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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7011). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005589 / 0.011353 (-0.005764) | 0.003855 / 0.011008 (-0.007153) | 0.063445 / 0.038508 (0.024937) | 0.030815 / 0.023109 (0.007706) | 0.244052 / 0.275898 (-0.031846) | 0.269916 / 0.323480 (-0.053563) | 0.003130 / 0.007986 (-0.004856) | 0.003349 / 0.004328 (-0.000980) | 0.049338 / 0.004250 (0.045088) | 0.045314 / 0.037052 (0.008261) | 0.250646 / 0.258489 (-0.007844) | 0.295828 / 0.293841 (0.001987) | 0.029808 / 0.128546 (-0.098738) | 0.012299 / 0.075646 (-0.063347) | 0.204946 / 0.419271 (-0.214325) | 0.036387 / 0.043533 (-0.007146) | 0.244316 / 0.255139 (-0.010823) | 0.269308 / 0.283200 (-0.013892) | 0.019226 / 0.141683 (-0.122457) | 1.138739 / 1.452155 (-0.313416) | 1.155265 / 1.492716 (-0.337451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094085 / 0.018006 (0.076078) | 0.299764 / 0.000490 (0.299275) | 0.000205 / 0.000200 (0.000005) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018361 / 0.037411 (-0.019050) | 0.062665 / 0.014526 (0.048139) | 0.075888 / 0.176557 (-0.100668) | 0.120915 / 0.737135 (-0.616221) | 0.075465 / 0.296338 (-0.220873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279698 / 0.215209 (0.064489) | 2.784544 / 2.077655 (0.706889) | 1.498441 / 1.504120 (-0.005679) | 1.379789 / 1.541195 (-0.161406) | 1.388480 / 1.468490 (-0.080011) | 0.724249 / 4.584777 (-3.860528) | 2.343139 / 3.745712 (-1.402573) | 2.816179 / 5.269862 (-2.453683) | 1.908737 / 4.565676 (-2.656940) | 0.077686 / 0.424275 (-0.346589) | 0.005444 / 0.007607 (-0.002163) | 0.344084 / 0.226044 (0.118039) | 3.367548 / 2.268929 (1.098619) | 1.849200 / 55.444624 (-53.595424) | 1.556390 / 6.876477 (-5.320087) | 1.672902 / 2.142072 (-0.469170) | 0.795457 / 4.805227 (-4.009770) | 0.133521 / 6.500664 (-6.367143) | 0.042883 / 0.075469 (-0.032586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959094 / 1.841788 (-0.882694) | 11.399783 / 8.074308 (3.325475) | 9.075784 / 10.191392 (-1.115608) | 0.142897 / 0.680424 (-0.537527) | 0.014765 / 0.534201 (-0.519436) | 0.302259 / 0.579283 (-0.277024) | 0.261148 / 0.434364 (-0.173216) | 0.340302 / 0.540337 (-0.200035) | 0.459203 / 1.386936 (-0.927733) |\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) | 0.005821 / 0.011353 (-0.005532) | 0.003964 / 0.011008 (-0.007044) | 0.049904 / 0.038508 (0.011396) | 0.031061 / 0.023109 (0.007952) | 0.270002 / 0.275898 (-0.005896) | 0.289489 / 0.323480 (-0.033991) | 0.004477 / 0.007986 (-0.003509) | 0.002800 / 0.004328 (-0.001528) | 0.048029 / 0.004250 (0.043779) | 0.040486 / 0.037052 (0.003434) | 0.278442 / 0.258489 (0.019953) | 0.312606 / 0.293841 (0.018765) | 0.032920 / 0.128546 (-0.095626) | 0.012572 / 0.075646 (-0.063075) | 0.060589 / 0.419271 (-0.358682) | 0.034147 / 0.043533 (-0.009386) | 0.275282 / 0.255139 (0.020143) | 0.314073 / 0.283200 (0.030873) | 0.017555 / 0.141683 (-0.124128) | 1.149974 / 1.452155 (-0.302181) | 1.183715 / 1.492716 (-0.309002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095616 / 0.018006 (0.077610) | 0.302101 / 0.000490 (0.301611) | 0.000201 / 0.000200 (0.000001) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022245 / 0.037411 (-0.015166) | 0.076890 / 0.014526 (0.062364) | 0.088471 / 0.176557 (-0.088085) | 0.128364 / 0.737135 (-0.608771) | 0.089907 / 0.296338 (-0.206431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302662 / 0.215209 (0.087453) | 2.979054 / 2.077655 (0.901399) | 1.576534 / 1.504120 (0.072414) | 1.443784 / 1.541195 (-0.097410) | 1.476000 / 1.468490 (0.007510) | 0.740580 / 4.584777 (-3.844197) | 0.953349 / 3.745712 (-2.792363) | 2.925619 / 5.269862 (-2.344243) | 1.904701 / 4.565676 (-2.660975) | 0.078404 / 0.424275 (-0.345872) | 0.005179 / 0.007607 (-0.002429) | 0.357217 / 0.226044 (0.131173) | 3.494812 / 2.268929 (1.225884) | 1.927345 / 55.444624 (-53.517280) | 1.627162 / 6.876477 (-5.249315) | 1.676748 / 2.142072 (-0.465324) | 0.798826 / 4.805227 (-4.006401) | 0.133617 / 6.500664 (-6.367047) | 0.041229 / 0.075469 (-0.034240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.017046 / 1.841788 (-0.824742) | 12.045942 / 8.074308 (3.971634) | 10.430383 / 10.191392 (0.238991) | 0.144497 / 0.680424 (-0.535926) | 0.015809 / 0.534201 (-0.518392) | 0.304701 / 0.579283 (-0.274582) | 0.126496 / 0.434364 (-0.307868) | 0.340308 / 0.540337 (-0.200030) | 0.434917 / 1.386936 (-0.952019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#054e57a8468af9fff5b75c08d2d6adf3e05fa763 \"CML watermark\")\n"
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} | Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers
- https://github.com/huggingface/transformers/pull/31007
was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0
Fix #7010. | {
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- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7009). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005481 / 0.011353 (-0.005872) | 0.003580 / 0.011008 (-0.007428) | 0.062682 / 0.038508 (0.024174) | 0.031125 / 0.023109 (0.008015) | 0.239443 / 0.275898 (-0.036455) | 0.262950 / 0.323480 (-0.060529) | 0.003129 / 0.007986 (-0.004857) | 0.003393 / 0.004328 (-0.000935) | 0.048765 / 0.004250 (0.044514) | 0.044363 / 0.037052 (0.007311) | 0.248632 / 0.258489 (-0.009857) | 0.285056 / 0.293841 (-0.008785) | 0.029674 / 0.128546 (-0.098872) | 0.011963 / 0.075646 (-0.063684) | 0.204122 / 0.419271 (-0.215150) | 0.035867 / 0.043533 (-0.007665) | 0.245422 / 0.255139 (-0.009717) | 0.267165 / 0.283200 (-0.016035) | 0.018556 / 0.141683 (-0.123127) | 1.132112 / 1.452155 (-0.320043) | 1.173512 / 1.492716 (-0.319204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092749 / 0.018006 (0.074743) | 0.298946 / 0.000490 (0.298457) | 0.000211 / 0.000200 (0.000011) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019496 / 0.037411 (-0.017915) | 0.062209 / 0.014526 (0.047683) | 0.074656 / 0.176557 (-0.101901) | 0.121238 / 0.737135 (-0.615897) | 0.075810 / 0.296338 (-0.220528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278089 / 0.215209 (0.062880) | 2.725602 / 2.077655 (0.647948) | 1.413346 / 1.504120 (-0.090774) | 1.290352 / 1.541195 (-0.250843) | 1.306732 / 1.468490 (-0.161758) | 0.713945 / 4.584777 (-3.870832) | 2.380131 / 3.745712 (-1.365581) | 2.804548 / 5.269862 (-2.465314) | 1.896506 / 4.565676 (-2.669170) | 0.078303 / 0.424275 (-0.345972) | 0.005475 / 0.007607 (-0.002132) | 0.340162 / 0.226044 (0.114117) | 3.355732 / 2.268929 (1.086803) | 1.776012 / 55.444624 (-53.668613) | 1.507006 / 6.876477 (-5.369471) | 1.607234 / 2.142072 (-0.534838) | 0.796458 / 4.805227 (-4.008769) | 0.135888 / 6.500664 (-6.364776) | 0.042352 / 0.075469 (-0.033118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988337 / 1.841788 (-0.853450) | 11.299311 / 8.074308 (3.225003) | 9.166845 / 10.191392 (-1.024547) | 0.140351 / 0.680424 (-0.540073) | 0.013932 / 0.534201 (-0.520269) | 0.302157 / 0.579283 (-0.277126) | 0.259355 / 0.434364 (-0.175009) | 0.339850 / 0.540337 (-0.200488) | 0.465345 / 1.386936 (-0.921591) |\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) | 0.005707 / 0.011353 (-0.005646) | 0.003846 / 0.011008 (-0.007162) | 0.050100 / 0.038508 (0.011591) | 0.031810 / 0.023109 (0.008701) | 0.265120 / 0.275898 (-0.010778) | 0.286635 / 0.323480 (-0.036845) | 0.004329 / 0.007986 (-0.003657) | 0.002757 / 0.004328 (-0.001571) | 0.050864 / 0.004250 (0.046614) | 0.039872 / 0.037052 (0.002820) | 0.277675 / 0.258489 (0.019186) | 0.310251 / 0.293841 (0.016410) | 0.032458 / 0.128546 (-0.096088) | 0.012072 / 0.075646 (-0.063574) | 0.060539 / 0.419271 (-0.358733) | 0.033772 / 0.043533 (-0.009761) | 0.265992 / 0.255139 (0.010853) | 0.286152 / 0.283200 (0.002953) | 0.018210 / 0.141683 (-0.123473) | 1.151461 / 1.452155 (-0.300694) | 1.199998 / 1.492716 (-0.292718) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094109 / 0.018006 (0.076103) | 0.298190 / 0.000490 (0.297701) | 0.000199 / 0.000200 (-0.000001) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022431 / 0.037411 (-0.014980) | 0.076319 / 0.014526 (0.061794) | 0.090023 / 0.176557 (-0.086533) | 0.128189 / 0.737135 (-0.608946) | 0.089564 / 0.296338 (-0.206774) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298887 / 0.215209 (0.083678) | 2.928580 / 2.077655 (0.850926) | 1.565379 / 1.504120 (0.061259) | 1.424704 / 1.541195 (-0.116490) | 1.446336 / 1.468490 (-0.022154) | 0.716348 / 4.584777 (-3.868429) | 0.967465 / 3.745712 (-2.778247) | 2.967318 / 5.269862 (-2.302544) | 1.918878 / 4.565676 (-2.646798) | 0.077167 / 0.424275 (-0.347108) | 0.005271 / 0.007607 (-0.002336) | 0.342376 / 0.226044 (0.116332) | 3.386044 / 2.268929 (1.117115) | 1.915308 / 55.444624 (-53.529316) | 1.612729 / 6.876477 (-5.263748) | 1.621278 / 2.142072 (-0.520794) | 0.804639 / 4.805227 (-4.000589) | 0.132596 / 6.500664 (-6.368069) | 0.041075 / 0.075469 (-0.034394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996521 / 1.841788 (-0.845267) | 12.328856 / 8.074308 (4.254548) | 10.585154 / 10.191392 (0.393762) | 0.131720 / 0.680424 (-0.548704) | 0.016777 / 0.534201 (-0.517424) | 0.300424 / 0.579283 (-0.278860) | 0.128526 / 0.434364 (-0.305838) | 0.339961 / 0.540337 (-0.200377) | 0.441661 / 1.386936 (-0.945275) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a16477ddf8f96e590e9597225a5d180cce343f26 \"CML watermark\")\n"
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} | Support ruff 0.5.0 in CI and revert:
- #7007
Fix #7008. | {
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Also revert:
- #7007 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7007). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005225 / 0.011353 (-0.006128) | 0.003856 / 0.011008 (-0.007152) | 0.063455 / 0.038508 (0.024947) | 0.030184 / 0.023109 (0.007075) | 0.248518 / 0.275898 (-0.027380) | 0.270596 / 0.323480 (-0.052884) | 0.003185 / 0.007986 (-0.004800) | 0.002739 / 0.004328 (-0.001590) | 0.049379 / 0.004250 (0.045129) | 0.043190 / 0.037052 (0.006138) | 0.257181 / 0.258489 (-0.001308) | 0.283385 / 0.293841 (-0.010456) | 0.029702 / 0.128546 (-0.098844) | 0.012022 / 0.075646 (-0.063624) | 0.204531 / 0.419271 (-0.214741) | 0.035621 / 0.043533 (-0.007912) | 0.257745 / 0.255139 (0.002606) | 0.269033 / 0.283200 (-0.014167) | 0.019283 / 0.141683 (-0.122400) | 1.152477 / 1.452155 (-0.299678) | 1.180929 / 1.492716 (-0.311788) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094520 / 0.018006 (0.076514) | 0.299383 / 0.000490 (0.298893) | 0.000224 / 0.000200 (0.000024) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019267 / 0.037411 (-0.018145) | 0.062458 / 0.014526 (0.047933) | 0.075743 / 0.176557 (-0.100814) | 0.128564 / 0.737135 (-0.608572) | 0.075549 / 0.296338 (-0.220789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288809 / 0.215209 (0.073600) | 2.854469 / 2.077655 (0.776814) | 1.581731 / 1.504120 (0.077611) | 1.460196 / 1.541195 (-0.080999) | 1.485567 / 1.468490 (0.017077) | 0.708824 / 4.584777 (-3.875953) | 2.362389 / 3.745712 (-1.383323) | 2.980804 / 5.269862 (-2.289057) | 1.918788 / 4.565676 (-2.646888) | 0.088389 / 0.424275 (-0.335886) | 0.005266 / 0.007607 (-0.002341) | 0.348598 / 0.226044 (0.122554) | 3.443202 / 2.268929 (1.174273) | 1.979311 / 55.444624 (-53.465314) | 1.655774 / 6.876477 (-5.220702) | 1.685538 / 2.142072 (-0.456535) | 0.788695 / 4.805227 (-4.016532) | 0.138403 / 6.500664 (-6.362261) | 0.043288 / 0.075469 (-0.032181) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975874 / 1.841788 (-0.865913) | 11.506991 / 8.074308 (3.432683) | 9.640619 / 10.191392 (-0.550773) | 0.131897 / 0.680424 (-0.548527) | 0.014912 / 0.534201 (-0.519289) | 0.304173 / 0.579283 (-0.275110) | 0.262483 / 0.434364 (-0.171881) | 0.342636 / 0.540337 (-0.197701) | 0.440337 / 1.386936 (-0.946599) |\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) | 0.005961 / 0.011353 (-0.005392) | 0.004023 / 0.011008 (-0.006985) | 0.050230 / 0.038508 (0.011722) | 0.033204 / 0.023109 (0.010095) | 0.263462 / 0.275898 (-0.012436) | 0.287517 / 0.323480 (-0.035963) | 0.004432 / 0.007986 (-0.003553) | 0.002938 / 0.004328 (-0.001390) | 0.049297 / 0.004250 (0.045047) | 0.041166 / 0.037052 (0.004113) | 0.279220 / 0.258489 (0.020731) | 0.312857 / 0.293841 (0.019016) | 0.032567 / 0.128546 (-0.095979) | 0.012566 / 0.075646 (-0.063080) | 0.060579 / 0.419271 (-0.358692) | 0.033760 / 0.043533 (-0.009773) | 0.264219 / 0.255139 (0.009080) | 0.282929 / 0.283200 (-0.000270) | 0.017434 / 0.141683 (-0.124248) | 1.148472 / 1.452155 (-0.303683) | 1.247434 / 1.492716 (-0.245282) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004566 / 0.018006 (-0.013440) | 0.296110 / 0.000490 (0.295621) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022514 / 0.037411 (-0.014897) | 0.076554 / 0.014526 (0.062029) | 0.090427 / 0.176557 (-0.086130) | 0.128611 / 0.737135 (-0.608524) | 0.090748 / 0.296338 (-0.205590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.315051 / 0.215209 (0.099842) | 3.099662 / 2.077655 (1.022007) | 1.706009 / 1.504120 (0.201889) | 1.574637 / 1.541195 (0.033442) | 1.592454 / 1.468490 (0.123964) | 0.724699 / 4.584777 (-3.860078) | 0.949954 / 3.745712 (-2.795758) | 2.818915 / 5.269862 (-2.450946) | 1.931290 / 4.565676 (-2.634386) | 0.079308 / 0.424275 (-0.344967) | 0.005414 / 0.007607 (-0.002193) | 0.373547 / 0.226044 (0.147503) | 3.742222 / 2.268929 (1.473293) | 2.076239 / 55.444624 (-53.368385) | 1.772359 / 6.876477 (-5.104118) | 1.894369 / 2.142072 (-0.247703) | 0.803650 / 4.805227 (-4.001578) | 0.136995 / 6.500664 (-6.363669) | 0.041565 / 0.075469 (-0.033905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989806 / 1.841788 (-0.851982) | 12.151497 / 8.074308 (4.077189) | 10.188075 / 10.191392 (-0.003317) | 0.141194 / 0.680424 (-0.539230) | 0.016351 / 0.534201 (-0.517850) | 0.303242 / 0.579283 (-0.276041) | 0.127446 / 0.434364 (-0.306918) | 0.339806 / 0.540337 (-0.200532) | 0.443527 / 1.386936 (-0.943409) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd631431cb73c3ca434dfd6b115a6c30c5a665a5 \"CML watermark\")\n"
] | "2024-06-28T05:09:17" | "2024-06-28T05:31:21" | "2024-06-28T05:25:17" | MEMBER | null | false | {
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} | As a hotfix for CI, temporarily pin ruff upper version < 0.5.0.
Fix #7006.
Revert once root cause is fixed. | {
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] | null | [] | "2024-06-28T05:03:28" | "2024-06-28T05:25:18" | "2024-06-28T05:25:18" | MEMBER | null | null | null | After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule.
See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks | {
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https://api.github.com/repos/huggingface/datasets/issues/7005 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7005/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7005/comments | https://api.github.com/repos/huggingface/datasets/issues/7005/events | https://github.com/huggingface/datasets/issues/7005 | 2,378,424,349 | I_kwDODunzps6Nw-Ad | 7,005 | EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files | {
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"Hi ! `data_dir=` is for directories, can you try using `data_files=` instead ?",
"If you are trying to load your image dataset from a local folder, you should replace \"data_dir=path/to/jsonl/metadata.jsonl\" with the real folder path in your computer.\r\n\r\nhttps://huggingface.co/docs/datasets/en/image_load#imagefolder",
"Ah yes. My bad. I was giving file name. I should have given the folder directory as the path. That solved my issue. Thank you @albertvillanova and @lhoestq. "
] | "2024-06-27T15:08:26" | "2024-06-28T09:56:19" | "2024-06-28T09:56:19" | NONE | null | null | null | ### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment. | {
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https://api.github.com/repos/huggingface/datasets/issues/7004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7004/comments | https://api.github.com/repos/huggingface/datasets/issues/7004/events | https://github.com/huggingface/datasets/pull/7004 | 2,376,064,264 | PR_kwDODunzps5zrIYR | 7,004 | Fix WebDatasets KeyError for user-defined Features when a field is missing in an example | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7004). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005188 / 0.011353 (-0.006165) | 0.003812 / 0.011008 (-0.007196) | 0.062408 / 0.038508 (0.023900) | 0.030734 / 0.023109 (0.007625) | 0.236528 / 0.275898 (-0.039370) | 0.267684 / 0.323480 (-0.055796) | 0.003182 / 0.007986 (-0.004804) | 0.004009 / 0.004328 (-0.000319) | 0.048966 / 0.004250 (0.044715) | 0.045259 / 0.037052 (0.008207) | 0.250586 / 0.258489 (-0.007903) | 0.287079 / 0.293841 (-0.006762) | 0.029235 / 0.128546 (-0.099311) | 0.012216 / 0.075646 (-0.063431) | 0.203864 / 0.419271 (-0.215408) | 0.036324 / 0.043533 (-0.007209) | 0.245180 / 0.255139 (-0.009959) | 0.270327 / 0.283200 (-0.012872) | 0.018676 / 0.141683 (-0.123007) | 1.115568 / 1.452155 (-0.336586) | 1.183267 / 1.492716 (-0.309449) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094307 / 0.018006 (0.076301) | 0.299071 / 0.000490 (0.298581) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018336 / 0.037411 (-0.019076) | 0.062973 / 0.014526 (0.048447) | 0.074137 / 0.176557 (-0.102420) | 0.120553 / 0.737135 (-0.616582) | 0.075411 / 0.296338 (-0.220927) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284751 / 0.215209 (0.069542) | 2.789294 / 2.077655 (0.711640) | 1.457789 / 1.504120 (-0.046331) | 1.339140 / 1.541195 (-0.202055) | 1.341685 / 1.468490 (-0.126805) | 0.714928 / 4.584777 (-3.869849) | 2.361197 / 3.745712 (-1.384516) | 2.791569 / 5.269862 (-2.478293) | 1.892261 / 4.565676 (-2.673416) | 0.077954 / 0.424275 (-0.346321) | 0.005454 / 0.007607 (-0.002153) | 0.350766 / 0.226044 (0.124721) | 3.416749 / 2.268929 (1.147820) | 1.835377 / 55.444624 (-53.609247) | 1.506456 / 6.876477 (-5.370020) | 1.642728 / 2.142072 (-0.499344) | 0.791543 / 4.805227 (-4.013684) | 0.133102 / 6.500664 (-6.367562) | 0.042572 / 0.075469 (-0.032897) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977958 / 1.841788 (-0.863830) | 11.438271 / 8.074308 (3.363963) | 9.305719 / 10.191392 (-0.885673) | 0.141239 / 0.680424 (-0.539185) | 0.014330 / 0.534201 (-0.519871) | 0.302201 / 0.579283 (-0.277082) | 0.261688 / 0.434364 (-0.172676) | 0.338752 / 0.540337 (-0.201586) | 0.468466 / 1.386936 (-0.918470) |\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) | 0.005629 / 0.011353 (-0.005723) | 0.003997 / 0.011008 (-0.007011) | 0.050447 / 0.038508 (0.011939) | 0.031694 / 0.023109 (0.008585) | 0.277392 / 0.275898 (0.001494) | 0.290440 / 0.323480 (-0.033040) | 0.004403 / 0.007986 (-0.003583) | 0.002851 / 0.004328 (-0.001478) | 0.048593 / 0.004250 (0.044343) | 0.040622 / 0.037052 (0.003570) | 0.282640 / 0.258489 (0.024151) | 0.309390 / 0.293841 (0.015549) | 0.031453 / 0.128546 (-0.097094) | 0.012424 / 0.075646 (-0.063223) | 0.060311 / 0.419271 (-0.358960) | 0.033195 / 0.043533 (-0.010338) | 0.266867 / 0.255139 (0.011728) | 0.281966 / 0.283200 (-0.001234) | 0.018026 / 0.141683 (-0.123657) | 1.136273 / 1.452155 (-0.315882) | 1.141643 / 1.492716 (-0.351073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095011 / 0.018006 (0.077005) | 0.300571 / 0.000490 (0.300082) | 0.000220 / 0.000200 (0.000020) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022903 / 0.037411 (-0.014508) | 0.077130 / 0.014526 (0.062604) | 0.089576 / 0.176557 (-0.086980) | 0.127156 / 0.737135 (-0.609980) | 0.090008 / 0.296338 (-0.206331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289270 / 0.215209 (0.074061) | 2.848239 / 2.077655 (0.770585) | 1.546788 / 1.504120 (0.042668) | 1.417275 / 1.541195 (-0.123920) | 1.456214 / 1.468490 (-0.012276) | 0.716688 / 4.584777 (-3.868088) | 0.940242 / 3.745712 (-2.805470) | 2.911956 / 5.269862 (-2.357906) | 1.871358 / 4.565676 (-2.694318) | 0.077553 / 0.424275 (-0.346722) | 0.005279 / 0.007607 (-0.002328) | 0.343338 / 0.226044 (0.117294) | 3.368694 / 2.268929 (1.099766) | 1.896765 / 55.444624 (-53.547859) | 1.612414 / 6.876477 (-5.264063) | 1.615934 / 2.142072 (-0.526138) | 0.794016 / 4.805227 (-4.011212) | 0.131821 / 6.500664 (-6.368843) | 0.041495 / 0.075469 (-0.033975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.003418 / 1.841788 (-0.838370) | 12.073906 / 8.074308 (3.999598) | 10.166291 / 10.191392 (-0.025101) | 0.131224 / 0.680424 (-0.549200) | 0.015246 / 0.534201 (-0.518955) | 0.299835 / 0.579283 (-0.279448) | 0.124308 / 0.434364 (-0.310056) | 0.336414 / 0.540337 (-0.203924) | 0.429569 / 1.386936 (-0.957367) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83d28601fad73755b74314a9bc1e327005514d54 \"CML watermark\")\n"
] | "2024-06-26T18:58:05" | "2024-06-28T09:36:00" | "2024-06-28T09:30:12" | CONTRIBUTOR | null | false | {
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} | Fixes: https://github.com/huggingface/datasets/issues/6900
Not sure if this needs any addition stuff before merging | {
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https://api.github.com/repos/huggingface/datasets/issues/7003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7003/comments | https://api.github.com/repos/huggingface/datasets/issues/7003/events | https://github.com/huggingface/datasets/pull/7003 | 2,373,084,132 | PR_kwDODunzps5zhRAK | 7,003 | minor fix for bfloat16 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7003). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005633 / 0.011353 (-0.005720) | 0.004366 / 0.011008 (-0.006642) | 0.064081 / 0.038508 (0.025573) | 0.031790 / 0.023109 (0.008681) | 0.239270 / 0.275898 (-0.036628) | 0.267424 / 0.323480 (-0.056055) | 0.003229 / 0.007986 (-0.004756) | 0.002849 / 0.004328 (-0.001479) | 0.050147 / 0.004250 (0.045897) | 0.046119 / 0.037052 (0.009066) | 0.253506 / 0.258489 (-0.004983) | 0.280464 / 0.293841 (-0.013377) | 0.030561 / 0.128546 (-0.097985) | 0.012258 / 0.075646 (-0.063388) | 0.212222 / 0.419271 (-0.207049) | 0.036695 / 0.043533 (-0.006838) | 0.242141 / 0.255139 (-0.012998) | 0.263014 / 0.283200 (-0.020186) | 0.020008 / 0.141683 (-0.121675) | 1.103701 / 1.452155 (-0.348453) | 1.151641 / 1.492716 (-0.341076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095884 / 0.018006 (0.077878) | 0.300858 / 0.000490 (0.300368) | 0.000209 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018713 / 0.037411 (-0.018698) | 0.063659 / 0.014526 (0.049134) | 0.074588 / 0.176557 (-0.101968) | 0.120779 / 0.737135 (-0.616356) | 0.077768 / 0.296338 (-0.218570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281680 / 0.215209 (0.066471) | 2.754658 / 2.077655 (0.677003) | 1.454036 / 1.504120 (-0.050084) | 1.333153 / 1.541195 (-0.208042) | 1.383616 / 1.468490 (-0.084874) | 0.728933 / 4.584777 (-3.855844) | 2.374989 / 3.745712 (-1.370723) | 2.990824 / 5.269862 (-2.279038) | 1.899065 / 4.565676 (-2.666612) | 0.078657 / 0.424275 (-0.345619) | 0.005162 / 0.007607 (-0.002445) | 0.335883 / 0.226044 (0.109838) | 3.323047 / 2.268929 (1.054119) | 1.848290 / 55.444624 (-53.596335) | 1.519510 / 6.876477 (-5.356966) | 1.563608 / 2.142072 (-0.578465) | 0.807890 / 4.805227 (-3.997337) | 0.134517 / 6.500664 (-6.366147) | 0.042208 / 0.075469 (-0.033262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963634 / 1.841788 (-0.878154) | 11.617868 / 8.074308 (3.543560) | 9.804648 / 10.191392 (-0.386744) | 0.142311 / 0.680424 (-0.538113) | 0.013748 / 0.534201 (-0.520453) | 0.300309 / 0.579283 (-0.278974) | 0.268214 / 0.434364 (-0.166150) | 0.342406 / 0.540337 (-0.197931) | 0.430315 / 1.386936 (-0.956621) |\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) | 0.005533 / 0.011353 (-0.005820) | 0.004208 / 0.011008 (-0.006800) | 0.051732 / 0.038508 (0.013224) | 0.031296 / 0.023109 (0.008187) | 0.275091 / 0.275898 (-0.000807) | 0.296889 / 0.323480 (-0.026591) | 0.004363 / 0.007986 (-0.003623) | 0.002807 / 0.004328 (-0.001522) | 0.049727 / 0.004250 (0.045476) | 0.039798 / 0.037052 (0.002746) | 0.284379 / 0.258489 (0.025890) | 0.317281 / 0.293841 (0.023440) | 0.031286 / 0.128546 (-0.097261) | 0.012384 / 0.075646 (-0.063263) | 0.061619 / 0.419271 (-0.357652) | 0.032974 / 0.043533 (-0.010559) | 0.274313 / 0.255139 (0.019174) | 0.296142 / 0.283200 (0.012943) | 0.017391 / 0.141683 (-0.124291) | 1.148369 / 1.452155 (-0.303786) | 1.171539 / 1.492716 (-0.321177) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097309 / 0.018006 (0.079302) | 0.304701 / 0.000490 (0.304212) | 0.000208 / 0.000200 (0.000008) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022382 / 0.037411 (-0.015030) | 0.077000 / 0.014526 (0.062474) | 0.088165 / 0.176557 (-0.088392) | 0.129060 / 0.737135 (-0.608075) | 0.090128 / 0.296338 (-0.206211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285308 / 0.215209 (0.070099) | 2.816680 / 2.077655 (0.739025) | 1.542418 / 1.504120 (0.038298) | 1.418567 / 1.541195 (-0.122628) | 1.447018 / 1.468490 (-0.021472) | 0.737055 / 4.584777 (-3.847722) | 0.968285 / 3.745712 (-2.777427) | 2.880120 / 5.269862 (-2.389741) | 1.921813 / 4.565676 (-2.643864) | 0.079110 / 0.424275 (-0.345165) | 0.005826 / 0.007607 (-0.001781) | 0.336441 / 0.226044 (0.110397) | 3.326384 / 2.268929 (1.057456) | 1.929205 / 55.444624 (-53.515419) | 1.618215 / 6.876477 (-5.258261) | 1.769688 / 2.142072 (-0.372385) | 0.808009 / 4.805227 (-3.997219) | 0.136384 / 6.500664 (-6.364280) | 0.041332 / 0.075469 (-0.034137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010884 / 1.841788 (-0.830903) | 12.266118 / 8.074308 (4.191810) | 10.287424 / 10.191392 (0.096032) | 0.143172 / 0.680424 (-0.537251) | 0.015798 / 0.534201 (-0.518403) | 0.301604 / 0.579283 (-0.277679) | 0.131079 / 0.434364 (-0.303285) | 0.338396 / 0.540337 (-0.201941) | 0.460721 / 1.386936 (-0.926215) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e1d31387aa594b2e745c8ed8964962c134d203d \"CML watermark\")\n"
] | "2024-06-25T16:10:04" | "2024-06-25T16:16:11" | "2024-06-25T16:10:10" | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/7002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7002/comments | https://api.github.com/repos/huggingface/datasets/issues/7002/events | https://github.com/huggingface/datasets/pull/7002 | 2,373,010,351 | PR_kwDODunzps5zhBld | 7,002 | Fix dump of bfloat16 torch tensor | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7002). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005321 / 0.011353 (-0.006032) | 0.003495 / 0.011008 (-0.007514) | 0.065577 / 0.038508 (0.027069) | 0.030876 / 0.023109 (0.007767) | 0.255216 / 0.275898 (-0.020682) | 0.265111 / 0.323480 (-0.058368) | 0.003149 / 0.007986 (-0.004837) | 0.004062 / 0.004328 (-0.000267) | 0.051142 / 0.004250 (0.046891) | 0.042460 / 0.037052 (0.005408) | 0.270692 / 0.258489 (0.012203) | 0.284957 / 0.293841 (-0.008884) | 0.030143 / 0.128546 (-0.098403) | 0.012148 / 0.075646 (-0.063498) | 0.203706 / 0.419271 (-0.215565) | 0.035948 / 0.043533 (-0.007584) | 0.251391 / 0.255139 (-0.003748) | 0.270908 / 0.283200 (-0.012292) | 0.018496 / 0.141683 (-0.123187) | 1.118567 / 1.452155 (-0.333587) | 1.157695 / 1.492716 (-0.335021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.135649 / 0.018006 (0.117643) | 0.281489 / 0.000490 (0.281000) | 0.000244 / 0.000200 (0.000044) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018700 / 0.037411 (-0.018711) | 0.062305 / 0.014526 (0.047779) | 0.074968 / 0.176557 (-0.101589) | 0.121490 / 0.737135 (-0.615645) | 0.075585 / 0.296338 (-0.220754) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276929 / 0.215209 (0.061720) | 2.733543 / 2.077655 (0.655888) | 1.414585 / 1.504120 (-0.089535) | 1.301975 / 1.541195 (-0.239220) | 1.336698 / 1.468490 (-0.131792) | 0.720650 / 4.584777 (-3.864127) | 2.374796 / 3.745712 (-1.370917) | 2.866534 / 5.269862 (-2.403327) | 1.819607 / 4.565676 (-2.746069) | 0.077914 / 0.424275 (-0.346361) | 0.005146 / 0.007607 (-0.002461) | 0.331722 / 0.226044 (0.105678) | 3.290875 / 2.268929 (1.021946) | 1.799806 / 55.444624 (-53.644818) | 1.476816 / 6.876477 (-5.399660) | 1.511441 / 2.142072 (-0.630631) | 0.798043 / 4.805227 (-4.007185) | 0.134577 / 6.500664 (-6.366087) | 0.042055 / 0.075469 (-0.033415) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967908 / 1.841788 (-0.873880) | 11.215688 / 8.074308 (3.141380) | 9.486403 / 10.191392 (-0.704989) | 0.141864 / 0.680424 (-0.538560) | 0.013462 / 0.534201 (-0.520739) | 0.302601 / 0.579283 (-0.276682) | 0.266870 / 0.434364 (-0.167494) | 0.336963 / 0.540337 (-0.203375) | 0.425374 / 1.386936 (-0.961562) |\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) | 0.005549 / 0.011353 (-0.005803) | 0.003464 / 0.011008 (-0.007544) | 0.051421 / 0.038508 (0.012913) | 0.032320 / 0.023109 (0.009211) | 0.269591 / 0.275898 (-0.006307) | 0.292015 / 0.323480 (-0.031465) | 0.004351 / 0.007986 (-0.003634) | 0.002772 / 0.004328 (-0.001556) | 0.048836 / 0.004250 (0.044586) | 0.039501 / 0.037052 (0.002449) | 0.282419 / 0.258489 (0.023930) | 0.312289 / 0.293841 (0.018448) | 0.031788 / 0.128546 (-0.096759) | 0.012074 / 0.075646 (-0.063572) | 0.060457 / 0.419271 (-0.358814) | 0.033106 / 0.043533 (-0.010427) | 0.270323 / 0.255139 (0.015184) | 0.287855 / 0.283200 (0.004655) | 0.017865 / 0.141683 (-0.123818) | 1.130406 / 1.452155 (-0.321749) | 1.178679 / 1.492716 (-0.314038) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093606 / 0.018006 (0.075600) | 0.297328 / 0.000490 (0.296838) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022498 / 0.037411 (-0.014913) | 0.076927 / 0.014526 (0.062401) | 0.088013 / 0.176557 (-0.088544) | 0.127279 / 0.737135 (-0.609857) | 0.089424 / 0.296338 (-0.206914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296441 / 0.215209 (0.081232) | 2.913051 / 2.077655 (0.835396) | 1.581816 / 1.504120 (0.077696) | 1.451575 / 1.541195 (-0.089620) | 1.458968 / 1.468490 (-0.009522) | 0.727191 / 4.584777 (-3.857586) | 0.954607 / 3.745712 (-2.791106) | 2.824357 / 5.269862 (-2.445505) | 1.886779 / 4.565676 (-2.678898) | 0.079397 / 0.424275 (-0.344878) | 0.005566 / 0.007607 (-0.002041) | 0.351655 / 0.226044 (0.125611) | 3.395790 / 2.268929 (1.126862) | 1.886238 / 55.444624 (-53.558387) | 1.615413 / 6.876477 (-5.261064) | 1.723922 / 2.142072 (-0.418150) | 0.807858 / 4.805227 (-3.997369) | 0.132998 / 6.500664 (-6.367667) | 0.040396 / 0.075469 (-0.035073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008527 / 1.841788 (-0.833261) | 11.736104 / 8.074308 (3.661796) | 10.283367 / 10.191392 (0.091975) | 0.141386 / 0.680424 (-0.539038) | 0.015722 / 0.534201 (-0.518479) | 0.301785 / 0.579283 (-0.277498) | 0.123073 / 0.434364 (-0.311291) | 0.340478 / 0.540337 (-0.199859) | 0.462936 / 1.386936 (-0.924000) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bfb0a414d68e945addf95a9419a8314c372e19ba \"CML watermark\")\n"
] | "2024-06-25T15:38:09" | "2024-06-25T16:10:16" | "2024-06-25T15:51:52" | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/7001 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7001/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7001/comments | https://api.github.com/repos/huggingface/datasets/issues/7001/events | https://github.com/huggingface/datasets/issues/7001 | 2,372,930,879 | I_kwDODunzps6NcA0_ | 7,001 | Datasetbuilder Local Download FileNotFoundError | {
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"Ok it seems the solution is to use the directory string without the trailing \"/\" which in my case as: \r\n\r\n`parquet_dir = \"~/data/Parquet\" `\r\n\r\nStill i think this is a weird behavior... "
] | "2024-06-25T15:02:34" | "2024-06-25T15:21:19" | null | NONE | null | null | null | ### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
``` | {
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"@lhoestq Thank you for merging #6607, but unfortunately the issue persists for `IterableDataset` :pensive: ",
"Hi ! I opened https://github.com/huggingface/datasets/pull/7002 to fix this bug",
"Amazing, thank you so much @lhoestq! :pray:"
] | "2024-06-25T14:43:26" | "2024-06-25T16:04:00" | "2024-06-25T15:51:53" | NONE | null | null | null | ### Describe the bug
`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6999). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6998). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005396 / 0.011353 (-0.005957) | 0.003974 / 0.011008 (-0.007034) | 0.063490 / 0.038508 (0.024982) | 0.030299 / 0.023109 (0.007189) | 0.244489 / 0.275898 (-0.031409) | 0.274116 / 0.323480 (-0.049364) | 0.003187 / 0.007986 (-0.004798) | 0.003433 / 0.004328 (-0.000896) | 0.049313 / 0.004250 (0.045062) | 0.043677 / 0.037052 (0.006624) | 0.260198 / 0.258489 (0.001709) | 0.283558 / 0.293841 (-0.010283) | 0.029728 / 0.128546 (-0.098819) | 0.011950 / 0.075646 (-0.063696) | 0.204371 / 0.419271 (-0.214901) | 0.035712 / 0.043533 (-0.007821) | 0.252715 / 0.255139 (-0.002424) | 0.268906 / 0.283200 (-0.014293) | 0.021153 / 0.141683 (-0.120529) | 1.125599 / 1.452155 (-0.326556) | 1.163122 / 1.492716 (-0.329594) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095089 / 0.018006 (0.077083) | 0.298576 / 0.000490 (0.298086) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018567 / 0.037411 (-0.018844) | 0.062337 / 0.014526 (0.047811) | 0.074231 / 0.176557 (-0.102326) | 0.120960 / 0.737135 (-0.616175) | 0.076124 / 0.296338 (-0.220215) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286936 / 0.215209 (0.071727) | 2.816656 / 2.077655 (0.739001) | 1.486772 / 1.504120 (-0.017348) | 1.373289 / 1.541195 (-0.167905) | 1.392739 / 1.468490 (-0.075752) | 0.708091 / 4.584777 (-3.876686) | 2.410034 / 3.745712 (-1.335678) | 2.912701 / 5.269862 (-2.357161) | 1.850924 / 4.565676 (-2.714752) | 0.078896 / 0.424275 (-0.345380) | 0.005116 / 0.007607 (-0.002491) | 0.332275 / 0.226044 (0.106231) | 3.306562 / 2.268929 (1.037633) | 1.853051 / 55.444624 (-53.591573) | 1.556210 / 6.876477 (-5.320267) | 1.558892 / 2.142072 (-0.583181) | 0.789917 / 4.805227 (-4.015310) | 0.133683 / 6.500664 (-6.366981) | 0.042566 / 0.075469 (-0.032904) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957050 / 1.841788 (-0.884738) | 11.401462 / 8.074308 (3.327154) | 9.782988 / 10.191392 (-0.408404) | 0.142127 / 0.680424 (-0.538296) | 0.014730 / 0.534201 (-0.519471) | 0.302647 / 0.579283 (-0.276636) | 0.264654 / 0.434364 (-0.169710) | 0.341340 / 0.540337 (-0.198998) | 0.425808 / 1.386936 (-0.961128) |\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) | 0.005679 / 0.011353 (-0.005674) | 0.003513 / 0.011008 (-0.007495) | 0.050135 / 0.038508 (0.011627) | 0.031614 / 0.023109 (0.008505) | 0.260064 / 0.275898 (-0.015834) | 0.285816 / 0.323480 (-0.037664) | 0.004428 / 0.007986 (-0.003558) | 0.002816 / 0.004328 (-0.001512) | 0.048441 / 0.004250 (0.044191) | 0.039622 / 0.037052 (0.002570) | 0.274940 / 0.258489 (0.016451) | 0.311837 / 0.293841 (0.017996) | 0.031439 / 0.128546 (-0.097107) | 0.012056 / 0.075646 (-0.063590) | 0.060109 / 0.419271 (-0.359163) | 0.033123 / 0.043533 (-0.010409) | 0.261563 / 0.255139 (0.006424) | 0.282640 / 0.283200 (-0.000560) | 0.017168 / 0.141683 (-0.124515) | 1.127859 / 1.452155 (-0.324295) | 1.182414 / 1.492716 (-0.310303) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095517 / 0.018006 (0.077510) | 0.300578 / 0.000490 (0.300088) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022192 / 0.037411 (-0.015220) | 0.076617 / 0.014526 (0.062091) | 0.087405 / 0.176557 (-0.089151) | 0.127011 / 0.737135 (-0.610124) | 0.088706 / 0.296338 (-0.207632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294260 / 0.215209 (0.079051) | 2.872879 / 2.077655 (0.795224) | 1.531374 / 1.504120 (0.027254) | 1.399232 / 1.541195 (-0.141962) | 1.400708 / 1.468490 (-0.067782) | 0.714003 / 4.584777 (-3.870773) | 0.943144 / 3.745712 (-2.802568) | 2.833396 / 5.269862 (-2.436466) | 1.890570 / 4.565676 (-2.675106) | 0.077664 / 0.424275 (-0.346611) | 0.005651 / 0.007607 (-0.001956) | 0.349476 / 0.226044 (0.123431) | 3.405768 / 2.268929 (1.136840) | 1.869739 / 55.444624 (-53.574885) | 1.575293 / 6.876477 (-5.301184) | 1.692981 / 2.142072 (-0.449092) | 0.795363 / 4.805227 (-4.009865) | 0.131532 / 6.500664 (-6.369132) | 0.041183 / 0.075469 (-0.034286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.000821 / 1.841788 (-0.840967) | 11.987795 / 8.074308 (3.913487) | 10.147652 / 10.191392 (-0.043740) | 0.141314 / 0.680424 (-0.539110) | 0.015506 / 0.534201 (-0.518695) | 0.305090 / 0.579283 (-0.274193) | 0.123403 / 0.434364 (-0.310960) | 0.346507 / 0.540337 (-0.193831) | 0.471318 / 1.386936 (-0.915618) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#186b560eb2393c7d1913f4b3e76e9e04a081e09b \"CML watermark\")\n"
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} | Fix tests using hf-internal-testing/librispeech_asr_dummy once that dataset has been converted to Parquet.
Fix #6997. | {
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```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6996). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | "2024-06-25T06:54:40" | "2024-06-27T13:29:58" | null | MEMBER | null | false | {
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First merge:
- [ ] #6983
- [x] #6987
- [ ] #6999 | {
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"What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hub\r\n```\r\n\r\nNote that `CommitInfo` was implemented in huggingface-hub 0.10.0 and datasets requires \"huggingface-hub>=0.21.2\"",
"The version of my huggingface-hub is 0.23.4.",
"The error message says there is no CommitInfo in your installed huggingface-hub library:\r\n```\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\\Anaconda3\\envs\\CS224S\\Lib\\site-packages\\huggingface_hub_init_.py)\r\n```\r\n\r\nAnd this is implemented since version 0.10.0:\r\n- https://github.com/huggingface/huggingface_hub/pull/1066"
] | "2024-06-24T17:07:22" | "2024-06-25T07:17:25" | "2024-06-25T06:11:37" | NONE | null | null | null | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
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"Sure~",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005538 / 0.011353 (-0.005815) | 0.003997 / 0.011008 (-0.007011) | 0.063444 / 0.038508 (0.024935) | 0.032552 / 0.023109 (0.009442) | 0.266574 / 0.275898 (-0.009324) | 0.282841 / 0.323480 (-0.040639) | 0.004279 / 0.007986 (-0.003706) | 0.002788 / 0.004328 (-0.001540) | 0.049226 / 0.004250 (0.044976) | 0.044688 / 0.037052 (0.007636) | 0.275464 / 0.258489 (0.016975) | 0.305278 / 0.293841 (0.011437) | 0.030097 / 0.128546 (-0.098450) | 0.012237 / 0.075646 (-0.063410) | 0.205526 / 0.419271 (-0.213745) | 0.036145 / 0.043533 (-0.007388) | 0.267395 / 0.255139 (0.012256) | 0.289149 / 0.283200 (0.005949) | 0.019044 / 0.141683 (-0.122639) | 1.162294 / 1.452155 (-0.289861) | 1.183642 / 1.492716 (-0.309074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.139125 / 0.018006 (0.121119) | 0.301743 / 0.000490 (0.301253) | 0.000260 / 0.000200 (0.000061) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019494 / 0.037411 (-0.017917) | 0.063078 / 0.014526 (0.048552) | 0.076989 / 0.176557 (-0.099567) | 0.121363 / 0.737135 (-0.615773) | 0.080040 / 0.296338 (-0.216298) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284401 / 0.215209 (0.069192) | 2.805397 / 2.077655 (0.727742) | 1.555609 / 1.504120 (0.051489) | 1.405662 / 1.541195 (-0.135533) | 1.459492 / 1.468490 (-0.008999) | 0.718376 / 4.584777 (-3.866401) | 2.395918 / 3.745712 (-1.349794) | 2.976753 / 5.269862 (-2.293108) | 1.883938 / 4.565676 (-2.681738) | 0.078867 / 0.424275 (-0.345408) | 0.005207 / 0.007607 (-0.002400) | 0.335178 / 0.226044 (0.109133) | 3.313414 / 2.268929 (1.044485) | 1.856929 / 55.444624 (-53.587696) | 1.565319 / 6.876477 (-5.311158) | 1.592723 / 2.142072 (-0.549350) | 0.793621 / 4.805227 (-4.011606) | 0.134208 / 6.500664 (-6.366456) | 0.042853 / 0.075469 (-0.032616) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981553 / 1.841788 (-0.860235) | 11.810438 / 8.074308 (3.736130) | 9.529874 / 10.191392 (-0.661518) | 0.142216 / 0.680424 (-0.538207) | 0.014303 / 0.534201 (-0.519898) | 0.304600 / 0.579283 (-0.274684) | 0.261869 / 0.434364 (-0.172495) | 0.347301 / 0.540337 (-0.193036) | 0.437395 / 1.386936 (-0.949541) |\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) | 0.005881 / 0.011353 (-0.005472) | 0.004039 / 0.011008 (-0.006969) | 0.050241 / 0.038508 (0.011733) | 0.032670 / 0.023109 (0.009561) | 0.264940 / 0.275898 (-0.010959) | 0.287105 / 0.323480 (-0.036374) | 0.004844 / 0.007986 (-0.003142) | 0.002867 / 0.004328 (-0.001462) | 0.048083 / 0.004250 (0.043833) | 0.040965 / 0.037052 (0.003913) | 0.274390 / 0.258489 (0.015901) | 0.312107 / 0.293841 (0.018266) | 0.031714 / 0.128546 (-0.096832) | 0.012603 / 0.075646 (-0.063043) | 0.060698 / 0.419271 (-0.358573) | 0.033130 / 0.043533 (-0.010402) | 0.264444 / 0.255139 (0.009305) | 0.282797 / 0.283200 (-0.000403) | 0.027872 / 0.141683 (-0.113811) | 1.139026 / 1.452155 (-0.313129) | 1.181431 / 1.492716 (-0.311285) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097314 / 0.018006 (0.079308) | 0.301326 / 0.000490 (0.300836) | 0.000215 / 0.000200 (0.000015) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023394 / 0.037411 (-0.014018) | 0.076270 / 0.014526 (0.061744) | 0.089065 / 0.176557 (-0.087491) | 0.129996 / 0.737135 (-0.607139) | 0.089642 / 0.296338 (-0.206697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295390 / 0.215209 (0.080181) | 2.877849 / 2.077655 (0.800194) | 1.537129 / 1.504120 (0.033009) | 1.409441 / 1.541195 (-0.131754) | 1.432468 / 1.468490 (-0.036023) | 0.718054 / 4.584777 (-3.866722) | 0.930872 / 3.745712 (-2.814841) | 2.841028 / 5.269862 (-2.428834) | 1.921990 / 4.565676 (-2.643686) | 0.077638 / 0.424275 (-0.346637) | 0.005494 / 0.007607 (-0.002113) | 0.336331 / 0.226044 (0.110287) | 3.330490 / 2.268929 (1.061561) | 1.887994 / 55.444624 (-53.556630) | 1.593332 / 6.876477 (-5.283144) | 1.726956 / 2.142072 (-0.415116) | 0.783612 / 4.805227 (-4.021615) | 0.129926 / 6.500664 (-6.370738) | 0.040792 / 0.075469 (-0.034677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980274 / 1.841788 (-0.861514) | 12.193871 / 8.074308 (4.119563) | 10.348934 / 10.191392 (0.157542) | 0.141584 / 0.680424 (-0.538840) | 0.015737 / 0.534201 (-0.518464) | 0.300725 / 0.579283 (-0.278558) | 0.127190 / 0.434364 (-0.307174) | 0.341142 / 0.540337 (-0.199196) | 0.459523 / 1.386936 (-0.927413) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#637246baf96f07b19b193ed101f34b65cb35cffb \"CML watermark\")\n"
] | "2024-06-24T15:07:47" | "2024-06-26T04:37:35" | "2024-06-25T16:19:17" | CONTRIBUTOR | null | false | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6993). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005810 / 0.011353 (-0.005543) | 0.003984 / 0.011008 (-0.007024) | 0.064347 / 0.038508 (0.025839) | 0.031943 / 0.023109 (0.008834) | 0.252596 / 0.275898 (-0.023302) | 0.274032 / 0.323480 (-0.049448) | 0.003494 / 0.007986 (-0.004492) | 0.002817 / 0.004328 (-0.001511) | 0.050132 / 0.004250 (0.045881) | 0.048008 / 0.037052 (0.010955) | 0.249037 / 0.258489 (-0.009452) | 0.288526 / 0.293841 (-0.005315) | 0.031038 / 0.128546 (-0.097509) | 0.012542 / 0.075646 (-0.063104) | 0.205682 / 0.419271 (-0.213590) | 0.038022 / 0.043533 (-0.005511) | 0.259001 / 0.255139 (0.003862) | 0.267455 / 0.283200 (-0.015744) | 0.021980 / 0.141683 (-0.119703) | 1.123996 / 1.452155 (-0.328159) | 1.173801 / 1.492716 (-0.318915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102827 / 0.018006 (0.084821) | 0.317210 / 0.000490 (0.316720) | 0.000222 / 0.000200 (0.000022) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019483 / 0.037411 (-0.017928) | 0.064098 / 0.014526 (0.049572) | 0.076219 / 0.176557 (-0.100337) | 0.122898 / 0.737135 (-0.614237) | 0.080657 / 0.296338 (-0.215681) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.215209 (0.063169) | 2.792314 / 2.077655 (0.714659) | 1.516439 / 1.504120 (0.012319) | 1.374052 / 1.541195 (-0.167143) | 1.370848 / 1.468490 (-0.097642) | 0.756002 / 4.584777 (-3.828775) | 2.349581 / 3.745712 (-1.396131) | 2.994094 / 5.269862 (-2.275768) | 1.904242 / 4.565676 (-2.661435) | 0.078769 / 0.424275 (-0.345506) | 0.005103 / 0.007607 (-0.002505) | 0.336331 / 0.226044 (0.110287) | 3.329502 / 2.268929 (1.060574) | 1.863545 / 55.444624 (-53.581079) | 1.554690 / 6.876477 (-5.321787) | 1.588448 / 2.142072 (-0.553624) | 0.787322 / 4.805227 (-4.017905) | 0.138345 / 6.500664 (-6.362320) | 0.042228 / 0.075469 (-0.033241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968607 / 1.841788 (-0.873181) | 11.972076 / 8.074308 (3.897768) | 9.927608 / 10.191392 (-0.263784) | 0.141666 / 0.680424 (-0.538758) | 0.014591 / 0.534201 (-0.519610) | 0.301995 / 0.579283 (-0.277288) | 0.274360 / 0.434364 (-0.160004) | 0.338396 / 0.540337 (-0.201941) | 0.431081 / 1.386936 (-0.955855) |\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) | 0.006122 / 0.011353 (-0.005231) | 0.004201 / 0.011008 (-0.006807) | 0.050204 / 0.038508 (0.011695) | 0.033222 / 0.023109 (0.010113) | 0.274357 / 0.275898 (-0.001542) | 0.296238 / 0.323480 (-0.027242) | 0.004542 / 0.007986 (-0.003444) | 0.002880 / 0.004328 (-0.001449) | 0.049103 / 0.004250 (0.044852) | 0.042294 / 0.037052 (0.005242) | 0.286459 / 0.258489 (0.027970) | 0.324988 / 0.293841 (0.031147) | 0.032084 / 0.128546 (-0.096462) | 0.012329 / 0.075646 (-0.063318) | 0.060261 / 0.419271 (-0.359010) | 0.034130 / 0.043533 (-0.009403) | 0.271432 / 0.255139 (0.016293) | 0.306251 / 0.283200 (0.023051) | 0.019744 / 0.141683 (-0.121939) | 1.153483 / 1.452155 (-0.298672) | 1.209126 / 1.492716 (-0.283591) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004737 / 0.018006 (-0.013270) | 0.313458 / 0.000490 (0.312968) | 0.000216 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022472 / 0.037411 (-0.014939) | 0.076725 / 0.014526 (0.062199) | 0.091356 / 0.176557 (-0.085201) | 0.132427 / 0.737135 (-0.604708) | 0.091072 / 0.296338 (-0.205266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294414 / 0.215209 (0.079205) | 2.913695 / 2.077655 (0.836040) | 1.567309 / 1.504120 (0.063189) | 1.448664 / 1.541195 (-0.092531) | 1.466386 / 1.468490 (-0.002105) | 0.718605 / 4.584777 (-3.866172) | 0.951963 / 3.745712 (-2.793749) | 2.812565 / 5.269862 (-2.457297) | 1.886483 / 4.565676 (-2.679193) | 0.077912 / 0.424275 (-0.346363) | 0.005371 / 0.007607 (-0.002236) | 0.349528 / 0.226044 (0.123484) | 3.431049 / 2.268929 (1.162121) | 1.920210 / 55.444624 (-53.524414) | 1.637927 / 6.876477 (-5.238549) | 1.767502 / 2.142072 (-0.374570) | 0.808672 / 4.805227 (-3.996555) | 0.134261 / 6.500664 (-6.366403) | 0.041295 / 0.075469 (-0.034174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.023454 / 1.841788 (-0.818334) | 12.433731 / 8.074308 (4.359423) | 10.413191 / 10.191392 (0.221799) | 0.156813 / 0.680424 (-0.523611) | 0.015446 / 0.534201 (-0.518755) | 0.301935 / 0.579283 (-0.277348) | 0.133655 / 0.434364 (-0.300709) | 0.340296 / 0.540337 (-0.200041) | 0.466314 / 1.386936 (-0.920622) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cf563fd57807e923a29ebbe327fecb4ef969877 \"CML watermark\")\n"
] | "2024-06-24T14:45:28" | "2024-06-27T09:37:23" | "2024-06-27T09:31:21" | MEMBER | null | false | {
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"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | "2024-06-22T16:12:08" | "2024-06-25T15:43:05" | null | NONE | null | null | null | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6991). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | "2024-06-22T09:19:53" | "2024-06-26T13:49:45" | null | NONE | null | false | {
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"ah yes good catch ! feel free to open a PR with your suggested fix"
] | "2024-06-21T14:25:26" | "2024-06-25T16:19:19" | "2024-06-25T16:19:19" | CONTRIBUTOR | null | null | null | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0 | {
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