.006637) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033830 / 0.037411 (-0.003581) | 0.136217 / 0.014526 (0.121691) | 0.147006 / 0.176557 (-0.029551) | 0.203950 / 0.737135 (-0.533185) | 0.150327 / 0.296338 (-0.146011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654287 / 0.215209 (0.439078) | 6.430306 / 2.077655 (4.352651) | 2.881750 / 1.504120 (1.377630) | 2.489505 / 1.541195 (0.948310) | 2.543037 / 1.468490 (1.074547) | 1.226682 / 4.584777 (-3.358094) | 5.902076 / 3.745712 (2.156364) | 3.335344 / 5.269862 (-1.934518) | 2.156738 / 4.565676 (-2.408939) | 0.151804 / 0.424275 (-0.272472) | 0.015238 / 0.007607 (0.007631) | 0.816364 / 0.226044 (0.590319) | 8.126367 / 2.268929 (5.857438) | 3.653222 / 55.444624 (-51.791402) | 2.886667 / 6.876477 (-3.989809) | 3.120852 / 2.142072 (0.978779) | 1.421423 / 4.805227 (-3.383804) | 0.264590 / 6.500664 (-6.236074) | 0.085716 / 0.075469 (0.010247) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745258 / 1.841788 (-0.096530) | 19.379253 / 8.074308 (11.304945) | 23.827046 / 10.191392 (13.635654) | 0.267702 / 0.680424 (-0.412722) | 0.030253 / 0.534201 (-0.503948) | 0.542037 / 0.579283 (-0.037246) | 0.655946 / 0.434364 (0.221582) | 0.683525 / 0.540337 (0.143188) | 0.831333 / 1.386936 (-0.555603) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b011a258329375aa4dc7b414bd4e7b6363c5357 \"CML watermark\")\n" ]

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[ "Hi ! Thanks for reporting, I'm working on it ;)" ]

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[ "The issue with multichannel TIFF images has already been reported in Pillow (https://github.com/python-pillow/Pillow/issues/1888). We can't do much about it on our side.\r\n\r\nStill, to avoid the error, you can bypass the default Pillow decoding and define a custom one as follows:\r\n```python\r\nimport tifffile # pip install tifffile\r\n\r\ndset = dset.cast_column(\"image\", datasets.Image(decode=False))\r\n\r\ndef decode_mutlichannel_tiff(batch):\r\n batch[\"image\"] = [tifffile.imread(image[\"path\"]) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndset.set_transform(decode_mutlichannel_tiff)\r\n```\r\n\r\nRegarding the annotations, in which format are they? In the COCO format? I think this is a bit too specific to have a built-in loader for it.", "This snippet is awesome! I know I probably should have gotten deeper in to the docs to find cast_column and set_transform, but perhaps a link ushering folks to that documentation or even this thread somewhere in https://huggingface.co/docs/datasets/image_load would be helpful? Thanks again for the snippet", "We have a section on custom decoding [here](https://huggingface.co/docs/datasets/process#format-transform) (for the audio case though)", "Btw, we can close this issue as it should be addressed in Pillow rather than here. ", "For sure, if image based stuff becomes a priority I think guiding folks to an image decoder section would be really helpful, but im just one dev :) and I know priorities gotta be balanced so no worries. Thanks again for the snippet, agreed we can close" ]

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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) | 0.007852 / 0.011353 (-0.003500) | 0.005804 / 0.011008 (-0.005204) | 0.098268 / 0.038508 (0.059760) | 0.036440 / 0.023109 (0.013331) | 0.299952 / 0.275898 (0.024054) | 0.335590 / 0.323480 (0.012111) | 0.006332 / 0.007986 (-0.001653) | 0.004218 / 0.004328 (-0.000110) | 0.074733 / 0.004250 (0.070483) | 0.055252 / 0.037052 (0.018200) | 0.300854 / 0.258489 (0.042365) | 0.353442 / 0.293841 (0.059601) | 0.036447 / 0.128546 (-0.092099) | 0.012638 / 0.075646 (-0.063009) | 0.336680 / 0.419271 (-0.082591) | 0.052436 / 0.043533 (0.008903) | 0.292606 / 0.255139 (0.037467) | 0.319676 / 0.283200 (0.036476) | 0.111137 / 0.141683 (-0.030546) | 1.449569 / 1.452155 (-0.002586) | 1.558110 / 1.492716 (0.065394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306043 / 0.018006 (0.288037) | 0.563174 / 0.000490 (0.562684) | 0.032227 / 0.000200 (0.032027) | 0.000491 / 0.000054 (0.000436) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029874 / 0.037411 (-0.007537) | 0.109330 / 0.014526 (0.094805) | 0.122579 / 0.176557 (-0.053978) | 0.181398 / 0.737135 (-0.555737) | 0.127124 / 0.296338 (-0.169215) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417950 / 0.215209 (0.202741) | 4.163883 / 2.077655 (2.086228) | 1.985209 / 1.504120 (0.481089) | 1.793660 / 1.541195 (0.252465) | 1.895193 / 1.468490 (0.426703) | 0.694331 / 4.584777 (-3.890446) | 3.820170 / 3.745712 (0.074458) | 2.180556 / 5.269862 (-3.089305) | 1.490671 / 4.565676 (-3.075006) | 0.086132 / 0.424275 (-0.338143) | 0.012289 / 0.007607 (0.004682) | 0.511182 / 0.226044 (0.285137) | 5.117855 / 2.268929 (2.848927) | 2.403914 / 55.444624 (-53.040710) | 2.071107 / 6.876477 (-4.805369) | 2.184108 / 2.142072 (0.042036) | 0.835028 / 4.805227 (-3.970199) | 0.167707 / 6.500664 (-6.332957) | 0.066724 / 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) | 1.203921 / 1.841788 (-0.637867) | 15.214676 / 8.074308 (7.140368) | 14.971337 / 10.191392 (4.779945) | 0.170225 / 0.680424 (-0.510199) | 0.017924 / 0.534201 (-0.516277) | 0.428532 / 0.579283 (-0.150751) | 0.449157 / 0.434364 (0.014793) | 0.507723 / 0.540337 (-0.032614) | 0.615331 / 1.386936 (-0.771605) |\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) | 0.008172 / 0.011353 (-0.003181) | 0.005405 / 0.011008 (-0.005603) | 0.074684 / 0.038508 (0.036176) | 0.039133 / 0.023109 (0.016024) | 0.342598 / 0.275898 (0.066700) | 0.377752 / 0.323480 (0.054272) | 0.006655 / 0.007986 (-0.001331) | 0.005788 / 0.004328 (0.001459) | 0.074014 / 0.004250 (0.069763) | 0.056225 / 0.037052 (0.019173) | 0.342330 / 0.258489 (0.083841) | 0.381052 / 0.293841 (0.087211) | 0.036574 / 0.128546 (-0.091973) | 0.012472 / 0.075646 (-0.063174) | 0.087574 / 0.419271 (-0.331698) | 0.050178 / 0.043533 (0.006646) | 0.351116 / 0.255139 (0.095977) | 0.363772 / 0.283200 (0.080572) | 0.118313 / 0.141683 (-0.023370) | 1.436691 / 1.452155 (-0.015463) | 1.551397 / 1.492716 (0.058680) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265201 / 0.018006 (0.247195) | 0.561855 / 0.000490 (0.561366) | 0.000463 / 0.000200 (0.000263) | 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) | 0.030540 / 0.037411 (-0.006871) | 0.118815 / 0.014526 (0.104289) | 0.127689 / 0.176557 (-0.048868) | 0.176211 / 0.737135 (-0.560924) | 0.133130 / 0.296338 (-0.163208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416318 / 0.215209 (0.201109) | 4.146806 / 2.077655 (2.069151) | 1.983437 / 1.504120 (0.479317) | 1.799733 / 1.541195 (0.258539) | 1.889026 / 1.468490 (0.420536) | 0.723330 / 4.584777 (-3.861447) | 3.817795 / 3.745712 (0.072083) | 2.158449 / 5.269862 (-3.111413) | 1.377348 / 4.565676 (-3.188328) | 0.088504 / 0.424275 (-0.335771) | 0.012560 / 0.007607 (0.004953) | 0.530382 / 0.226044 (0.304337) | 5.308529 / 2.268929 (3.039600) | 2.469655 / 55.444624 (-52.974970) | 2.136209 / 6.876477 (-4.740267) | 2.322997 / 2.142072 (0.180924) | 0.861396 / 4.805227 (-3.943831) | 0.172747 / 6.500664 (-6.327917) | 0.067617 / 0.075469 (-0.007852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263225 / 1.841788 (-0.578563) | 15.878025 / 8.074308 (7.803717) | 14.815627 / 10.191392 (4.624235) | 0.148722 / 0.680424 (-0.531702) | 0.018071 / 0.534201 (-0.516130) | 0.428389 / 0.579283 (-0.150894) | 0.428635 / 0.434364 (-0.005729) | 0.496953 / 0.540337 (-0.043385) | 0.592783 / 1.386936 (-0.794153) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d2e5568dc7a47f9a99678d2889bd2e3c33afdd00 \"CML watermark\")\n" ]

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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) | 0.007343 / 0.011353 (-0.004010) | 0.005145 / 0.011008 (-0.005863) | 0.099820 / 0.038508 (0.061312) | 0.033487 / 0.023109 (0.010378) | 0.313069 / 0.275898 (0.037171) | 0.335420 / 0.323480 (0.011940) | 0.005959 / 0.007986 (-0.002027) | 0.005373 / 0.004328 (0.001044) | 0.076568 / 0.004250 (0.072317) | 0.048702 / 0.037052 (0.011650) | 0.322957 / 0.258489 (0.064468) | 0.363044 / 0.293841 (0.069203) | 0.035070 / 0.128546 (-0.093476) | 0.012029 / 0.075646 (-0.063618) | 0.334664 / 0.419271 (-0.084607) | 0.050549 / 0.043533 (0.007017) | 0.310113 / 0.255139 (0.054974) | 0.324405 / 0.283200 (0.041205) | 0.097596 / 0.141683 (-0.044087) | 1.440741 / 1.452155 (-0.011414) | 1.531194 / 1.492716 (0.038478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220799 / 0.018006 (0.202793) | 0.438158 / 0.000490 (0.437668) | 0.007737 / 0.000200 (0.007537) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026888 / 0.037411 (-0.010523) | 0.106281 / 0.014526 (0.091755) | 0.117419 / 0.176557 (-0.059138) | 0.179144 / 0.737135 (-0.557992) | 0.122477 / 0.296338 (-0.173861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412667 / 0.215209 (0.197458) | 4.108784 / 2.077655 (2.031129) | 1.834300 / 1.504120 (0.330180) | 1.627256 / 1.541195 (0.086061) | 1.691036 / 1.468490 (0.222546) | 0.713405 / 4.584777 (-3.871372) | 3.839262 / 3.745712 (0.093550) | 2.108453 / 5.269862 (-3.161408) | 1.340740 / 4.565676 (-3.224936) | 0.087776 / 0.424275 (-0.336499) | 0.012730 / 0.007607 (0.005123) | 0.505323 / 0.226044 (0.279279) | 5.085176 / 2.268929 (2.816247) | 2.307165 / 55.444624 (-53.137459) | 1.936771 / 6.876477 (-4.939706) | 2.097391 / 2.142072 (-0.044681) | 0.856215 / 4.805227 (-3.949012) | 0.171826 / 6.500664 (-6.328838) | 0.066603 / 0.075469 (-0.008866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202126 / 1.841788 (-0.639661) | 15.173598 / 8.074308 (7.099290) | 15.012645 / 10.191392 (4.821253) | 0.162187 / 0.680424 (-0.518237) | 0.017462 / 0.534201 (-0.516739) | 0.423895 / 0.579283 (-0.155388) | 0.432010 / 0.434364 (-0.002354) | 0.503234 / 0.540337 (-0.037104) | 0.598948 / 1.386936 (-0.787988) |\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) | 0.007099 / 0.011353 (-0.004254) | 0.005167 / 0.011008 (-0.005841) | 0.075551 / 0.038508 (0.037043) | 0.033050 / 0.023109 (0.009940) | 0.339629 / 0.275898 (0.063731) | 0.380486 / 0.323480 (0.057006) | 0.005776 / 0.007986 (-0.002209) | 0.004029 / 0.004328 (-0.000299) | 0.075074 / 0.004250 (0.070823) | 0.046709 / 0.037052 (0.009656) | 0.340203 / 0.258489 (0.081714) | 0.380849 / 0.293841 (0.087008) | 0.035027 / 0.128546 (-0.093519) | 0.012226 / 0.075646 (-0.063420) | 0.087525 / 0.419271 (-0.331747) | 0.049361 / 0.043533 (0.005828) | 0.341854 / 0.255139 (0.086715) | 0.359590 / 0.283200 (0.076390) | 0.100102 / 0.141683 (-0.041581) | 1.482759 / 1.452155 (0.030605) | 1.569905 / 1.492716 (0.077189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213615 / 0.018006 (0.195609) | 0.441117 / 0.000490 (0.440628) | 0.004932 / 0.000200 (0.004732) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031313 / 0.037411 (-0.006098) | 0.110191 / 0.014526 (0.095665) | 0.125320 / 0.176557 (-0.051237) | 0.177658 / 0.737135 (-0.559477) | 0.127928 / 0.296338 (-0.168410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426952 / 0.215209 (0.211743) | 4.247731 / 2.077655 (2.170076) | 2.107318 / 1.504120 (0.603198) | 1.843845 / 1.541195 (0.302650) | 1.894822 / 1.468490 (0.426332) | 0.696232 / 4.584777 (-3.888545) | 3.826516 / 3.745712 (0.080804) | 2.126688 / 5.269862 (-3.143174) | 1.327062 / 4.565676 (-3.238615) | 0.085693 / 0.424275 (-0.338582) | 0.012226 / 0.007607 (0.004619) | 0.521904 / 0.226044 (0.295859) | 5.219798 / 2.268929 (2.950869) | 2.524908 / 55.444624 (-52.919716) | 2.212078 / 6.876477 (-4.664399) | 2.373944 / 2.142072 (0.231871) | 0.833846 / 4.805227 (-3.971381) | 0.169639 / 6.500664 (-6.331025) | 0.064538 / 0.075469 (-0.010931) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254930 / 1.841788 (-0.586858) | 15.585277 / 8.074308 (7.510969) | 14.762857 / 10.191392 (4.571465) | 0.146959 / 0.680424 (-0.533465) | 0.017451 / 0.534201 (-0.516750) | 0.424469 / 0.579283 (-0.154814) | 0.422359 / 0.434364 (-0.012004) | 0.489930 / 0.540337 (-0.050408) | 0.595856 / 1.386936 (-0.791080) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#213c72f52ae52b662f967d3218f66c70a3043048 \"CML watermark\")\n", "@albertvillanova thanks for the review. As you prefer for the github CI config. I just took it from @lhoestq's branch when testing hfh==0.14.0. I think it's still relevant for next releases. In any case, I let you handle merging the PR :)", "<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) | 0.008371 / 0.011353 (-0.002982) | 0.005210 / 0.011008 (-0.005798) | 0.105639 / 0.038508 (0.067131) | 0.045903 / 0.023109 (0.022794) | 0.391231 / 0.275898 (0.115333) | 0.438824 / 0.323480 (0.115345) | 0.006270 / 0.007986 (-0.001715) | 0.005950 / 0.004328 (0.001621) | 0.079685 / 0.004250 (0.075434) | 0.052121 / 0.037052 (0.015069) | 0.387787 / 0.258489 (0.129298) | 0.434322 / 0.293841 (0.140481) | 0.032598 / 0.128546 (-0.095948) | 0.012126 / 0.075646 (-0.063520) | 0.359658 / 0.419271 (-0.059613) | 0.046686 / 0.043533 (0.003154) | 0.391973 / 0.255139 (0.136834) | 0.421149 / 0.283200 (0.137949) | 0.105920 / 0.141683 (-0.035763) | 1.483008 / 1.452155 (0.030854) | 1.617010 / 1.492716 (0.124294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199111 / 0.018006 (0.181105) | 0.407995 / 0.000490 (0.407505) | 0.006706 / 0.000200 (0.006506) | 0.000229 / 0.000054 (0.000175) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030247 / 0.037411 (-0.007164) | 0.115977 / 0.014526 (0.101451) | 0.118112 / 0.176557 (-0.058444) | 0.182710 / 0.737135 (-0.554426) | 0.122483 / 0.296338 (-0.173855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430455 / 0.215209 (0.215246) | 4.314298 / 2.077655 (2.236643) | 1.898124 / 1.504120 (0.394005) | 1.734909 / 1.541195 (0.193715) | 1.802400 / 1.468490 (0.333910) | 0.717237 / 4.584777 (-3.867539) | 4.004705 / 3.745712 (0.258993) | 2.138901 / 5.269862 (-3.130960) | 1.254037 / 4.565676 (-3.311640) | 0.085594 / 0.424275 (-0.338681) | 0.013774 / 0.007607 (0.006166) | 0.535218 / 0.226044 (0.309174) | 5.373730 / 2.268929 (3.104801) | 2.371194 / 55.444624 (-53.073430) | 2.111206 / 6.876477 (-4.765270) | 2.225137 / 2.142072 (0.083064) | 0.838325 / 4.805227 (-3.966902) | 0.159176 / 6.500664 (-6.341488) | 0.072285 / 0.075469 (-0.003184) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352232 / 1.841788 (-0.489555) | 16.926722 / 8.074308 (8.852414) | 16.709531 / 10.191392 (6.518139) | 0.159249 / 0.680424 (-0.521175) | 0.017667 / 0.534201 (-0.516534) | 0.426894 / 0.579283 (-0.152390) | 0.539903 / 0.434364 (0.105539) | 0.537471 / 0.540337 (-0.002866) | 0.619592 / 1.386936 (-0.767344) |\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) | 0.005366 / 0.011008 (-0.005642) | 0.080961 / 0.038508 (0.042453) | 0.046574 / 0.023109 (0.023465) | 0.345949 / 0.275898 (0.070051) | 0.394041 / 0.323480 (0.070562) | 0.006209 / 0.007986 (-0.001777) | 0.005980 / 0.004328 (0.001651) | 0.076235 / 0.004250 (0.071984) | 0.051833 / 0.037052 (0.014780) | 0.348786 / 0.258489 (0.090297) | 0.397421 / 0.293841 (0.103580) | 0.033026 / 0.128546 (-0.095520) | 0.012217 / 0.075646 (-0.063429) | 0.087439 / 0.419271 (-0.331832) | 0.045488 / 0.043533 (0.001955) | 0.352160 / 0.255139 (0.097021) | 0.379079 / 0.283200 (0.095879) | 0.116111 / 0.141683 (-0.025572) | 1.470177 / 1.452155 (0.018022) | 1.587499 / 1.492716 (0.094783) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296149 / 0.018006 (0.278143) | 0.592362 / 0.000490 (0.591872) | 0.000492 / 0.000200 (0.000292) | 0.000064 / 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) | 0.036599 / 0.037411 (-0.000813) | 0.113768 / 0.014526 (0.099242) | 0.116198 / 0.176557 (-0.060358) | 0.180329 / 0.737135 (-0.556806) | 0.123942 / 0.296338 (-0.172396) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452445 / 0.215209 (0.237236) | 4.504330 / 2.077655 (2.426675) | 2.275645 / 1.504120 (0.771525) | 2.107765 / 1.541195 (0.566571) | 2.086363 / 1.468490 (0.617873) | 0.723721 / 4.584777 (-3.861056) | 3.825330 / 3.745712 (0.079618) | 2.162743 / 5.269862 (-3.107119) | 1.255953 / 4.565676 (-3.309724) | 0.085860 / 0.424275 (-0.338415) | 0.013790 / 0.007607 (0.006183) | 0.560257 / 0.226044 (0.334213) | 5.618180 / 2.268929 (3.349251) | 2.625423 / 55.444624 (-52.819202) | 2.374381 / 6.876477 (-4.502095) | 2.496560 / 2.142072 (0.354488) | 0.841120 / 4.805227 (-3.964107) | 0.161541 / 6.500664 (-6.339123) | 0.075270 / 0.075469 (-0.000199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432916 / 1.841788 (-0.408872) | 14.858534 / 8.074308 (6.784226) | 14.973521 / 10.191392 (4.782129) | 0.148312 / 0.680424 (-0.532112) | 0.016811 / 0.534201 (-0.517390) | 0.382623 / 0.579283 (-0.196660) | 0.389767 / 0.434364 (-0.044596) | 0.449657 / 0.540337 (-0.090680) | 0.533723 / 1.386936 (-0.853214) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8344350f15265a585188ac986ae49a8ed8289fe \"CML watermark\")\n", "I agree it is good to have a way to run the CI on push, without needing to open a PR.\r\n\r\nBut I think the branch name should be more generic (and this is not specific to this PR). See:\r\n- #5790 ", "<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) | 0.007208 / 0.011353 (-0.004145) | 0.005600 / 0.011008 (-0.005408) | 0.096129 / 0.038508 (0.057621) | 0.027834 / 0.023109 (0.004725) | 0.295106 / 0.275898 (0.019208) | 0.323983 / 0.323480 (0.000503) | 0.005164 / 0.007986 (-0.002822) | 0.003962 / 0.004328 (-0.000366) | 0.078339 / 0.004250 (0.074089) | 0.036974 / 0.037052 (-0.000078) | 0.310315 / 0.258489 (0.051826) | 0.338036 / 0.293841 (0.044195) | 0.042124 / 0.128546 (-0.086422) | 0.015886 / 0.075646 (-0.059760) | 0.337961 / 0.419271 (-0.081310) | 0.051507 / 0.043533 (0.007974) | 0.297505 / 0.255139 (0.042366) | 0.310728 / 0.283200 (0.027528) | 0.086312 / 0.141683 (-0.055371) | 1.356923 / 1.452155 (-0.095232) | 1.429366 / 1.492716 (-0.063350) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205495 / 0.018006 (0.187489) | 0.460639 / 0.000490 (0.460149) | 0.003996 / 0.000200 (0.003796) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021970 / 0.037411 (-0.015442) | 0.090283 / 0.014526 (0.075757) | 0.098579 / 0.176557 (-0.077978) | 0.160437 / 0.737135 (-0.576699) | 0.102738 / 0.296338 (-0.193600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494474 / 0.215209 (0.279265) | 4.967453 / 2.077655 (2.889799) | 2.045852 / 1.504120 (0.541732) | 1.858022 / 1.541195 (0.316827) | 1.771874 / 1.468490 (0.303384) | 1.186368 / 4.584777 (-3.398408) | 4.974762 / 3.745712 (1.229050) | 2.616225 / 5.269862 (-2.653636) | 1.702971 / 4.565676 (-2.862705) | 0.124929 / 0.424275 (-0.299346) | 0.011774 / 0.007607 (0.004167) | 0.569643 / 0.226044 (0.343598) | 5.793114 / 2.268929 (3.524186) | 2.441561 / 55.444624 (-53.003064) | 1.862233 / 6.876477 (-5.014243) | 1.931142 / 2.142072 (-0.210931) | 1.148915 / 4.805227 (-3.656313) | 0.203914 / 6.500664 (-6.296750) | 0.062468 / 0.075469 (-0.013001) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188708 / 1.841788 (-0.653080) | 13.710830 / 8.074308 (5.636522) | 15.695153 / 10.191392 (5.503761) | 0.171467 / 0.680424 (-0.508957) | 0.024509 / 0.534201 (-0.509692) | 0.450270 / 0.579283 (-0.129014) | 0.500712 / 0.434364 (0.066348) | 0.488632 / 0.540337 (-0.051706) | 0.574893 / 1.386936 (-0.812043) |\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) | 0.007254 / 0.011353 (-0.004099) | 0.006199 / 0.011008 (-0.004809) | 0.072079 / 0.038508 (0.033571) | 0.026909 / 0.023109 (0.003800) | 0.355538 / 0.275898 (0.079640) | 0.358625 / 0.323480 (0.035145) | 0.005564 / 0.007986 (-0.002421) | 0.005278 / 0.004328 (0.000950) | 0.076469 / 0.004250 (0.072219) | 0.038269 / 0.037052 (0.001216) | 0.355214 / 0.258489 (0.096725) | 0.383219 / 0.293841 (0.089378) | 0.046516 / 0.128546 (-0.082030) | 0.015393 / 0.075646 (-0.060254) | 0.088506 / 0.419271 (-0.330765) | 0.050326 / 0.043533 (0.006793) | 0.327265 / 0.255139 (0.072126) | 0.370176 / 0.283200 (0.086976) | 0.102438 / 0.141683 (-0.039245) | 1.378969 / 1.452155 (-0.073186) | 1.441998 / 1.492716 (-0.050719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209044 / 0.018006 (0.191038) | 0.455733 / 0.000490 (0.455243) | 0.005856 / 0.000200 (0.005656) | 0.000116 / 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) | 0.025336 / 0.037411 (-0.012075) | 0.097449 / 0.014526 (0.082923) | 0.106301 / 0.176557 (-0.070255) | 0.153053 / 0.737135 (-0.584082) | 0.107938 / 0.296338 (-0.188401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491070 / 0.215209 (0.275861) | 5.049637 / 2.077655 (2.971982) | 2.064709 / 1.504120 (0.560589) | 1.782266 / 1.541195 (0.241072) | 1.798570 / 1.468490 (0.330080) | 0.988886 / 4.584777 (-3.595891) | 4.690324 / 3.745712 (0.944612) | 4.317355 / 5.269862 (-0.952507) | 2.347596 / 4.565676 (-2.218081) | 0.117249 / 0.424275 (-0.307026) | 0.011614 / 0.007607 (0.004007) | 0.630033 / 0.226044 (0.403988) | 6.140108 / 2.268929 (3.871180) | 2.638080 / 55.444624 (-52.806545) | 2.133017 / 6.876477 (-4.743459) | 2.123392 / 2.142072 (-0.018680) | 1.178056 / 4.805227 (-3.627171) | 0.209465 / 6.500664 (-6.291199) | 0.063234 / 0.075469 (-0.012235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238089 / 1.841788 (-0.603699) | 14.066866 / 8.074308 (5.992558) | 16.225480 / 10.191392 (6.034088) | 0.206466 / 0.680424 (-0.473958) | 0.027279 / 0.534201 (-0.506922) | 0.443006 / 0.579283 (-0.136277) | 0.509512 / 0.434364 (0.075148) | 0.479075 / 0.540337 (-0.061263) | 0.573546 / 1.386936 (-0.813390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c6015a070c66a5bbd84603d415ccc57cb668b44b \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think you can revert the last commit - it should fail if data_files={} IMO", "The validation of non-empty data_files is addressed in this PR:\r\n- #5802", "<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) | 0.008622 / 0.011353 (-0.002730) | 0.005970 / 0.011008 (-0.005038) | 0.117797 / 0.038508 (0.079289) | 0.040955 / 0.023109 (0.017846) | 0.419538 / 0.275898 (0.143640) | 0.455816 / 0.323480 (0.132336) | 0.006481 / 0.007986 (-0.001505) | 0.004507 / 0.004328 (0.000178) | 0.089073 / 0.004250 (0.084822) | 0.052389 / 0.037052 (0.015337) | 0.420053 / 0.258489 (0.161564) | 0.466886 / 0.293841 (0.173045) | 0.042660 / 0.128546 (-0.085886) | 0.014673 / 0.075646 (-0.060973) | 0.411229 / 0.419271 (-0.008042) | 0.076993 / 0.043533 (0.033460) | 0.431693 / 0.255139 (0.176554) | 0.446283 / 0.283200 (0.163084) | 0.131408 / 0.141683 (-0.010275) | 1.820339 / 1.452155 (0.368184) | 1.952946 / 1.492716 (0.460230) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246543 / 0.018006 (0.228537) | 0.489806 / 0.000490 (0.489317) | 0.013999 / 0.000200 (0.013800) | 0.000323 / 0.000054 (0.000269) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032541 / 0.037411 (-0.004870) | 0.130569 / 0.014526 (0.116043) | 0.139630 / 0.176557 (-0.036926) | 0.217018 / 0.737135 (-0.520118) | 0.147914 / 0.296338 (-0.148425) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494767 / 0.215209 (0.279558) | 4.949313 / 2.077655 (2.871658) | 2.277023 / 1.504120 (0.772903) | 2.036677 / 1.541195 (0.495482) | 2.064461 / 1.468490 (0.595970) | 0.842484 / 4.584777 (-3.742293) | 4.720646 / 3.745712 (0.974934) | 4.025673 / 5.269862 (-1.244189) | 2.198606 / 4.565676 (-2.367070) | 0.103042 / 0.424275 (-0.321233) | 0.014794 / 0.007607 (0.007187) | 0.617867 / 0.226044 (0.391822) | 6.197146 / 2.268929 (3.928218) | 2.804927 / 55.444624 (-52.639697) | 2.426420 / 6.876477 (-4.450057) | 2.515182 / 2.142072 (0.373109) | 1.008098 / 4.805227 (-3.797129) | 0.204982 / 6.500664 (-6.295682) | 0.078643 / 0.075469 (0.003174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.490790 / 1.841788 (-0.350997) | 17.268042 / 8.074308 (9.193734) | 17.129647 / 10.191392 (6.938255) | 0.170351 / 0.680424 (-0.510073) | 0.021317 / 0.534201 (-0.512884) | 0.517068 / 0.579283 (-0.062215) | 0.500200 / 0.434364 (0.065836) | 0.641974 / 0.540337 (0.101637) | 0.763984 / 1.386936 (-0.622952) |\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) | 0.008358 / 0.011353 (-0.002995) | 0.005710 / 0.011008 (-0.005298) | 0.091077 / 0.038508 (0.052569) | 0.040413 / 0.023109 (0.017303) | 0.416634 / 0.275898 (0.140736) | 0.451122 / 0.323480 (0.127642) | 0.006417 / 0.007986 (-0.001569) | 0.004360 / 0.004328 (0.000032) | 0.089543 / 0.004250 (0.085292) | 0.051137 / 0.037052 (0.014085) | 0.420228 / 0.258489 (0.161739) | 0.458649 / 0.293841 (0.164808) | 0.041828 / 0.128546 (-0.086718) | 0.014268 / 0.075646 (-0.061379) | 0.105301 / 0.419271 (-0.313970) | 0.058931 / 0.043533 (0.015398) | 0.413445 / 0.255139 (0.158306) | 0.443882 / 0.283200 (0.160682) | 0.124946 / 0.141683 (-0.016737) | 1.842259 / 1.452155 (0.390104) | 1.948162 / 1.492716 (0.455445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235799 / 0.018006 (0.217792) | 0.487667 / 0.000490 (0.487177) | 0.001112 / 0.000200 (0.000912) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034233 / 0.037411 (-0.003178) | 0.136593 / 0.014526 (0.122068) | 0.145598 / 0.176557 (-0.030959) | 0.206545 / 0.737135 (-0.530590) | 0.150781 / 0.296338 (-0.145558) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522345 / 0.215209 (0.307136) | 5.192092 / 2.077655 (3.114438) | 2.543182 / 1.504120 (1.039062) | 2.285212 / 1.541195 (0.744018) | 2.312803 / 1.468490 (0.844313) | 0.859334 / 4.584777 (-3.725443) | 4.620235 / 3.745712 (0.874523) | 3.964060 / 5.269862 (-1.305802) | 2.046347 / 4.565676 (-2.519330) | 0.105284 / 0.424275 (-0.318991) | 0.015051 / 0.007607 (0.007444) | 0.646530 / 0.226044 (0.420485) | 6.386396 / 2.268929 (4.117467) | 3.131833 / 55.444624 (-52.312791) | 2.761898 / 6.876477 (-4.114579) | 2.833216 / 2.142072 (0.691143) | 1.026024 / 4.805227 (-3.779204) | 0.206776 / 6.500664 (-6.293888) | 0.078845 / 0.075469 (0.003376) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.580851 / 1.841788 (-0.260937) | 17.826213 / 8.074308 (9.751905) | 16.929460 / 10.191392 (6.738068) | 0.232483 / 0.680424 (-0.447941) | 0.021123 / 0.534201 (-0.513078) | 0.522196 / 0.579283 (-0.057087) | 0.503495 / 0.434364 (0.069131) | 0.622777 / 0.540337 (0.082440) | 0.753272 / 1.386936 (-0.633664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f9dfbd93707665132abc862b14bb9b50597b739 \"CML watermark\")\n" ]

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[ "Hi ! PyTorch may hang when calling `load_state_dict()` in a subprocess. To fix that, set the multiprocessing start method to \"spawn\". Since `datasets` uses `multiprocess`, you should do:\r\n\r\n```python\r\n# Required to avoid issues with pytorch (otherwise hangs during load_state_dict in multiprocessing)\r\nimport multiprocess.context as ctx\r\nctx._force_start_method('spawn')\r\n```\r\n\r\nAlso make sure to run your main code in `if __name__ == \"__main__\":` to avoid issues with python multiprocesing", "Thanks!", "@lhoestq Hello, I also encountered this problem but maybe with another reason. Here is my code:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir, model_max_length=training_args.model_max_length)\r\ndata = load_dataset(\"json\", data_files=data_args.train_file, cache_dir=data_args.data_cache_dir)\r\ndef func(samples):\r\n # main operation\r\n for sentence_value in samples:\r\n sentence_ids = tokenizer.encode(sentence_value, add_special_tokens=False, max_length=tokenizer.model_max_length, truncation=True)\r\n ... ...\r\ntrain_data = data[\"train\"].shuffle().map(func, num_proc=os.cpu_count())\r\n```\r\nIt hangs after the progress reaches 100%. Could you help me point out the reason?", "@SkyAndCloud your issue doesn't seem related to the original post - could you open a new issue and provide more details ? (size of the dataset, number of cpus, how much time it took to run, `datasets` version)", "@lhoestq Hi, I just solved this problem. Because the input is extremely long and the tokenizer requests a large amount of memory, which leads to a OOM error and may eventually causes the hang. I didn't filter those too-long sentences because I thought `tokenizer` would stop once the length exceeds the `max_length`. However, it actually firstly complete the tokenization of entire sentence and then truncate it." ]

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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) | 0.008959 / 0.011353 (-0.002394) | 0.005804 / 0.011008 (-0.005204) | 0.112663 / 0.038508 (0.074155) | 0.043406 / 0.023109 (0.020297) | 0.348582 / 0.275898 (0.072684) | 0.382332 / 0.323480 (0.058852) | 0.007469 / 0.007986 (-0.000517) | 0.006211 / 0.004328 (0.001883) | 0.086576 / 0.004250 (0.082326) | 0.059223 / 0.037052 (0.022170) | 0.361051 / 0.258489 (0.102562) | 0.411359 / 0.293841 (0.117518) | 0.043640 / 0.128546 (-0.084906) | 0.014239 / 0.075646 (-0.061408) | 0.389729 / 0.419271 (-0.029542) | 0.072319 / 0.043533 (0.028786) | 0.351025 / 0.255139 (0.095886) | 0.371893 / 0.283200 (0.088693) | 0.125994 / 0.141683 (-0.015688) | 1.675249 / 1.452155 (0.223094) | 1.808740 / 1.492716 (0.316024) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255172 / 0.018006 (0.237166) | 0.536003 / 0.000490 (0.535514) | 0.000365 / 0.000200 (0.000165) | 0.000070 / 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) | 0.031989 / 0.037411 (-0.005423) | 0.126854 / 0.014526 (0.112328) | 0.142458 / 0.176557 (-0.034098) | 0.207821 / 0.737135 (-0.529314) | 0.145610 / 0.296338 (-0.150728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468924 / 0.215209 (0.253715) | 4.696677 / 2.077655 (2.619023) | 2.183133 / 1.504120 (0.679013) | 1.994219 / 1.541195 (0.453024) | 2.101375 / 1.468490 (0.632885) | 0.827168 / 4.584777 (-3.757609) | 4.710167 / 3.745712 (0.964455) | 2.377062 / 5.269862 (-2.892800) | 1.712245 / 4.565676 (-2.853431) | 0.100620 / 0.424275 (-0.323655) | 0.014302 / 0.007607 (0.006695) | 0.590813 / 0.226044 (0.364769) | 5.871991 / 2.268929 (3.603063) | 2.722229 / 55.444624 (-52.722395) | 2.323585 / 6.876477 (-4.552892) | 2.503289 / 2.142072 (0.361217) | 0.983644 / 4.805227 (-3.821583) | 0.193942 / 6.500664 (-6.306722) | 0.076493 / 0.075469 (0.001024) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.463107 / 1.841788 (-0.378681) | 17.876918 / 8.074308 (9.802610) | 16.755740 / 10.191392 (6.564348) | 0.167556 / 0.680424 (-0.512868) | 0.020514 / 0.534201 (-0.513687) | 0.508385 / 0.579283 (-0.070898) | 0.505873 / 0.434364 (0.071509) | 0.603630 / 0.540337 (0.063293) | 0.708856 / 1.386936 (-0.678080) |\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) | 0.008504 / 0.011353 (-0.002849) | 0.005894 / 0.011008 (-0.005114) | 0.085523 / 0.038508 (0.047015) | 0.038780 / 0.023109 (0.015671) | 0.402869 / 0.275898 (0.126971) | 0.423819 / 0.323480 (0.100339) | 0.006427 / 0.007986 (-0.001559) | 0.004598 / 0.004328 (0.000269) | 0.079807 / 0.004250 (0.075556) | 0.050852 / 0.037052 (0.013799) | 0.403232 / 0.258489 (0.144743) | 0.452489 / 0.293841 (0.158648) | 0.041501 / 0.128546 (-0.087045) | 0.014996 / 0.075646 (-0.060650) | 0.101548 / 0.419271 (-0.317724) | 0.056993 / 0.043533 (0.013461) | 0.403153 / 0.255139 (0.148014) | 0.424587 / 0.283200 (0.141388) | 0.114507 / 0.141683 (-0.027176) | 1.707098 / 1.452155 (0.254943) | 1.799008 / 1.492716 (0.306291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288003 / 0.018006 (0.269996) | 0.496526 / 0.000490 (0.496036) | 0.010923 / 0.000200 (0.010723) | 0.000159 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033948 / 0.037411 (-0.003463) | 0.142343 / 0.014526 (0.127817) | 0.143862 / 0.176557 (-0.032695) | 0.202655 / 0.737135 (-0.534480) | 0.151177 / 0.296338 (-0.145162) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508003 / 0.215209 (0.292794) | 5.320394 / 2.077655 (3.242740) | 2.409854 / 1.504120 (0.905734) | 2.190656 / 1.541195 (0.649462) | 2.272171 / 1.468490 (0.803681) | 0.809492 / 4.584777 (-3.775285) | 4.554412 / 3.745712 (0.808699) | 4.413643 / 5.269862 (-0.856218) | 2.374034 / 4.565676 (-2.191642) | 0.099458 / 0.424275 (-0.324817) | 0.014553 / 0.007607 (0.006946) | 0.613916 / 0.226044 (0.387871) | 6.121430 / 2.268929 (3.852502) | 2.945661 / 55.444624 (-52.498964) | 2.595247 / 6.876477 (-4.281230) | 2.734047 / 2.142072 (0.591975) | 0.952217 / 4.805227 (-3.853010) | 0.196933 / 6.500664 (-6.303731) | 0.073391 / 0.075469 (-0.002078) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475666 / 1.841788 (-0.366122) | 18.564281 / 8.074308 (10.489973) | 16.865259 / 10.191392 (6.673867) | 0.166494 / 0.680424 (-0.513930) | 0.020655 / 0.534201 (-0.513546) | 0.495120 / 0.579283 (-0.084163) | 0.502602 / 0.434364 (0.068238) | 0.622448 / 0.540337 (0.082110) | 0.721036 / 1.386936 (-0.665900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40c204c777793d64e8bb8ce357e9c07b3b303e41 \"CML watermark\")\n", "Whoops mario you're off this week sorry. I'm taking the liberty to merge this one", "<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) | 0.009079 / 0.011353 (-0.002274) | 0.005960 / 0.011008 (-0.005049) | 0.116530 / 0.038508 (0.078022) | 0.046649 / 0.023109 (0.023540) | 0.391906 / 0.275898 (0.116008) | 0.438892 / 0.323480 (0.115412) | 0.007134 / 0.007986 (-0.000851) | 0.004997 / 0.004328 (0.000668) | 0.085947 / 0.004250 (0.081697) | 0.059814 / 0.037052 (0.022762) | 0.396423 / 0.258489 (0.137934) | 0.455941 / 0.293841 (0.162100) | 0.042535 / 0.128546 (-0.086011) | 0.014667 / 0.075646 (-0.060980) | 0.402023 / 0.419271 (-0.017249) | 0.060381 / 0.043533 (0.016848) | 0.393829 / 0.255139 (0.138690) | 0.426557 / 0.283200 (0.143358) | 0.131519 / 0.141683 (-0.010163) | 1.758098 / 1.452155 (0.305943) | 1.848194 / 1.492716 (0.355478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236405 / 0.018006 (0.218399) | 0.611442 / 0.000490 (0.610952) | 0.005143 / 0.000200 (0.004943) | 0.000146 / 0.000054 (0.000092) |\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) | 0.182485 / 0.014526 (0.167959) | 0.183149 / 0.176557 (0.006592) | 0.293592 / 0.737135 (-0.443543) | 0.197137 / 0.296338 (-0.099202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475690 / 0.215209 (0.260481) | 4.757344 / 2.077655 (2.679690) | 2.184079 / 1.504120 (0.679959) | 1.956599 / 1.541195 (0.415404) | 2.043041 / 1.468490 (0.574551) | 0.817602 / 4.584777 (-3.767175) | 6.432267 / 3.745712 (2.686555) | 5.999402 / 5.269862 (0.729541) | 3.095970 / 4.565676 (-1.469706) | 0.181589 / 0.424275 (-0.242686) | 0.023286 / 0.007607 (0.015679) | 1.090318 / 0.226044 (0.864274) | 7.919330 / 2.268929 (5.650401) | 2.702821 / 55.444624 (-52.741804) | 2.375442 / 6.876477 (-4.501034) | 2.543075 / 2.142072 (0.401003) | 1.011763 / 4.805227 (-3.793464) | 0.203676 / 6.500664 (-6.296988) | 0.080075 / 0.075469 (0.004606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.875420 / 1.841788 (0.033632) | 23.059278 / 8.074308 (14.984970) | 19.250807 / 10.191392 (9.059415) | 0.323678 / 0.680424 (-0.356746) | 0.028682 / 0.534201 (-0.505519) | 0.698231 / 0.579283 (0.118948) | 0.668129 / 0.434364 (0.233765) | 0.831218 / 0.540337 (0.290880) | 0.941191 / 1.386936 (-0.445745) |\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) | 0.013122 / 0.011353 (0.001769) | 0.006123 / 0.011008 (-0.004886) | 0.090493 / 0.038508 (0.051985) | 0.070660 / 0.023109 (0.047551) | 0.413486 / 0.275898 (0.137588) | 0.450364 / 0.323480 (0.126884) | 0.010288 / 0.007986 (0.002302) | 0.006590 / 0.004328 (0.002261) | 0.087174 / 0.004250 (0.082923) | 0.077304 / 0.037052 (0.040252) | 0.428480 / 0.258489 (0.169991) | 0.459872 / 0.293841 (0.166032) | 0.060477 / 0.128546 (-0.068069) | 0.014859 / 0.075646 (-0.060788) | 0.103915 / 0.419271 (-0.315356) | 0.087466 / 0.043533 (0.043933) | 0.418644 / 0.255139 (0.163505) | 0.433409 / 0.283200 (0.150209) | 0.166716 / 0.141683 (0.025033) | 1.712068 / 1.452155 (0.259914) | 1.827869 / 1.492716 (0.335153) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.372491 / 0.018006 (0.354484) | 0.493426 / 0.000490 (0.492937) | 0.005497 / 0.000200 (0.005297) | 0.000129 / 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) | 0.036531 / 0.037411 (-0.000880) | 0.142152 / 0.014526 (0.127626) | 0.148183 / 0.176557 (-0.028373) | 0.212918 / 0.737135 (-0.524217) | 0.154092 / 0.296338 (-0.142246) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.551733 / 0.215209 (0.336524) | 5.421498 / 2.077655 (3.343843) | 2.418848 / 1.504120 (0.914728) | 2.213185 / 1.541195 (0.671991) | 2.294881 / 1.468490 (0.826391) | 0.827031 / 4.584777 (-3.757746) | 6.365622 / 3.745712 (2.619910) | 4.927996 / 5.269862 (-0.341866) | 2.756133 / 4.565676 (-1.809544) | 0.101474 / 0.424275 (-0.322801) | 0.014523 / 0.007607 (0.006916) | 0.619082 / 0.226044 (0.393037) | 6.200132 / 2.268929 (3.931204) | 3.015590 / 55.444624 (-52.429034) | 2.711181 / 6.876477 (-4.165296) | 2.857157 / 2.142072 (0.715084) | 0.993329 / 4.805227 (-3.811898) | 0.203364 / 6.500664 (-6.297301) | 0.079167 / 0.075469 (0.003698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.709881 / 1.841788 (-0.131907) | 24.867536 / 8.074308 (16.793228) | 21.755361 / 10.191392 (11.563969) | 0.295837 / 0.680424 (-0.384586) | 0.031934 / 0.534201 (-0.502267) | 0.709994 / 0.579283 (0.130711) | 0.779656 / 0.434364 (0.345293) | 0.780669 / 0.540337 (0.240331) | 0.712808 / 1.386936 (-0.674128) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4a1951bdca7175adac9c8b85550e89dcceb6fa \"CML watermark\")\n" ]

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[ "Hi! This looks like an Arrow bug, but it can be avoided by reducing the `writer_batch_size`.\r\n\r\n(`ds = ds.map(get_text_caption, writer_batch_size=100)` in Colab runs without issues)\r\n", "@mariosasko I ran into this problem with load_dataset. What should I do", "@AisingioroHao0 You can also pass the `writer_batch_size` parameter to `load_dataset`, e.g., `load_dataset(\"mnist\", writer_batch_size=100)`", "@mariosasko How do I determine the optimal size of write_batch_size? My training is sometimes fast and sometimes slow. Is it because write_batch_size is too small? Each batch of the current dataloader should be the same size. I preprocessed the dataset using map", "@aihao2000 It's unlikely `writer_batch_size` is the problem. You can use the following code to profile the training loop (e.g., on a subset of data) and find slow parts:\r\n```python\r\nimport cProfile, pstats\r\n\r\nwith cProfile.Profile() as profiler:\r\n ... # training loop code\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\n", "@nishanthcgit ok，thanks.Recently I found dataset.with_transform to be faster and more stable with multiple processes", "@mariosasko Is the larger the num_proc of load_dataset within the number of cpu cores, the better? Then the num_proc of data_loader is the number of cpu cores/number of training processes" ]

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[ "Hi! \r\n\r\nYou can use `set_transform`/`with_transform` to define a custom decoding for audio formats not supported by `soundfile`:\r\n```python\r\naudio_dataset_amr = Dataset.from_dict({\"audio\": [\"audio_samples/audio.amr\"]})\r\n\r\ndef decode_audio(batch):\r\n batch[\"audio\"] = [read_ffmpeg(audio_path) for audio_path in batch[\"audio\"]]\r\n return batch\r\n\r\naudio_dataset_amr.set_transform(decode_amr) \r\n```\r\n\r\nSupporting multiple backends is more work to maintain, but we could consider this if we get more requests such as this one.", "Could it be put somewhere as an example tip or something?", "Considering the number of times a custom decoding transform has been suggested as a solution, an example in the [docs](https://huggingface.co/docs/datasets/process#format-transform) would be nice.\r\n\r\ncc @stevhliu " ]

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[ "It looks like an issue with your installation of scipy, can you try reinstalling it ?", "Sorry for the late reply, but that worked @lhoestq . Thanks for the assist." ]

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