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serhii-korobchenko

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6639). 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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[ "Looks like it works with latest datasets repository\r\n```\r\n- `datasets` version: 2.16.2.dev0\r\n- Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 2.0.1\r\n- `fsspec` version: 2023.10.0\r\n```\r\n\r\nCould you explain which is the minimum version that fixes this?\r\nEdit: Looks like that's 2.16.0, will close out issue" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I still saw that it took ~3.5 minutes per batch on 6000 features when using `dataset.map(lambda x: x, batched=True)`. From the profile, the culprits were mainly with `ArrowWriter.write_batch` and `ArrowWriter._build_writer`. The slow down from `_build_writer` is due to updating existing features with the inferred ones. I don't think this can be optimized any further, but fortunately, I can avoid this by setting the `features` in `map`. On the other hand, `write_batch` selects cols based on intersection and difference between schema names and example keys using two for loops. The same exists in `ArrowWriter.write_examples_on_file`. Optimizing the column selection using set operations effectively brings it from 3.5 minutes per batch down to 6 seconds per batch. Can we add these changes along with this PR?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6635). 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_6634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6633). 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) | 0.005172 / 0.011353 (-0.006181) | 0.003694 / 0.011008 (-0.007314) | 0.063098 / 0.038508 (0.024590) | 0.028161 / 0.023109 (0.005052) | 0.262288 / 0.275898 (-0.013610) | 0.281867 / 0.323480 (-0.041613) | 0.004088 / 0.007986 (-0.003898) | 0.002745 / 0.004328 (-0.001583) | 0.049071 / 0.004250 (0.044820) | 0.040629 / 0.037052 (0.003577) | 0.282766 / 0.258489 (0.024277) | 0.297998 / 0.293841 (0.004157) | 0.028057 / 0.128546 (-0.100489) | 0.010878 / 0.075646 (-0.064768) | 0.207410 / 0.419271 (-0.211861) | 0.035600 / 0.043533 (-0.007933) | 0.260157 / 0.255139 (0.005018) | 0.273252 / 0.283200 (-0.009948) | 0.017403 / 0.141683 (-0.124280) | 1.150798 / 1.452155 (-0.301356) | 1.200485 / 1.492716 (-0.292231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093783 / 0.018006 (0.075777) | 0.302112 / 0.000490 (0.301622) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018254 / 0.037411 (-0.019158) | 0.061083 / 0.014526 (0.046557) | 0.074899 / 0.176557 (-0.101657) | 0.119616 / 0.737135 (-0.617520) | 0.075269 / 0.296338 (-0.221069) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275878 / 0.215209 (0.060669) | 2.694778 / 2.077655 (0.617123) | 1.423810 / 1.504120 (-0.080310) | 1.309444 / 1.541195 (-0.231750) | 1.327898 / 1.468490 (-0.140592) | 0.568621 / 4.584777 (-4.016155) | 2.345849 / 3.745712 (-1.399863) | 2.901281 / 5.269862 (-2.368580) | 1.777959 / 4.565676 (-2.787717) | 0.063539 / 0.424275 (-0.360736) | 0.005011 / 0.007607 (-0.002596) | 0.331212 / 0.226044 (0.105168) | 3.200379 / 2.268929 (0.931451) | 1.780766 / 55.444624 (-53.663859) | 1.517178 / 6.876477 (-5.359299) | 1.587307 / 2.142072 (-0.554765) | 0.651939 / 4.805227 (-4.153288) | 0.116646 / 6.500664 (-6.384018) | 0.043325 / 0.075469 (-0.032144) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996894 / 1.841788 (-0.844894) | 11.495397 / 8.074308 (3.421089) | 10.255784 / 10.191392 (0.064392) | 0.129006 / 0.680424 (-0.551418) | 0.013967 / 0.534201 (-0.520234) | 0.284847 / 0.579283 (-0.294436) | 0.265610 / 0.434364 (-0.168754) | 0.320176 / 0.540337 (-0.220162) | 0.429526 / 1.386936 (-0.957410) |\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) | 0.005582 / 0.011353 (-0.005771) | 0.003867 / 0.011008 (-0.007142) | 0.050416 / 0.038508 (0.011908) | 0.030996 / 0.023109 (0.007887) | 0.275987 / 0.275898 (0.000089) | 0.289487 / 0.323480 (-0.033993) | 0.005149 / 0.007986 (-0.002837) | 0.002806 / 0.004328 (-0.001522) | 0.049617 / 0.004250 (0.045366) | 0.046949 / 0.037052 (0.009897) | 0.281596 / 0.258489 (0.023107) | 0.330948 / 0.293841 (0.037108) | 0.049645 / 0.128546 (-0.078901) | 0.010953 / 0.075646 (-0.064693) | 0.058546 / 0.419271 (-0.360725) | 0.034010 / 0.043533 (-0.009523) | 0.270525 / 0.255139 (0.015386) | 0.289749 / 0.283200 (0.006550) | 0.018755 / 0.141683 (-0.122927) | 1.163072 / 1.452155 (-0.289082) | 1.213400 / 1.492716 (-0.279316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092397 / 0.018006 (0.074390) | 0.299376 / 0.000490 (0.298886) | 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) | 0.022496 / 0.037411 (-0.014916) | 0.076886 / 0.014526 (0.062361) | 0.087186 / 0.176557 (-0.089371) | 0.126092 / 0.737135 (-0.611044) | 0.088832 / 0.296338 (-0.207507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288885 / 0.215209 (0.073676) | 2.839851 / 2.077655 (0.762196) | 1.587556 / 1.504120 (0.083436) | 1.470249 / 1.541195 (-0.070945) | 1.518080 / 1.468490 (0.049590) | 0.569646 / 4.584777 (-4.015131) | 2.417574 / 3.745712 (-1.328138) | 2.737368 / 5.269862 (-2.532494) | 1.784419 / 4.565676 (-2.781257) | 0.064104 / 0.424275 (-0.360171) | 0.005138 / 0.007607 (-0.002469) | 0.346214 / 0.226044 (0.120169) | 3.439541 / 2.268929 (1.170612) | 1.944792 / 55.444624 (-53.499832) | 1.675762 / 6.876477 (-5.200714) | 1.851871 / 2.142072 (-0.290201) | 0.652932 / 4.805227 (-4.152295) | 0.118953 / 6.500664 (-6.381711) | 0.041011 / 0.075469 (-0.034459) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.017690 / 1.841788 (-0.824098) | 12.610531 / 8.074308 (4.536223) | 11.223165 / 10.191392 (1.031773) | 0.131637 / 0.680424 (-0.548786) | 0.016733 / 0.534201 (-0.517468) | 0.288491 / 0.579283 (-0.290792) | 0.275899 / 0.434364 (-0.158465) | 0.331837 / 0.540337 (-0.208500) | 0.421695 / 1.386936 (-0.965241) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d9dfa9a8c077c783729a279623926faa9e2f3f1 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6632). 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_6631). 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) | 0.005076 / 0.011353 (-0.006277) | 0.003665 / 0.011008 (-0.007343) | 0.063602 / 0.038508 (0.025094) | 0.029103 / 0.023109 (0.005993) | 0.233133 / 0.275898 (-0.042765) | 0.257000 / 0.323480 (-0.066480) | 0.003059 / 0.007986 (-0.004926) | 0.004007 / 0.004328 (-0.000321) | 0.049804 / 0.004250 (0.045553) | 0.039946 / 0.037052 (0.002893) | 0.248003 / 0.258489 (-0.010486) | 0.272729 / 0.293841 (-0.021112) | 0.027542 / 0.128546 (-0.101004) | 0.010745 / 0.075646 (-0.064901) | 0.207686 / 0.419271 (-0.211586) | 0.035438 / 0.043533 (-0.008095) | 0.236864 / 0.255139 (-0.018275) | 0.258610 / 0.283200 (-0.024590) | 0.017225 / 0.141683 (-0.124458) | 1.130894 / 1.452155 (-0.321261) | 1.171266 / 1.492716 (-0.321450) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092532 / 0.018006 (0.074525) | 0.301650 / 0.000490 (0.301161) | 0.000216 / 0.000200 (0.000016) | 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) | 0.018175 / 0.037411 (-0.019237) | 0.061538 / 0.014526 (0.047012) | 0.073673 / 0.176557 (-0.102884) | 0.120676 / 0.737135 (-0.616460) | 0.074753 / 0.296338 (-0.221586) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283625 / 0.215209 (0.068416) | 2.794903 / 2.077655 (0.717248) | 1.485149 / 1.504120 (-0.018970) | 1.361154 / 1.541195 (-0.180041) | 1.371436 / 1.468490 (-0.097054) | 0.580401 / 4.584777 (-4.004376) | 2.457068 / 3.745712 (-1.288644) | 2.760878 / 5.269862 (-2.508984) | 1.725507 / 4.565676 (-2.840169) | 0.063632 / 0.424275 (-0.360644) | 0.005036 / 0.007607 (-0.002572) | 0.337167 / 0.226044 (0.111122) | 3.314508 / 2.268929 (1.045579) | 1.863412 / 55.444624 (-53.581213) | 1.621966 / 6.876477 (-5.254511) | 1.600422 / 2.142072 (-0.541651) | 0.647753 / 4.805227 (-4.157475) | 0.117169 / 6.500664 (-6.383495) | 0.042338 / 0.075469 (-0.033131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981818 / 1.841788 (-0.859969) | 12.044657 / 8.074308 (3.970349) | 10.654091 / 10.191392 (0.462699) | 0.130693 / 0.680424 (-0.549731) | 0.014733 / 0.534201 (-0.519468) | 0.317432 / 0.579283 (-0.261851) | 0.267196 / 0.434364 (-0.167168) | 0.329310 / 0.540337 (-0.211028) | 0.433379 / 1.386936 (-0.953557) |\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) | 0.005502 / 0.011353 (-0.005851) | 0.003951 / 0.011008 (-0.007057) | 0.050651 / 0.038508 (0.012143) | 0.031802 / 0.023109 (0.008693) | 0.281384 / 0.275898 (0.005485) | 0.303900 / 0.323480 (-0.019580) | 0.004451 / 0.007986 (-0.003534) | 0.002801 / 0.004328 (-0.001527) | 0.048688 / 0.004250 (0.044438) | 0.044717 / 0.037052 (0.007664) | 0.295017 / 0.258489 (0.036528) | 0.328003 / 0.293841 (0.034162) | 0.048421 / 0.128546 (-0.080125) | 0.011254 / 0.075646 (-0.064392) | 0.058223 / 0.419271 (-0.361048) | 0.033915 / 0.043533 (-0.009618) | 0.279893 / 0.255139 (0.024754) | 0.297605 / 0.283200 (0.014405) | 0.017115 / 0.141683 (-0.124568) | 1.146966 / 1.452155 (-0.305189) | 1.191650 / 1.492716 (-0.301066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092524 / 0.018006 (0.074518) | 0.309332 / 0.000490 (0.308842) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022265 / 0.037411 (-0.015146) | 0.075732 / 0.014526 (0.061206) | 0.087340 / 0.176557 (-0.089217) | 0.126079 / 0.737135 (-0.611056) | 0.090349 / 0.296338 (-0.205990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288882 / 0.215209 (0.073673) | 2.833046 / 2.077655 (0.755392) | 1.602905 / 1.504120 (0.098785) | 1.473110 / 1.541195 (-0.068085) | 1.491300 / 1.468490 (0.022810) | 0.557799 / 4.584777 (-4.026978) | 2.439526 / 3.745712 (-1.306186) | 2.669336 / 5.269862 (-2.600526) | 1.719472 / 4.565676 (-2.846204) | 0.062456 / 0.424275 (-0.361819) | 0.005058 / 0.007607 (-0.002549) | 0.343706 / 0.226044 (0.117662) | 3.422397 / 2.268929 (1.153469) | 1.983679 / 55.444624 (-53.460946) | 1.673784 / 6.876477 (-5.202693) | 1.785144 / 2.142072 (-0.356928) | 0.643127 / 4.805227 (-4.162100) | 0.115254 / 6.500664 (-6.385410) | 0.041235 / 0.075469 (-0.034235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005448 / 1.841788 (-0.836340) | 12.240100 / 8.074308 (4.165792) | 11.051965 / 10.191392 (0.860573) | 0.130438 / 0.680424 (-0.549986) | 0.015918 / 0.534201 (-0.518283) | 0.287468 / 0.579283 (-0.291815) | 0.287699 / 0.434364 (-0.146665) | 0.324561 / 0.540337 (-0.215777) | 0.418820 / 1.386936 (-0.968116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#237a2a688155e23cfbcdfadd2d491ce1667fa494 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6630). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hmm these errors look pretty weird... can they be retried?", "Hi, thanks for working on this! To fix the errors, you also need to update [this file](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/_dill.py) (by adding `version.parse(\"0.3.8\").release` to the lists)", "<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) | 0.005068 / 0.011353 (-0.006285) | 0.003657 / 0.011008 (-0.007351) | 0.062914 / 0.038508 (0.024406) | 0.027965 / 0.023109 (0.004855) | 0.241804 / 0.275898 (-0.034094) | 0.268069 / 0.323480 (-0.055411) | 0.004066 / 0.007986 (-0.003920) | 0.002704 / 0.004328 (-0.001624) | 0.048745 / 0.004250 (0.044495) | 0.042158 / 0.037052 (0.005106) | 0.257670 / 0.258489 (-0.000819) | 0.279419 / 0.293841 (-0.014422) | 0.027193 / 0.128546 (-0.101353) | 0.010379 / 0.075646 (-0.065267) | 0.207009 / 0.419271 (-0.212262) | 0.035494 / 0.043533 (-0.008039) | 0.246025 / 0.255139 (-0.009114) | 0.265906 / 0.283200 (-0.017294) | 0.017335 / 0.141683 (-0.124348) | 1.134052 / 1.452155 (-0.318103) | 1.184668 / 1.492716 (-0.308049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093137 / 0.018006 (0.075130) | 0.302279 / 0.000490 (0.301789) | 0.000210 / 0.000200 (0.000010) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018190 / 0.037411 (-0.019221) | 0.061436 / 0.014526 (0.046910) | 0.073102 / 0.176557 (-0.103454) | 0.119782 / 0.737135 (-0.617354) | 0.074292 / 0.296338 (-0.222046) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285905 / 0.215209 (0.070696) | 2.809051 / 2.077655 (0.731397) | 1.470305 / 1.504120 (-0.033814) | 1.350457 / 1.541195 (-0.190738) | 1.349111 / 1.468490 (-0.119379) | 0.568277 / 4.584777 (-4.016500) | 2.353046 / 3.745712 (-1.392666) | 2.805862 / 5.269862 (-2.463999) | 1.750275 / 4.565676 (-2.815401) | 0.062370 / 0.424275 (-0.361905) | 0.004954 / 0.007607 (-0.002653) | 0.335609 / 0.226044 (0.109564) | 3.367200 / 2.268929 (1.098271) | 1.829431 / 55.444624 (-53.615193) | 1.545093 / 6.876477 (-5.331384) | 1.571107 / 2.142072 (-0.570966) | 0.640279 / 4.805227 (-4.164949) | 0.116209 / 6.500664 (-6.384455) | 0.042308 / 0.075469 (-0.033161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982972 / 1.841788 (-0.858816) | 11.424370 / 8.074308 (3.350062) | 10.427111 / 10.191392 (0.235719) | 0.129477 / 0.680424 (-0.550946) | 0.014166 / 0.534201 (-0.520035) | 0.287597 / 0.579283 (-0.291686) | 0.265588 / 0.434364 (-0.168776) | 0.324007 / 0.540337 (-0.216330) | 0.430766 / 1.386936 (-0.956170) |\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) | 0.005347 / 0.011353 (-0.006005) | 0.003733 / 0.011008 (-0.007275) | 0.049520 / 0.038508 (0.011011) | 0.031177 / 0.023109 (0.008068) | 0.281854 / 0.275898 (0.005956) | 0.300937 / 0.323480 (-0.022543) | 0.004385 / 0.007986 (-0.003601) | 0.002841 / 0.004328 (-0.001488) | 0.048661 / 0.004250 (0.044411) | 0.044258 / 0.037052 (0.007205) | 0.295651 / 0.258489 (0.037162) | 0.322872 / 0.293841 (0.029031) | 0.048924 / 0.128546 (-0.079622) | 0.010742 / 0.075646 (-0.064905) | 0.059327 / 0.419271 (-0.359944) | 0.033938 / 0.043533 (-0.009595) | 0.282235 / 0.255139 (0.027096) | 0.297432 / 0.283200 (0.014233) | 0.018295 / 0.141683 (-0.123388) | 1.164459 / 1.452155 (-0.287696) | 1.214511 / 1.492716 (-0.278205) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091441 / 0.018006 (0.073435) | 0.303023 / 0.000490 (0.302533) | 0.000211 / 0.000200 (0.000011) | 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) | 0.022024 / 0.037411 (-0.015388) | 0.075570 / 0.014526 (0.061044) | 0.086761 / 0.176557 (-0.089796) | 0.126437 / 0.737135 (-0.610698) | 0.088354 / 0.296338 (-0.207984) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289360 / 0.215209 (0.074151) | 2.816433 / 2.077655 (0.738779) | 1.561442 / 1.504120 (0.057322) | 1.438168 / 1.541195 (-0.103027) | 1.453398 / 1.468490 (-0.015092) | 0.579474 / 4.584777 (-4.005303) | 2.458640 / 3.745712 (-1.287072) | 2.638572 / 5.269862 (-2.631290) | 1.725218 / 4.565676 (-2.840458) | 0.063550 / 0.424275 (-0.360725) | 0.005220 / 0.007607 (-0.002387) | 0.338883 / 0.226044 (0.112838) | 3.353585 / 2.268929 (1.084656) | 1.913186 / 55.444624 (-53.531438) | 1.667445 / 6.876477 (-5.209032) | 1.740085 / 2.142072 (-0.401987) | 0.646369 / 4.805227 (-4.158859) | 0.116737 / 6.500664 (-6.383927) | 0.041052 / 0.075469 (-0.034417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.023180 / 1.841788 (-0.818608) | 12.078398 / 8.074308 (4.004090) | 10.952012 / 10.191392 (0.760620) | 0.131335 / 0.680424 (-0.549089) | 0.015701 / 0.534201 (-0.518499) | 0.289709 / 0.579283 (-0.289574) | 0.270495 / 0.434364 (-0.163869) | 0.331773 / 0.540337 (-0.208565) | 0.417660 / 1.386936 (-0.969276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b21d74f5c0ab8a85838af04de8ad85e71b0ac4f \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6629). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release." ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6628). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release." ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6627). 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) | 0.004944 / 0.011353 (-0.006409) | 0.003279 / 0.011008 (-0.007729) | 0.063041 / 0.038508 (0.024533) | 0.029888 / 0.023109 (0.006779) | 0.259138 / 0.275898 (-0.016760) | 0.276907 / 0.323480 (-0.046573) | 0.004015 / 0.007986 (-0.003970) | 0.002647 / 0.004328 (-0.001682) | 0.048944 / 0.004250 (0.044693) | 0.039412 / 0.037052 (0.002360) | 0.278069 / 0.258489 (0.019580) | 0.299139 / 0.293841 (0.005298) | 0.027272 / 0.128546 (-0.101274) | 0.010445 / 0.075646 (-0.065202) | 0.206925 / 0.419271 (-0.212347) | 0.035589 / 0.043533 (-0.007944) | 0.256805 / 0.255139 (0.001666) | 0.275128 / 0.283200 (-0.008072) | 0.017888 / 0.141683 (-0.123795) | 1.136983 / 1.452155 (-0.315172) | 1.167495 / 1.492716 (-0.325222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088167 / 0.018006 (0.070161) | 0.297360 / 0.000490 (0.296871) | 0.000231 / 0.000200 (0.000031) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018114 / 0.037411 (-0.019297) | 0.061217 / 0.014526 (0.046691) | 0.072269 / 0.176557 (-0.104288) | 0.120607 / 0.737135 (-0.616528) | 0.073517 / 0.296338 (-0.222822) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282580 / 0.215209 (0.067371) | 2.758650 / 2.077655 (0.680995) | 1.425125 / 1.504120 (-0.078995) | 1.303182 / 1.541195 (-0.238013) | 1.341035 / 1.468490 (-0.127455) | 0.549485 / 4.584777 (-4.035292) | 2.346297 / 3.745712 (-1.399415) | 2.686457 / 5.269862 (-2.583405) | 1.684789 / 4.565676 (-2.880888) | 0.061279 / 0.424275 (-0.362996) | 0.004902 / 0.007607 (-0.002705) | 0.333089 / 0.226044 (0.107044) | 3.297016 / 2.268929 (1.028087) | 1.765614 / 55.444624 (-53.679010) | 1.499314 / 6.876477 (-5.377162) | 1.501275 / 2.142072 (-0.640797) | 0.619039 / 4.805227 (-4.186189) | 0.114284 / 6.500664 (-6.386380) | 0.041481 / 0.075469 (-0.033988) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973924 / 1.841788 (-0.867863) | 11.268266 / 8.074308 (3.193958) | 10.304738 / 10.191392 (0.113346) | 0.129297 / 0.680424 (-0.551127) | 0.014894 / 0.534201 (-0.519307) | 0.287658 / 0.579283 (-0.291626) | 0.266476 / 0.434364 (-0.167888) | 0.322199 / 0.540337 (-0.218138) | 0.419568 / 1.386936 (-0.967368) |\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) | 0.005220 / 0.011353 (-0.006133) | 0.003310 / 0.011008 (-0.007698) | 0.049707 / 0.038508 (0.011199) | 0.031148 / 0.023109 (0.008039) | 0.284644 / 0.275898 (0.008746) | 0.302767 / 0.323480 (-0.020712) | 0.004245 / 0.007986 (-0.003740) | 0.002677 / 0.004328 (-0.001651) | 0.049870 / 0.004250 (0.045620) | 0.043922 / 0.037052 (0.006870) | 0.294955 / 0.258489 (0.036466) | 0.322144 / 0.293841 (0.028303) | 0.047211 / 0.128546 (-0.081336) | 0.010492 / 0.075646 (-0.065155) | 0.058152 / 0.419271 (-0.361120) | 0.033508 / 0.043533 (-0.010025) | 0.281266 / 0.255139 (0.026127) | 0.300010 / 0.283200 (0.016810) | 0.017616 / 0.141683 (-0.124067) | 1.124658 / 1.452155 (-0.327496) | 1.167222 / 1.492716 (-0.325495) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089085 / 0.018006 (0.071079) | 0.297912 / 0.000490 (0.297423) | 0.000211 / 0.000200 (0.000011) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021669 / 0.037411 (-0.015742) | 0.075648 / 0.014526 (0.061123) | 0.086054 / 0.176557 (-0.090503) | 0.125236 / 0.737135 (-0.611899) | 0.088146 / 0.296338 (-0.208192) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295238 / 0.215209 (0.080029) | 2.870002 / 2.077655 (0.792347) | 1.582534 / 1.504120 (0.078414) | 1.466710 / 1.541195 (-0.074485) | 1.475352 / 1.468490 (0.006861) | 0.554745 / 4.584777 (-4.030032) | 2.412533 / 3.745712 (-1.333179) | 2.583863 / 5.269862 (-2.685999) | 1.689124 / 4.565676 (-2.876552) | 0.061353 / 0.424275 (-0.362922) | 0.005015 / 0.007607 (-0.002592) | 0.338733 / 0.226044 (0.112688) | 3.356710 / 2.268929 (1.087781) | 1.932143 / 55.444624 (-53.512481) | 1.660081 / 6.876477 (-5.216396) | 1.764961 / 2.142072 (-0.377111) | 0.640002 / 4.805227 (-4.165225) | 0.115251 / 6.500664 (-6.385413) | 0.040627 / 0.075469 (-0.034842) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992296 / 1.841788 (-0.849492) | 11.821259 / 8.074308 (3.746951) | 10.715570 / 10.191392 (0.524178) | 0.142934 / 0.680424 (-0.537489) | 0.015680 / 0.534201 (-0.518521) | 0.287435 / 0.579283 (-0.291848) | 0.276817 / 0.434364 (-0.157547) | 0.327823 / 0.540337 (-0.212515) | 0.413404 / 1.386936 (-0.973532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#82c78b614d34ee42180d35a882875a28d6281db0 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6626). 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) | 0.005085 / 0.011353 (-0.006268) | 0.003592 / 0.011008 (-0.007417) | 0.062591 / 0.038508 (0.024083) | 0.031063 / 0.023109 (0.007954) | 0.247029 / 0.275898 (-0.028869) | 0.273706 / 0.323480 (-0.049774) | 0.004034 / 0.007986 (-0.003951) | 0.002672 / 0.004328 (-0.001657) | 0.048407 / 0.004250 (0.044156) | 0.049229 / 0.037052 (0.012177) | 0.264316 / 0.258489 (0.005827) | 0.284953 / 0.293841 (-0.008888) | 0.027712 / 0.128546 (-0.100834) | 0.010619 / 0.075646 (-0.065027) | 0.210017 / 0.419271 (-0.209254) | 0.035636 / 0.043533 (-0.007897) | 0.252830 / 0.255139 (-0.002309) | 0.278772 / 0.283200 (-0.004428) | 0.017356 / 0.141683 (-0.124326) | 1.140202 / 1.452155 (-0.311953) | 1.204807 / 1.492716 (-0.287909) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089130 / 0.018006 (0.071123) | 0.300115 / 0.000490 (0.299626) | 0.000213 / 0.000200 (0.000013) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018352 / 0.037411 (-0.019059) | 0.061431 / 0.014526 (0.046905) | 0.073911 / 0.176557 (-0.102646) | 0.121230 / 0.737135 (-0.615906) | 0.074867 / 0.296338 (-0.221471) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282272 / 0.215209 (0.067063) | 2.737413 / 2.077655 (0.659759) | 1.446651 / 1.504120 (-0.057469) | 1.319686 / 1.541195 (-0.221508) | 1.327479 / 1.468490 (-0.141011) | 0.558003 / 4.584777 (-4.026774) | 2.361623 / 3.745712 (-1.384089) | 2.770436 / 5.269862 (-2.499425) | 1.703450 / 4.565676 (-2.862227) | 0.062034 / 0.424275 (-0.362241) | 0.005070 / 0.007607 (-0.002537) | 0.337265 / 0.226044 (0.111221) | 3.299438 / 2.268929 (1.030509) | 1.781273 / 55.444624 (-53.663351) | 1.512743 / 6.876477 (-5.363734) | 1.530995 / 2.142072 (-0.611077) | 0.630210 / 4.805227 (-4.175017) | 0.116219 / 6.500664 (-6.384445) | 0.042220 / 0.075469 (-0.033249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946341 / 1.841788 (-0.895446) | 11.462179 / 8.074308 (3.387871) | 10.603314 / 10.191392 (0.411922) | 0.128826 / 0.680424 (-0.551598) | 0.013994 / 0.534201 (-0.520207) | 0.288142 / 0.579283 (-0.291141) | 0.266941 / 0.434364 (-0.167422) | 0.329392 / 0.540337 (-0.210946) | 0.431720 / 1.386936 (-0.955216) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005303 / 0.011353 (-0.006050) | 0.003587 / 0.011008 (-0.007422) | 0.049437 / 0.038508 (0.010929) | 0.031940 / 0.023109 (0.008831) | 0.276651 / 0.275898 (0.000752) | 0.297240 / 0.323480 (-0.026240) | 0.004202 / 0.007986 (-0.003784) | 0.002709 / 0.004328 (-0.001619) | 0.048647 / 0.004250 (0.044397) | 0.044147 / 0.037052 (0.007095) | 0.291171 / 0.258489 (0.032682) | 0.319297 / 0.293841 (0.025456) | 0.048167 / 0.128546 (-0.080379) | 0.010630 / 0.075646 (-0.065016) | 0.058402 / 0.419271 (-0.360869) | 0.033817 / 0.043533 (-0.009716) | 0.300546 / 0.255139 (0.045407) | 0.319396 / 0.283200 (0.036197) | 0.017736 / 0.141683 (-0.123946) | 1.159590 / 1.452155 (-0.292565) | 1.191778 / 1.492716 (-0.300939) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088971 / 0.018006 (0.070965) | 0.299721 / 0.000490 (0.299231) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021895 / 0.037411 (-0.015516) | 0.075388 / 0.014526 (0.060862) | 0.087446 / 0.176557 (-0.089111) | 0.126339 / 0.737135 (-0.610796) | 0.089329 / 0.296338 (-0.207010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296642 / 0.215209 (0.081433) | 2.916023 / 2.077655 (0.838368) | 1.593180 / 1.504120 (0.089060) | 1.470491 / 1.541195 (-0.070704) | 1.485713 / 1.468490 (0.017223) | 0.577204 / 4.584777 (-4.007573) | 2.436463 / 3.745712 (-1.309249) | 2.651004 / 5.269862 (-2.618858) | 1.754026 / 4.565676 (-2.811651) | 0.064943 / 0.424275 (-0.359332) | 0.005115 / 0.007607 (-0.002492) | 0.362082 / 0.226044 (0.136038) | 3.498198 / 2.268929 (1.229270) | 1.951936 / 55.444624 (-53.492688) | 1.682027 / 6.876477 (-5.194450) | 1.751768 / 2.142072 (-0.390304) | 0.668479 / 4.805227 (-4.136748) | 0.119934 / 6.500664 (-6.380730) | 0.041419 / 0.075469 (-0.034050) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978145 / 1.841788 (-0.863643) | 11.984984 / 8.074308 (3.910676) | 10.732377 / 10.191392 (0.540985) | 0.141868 / 0.680424 (-0.538555) | 0.015256 / 0.534201 (-0.518945) | 0.288488 / 0.579283 (-0.290795) | 0.276091 / 0.434364 (-0.158273) | 0.330429 / 0.540337 (-0.209908) | 0.423964 / 1.386936 (-0.962972) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb8497b9dec2a3807c887b8184f902d1d8d7c25a \"CML watermark\")\n" ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/6626.diff", "html_url": "https://github.com/huggingface/datasets/pull/6626", "merged_at": "2024-01-29T15:12:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6626.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6626" }

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[ "Hi, this dataset has been disabled by the authors, so unfortunately it's no longer possible to download it." ]

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[ "@mariosasko, @lhoestq, @albertvillanova\r\nhey guys! can anyone help? or can you guys suggest who can help with this?", "Hi ! \r\n\r\n1. When the dataset is running of of examples, the last batches received by the GPU can be incomplete or empty/missing. We haven't implemented yet a way to ignore the last batch. It might require the datasets to provide the number of examples per shard though, so that we can know when to stop.\r\n2. Samplers are not compatible with IterableDatasets in pytorch\r\n3. if `dataset.n_shards % world_size != 0` then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of `world_size` so that each example goes to one exactly one GPU.\r\n4. no, sharding should be down up-front and can take some time depending on the dataset size and format", "> if dataset.n_shards % world_size != 0 then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of world_size so that each example goes to one exactly one GPU.\r\n\r\nconsidering there's just 1 shard and 2 worker nodes, do you mean each worker node will load the whole dataset but still receive half of that shard while streaming?", "Yes both nodes will stream from the 1 shard, but each node will skip half of the examples. This way in total each example is seen once and exactly once during you distributed training.\r\n\r\nThough it terms of I/O, the dataset is effectively read/streamed twice.", "what if the number of samples in that shard % num_nodes != 0? it will break/get stuck? or is the data repeated in that case for gradient sync?", "In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n\r\nIn the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). 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) | 0.005066 / 0.011353 (-0.006287) | 0.003678 / 0.011008 (-0.007330) | 0.063057 / 0.038508 (0.024549) | 0.031250 / 0.023109 (0.008140) | 0.248856 / 0.275898 (-0.027042) | 0.266932 / 0.323480 (-0.056548) | 0.003814 / 0.007986 (-0.004172) | 0.002843 / 0.004328 (-0.001485) | 0.049210 / 0.004250 (0.044959) | 0.041514 / 0.037052 (0.004462) | 0.264874 / 0.258489 (0.006385) | 0.288834 / 0.293841 (-0.005007) | 0.027457 / 0.128546 (-0.101089) | 0.011071 / 0.075646 (-0.064575) | 0.206433 / 0.419271 (-0.212839) | 0.035381 / 0.043533 (-0.008152) | 0.246829 / 0.255139 (-0.008310) | 0.271094 / 0.283200 (-0.012106) | 0.017790 / 0.141683 (-0.123893) | 1.134618 / 1.452155 (-0.317536) | 1.182600 / 1.492716 (-0.310116) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094970 / 0.018006 (0.076964) | 0.306438 / 0.000490 (0.305949) | 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) | 0.017786 / 0.037411 (-0.019625) | 0.060652 / 0.014526 (0.046127) | 0.072619 / 0.176557 (-0.103937) | 0.119460 / 0.737135 (-0.617676) | 0.073580 / 0.296338 (-0.222759) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279304 / 0.215209 (0.064095) | 2.747179 / 2.077655 (0.669524) | 1.438291 / 1.504120 (-0.065829) | 1.313405 / 1.541195 (-0.227789) | 1.354569 / 1.468490 (-0.113921) | 0.578375 / 4.584777 (-4.006402) | 2.424576 / 3.745712 (-1.321136) | 2.831513 / 5.269862 (-2.438348) | 1.756062 / 4.565676 (-2.809614) | 0.064460 / 0.424275 (-0.359815) | 0.005065 / 0.007607 (-0.002542) | 0.335003 / 0.226044 (0.108958) | 3.310500 / 2.268929 (1.041571) | 1.778017 / 55.444624 (-53.666607) | 1.504743 / 6.876477 (-5.371734) | 1.532843 / 2.142072 (-0.609229) | 0.662110 / 4.805227 (-4.143118) | 0.118239 / 6.500664 (-6.382425) | 0.042135 / 0.075469 (-0.033335) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945650 / 1.841788 (-0.896137) | 11.623179 / 8.074308 (3.548871) | 10.927315 / 10.191392 (0.735923) | 0.131050 / 0.680424 (-0.549374) | 0.014725 / 0.534201 (-0.519476) | 0.290716 / 0.579283 (-0.288567) | 0.272357 / 0.434364 (-0.162007) | 0.323274 / 0.540337 (-0.217064) | 0.426692 / 1.386936 (-0.960244) |\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) | 0.005478 / 0.011353 (-0.005875) | 0.003618 / 0.011008 (-0.007390) | 0.049599 / 0.038508 (0.011091) | 0.030814 / 0.023109 (0.007705) | 0.273663 / 0.275898 (-0.002235) | 0.292099 / 0.323480 (-0.031381) | 0.004196 / 0.007986 (-0.003790) | 0.002779 / 0.004328 (-0.001550) | 0.047812 / 0.004250 (0.043562) | 0.045095 / 0.037052 (0.008043) | 0.286288 / 0.258489 (0.027799) | 0.314125 / 0.293841 (0.020284) | 0.047940 / 0.128546 (-0.080606) | 0.010714 / 0.075646 (-0.064932) | 0.057453 / 0.419271 (-0.361819) | 0.033482 / 0.043533 (-0.010051) | 0.273391 / 0.255139 (0.018252) | 0.284936 / 0.283200 (0.001736) | 0.017805 / 0.141683 (-0.123878) | 1.148303 / 1.452155 (-0.303852) | 1.185268 / 1.492716 (-0.307448) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092442 / 0.018006 (0.074436) | 0.309908 / 0.000490 (0.309418) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022874 / 0.037411 (-0.014537) | 0.078238 / 0.014526 (0.063712) | 0.088844 / 0.176557 (-0.087713) | 0.127054 / 0.737135 (-0.610081) | 0.089809 / 0.296338 (-0.206530) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292360 / 0.215209 (0.077151) | 2.842700 / 2.077655 (0.765045) | 1.571071 / 1.504120 (0.066951) | 1.450773 / 1.541195 (-0.090422) | 1.467090 / 1.468490 (-0.001400) | 0.583529 / 4.584777 (-4.001248) | 2.469284 / 3.745712 (-1.276428) | 2.844426 / 5.269862 (-2.425435) | 1.773336 / 4.565676 (-2.792341) | 0.064585 / 0.424275 (-0.359690) | 0.005098 / 0.007607 (-0.002509) | 0.342816 / 0.226044 (0.116771) | 3.363309 / 2.268929 (1.094381) | 1.922834 / 55.444624 (-53.521790) | 1.649702 / 6.876477 (-5.226774) | 1.672727 / 2.142072 (-0.469345) | 0.665015 / 4.805227 (-4.140212) | 0.124764 / 6.500664 (-6.375900) | 0.041564 / 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) | 0.988970 / 1.841788 (-0.852818) | 12.148983 / 8.074308 (4.074675) | 11.132697 / 10.191392 (0.941305) | 0.131596 / 0.680424 (-0.548828) | 0.015700 / 0.534201 (-0.518501) | 0.288819 / 0.579283 (-0.290464) | 0.276692 / 0.434364 (-0.157672) | 0.330260 / 0.540337 (-0.210078) | 0.421612 / 1.386936 (-0.965324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d627fb8357f39d78d79e704712609c7b34bdeba4 \"CML watermark\")\n" ]

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[ "Hi! Can you please share the error's stack trace so we can see where it comes from?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6617). 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) | 0.004774 / 0.011353 (-0.006579) | 0.003397 / 0.011008 (-0.007611) | 0.063862 / 0.038508 (0.025354) | 0.029353 / 0.023109 (0.006244) | 0.245921 / 0.275898 (-0.029977) | 0.268414 / 0.323480 (-0.055066) | 0.002834 / 0.007986 (-0.005152) | 0.002606 / 0.004328 (-0.001723) | 0.049690 / 0.004250 (0.045439) | 0.041637 / 0.037052 (0.004585) | 0.262526 / 0.258489 (0.004037) | 0.288200 / 0.293841 (-0.005641) | 0.027233 / 0.128546 (-0.101313) | 0.010322 / 0.075646 (-0.065324) | 0.213860 / 0.419271 (-0.205411) | 0.034930 / 0.043533 (-0.008602) | 0.249256 / 0.255139 (-0.005883) | 0.270016 / 0.283200 (-0.013184) | 0.019413 / 0.141683 (-0.122270) | 1.124801 / 1.452155 (-0.327354) | 1.166224 / 1.492716 (-0.326492) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091641 / 0.018006 (0.073635) | 0.299679 / 0.000490 (0.299189) | 0.000209 / 0.000200 (0.000009) | 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) | 0.018084 / 0.037411 (-0.019327) | 0.060143 / 0.014526 (0.045617) | 0.072556 / 0.176557 (-0.104001) | 0.118555 / 0.737135 (-0.618580) | 0.073786 / 0.296338 (-0.222553) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278193 / 0.215209 (0.062984) | 2.707954 / 2.077655 (0.630300) | 1.483575 / 1.504120 (-0.020545) | 1.371939 / 1.541195 (-0.169256) | 1.395009 / 1.468490 (-0.073481) | 0.559949 / 4.584777 (-4.024828) | 2.372529 / 3.745712 (-1.373183) | 2.823641 / 5.269862 (-2.446221) | 1.722999 / 4.565676 (-2.842678) | 0.062535 / 0.424275 (-0.361741) | 0.004970 / 0.007607 (-0.002637) | 0.338625 / 0.226044 (0.112580) | 3.317576 / 2.268929 (1.048648) | 1.854552 / 55.444624 (-53.590073) | 1.589323 / 6.876477 (-5.287154) | 1.624630 / 2.142072 (-0.517442) | 0.638388 / 4.805227 (-4.166839) | 0.116675 / 6.500664 (-6.383989) | 0.041850 / 0.075469 (-0.033619) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938025 / 1.841788 (-0.903763) | 11.450072 / 8.074308 (3.375764) | 10.414943 / 10.191392 (0.223551) | 0.128416 / 0.680424 (-0.552007) | 0.013798 / 0.534201 (-0.520403) | 0.287997 / 0.579283 (-0.291286) | 0.259976 / 0.434364 (-0.174387) | 0.320737 / 0.540337 (-0.219601) | 0.424292 / 1.386936 (-0.962644) |\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) | 0.005107 / 0.011353 (-0.006246) | 0.003374 / 0.011008 (-0.007634) | 0.050067 / 0.038508 (0.011559) | 0.031419 / 0.023109 (0.008310) | 0.275303 / 0.275898 (-0.000595) | 0.286736 / 0.323480 (-0.036744) | 0.004177 / 0.007986 (-0.003808) | 0.002742 / 0.004328 (-0.001586) | 0.049011 / 0.004250 (0.044761) | 0.044373 / 0.037052 (0.007321) | 0.289189 / 0.258489 (0.030700) | 0.320117 / 0.293841 (0.026276) | 0.050154 / 0.128546 (-0.078392) | 0.010541 / 0.075646 (-0.065106) | 0.058318 / 0.419271 (-0.360954) | 0.033090 / 0.043533 (-0.010443) | 0.276820 / 0.255139 (0.021681) | 0.290854 / 0.283200 (0.007654) | 0.017268 / 0.141683 (-0.124415) | 1.159345 / 1.452155 (-0.292809) | 1.224829 / 1.492716 (-0.267887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092468 / 0.018006 (0.074462) | 0.301176 / 0.000490 (0.300686) | 0.000216 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021858 / 0.037411 (-0.015553) | 0.074873 / 0.014526 (0.060347) | 0.086238 / 0.176557 (-0.090318) | 0.125555 / 0.737135 (-0.611580) | 0.087791 / 0.296338 (-0.208547) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292283 / 0.215209 (0.077073) | 2.847306 / 2.077655 (0.769651) | 1.600833 / 1.504120 (0.096713) | 1.474253 / 1.541195 (-0.066942) | 1.474871 / 1.468490 (0.006381) | 0.576427 / 4.584777 (-4.008350) | 2.380116 / 3.745712 (-1.365596) | 2.782059 / 5.269862 (-2.487803) | 1.730642 / 4.565676 (-2.835035) | 0.063860 / 0.424275 (-0.360415) | 0.005019 / 0.007607 (-0.002588) | 0.343247 / 0.226044 (0.117202) | 3.393427 / 2.268929 (1.124498) | 1.935346 / 55.444624 (-53.509278) | 1.680124 / 6.876477 (-5.196353) | 1.665788 / 2.142072 (-0.476285) | 0.648767 / 4.805227 (-4.156460) | 0.121962 / 6.500664 (-6.378702) | 0.040669 / 0.075469 (-0.034800) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996535 / 1.841788 (-0.845252) | 12.074553 / 8.074308 (4.000245) | 10.812740 / 10.191392 (0.621348) | 0.142690 / 0.680424 (-0.537734) | 0.014977 / 0.534201 (-0.519224) | 0.285619 / 0.579283 (-0.293664) | 0.269401 / 0.434364 (-0.164963) | 0.329882 / 0.540337 (-0.210456) | 0.416169 / 1.386936 (-0.970767) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#129b9e0565e7a2ceaca64b99dcbf39504661cfa9 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6616). 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) | 0.005382 / 0.011353 (-0.005970) | 0.003853 / 0.011008 (-0.007155) | 0.062629 / 0.038508 (0.024121) | 0.030344 / 0.023109 (0.007234) | 0.245394 / 0.275898 (-0.030505) | 0.266004 / 0.323480 (-0.057476) | 0.003183 / 0.007986 (-0.004802) | 0.002795 / 0.004328 (-0.001533) | 0.048357 / 0.004250 (0.044107) | 0.043834 / 0.037052 (0.006782) | 0.255979 / 0.258489 (-0.002510) | 0.280803 / 0.293841 (-0.013038) | 0.028200 / 0.128546 (-0.100347) | 0.010856 / 0.075646 (-0.064791) | 0.207076 / 0.419271 (-0.212195) | 0.036286 / 0.043533 (-0.007247) | 0.246492 / 0.255139 (-0.008647) | 0.265861 / 0.283200 (-0.017338) | 0.018309 / 0.141683 (-0.123374) | 1.155136 / 1.452155 (-0.297018) | 1.214342 / 1.492716 (-0.278375) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092530 / 0.018006 (0.074524) | 0.344951 / 0.000490 (0.344461) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018324 / 0.037411 (-0.019087) | 0.063137 / 0.014526 (0.048611) | 0.074683 / 0.176557 (-0.101874) | 0.120224 / 0.737135 (-0.616912) | 0.083107 / 0.296338 (-0.213232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288631 / 0.215209 (0.073422) | 2.817992 / 2.077655 (0.740337) | 1.473609 / 1.504120 (-0.030511) | 1.336610 / 1.541195 (-0.204585) | 1.354807 / 1.468490 (-0.113683) | 0.568776 / 4.584777 (-4.016001) | 2.412607 / 3.745712 (-1.333105) | 2.832816 / 5.269862 (-2.437045) | 1.789899 / 4.565676 (-2.775778) | 0.063602 / 0.424275 (-0.360673) | 0.004993 / 0.007607 (-0.002615) | 0.338830 / 0.226044 (0.112786) | 3.302550 / 2.268929 (1.033621) | 1.827907 / 55.444624 (-53.616717) | 1.589857 / 6.876477 (-5.286620) | 1.647746 / 2.142072 (-0.494326) | 0.658461 / 4.805227 (-4.146766) | 0.120360 / 6.500664 (-6.380304) | 0.042989 / 0.075469 (-0.032480) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945487 / 1.841788 (-0.896301) | 11.846335 / 8.074308 (3.772027) | 10.483199 / 10.191392 (0.291807) | 0.131853 / 0.680424 (-0.548570) | 0.014230 / 0.534201 (-0.519971) | 0.288700 / 0.579283 (-0.290584) | 0.276086 / 0.434364 (-0.158278) | 0.326225 / 0.540337 (-0.214112) | 0.422874 / 1.386936 (-0.964062) |\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) | 0.006234 / 0.011353 (-0.005118) | 0.004104 / 0.011008 (-0.006904) | 0.049967 / 0.038508 (0.011459) | 0.037157 / 0.023109 (0.014048) | 0.261892 / 0.275898 (-0.014006) | 0.284304 / 0.323480 (-0.039176) | 0.004482 / 0.007986 (-0.003504) | 0.002920 / 0.004328 (-0.001409) | 0.048827 / 0.004250 (0.044577) | 0.052258 / 0.037052 (0.015206) | 0.277121 / 0.258489 (0.018632) | 0.304177 / 0.293841 (0.010336) | 0.053537 / 0.128546 (-0.075009) | 0.011137 / 0.075646 (-0.064509) | 0.058188 / 0.419271 (-0.361083) | 0.034283 / 0.043533 (-0.009250) | 0.261912 / 0.255139 (0.006773) | 0.273851 / 0.283200 (-0.009348) | 0.017824 / 0.141683 (-0.123859) | 1.130454 / 1.452155 (-0.321701) | 1.176834 / 1.492716 (-0.315882) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102104 / 0.018006 (0.084098) | 0.302873 / 0.000490 (0.302383) | 0.000208 / 0.000200 (0.000008) | 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) | 0.022470 / 0.037411 (-0.014941) | 0.076776 / 0.014526 (0.062250) | 0.088220 / 0.176557 (-0.088337) | 0.130030 / 0.737135 (-0.607105) | 0.089955 / 0.296338 (-0.206383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284070 / 0.215209 (0.068861) | 2.769130 / 2.077655 (0.691475) | 1.546379 / 1.504120 (0.042259) | 1.435849 / 1.541195 (-0.105346) | 1.478616 / 1.468490 (0.010126) | 0.569185 / 4.584777 (-4.015592) | 2.504721 / 3.745712 (-1.240992) | 2.778267 / 5.269862 (-2.491595) | 1.860360 / 4.565676 (-2.705316) | 0.073465 / 0.424275 (-0.350810) | 0.005108 / 0.007607 (-0.002499) | 0.335185 / 0.226044 (0.109140) | 3.314799 / 2.268929 (1.045870) | 1.934824 / 55.444624 (-53.509801) | 1.656247 / 6.876477 (-5.220229) | 1.785422 / 2.142072 (-0.356650) | 0.673677 / 4.805227 (-4.131551) | 0.117692 / 6.500664 (-6.382972) | 0.041648 / 0.075469 (-0.033821) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972143 / 1.841788 (-0.869645) | 12.980353 / 8.074308 (4.906045) | 11.056189 / 10.191392 (0.864797) | 0.134592 / 0.680424 (-0.545832) | 0.015972 / 0.534201 (-0.518229) | 0.301691 / 0.579283 (-0.277593) | 0.286332 / 0.434364 (-0.148032) | 0.329025 / 0.540337 (-0.211312) | 0.422585 / 1.386936 (-0.964351) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6eb492c7072f21cb417801957c087888f252d2d1 \"CML watermark\")\n" ]

[]

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[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]

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[ "Hi ! We recently updated `cnn_dailymail` and now `datasets>=2.14` is needed to load it.\r\n\r\nYou can update `datasets` with\r\n\r\n```\r\npip install -U datasets\r\n```" ]

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[ "Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```", "> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks" ]

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[ "+1", "same error in 2.16.1", "@kongjiellx any luck with the issue?", "I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`", "Thanks @lhoestq !" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6608). 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) | 0.005376 / 0.011353 (-0.005977) | 0.004691 / 0.011008 (-0.006317) | 0.064061 / 0.038508 (0.025553) | 0.030397 / 0.023109 (0.007288) | 0.242656 / 0.275898 (-0.033242) | 0.275586 / 0.323480 (-0.047894) | 0.003460 / 0.007986 (-0.004526) | 0.003125 / 0.004328 (-0.001203) | 0.050496 / 0.004250 (0.046246) | 0.045833 / 0.037052 (0.008781) | 0.255222 / 0.258489 (-0.003267) | 0.287303 / 0.293841 (-0.006538) | 0.027755 / 0.128546 (-0.100791) | 0.011251 / 0.075646 (-0.064396) | 0.208456 / 0.419271 (-0.210816) | 0.037219 / 0.043533 (-0.006314) | 0.249592 / 0.255139 (-0.005547) | 0.261243 / 0.283200 (-0.021957) | 0.020735 / 0.141683 (-0.120948) | 1.130017 / 1.452155 (-0.322137) | 1.208558 / 1.492716 (-0.284158) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098891 / 0.018006 (0.080885) | 0.439042 / 0.000490 (0.438552) | 0.000333 / 0.000200 (0.000133) | 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) | 0.018356 / 0.037411 (-0.019055) | 0.062416 / 0.014526 (0.047891) | 0.075613 / 0.176557 (-0.100944) | 0.122009 / 0.737135 (-0.615126) | 0.078195 / 0.296338 (-0.218144) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273804 / 0.215209 (0.058595) | 2.706480 / 2.077655 (0.628826) | 1.456196 / 1.504120 (-0.047924) | 1.353301 / 1.541195 (-0.187893) | 1.378913 / 1.468490 (-0.089577) | 0.556885 / 4.584777 (-4.027892) | 2.358961 / 3.745712 (-1.386752) | 2.871830 / 5.269862 (-2.398031) | 1.765212 / 4.565676 (-2.800464) | 0.062172 / 0.424275 (-0.362103) | 0.004974 / 0.007607 (-0.002633) | 0.330375 / 0.226044 (0.104331) | 3.264550 / 2.268929 (0.995621) | 1.824444 / 55.444624 (-53.620181) | 1.561189 / 6.876477 (-5.315287) | 1.671020 / 2.142072 (-0.471052) | 0.633408 / 4.805227 (-4.171819) | 0.116080 / 6.500664 (-6.384584) | 0.044606 / 0.075469 (-0.030863) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980757 / 1.841788 (-0.861031) | 12.553534 / 8.074308 (4.479225) | 10.517668 / 10.191392 (0.326276) | 0.130528 / 0.680424 (-0.549896) | 0.013960 / 0.534201 (-0.520241) | 0.289615 / 0.579283 (-0.289668) | 0.267277 / 0.434364 (-0.167087) | 0.324139 / 0.540337 (-0.216198) | 0.440325 / 1.386936 (-0.946611) |\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) | 0.005388 / 0.011353 (-0.005965) | 0.004043 / 0.011008 (-0.006966) | 0.050514 / 0.038508 (0.012005) | 0.031413 / 0.023109 (0.008303) | 0.275122 / 0.275898 (-0.000776) | 0.307518 / 0.323480 (-0.015962) | 0.004440 / 0.007986 (-0.003546) | 0.003301 / 0.004328 (-0.001027) | 0.049200 / 0.004250 (0.044949) | 0.045704 / 0.037052 (0.008651) | 0.285265 / 0.258489 (0.026776) | 0.318942 / 0.293841 (0.025101) | 0.053893 / 0.128546 (-0.074653) | 0.011855 / 0.075646 (-0.063791) | 0.060951 / 0.419271 (-0.358321) | 0.034397 / 0.043533 (-0.009136) | 0.276108 / 0.255139 (0.020969) | 0.290981 / 0.283200 (0.007781) | 0.019986 / 0.141683 (-0.121697) | 1.205695 / 1.452155 (-0.246460) | 1.255942 / 1.492716 (-0.236774) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101910 / 0.018006 (0.083904) | 0.320551 / 0.000490 (0.320061) | 0.000299 / 0.000200 (0.000099) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022387 / 0.037411 (-0.015024) | 0.076380 / 0.014526 (0.061854) | 0.090404 / 0.176557 (-0.086153) | 0.127106 / 0.737135 (-0.610030) | 0.089873 / 0.296338 (-0.206465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288433 / 0.215209 (0.073223) | 2.827005 / 2.077655 (0.749350) | 1.548760 / 1.504120 (0.044640) | 1.419545 / 1.541195 (-0.121650) | 1.456531 / 1.468490 (-0.011959) | 0.570254 / 4.584777 (-4.014523) | 2.441318 / 3.745712 (-1.304394) | 2.778647 / 5.269862 (-2.491215) | 1.755255 / 4.565676 (-2.810422) | 0.062581 / 0.424275 (-0.361694) | 0.005205 / 0.007607 (-0.002402) | 0.342189 / 0.226044 (0.116145) | 3.401208 / 2.268929 (1.132279) | 1.941447 / 55.444624 (-53.503178) | 1.652578 / 6.876477 (-5.223899) | 1.768558 / 2.142072 (-0.373514) | 0.656537 / 4.805227 (-4.148690) | 0.116901 / 6.500664 (-6.383763) | 0.041408 / 0.075469 (-0.034061) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001715 / 1.841788 (-0.840073) | 12.533073 / 8.074308 (4.458765) | 11.086084 / 10.191392 (0.894692) | 0.134368 / 0.680424 (-0.546055) | 0.015255 / 0.534201 (-0.518946) | 0.291769 / 0.579283 (-0.287514) | 0.283311 / 0.434364 (-0.151053) | 0.327857 / 0.540337 (-0.212481) | 0.413854 / 1.386936 (-0.973083) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46931085bd8a3fdbc63b68b5ee4b8f62029c7557 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). 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) | 0.005625 / 0.011353 (-0.005728) | 0.003313 / 0.011008 (-0.007695) | 0.063997 / 0.038508 (0.025489) | 0.028949 / 0.023109 (0.005839) | 0.250069 / 0.275898 (-0.025829) | 0.271412 / 0.323480 (-0.052068) | 0.003837 / 0.007986 (-0.004148) | 0.002632 / 0.004328 (-0.001697) | 0.048351 / 0.004250 (0.044100) | 0.040664 / 0.037052 (0.003612) | 0.267540 / 0.258489 (0.009051) | 0.285237 / 0.293841 (-0.008604) | 0.026962 / 0.128546 (-0.101584) | 0.010417 / 0.075646 (-0.065229) | 0.211430 / 0.419271 (-0.207842) | 0.035411 / 0.043533 (-0.008122) | 0.258867 / 0.255139 (0.003728) | 0.278562 / 0.283200 (-0.004638) | 0.017690 / 0.141683 (-0.123993) | 1.128813 / 1.452155 (-0.323342) | 1.169384 / 1.492716 (-0.323333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091322 / 0.018006 (0.073316) | 0.303272 / 0.000490 (0.302782) | 0.000202 / 0.000200 (0.000002) | 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) | 0.017551 / 0.037411 (-0.019861) | 0.060027 / 0.014526 (0.045502) | 0.073431 / 0.176557 (-0.103125) | 0.120550 / 0.737135 (-0.616585) | 0.073107 / 0.296338 (-0.223231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283064 / 0.215209 (0.067855) | 2.754593 / 2.077655 (0.676938) | 1.477303 / 1.504120 (-0.026817) | 1.341072 / 1.541195 (-0.200123) | 1.366625 / 1.468490 (-0.101865) | 0.573467 / 4.584777 (-4.011310) | 2.395225 / 3.745712 (-1.350487) | 2.777021 / 5.269862 (-2.492841) | 1.720733 / 4.565676 (-2.844944) | 0.063339 / 0.424275 (-0.360936) | 0.004954 / 0.007607 (-0.002653) | 0.350359 / 0.226044 (0.124315) | 3.376221 / 2.268929 (1.107293) | 1.835539 / 55.444624 (-53.609086) | 1.558064 / 6.876477 (-5.318413) | 1.582778 / 2.142072 (-0.559294) | 0.649918 / 4.805227 (-4.155309) | 0.117761 / 6.500664 (-6.382903) | 0.041771 / 0.075469 (-0.033698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950202 / 1.841788 (-0.891586) | 11.476160 / 8.074308 (3.401852) | 10.290618 / 10.191392 (0.099226) | 0.140659 / 0.680424 (-0.539765) | 0.014525 / 0.534201 (-0.519676) | 0.287253 / 0.579283 (-0.292030) | 0.266204 / 0.434364 (-0.168160) | 0.327818 / 0.540337 (-0.212519) | 0.431680 / 1.386936 (-0.955256) |\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) | 0.005096 / 0.011353 (-0.006257) | 0.003460 / 0.011008 (-0.007548) | 0.049474 / 0.038508 (0.010966) | 0.031063 / 0.023109 (0.007954) | 0.272899 / 0.275898 (-0.002999) | 0.291859 / 0.323480 (-0.031621) | 0.004858 / 0.007986 (-0.003128) | 0.002598 / 0.004328 (-0.001731) | 0.049074 / 0.004250 (0.044824) | 0.044722 / 0.037052 (0.007669) | 0.285262 / 0.258489 (0.026772) | 0.314168 / 0.293841 (0.020327) | 0.046346 / 0.128546 (-0.082200) | 0.010384 / 0.075646 (-0.065262) | 0.058331 / 0.419271 (-0.360940) | 0.033728 / 0.043533 (-0.009805) | 0.276217 / 0.255139 (0.021078) | 0.295465 / 0.283200 (0.012265) | 0.018215 / 0.141683 (-0.123467) | 1.163847 / 1.452155 (-0.288308) | 1.213901 / 1.492716 (-0.278816) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091953 / 0.018006 (0.073947) | 0.299977 / 0.000490 (0.299487) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022031 / 0.037411 (-0.015381) | 0.075067 / 0.014526 (0.060541) | 0.087305 / 0.176557 (-0.089251) | 0.125530 / 0.737135 (-0.611605) | 0.088761 / 0.296338 (-0.207578) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302682 / 0.215209 (0.087473) | 2.941509 / 2.077655 (0.863854) | 1.643399 / 1.504120 (0.139280) | 1.530148 / 1.541195 (-0.011046) | 1.542067 / 1.468490 (0.073577) | 0.575883 / 4.584777 (-4.008894) | 2.434320 / 3.745712 (-1.311392) | 2.761683 / 5.269862 (-2.508179) | 1.732068 / 4.565676 (-2.833609) | 0.063543 / 0.424275 (-0.360732) | 0.005089 / 0.007607 (-0.002518) | 0.351314 / 0.226044 (0.125269) | 3.494572 / 2.268929 (1.225643) | 2.032503 / 55.444624 (-53.412121) | 1.697949 / 6.876477 (-5.178528) | 1.700392 / 2.142072 (-0.441680) | 0.650757 / 4.805227 (-4.154471) | 0.116719 / 6.500664 (-6.383945) | 0.040559 / 0.075469 (-0.034910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978218 / 1.841788 (-0.863570) | 11.972379 / 8.074308 (3.898071) | 10.725735 / 10.191392 (0.534343) | 0.130564 / 0.680424 (-0.549860) | 0.015396 / 0.534201 (-0.518805) | 0.286900 / 0.579283 (-0.292383) | 0.279633 / 0.434364 (-0.154730) | 0.327483 / 0.540337 (-0.212854) | 0.417848 / 1.386936 (-0.969088) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adfe8f8fa37b9f220c152f5b8b2473ba2cef0307 \"CML watermark\")\n" ]

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[ "Addressed in https://github.com/huggingface/transformers/pull/28715." ]

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[ "I've opened a PR with a fix.", "I don't think the PR fixes the root cause, since it still relies on the `random` library which will often have its seed fixed. I think the builtin `uuid.uuid4()` is a better choice: https://docs.python.org/3/library/uuid.html" ]

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[ "Unfortunately, I'm unable to reproduce this error. Can you share the reproducer?", "```\r\nds = datasets.Dataset.from_dict(dict(a=[i for i in range(100)]))\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-fn\") # this worked\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/filename\") # this failed\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/\") # this failed\r\n\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/whatever-folder/tmp1_izxvoo'\r\n```\r\n\r\nIt will fail if the filename parents do not exists. If we have `os.makedirs(\"/tmp/whatever-folder\")`, then it worked.\r\n\r\nMaybe add the `mkdir -p` into the map function?" ]

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[ "Hi! Parquet is the only format that supports complex/nested features such as `Translation`. So, this should work:\r\n```python\r\ntest_dataset = load_dataset(\"opus100\", name=\"en-fr\", split=\"test\")\r\n\r\n# Save with .to_parquet()\r\ntest_parquet_path = \"try_testset_save.parquet\"\r\ntest_dataset.to_parquet(test_parquet_path)\r\n\r\n# Load dataset from the Parquet\r\nloaded_dataset = load_dataset(\"parquet\", data_files=test_parquet_path)\r\nprint(test_dataset_fromfile[0][\"translation\"])\r\nprint(test_dataset_fromfile[0][\"translation\"][\"en\"])\r\n```", "Indeed this works great, thank you !" ]

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[ "Hi! Non-generic data processing is out of this library's scope, so it's downstream libraries/users' responsibility to implement such logic.", "That's fair. Thanks" ]

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[ "I am facing similar issue while reading a csv file from s3. Wondering if somebody has found a workaround. ", "same thing happened to other formats like parquet" ]

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[ "It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694", "Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585\r\n\r\n> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\r\n\r\nThis behavior was \"reverted\" by the PR: \r\n- #6519\r\n\r\nWe have therefore contradictory requirements. We should decide:\r\n- whether to support passing dataset_namespace without user/org that defaults to the logged-in user (and not support canonical datasets)\r\n- or vice-versa, to support canonical datasets and not support passing only dataset_name\r\n\r\nAs canonical datasets are \"deprecated\" (and will eventually disappear), I would choose the first option. However, if so, the Space to convert datasets to Parquet will not work for canonical datasets: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet", "IIUC, this could also be \"fixed\" by `create_repo(\"dataset_name\")` not defaulting to `create_repo(\"user/dataset_name\")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but justified to make the API more consistent.", "I tag @Wauplin to have his opinion as well.", "Hmm, creating repo with implicit namespace (e.g. `create_repo(\"dataset_name\")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace/repo_name`) that has to be passed to every `HfApi` method. Otherwise, libraries/scripts would often need to do a `whoami` call to get the namespace before creating a repo.\r\n\r\n Another solution for https://github.com/huggingface/datasets/issues/6597#issuecomment-1893746690 could be that implicit namespace is allowed (same as today) except if the `repo_id` is in a hard-coded list of canonical datasets. This list can be maintained automatically and should be slowly decreasing. **Caveat:** as a normal user I wouldn't be able to implicitly push to `imagenet-1k` if I wanted to push to `Wauplin/imagenet-1k`. Shouldn't be too problematic, no? Worse case, would need to add a `whoami` call and allow implicit-canonical-name for non-HF users for instance (a bit too over-engineered IMO but doable). ", "As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.\r\n\r\nI propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: \"Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\"\r\n\r\nI'm opening a PR." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6596). 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) | 0.004768 / 0.011353 (-0.006585) | 0.003084 / 0.011008 (-0.007924) | 0.062775 / 0.038508 (0.024267) | 0.029909 / 0.023109 (0.006800) | 0.242905 / 0.275898 (-0.032993) | 0.265609 / 0.323480 (-0.057871) | 0.003856 / 0.007986 (-0.004130) | 0.002610 / 0.004328 (-0.001718) | 0.048631 / 0.004250 (0.044381) | 0.040464 / 0.037052 (0.003412) | 0.256023 / 0.258489 (-0.002467) | 0.285914 / 0.293841 (-0.007927) | 0.027305 / 0.128546 (-0.101241) | 0.010345 / 0.075646 (-0.065301) | 0.206264 / 0.419271 (-0.213008) | 0.035290 / 0.043533 (-0.008243) | 0.247785 / 0.255139 (-0.007353) | 0.267053 / 0.283200 (-0.016147) | 0.017910 / 0.141683 (-0.123773) | 1.166096 / 1.452155 (-0.286059) | 1.210717 / 1.492716 (-0.281999) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095759 / 0.018006 (0.077753) | 0.311030 / 0.000490 (0.310540) | 0.000234 / 0.000200 (0.000034) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017828 / 0.037411 (-0.019583) | 0.060123 / 0.014526 (0.045597) | 0.071947 / 0.176557 (-0.104610) | 0.119353 / 0.737135 (-0.617782) | 0.073529 / 0.296338 (-0.222809) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282737 / 0.215209 (0.067528) | 2.761914 / 2.077655 (0.684260) | 1.480310 / 1.504120 (-0.023810) | 1.329977 / 1.541195 (-0.211218) | 1.332686 / 1.468490 (-0.135804) | 0.566309 / 4.584777 (-4.018468) | 2.361838 / 3.745712 (-1.383874) | 2.775613 / 5.269862 (-2.494249) | 1.744985 / 4.565676 (-2.820692) | 0.063038 / 0.424275 (-0.361237) | 0.004969 / 0.007607 (-0.002638) | 0.335543 / 0.226044 (0.109499) | 3.293779 / 2.268929 (1.024851) | 1.816093 / 55.444624 (-53.628532) | 1.562658 / 6.876477 (-5.313819) | 1.544888 / 2.142072 (-0.597185) | 0.641762 / 4.805227 (-4.163465) | 0.117904 / 6.500664 (-6.382760) | 0.042534 / 0.075469 (-0.032935) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935577 / 1.841788 (-0.906211) | 11.565833 / 8.074308 (3.491525) | 10.314723 / 10.191392 (0.123331) | 0.138912 / 0.680424 (-0.541512) | 0.013968 / 0.534201 (-0.520233) | 0.296270 / 0.579283 (-0.283013) | 0.266106 / 0.434364 (-0.168258) | 0.334729 / 0.540337 (-0.205609) | 0.443191 / 1.386936 (-0.943745) |\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) | 0.004865 / 0.011353 (-0.006488) | 0.003523 / 0.011008 (-0.007485) | 0.049303 / 0.038508 (0.010795) | 0.029252 / 0.023109 (0.006143) | 0.271288 / 0.275898 (-0.004610) | 0.290529 / 0.323480 (-0.032951) | 0.003982 / 0.007986 (-0.004004) | 0.002740 / 0.004328 (-0.001589) | 0.048513 / 0.004250 (0.044262) | 0.044473 / 0.037052 (0.007420) | 0.282072 / 0.258489 (0.023583) | 0.311321 / 0.293841 (0.017480) | 0.028825 / 0.128546 (-0.099721) | 0.010311 / 0.075646 (-0.065335) | 0.057071 / 0.419271 (-0.362200) | 0.052629 / 0.043533 (0.009097) | 0.273134 / 0.255139 (0.017995) | 0.290989 / 0.283200 (0.007789) | 0.018074 / 0.141683 (-0.123609) | 1.171724 / 1.452155 (-0.280431) | 1.236178 / 1.492716 (-0.256538) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097099 / 0.018006 (0.079093) | 0.309788 / 0.000490 (0.309298) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021703 / 0.037411 (-0.015708) | 0.076104 / 0.014526 (0.061578) | 0.088202 / 0.176557 (-0.088355) | 0.127351 / 0.737135 (-0.609784) | 0.089754 / 0.296338 (-0.206585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294574 / 0.215209 (0.079365) | 2.851581 / 2.077655 (0.773926) | 1.599117 / 1.504120 (0.094997) | 1.476183 / 1.541195 (-0.065012) | 1.512309 / 1.468490 (0.043819) | 0.559785 / 4.584777 (-4.024992) | 2.453287 / 3.745712 (-1.292425) | 2.660101 / 5.269862 (-2.609760) | 1.743043 / 4.565676 (-2.822633) | 0.063450 / 0.424275 (-0.360825) | 0.005019 / 0.007607 (-0.002589) | 0.351507 / 0.226044 (0.125462) | 3.431587 / 2.268929 (1.162658) | 1.943349 / 55.444624 (-53.501275) | 1.658706 / 6.876477 (-5.217771) | 1.780042 / 2.142072 (-0.362030) | 0.641364 / 4.805227 (-4.163863) | 0.118052 / 6.500664 (-6.382612) | 0.040961 / 0.075469 (-0.034508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974219 / 1.841788 (-0.867568) | 12.257824 / 8.074308 (4.183516) | 10.821225 / 10.191392 (0.629833) | 0.139399 / 0.680424 (-0.541025) | 0.015277 / 0.534201 (-0.518924) | 0.286975 / 0.579283 (-0.292309) | 0.283419 / 0.434364 (-0.150945) | 0.324299 / 0.540337 (-0.216039) | 0.424538 / 1.386936 (-0.962398) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2ba3f30bae5ff75ac48f0e653240b924d7982d5 \"CML watermark\")\n" ]

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[ "Hi ! I think the issue comes from the \"float16\" features that are not supported yet in Parquet\r\n\r\nFeel free to open an issue in `pyarrow` about this. In the meantime, I'd encourage you to use \"float32\" for your \"pooled_prompt_embeds\" and \"prompt_embeds\" features.\r\n\r\nYou can cast them to \"float32\" using\r\n\r\n```python\r\nfrom datasets import Value\r\n\r\nds = ds.cast_column(\"pooled_prompt_embeds\", Value(\"float32\"))\r\nds = ds.cast_column(\"prompt_embeds\", Value(\"float32\"))\r\n```", "@lhoestq hm. Thank you very much.\r\n\r\nDo you think it won't have any impact on the training? That it won't break it or the quality won't degrade because of this?\r\n\r\nI need to use it for [SDXL training](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)", "Increasing the precision should not degrade training (it only increases the precision), but make sure that it doesn't break your pytorch code (e.g. if it expects a float16 instead of a float32 somewhere)", "@lhoestq just fyi pyarrow 15.0.0 (just released) supports float16 as the underlying parquetcpp does as well now :)", "Oh that's amazing ! (and great timing ^^)\r\n\r\n@kopyl can you try to update `pyarrow` and try again ?\r\n\r\nBtw @assignUser there seems to be some casting implementations missing with float16 in 15.0.0, e.g.\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\n```\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float\r\n```", "Ah you are right casting is not implemented yet, it's even mentioned in the docs. This pr references the relevant issues if you'd like to track them\nhttps://github.com/apache/arrow/pull/38494", "Cool thank you :)", "@lhoestq i just recently found out that it's supported in 15.0.0, but wanted to try it first before telling you...\r\n\r\nTrying this right now and it seemingly works (although i need to wait till the end to make sure there is nothing wrong). Will update you when it's finished.\r\n\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/4821e215-e782-4736-8c76-d06187078175\">\r\n\r\nA couple of questions though:\r\n\r\n1. What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n2. Do you know how to `push_to_hub` with multiple processes?", "@lhoestq also it's strange that there was no error for a dataset with the same features, same data type, but smaller (much smaller).\r\n\r\nAltho i'm not sure about this, but chances are the dataset was loaded directly, not `load_from_disk`.... Maybe because of this.", "> What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n\r\nNothing for you, just that casting to float16 using `.cast_column(\"my_column_name\", Value(\"float16\"))` raises an error\r\n\r\n> Do you know how to push_to_hub with multiple processes?\r\n\r\nIt's not possible (yet ?). Mostly because we haven't implemented yet how to do parallel uploads to the Hub from `datasets`.\r\nThough if you want faster uploads you can already enable `hf_transfer` \r\n\r\n```\r\npip install hf_transfer\r\n```\r\n\r\nand setting `HF_HUB_ENABLE_HF_TRANSFER=1` as an environment variable\r\n\r\nsee https://huggingface.co/docs/huggingface_hub/guides/upload#tips-and-tricks-for-large-uploads", "@lhoestq thank you very much.\r\n\r\nThat would be amazing, I need to create a feature request for this :)\r\n\r\nBy the way, in short, how does hf_transfer improves the upload speed under the hood?", "@lhoestq i was just able to successfully upload without the dataset with the new pyarrow update and without increasing the precision :)", "Awesome !\r\n\r\nRegarding hf_transfer: it's been optimized in rust ;)", "@lhoestq wow, cool :)" ]

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[ "Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress." ]

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[ "Hi! Indeed, Dropbox is not a reliable host. I've just merged https://huggingface.co/datasets/PolyAI/minds14/discussions/24 to fix this by hosting the data files inside the repo." ]

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[ "We'll do a new release of `datasets` in the coming days with a fix !", "@lhoestq Thank you very much!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "friendly bump" ]

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[ "@JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`.\r\nThere is short discussion about it [here](https://github.com/huggingface/blog/issues/1623).\r\nWould be great if you can check if your PR would also fix that!", "> @JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`. There is short discussion about it [here](https://github.com/huggingface/blog/issues/1623). Would be great if you can check if your PR would also fix that!\r\n\r\nHi @thiagobarbosa, it might be related but I didn't worked with `push_to_hub` yet. I don't see a minimal example reproducing the specific error in your link. However, if you have a running version producing the error locally you can test it by pulling this PR and run your specific example locally. ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6586). 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) | 0.004729 / 0.011353 (-0.006624) | 0.002983 / 0.011008 (-0.008025) | 0.062482 / 0.038508 (0.023974) | 0.028406 / 0.023109 (0.005297) | 0.255896 / 0.275898 (-0.020002) | 0.276423 / 0.323480 (-0.047057) | 0.003828 / 0.007986 (-0.004157) | 0.002601 / 0.004328 (-0.001728) | 0.048954 / 0.004250 (0.044704) | 0.040661 / 0.037052 (0.003609) | 0.277710 / 0.258489 (0.019221) | 0.290360 / 0.293841 (-0.003481) | 0.027105 / 0.128546 (-0.101441) | 0.010168 / 0.075646 (-0.065478) | 0.206835 / 0.419271 (-0.212436) | 0.035226 / 0.043533 (-0.008306) | 0.262567 / 0.255139 (0.007428) | 0.273979 / 0.283200 (-0.009221) | 0.017576 / 0.141683 (-0.124106) | 1.125588 / 1.452155 (-0.326566) | 1.185018 / 1.492716 (-0.307698) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092192 / 0.018006 (0.074186) | 0.298350 / 0.000490 (0.297861) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017925 / 0.037411 (-0.019486) | 0.060285 / 0.014526 (0.045759) | 0.076579 / 0.176557 (-0.099978) | 0.118830 / 0.737135 (-0.618305) | 0.073017 / 0.296338 (-0.223322) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288149 / 0.215209 (0.072940) | 2.840004 / 2.077655 (0.762349) | 1.495758 / 1.504120 (-0.008361) | 1.362338 / 1.541195 (-0.178857) | 1.389746 / 1.468490 (-0.078744) | 0.576891 / 4.584777 (-4.007886) | 2.375724 / 3.745712 (-1.369988) | 2.707405 / 5.269862 (-2.562457) | 1.719850 / 4.565676 (-2.845826) | 0.067055 / 0.424275 (-0.357220) | 0.005039 / 0.007607 (-0.002568) | 0.346626 / 0.226044 (0.120581) | 3.468346 / 2.268929 (1.199418) | 1.860686 / 55.444624 (-53.583938) | 1.582929 / 6.876477 (-5.293548) | 1.613131 / 2.142072 (-0.528941) | 0.659022 / 4.805227 (-4.146206) | 0.118477 / 6.500664 (-6.382187) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005062 / 1.841788 (-0.836726) | 11.203210 / 8.074308 (3.128902) | 10.320764 / 10.191392 (0.129372) | 0.128541 / 0.680424 (-0.551883) | 0.014646 / 0.534201 (-0.519555) | 0.285280 / 0.579283 (-0.294003) | 0.263613 / 0.434364 (-0.170751) | 0.321161 / 0.540337 (-0.219177) | 0.420565 / 1.386936 (-0.966371) |\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) | 0.005288 / 0.011353 (-0.006065) | 0.003048 / 0.011008 (-0.007960) | 0.049196 / 0.038508 (0.010688) | 0.032104 / 0.023109 (0.008994) | 0.279345 / 0.275898 (0.003447) | 0.300194 / 0.323480 (-0.023286) | 0.004045 / 0.007986 (-0.003941) | 0.002594 / 0.004328 (-0.001735) | 0.047680 / 0.004250 (0.043430) | 0.044294 / 0.037052 (0.007241) | 0.292330 / 0.258489 (0.033841) | 0.318610 / 0.293841 (0.024769) | 0.050417 / 0.128546 (-0.078129) | 0.010326 / 0.075646 (-0.065320) | 0.057372 / 0.419271 (-0.361899) | 0.032985 / 0.043533 (-0.010548) | 0.277717 / 0.255139 (0.022579) | 0.295692 / 0.283200 (0.012493) | 0.017756 / 0.141683 (-0.123927) | 1.166277 / 1.452155 (-0.285877) | 1.213337 / 1.492716 (-0.279380) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091365 / 0.018006 (0.073359) | 0.296261 / 0.000490 (0.295772) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021973 / 0.037411 (-0.015438) | 0.074631 / 0.014526 (0.060106) | 0.085645 / 0.176557 (-0.090911) | 0.125181 / 0.737135 (-0.611955) | 0.086893 / 0.296338 (-0.209445) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294110 / 0.215209 (0.078901) | 2.855531 / 2.077655 (0.777876) | 1.583204 / 1.504120 (0.079084) | 1.453911 / 1.541195 (-0.087284) | 1.467031 / 1.468490 (-0.001460) | 0.581214 / 4.584777 (-4.003562) | 2.423626 / 3.745712 (-1.322086) | 2.736665 / 5.269862 (-2.533197) | 1.707000 / 4.565676 (-2.858676) | 0.061171 / 0.424275 (-0.363104) | 0.004789 / 0.007607 (-0.002818) | 0.344546 / 0.226044 (0.118502) | 3.530955 / 2.268929 (1.262027) | 1.962532 / 55.444624 (-53.482092) | 1.670207 / 6.876477 (-5.206270) | 1.669041 / 2.142072 (-0.473031) | 0.642298 / 4.805227 (-4.162929) | 0.115503 / 6.500664 (-6.385161) | 0.040729 / 0.075469 (-0.034740) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973101 / 1.841788 (-0.868687) | 11.823894 / 8.074308 (3.749586) | 10.664592 / 10.191392 (0.473200) | 0.139848 / 0.680424 (-0.540576) | 0.015728 / 0.534201 (-0.518473) | 0.289135 / 0.579283 (-0.290148) | 0.271325 / 0.434364 (-0.163039) | 0.332253 / 0.540337 (-0.208085) | 0.416982 / 1.386936 (-0.969954) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca76ca1152fce82bfeaab9f9a33849d4d7f9dd63 \"CML watermark\")\n" ]

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[ "Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270", "#self-assign" ]

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[ "@lhoestq\r\nCan you provide me with some ideas？", "Hi ! What's the error ?", "@lhoestq \r\n```\r\nTraceback (most recent call last):\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/example/test.py\", line 83, in <module>\r\n data = xnumpy_fromfile(current_dir, download_config=config,dtype=numpy.float32,)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/src/datasets/download/streaming_download_manager.py\", line 765, in xnumpy_fromfile\r\n return np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config).read(), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: embedded null byte\r\n```", " not add read() \r\nthe error is \r\n\r\nreturn np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nio.UnsupportedOperation: fileno", "xopen return obj do not have fileno function\r\nI don't know why?", "I used this method to read point cloud data in the script\r\n\r\n\r\n```python\r\nwith open(velodyne_filepath,\"rb\") as obj:\r\n velodyne_data = numpy.frombuffer(obj.read(), dtype=numpy.float32).reshape([-1, 4])\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6583). 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) | 0.005024 / 0.011353 (-0.006329) | 0.003172 / 0.011008 (-0.007836) | 0.062934 / 0.038508 (0.024426) | 0.031737 / 0.023109 (0.008628) | 0.249251 / 0.275898 (-0.026647) | 0.273084 / 0.323480 (-0.050396) | 0.002958 / 0.007986 (-0.005027) | 0.002726 / 0.004328 (-0.001603) | 0.048519 / 0.004250 (0.044269) | 0.043608 / 0.037052 (0.006556) | 0.253648 / 0.258489 (-0.004841) | 0.280095 / 0.293841 (-0.013746) | 0.027500 / 0.128546 (-0.101046) | 0.010545 / 0.075646 (-0.065101) | 0.206781 / 0.419271 (-0.212490) | 0.035515 / 0.043533 (-0.008018) | 0.259449 / 0.255139 (0.004310) | 0.271488 / 0.283200 (-0.011712) | 0.019352 / 0.141683 (-0.122331) | 1.152002 / 1.452155 (-0.300153) | 1.190325 / 1.492716 (-0.302391) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093253 / 0.018006 (0.075247) | 0.302182 / 0.000490 (0.301692) | 0.000216 / 0.000200 (0.000016) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017889 / 0.037411 (-0.019523) | 0.060292 / 0.014526 (0.045766) | 0.072640 / 0.176557 (-0.103917) | 0.121320 / 0.737135 (-0.615815) | 0.073866 / 0.296338 (-0.222472) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282910 / 0.215209 (0.067701) | 2.779815 / 2.077655 (0.702160) | 1.537929 / 1.504120 (0.033809) | 1.405990 / 1.541195 (-0.135205) | 1.407911 / 1.468490 (-0.060579) | 0.561551 / 4.584777 (-4.023226) | 2.368053 / 3.745712 (-1.377659) | 2.732608 / 5.269862 (-2.537254) | 1.710274 / 4.565676 (-2.855402) | 0.061925 / 0.424275 (-0.362350) | 0.004975 / 0.007607 (-0.002632) | 0.338843 / 0.226044 (0.112799) | 3.328579 / 2.268929 (1.059650) | 1.865994 / 55.444624 (-53.578631) | 1.603145 / 6.876477 (-5.273332) | 1.615440 / 2.142072 (-0.526633) | 0.635646 / 4.805227 (-4.169581) | 0.116185 / 6.500664 (-6.384479) | 0.041964 / 0.075469 (-0.033505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956977 / 1.841788 (-0.884811) | 11.539802 / 8.074308 (3.465494) | 10.048855 / 10.191392 (-0.142537) | 0.128758 / 0.680424 (-0.551666) | 0.013491 / 0.534201 (-0.520710) | 0.287330 / 0.579283 (-0.291953) | 0.262416 / 0.434364 (-0.171947) | 0.327327 / 0.540337 (-0.213011) | 0.418423 / 1.386936 (-0.968513) |\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) | 0.004963 / 0.011353 (-0.006390) | 0.003335 / 0.011008 (-0.007673) | 0.052082 / 0.038508 (0.013574) | 0.029302 / 0.023109 (0.006192) | 0.284986 / 0.275898 (0.009088) | 0.304082 / 0.323480 (-0.019398) | 0.004065 / 0.007986 (-0.003921) | 0.002643 / 0.004328 (-0.001685) | 0.049504 / 0.004250 (0.045253) | 0.044514 / 0.037052 (0.007461) | 0.287064 / 0.258489 (0.028575) | 0.312921 / 0.293841 (0.019080) | 0.029195 / 0.128546 (-0.099351) | 0.010471 / 0.075646 (-0.065175) | 0.057620 / 0.419271 (-0.361651) | 0.050221 / 0.043533 (0.006689) | 0.285392 / 0.255139 (0.030253) | 0.302111 / 0.283200 (0.018912) | 0.018690 / 0.141683 (-0.122993) | 1.165637 / 1.452155 (-0.286518) | 1.203757 / 1.492716 (-0.288959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095035 / 0.018006 (0.077028) | 0.304447 / 0.000490 (0.303957) | 0.000231 / 0.000200 (0.000031) | 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) | 0.022345 / 0.037411 (-0.015066) | 0.077195 / 0.014526 (0.062669) | 0.089564 / 0.176557 (-0.086992) | 0.129248 / 0.737135 (-0.607887) | 0.091974 / 0.296338 (-0.204365) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300641 / 0.215209 (0.085432) | 2.936669 / 2.077655 (0.859014) | 1.649100 / 1.504120 (0.144980) | 1.510693 / 1.541195 (-0.030502) | 1.517011 / 1.468490 (0.048521) | 0.572511 / 4.584777 (-4.012266) | 2.442704 / 3.745712 (-1.303009) | 2.833089 / 5.269862 (-2.436772) | 1.762668 / 4.565676 (-2.803008) | 0.063754 / 0.424275 (-0.360521) | 0.005034 / 0.007607 (-0.002573) | 0.401631 / 0.226044 (0.175586) | 3.418986 / 2.268929 (1.150057) | 1.989639 / 55.444624 (-53.454986) | 1.695776 / 6.876477 (-5.180701) | 1.712822 / 2.142072 (-0.429250) | 0.654029 / 4.805227 (-4.151198) | 0.117624 / 6.500664 (-6.383040) | 0.041058 / 0.075469 (-0.034411) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986008 / 1.841788 (-0.855779) | 12.146838 / 8.074308 (4.072530) | 11.105900 / 10.191392 (0.914508) | 0.139938 / 0.680424 (-0.540486) | 0.015117 / 0.534201 (-0.519084) | 0.286151 / 0.579283 (-0.293132) | 0.272960 / 0.434364 (-0.161404) | 0.323370 / 0.540337 (-0.216967) | 0.427379 / 1.386936 (-0.959557) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91888ea888fec1f2c96d8316a569439e64eb508e \"CML watermark\")\n" ]

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[ "A toy example to reveal the bug.\r\n\r\n```python\r\n\"\"\"\r\nDATASETS_VERBOSITY=debug torchrun --nproc-per-node 2 main.py \r\n\"\"\"\r\nimport torch.utils.data\r\nimport torch.distributed\r\nimport datasets.distributed\r\nimport datasets\r\n\r\n# num shards = 4\r\nshards = [(0, 100), (100, 200), (200, 300), (300, 400)]\r\n\r\n\r\ndef gen(shards):\r\n for st, ed in shards:\r\n yield from range(st, ed)\r\n\r\ntorch.distributed.init_process_group()\r\n\r\n# want to create total worker = world_size * 8\r\nds = datasets.IterableDataset.from_generator(gen, gen_kwargs={'shards': shards})\r\nds = datasets.distributed.split_dataset_by_node(\r\n ds,\r\n rank=torch.distributed.get_rank(),\r\n world_size=torch.distributed.get_world_size(),\r\n)\r\ndl = torch.utils.data.DataLoader(ds, batch_size=10, num_workers=8)\r\n\r\nfor x in dl:\r\n print(f\"RANK={torch.distributed.get_rank()} {x}\")\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6581). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "\r\nObj [\"name\"] ends with \"/\"", "@lhoestq \r\n\r\nhello,\r\nCan you help me check if there are any issues with this PR? Why hasn't anyone merged?\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004968 / 0.011353 (-0.006385) | 0.003516 / 0.011008 (-0.007492) | 0.063787 / 0.038508 (0.025279) | 0.031695 / 0.023109 (0.008586) | 0.240081 / 0.275898 (-0.035817) | 0.260984 / 0.323480 (-0.062496) | 0.003832 / 0.007986 (-0.004153) | 0.002680 / 0.004328 (-0.001648) | 0.049199 / 0.004250 (0.044948) | 0.044720 / 0.037052 (0.007668) | 0.255812 / 0.258489 (-0.002677) | 0.275923 / 0.293841 (-0.017918) | 0.026849 / 0.128546 (-0.101697) | 0.010473 / 0.075646 (-0.065174) | 0.209069 / 0.419271 (-0.210202) | 0.035731 / 0.043533 (-0.007802) | 0.246596 / 0.255139 (-0.008543) | 0.265889 / 0.283200 (-0.017311) | 0.017607 / 0.141683 (-0.124075) | 1.128648 / 1.452155 (-0.323507) | 1.174379 / 1.492716 (-0.318338) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098214 / 0.018006 (0.080207) | 0.311969 / 0.000490 (0.311480) | 0.000266 / 0.000200 (0.000066) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018401 / 0.037411 (-0.019010) | 0.061347 / 0.014526 (0.046821) | 0.073628 / 0.176557 (-0.102928) | 0.121359 / 0.737135 (-0.615776) | 0.075148 / 0.296338 (-0.221190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.274098 / 0.215209 (0.058889) | 2.707633 / 2.077655 (0.629978) | 1.453615 / 1.504120 (-0.050504) | 1.311942 / 1.541195 (-0.229253) | 1.332394 / 1.468490 (-0.136096) | 0.566947 / 4.584777 (-4.017830) | 2.383291 / 3.745712 (-1.362421) | 2.754779 / 5.269862 (-2.515083) | 1.725164 / 4.565676 (-2.840512) | 0.062124 / 0.424275 (-0.362152) | 0.005111 / 0.007607 (-0.002496) | 0.334217 / 0.226044 (0.108173) | 3.271619 / 2.268929 (1.002690) | 1.776906 / 55.444624 (-53.667718) | 1.519238 / 6.876477 (-5.357239) | 1.534722 / 2.142072 (-0.607351) | 0.646143 / 4.805227 (-4.159084) | 0.117015 / 6.500664 (-6.383649) | 0.042578 / 0.075469 (-0.032891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948488 / 1.841788 (-0.893299) | 11.598027 / 8.074308 (3.523719) | 10.269199 / 10.191392 (0.077807) | 0.144887 / 0.680424 (-0.535537) | 0.014745 / 0.534201 (-0.519456) | 0.289185 / 0.579283 (-0.290099) | 0.275243 / 0.434364 (-0.159120) | 0.328088 / 0.540337 (-0.212250) | 0.430161 / 1.386936 (-0.956775) |\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) | 0.005020 / 0.011353 (-0.006333) | 0.003246 / 0.011008 (-0.007762) | 0.049810 / 0.038508 (0.011302) | 0.032215 / 0.023109 (0.009105) | 0.271033 / 0.275898 (-0.004866) | 0.294957 / 0.323480 (-0.028523) | 0.004192 / 0.007986 (-0.003793) | 0.002652 / 0.004328 (-0.001677) | 0.049132 / 0.004250 (0.044881) | 0.047818 / 0.037052 (0.010766) | 0.292370 / 0.258489 (0.033881) | 0.316142 / 0.293841 (0.022301) | 0.049539 / 0.128546 (-0.079007) | 0.010533 / 0.075646 (-0.065113) | 0.058131 / 0.419271 (-0.361141) | 0.033807 / 0.043533 (-0.009725) | 0.277623 / 0.255139 (0.022484) | 0.292294 / 0.283200 (0.009094) | 0.021110 / 0.141683 (-0.120573) | 1.160997 / 1.452155 (-0.291157) | 1.213553 / 1.492716 (-0.279163) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098220 / 0.018006 (0.080214) | 0.312342 / 0.000490 (0.311852) | 0.000231 / 0.000200 (0.000031) | 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) | 0.022893 / 0.037411 (-0.014519) | 0.075572 / 0.014526 (0.061046) | 0.088357 / 0.176557 (-0.088199) | 0.126354 / 0.737135 (-0.610782) | 0.089763 / 0.296338 (-0.206575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284368 / 0.215209 (0.069159) | 2.785497 / 2.077655 (0.707842) | 1.499364 / 1.504120 (-0.004756) | 1.376020 / 1.541195 (-0.165175) | 1.394270 / 1.468490 (-0.074220) | 0.571945 / 4.584777 (-4.012832) | 2.419148 / 3.745712 (-1.326564) | 2.796974 / 5.269862 (-2.472887) | 1.749531 / 4.565676 (-2.816145) | 0.064088 / 0.424275 (-0.360187) | 0.005294 / 0.007607 (-0.002313) | 0.336250 / 0.226044 (0.110206) | 3.315933 / 2.268929 (1.047004) | 1.877165 / 55.444624 (-53.567459) | 1.592336 / 6.876477 (-5.284140) | 1.599979 / 2.142072 (-0.542093) | 0.655617 / 4.805227 (-4.149610) | 0.117636 / 6.500664 (-6.383028) | 0.040813 / 0.075469 (-0.034656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976887 / 1.841788 (-0.864901) | 12.668753 / 8.074308 (4.594445) | 11.081253 / 10.191392 (0.889861) | 0.134494 / 0.680424 (-0.545930) | 0.016053 / 0.534201 (-0.518148) | 0.291607 / 0.579283 (-0.287676) | 0.287726 / 0.434364 (-0.146638) | 0.328108 / 0.540337 (-0.212229) | 0.425194 / 1.386936 (-0.961742) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32672349e3e5abe21505fdbda122dd3426f8920f \"CML watermark\")\n" ]

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[ "Hi @haok1402, I have created an issue in the Discussion tab of the corresponding dataset: https://huggingface.co/datasets/eli5/discussions/7\r\nLet's continue the discussion there!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6578). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I added faster streaming support using streaming Requests instances in `huggingface_hub` and will be available in 0.21.\r\n\r\nThis PR can be used with https://github.com/huggingface/huggingface_hub/pull/1967 to get fast WebDataset streaming", "<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) | 0.004941 / 0.011353 (-0.006412) | 0.003431 / 0.011008 (-0.007577) | 0.062768 / 0.038508 (0.024260) | 0.029212 / 0.023109 (0.006103) | 0.253053 / 0.275898 (-0.022845) | 0.273061 / 0.323480 (-0.050419) | 0.004114 / 0.007986 (-0.003871) | 0.002713 / 0.004328 (-0.001616) | 0.048481 / 0.004250 (0.044231) | 0.040001 / 0.037052 (0.002949) | 0.268461 / 0.258489 (0.009971) | 0.287767 / 0.293841 (-0.006074) | 0.027885 / 0.128546 (-0.100661) | 0.010474 / 0.075646 (-0.065172) | 0.207989 / 0.419271 (-0.211282) | 0.035893 / 0.043533 (-0.007640) | 0.256833 / 0.255139 (0.001694) | 0.274197 / 0.283200 (-0.009003) | 0.017283 / 0.141683 (-0.124400) | 1.133597 / 1.452155 (-0.318558) | 1.206661 / 1.492716 (-0.286055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089610 / 0.018006 (0.071604) | 0.306051 / 0.000490 (0.305562) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018686 / 0.037411 (-0.018725) | 0.061253 / 0.014526 (0.046727) | 0.073654 / 0.176557 (-0.102903) | 0.120499 / 0.737135 (-0.616637) | 0.074827 / 0.296338 (-0.221511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293756 / 0.215209 (0.078547) | 2.897755 / 2.077655 (0.820100) | 1.558146 / 1.504120 (0.054026) | 1.458020 / 1.541195 (-0.083174) | 1.453489 / 1.468490 (-0.015001) | 0.576666 / 4.584777 (-4.008111) | 2.423441 / 3.745712 (-1.322271) | 2.727760 / 5.269862 (-2.542102) | 1.750287 / 4.565676 (-2.815390) | 0.062094 / 0.424275 (-0.362181) | 0.004940 / 0.007607 (-0.002667) | 0.338815 / 0.226044 (0.112770) | 3.342677 / 2.268929 (1.073748) | 1.928335 / 55.444624 (-53.516290) | 1.629965 / 6.876477 (-5.246511) | 1.651836 / 2.142072 (-0.490236) | 0.644354 / 4.805227 (-4.160874) | 0.117890 / 6.500664 (-6.382774) | 0.041907 / 0.075469 (-0.033562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984399 / 1.841788 (-0.857389) | 11.516572 / 8.074308 (3.442264) | 10.326922 / 10.191392 (0.135530) | 0.130821 / 0.680424 (-0.549603) | 0.014084 / 0.534201 (-0.520117) | 0.287078 / 0.579283 (-0.292205) | 0.263466 / 0.434364 (-0.170898) | 0.326867 / 0.540337 (-0.213470) | 0.425313 / 1.386936 (-0.961623) |\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) | 0.005305 / 0.011353 (-0.006048) | 0.003646 / 0.011008 (-0.007362) | 0.049402 / 0.038508 (0.010894) | 0.031719 / 0.023109 (0.008610) | 0.272579 / 0.275898 (-0.003319) | 0.295241 / 0.323480 (-0.028239) | 0.004309 / 0.007986 (-0.003677) | 0.002781 / 0.004328 (-0.001548) | 0.048134 / 0.004250 (0.043883) | 0.044702 / 0.037052 (0.007650) | 0.288201 / 0.258489 (0.029712) | 0.320351 / 0.293841 (0.026510) | 0.051327 / 0.128546 (-0.077219) | 0.011019 / 0.075646 (-0.064628) | 0.057983 / 0.419271 (-0.361288) | 0.034211 / 0.043533 (-0.009322) | 0.272856 / 0.255139 (0.017717) | 0.290007 / 0.283200 (0.006807) | 0.018656 / 0.141683 (-0.123027) | 1.135017 / 1.452155 (-0.317138) | 1.183904 / 1.492716 (-0.308813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090854 / 0.018006 (0.072847) | 0.299654 / 0.000490 (0.299165) | 0.000224 / 0.000200 (0.000024) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021882 / 0.037411 (-0.015529) | 0.075297 / 0.014526 (0.060771) | 0.086620 / 0.176557 (-0.089937) | 0.127125 / 0.737135 (-0.610011) | 0.088622 / 0.296338 (-0.207717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287104 / 0.215209 (0.071895) | 2.802723 / 2.077655 (0.725068) | 1.570137 / 1.504120 (0.066017) | 1.452234 / 1.541195 (-0.088961) | 1.465457 / 1.468490 (-0.003033) | 0.564965 / 4.584777 (-4.019812) | 2.416724 / 3.745712 (-1.328988) | 2.645057 / 5.269862 (-2.624805) | 1.727599 / 4.565676 (-2.838078) | 0.063338 / 0.424275 (-0.360937) | 0.005018 / 0.007607 (-0.002589) | 0.345280 / 0.226044 (0.119235) | 3.384323 / 2.268929 (1.115395) | 1.957227 / 55.444624 (-53.487397) | 1.667620 / 6.876477 (-5.208856) | 1.795339 / 2.142072 (-0.346733) | 0.642049 / 4.805227 (-4.163178) | 0.114853 / 6.500664 (-6.385811) | 0.040459 / 0.075469 (-0.035010) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.023640 / 1.841788 (-0.818147) | 11.998130 / 8.074308 (3.923822) | 10.858137 / 10.191392 (0.666744) | 0.130235 / 0.680424 (-0.550189) | 0.016201 / 0.534201 (-0.518000) | 0.289743 / 0.579283 (-0.289540) | 0.275100 / 0.434364 (-0.159264) | 0.329299 / 0.540337 (-0.211039) | 0.418632 / 1.386936 (-0.968304) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98495237883c5ed5a36fac125e68cad97598916f \"CML watermark\")\n" ]

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[ "cc @mariosasko @lhoestq ", "Hi! We should be able to avoid this error by retrying to read the data when it happens. I'll open a PR in `huggingface_hub` to address this.", "Thanks for the fix @mariosasko! Just wondering whether \"500 error\" should also be excluded? I got these errors overnight:\r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256/resolve/91e6a0cd0356605b021384ded813cfcf356a221c/train/tra\r\nin-02618-of-04012.parquet (Request ID: Root=1-65b18b81-627f2c2943bbb8ab68d19ee2;129537bd-1934-4257-a4d8-1cb774f8e1f8) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! \r\n```" ]

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[ "Thanks for reporting! I've opened a PR with a fix." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6575). 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) | 0.005095 / 0.011353 (-0.006257) | 0.003531 / 0.011008 (-0.007478) | 0.063634 / 0.038508 (0.025126) | 0.031187 / 0.023109 (0.008078) | 0.246375 / 0.275898 (-0.029523) | 0.261204 / 0.323480 (-0.062276) | 0.002898 / 0.007986 (-0.005088) | 0.003280 / 0.004328 (-0.001049) | 0.050739 / 0.004250 (0.046488) | 0.042905 / 0.037052 (0.005852) | 0.244506 / 0.258489 (-0.013983) | 0.269403 / 0.293841 (-0.024438) | 0.027588 / 0.128546 (-0.100959) | 0.010860 / 0.075646 (-0.064787) | 0.208332 / 0.419271 (-0.210939) | 0.035762 / 0.043533 (-0.007771) | 0.244448 / 0.255139 (-0.010691) | 0.278464 / 0.283200 (-0.004735) | 0.019839 / 0.141683 (-0.121844) | 1.145340 / 1.452155 (-0.306815) | 1.173240 / 1.492716 (-0.319476) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090472 / 0.018006 (0.072466) | 0.300883 / 0.000490 (0.300394) | 0.000202 / 0.000200 (0.000003) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017884 / 0.037411 (-0.019527) | 0.060629 / 0.014526 (0.046103) | 0.073157 / 0.176557 (-0.103400) | 0.120065 / 0.737135 (-0.617070) | 0.074519 / 0.296338 (-0.221820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289586 / 0.215209 (0.074377) | 2.821042 / 2.077655 (0.743387) | 1.515515 / 1.504120 (0.011395) | 1.390569 / 1.541195 (-0.150625) | 1.433238 / 1.468490 (-0.035252) | 0.567357 / 4.584777 (-4.017420) | 2.345483 / 3.745712 (-1.400229) | 2.803964 / 5.269862 (-2.465898) | 1.775343 / 4.565676 (-2.790334) | 0.063186 / 0.424275 (-0.361089) | 0.005013 / 0.007607 (-0.002594) | 0.335607 / 0.226044 (0.109562) | 3.307071 / 2.268929 (1.038143) | 1.875228 / 55.444624 (-53.569396) | 1.618286 / 6.876477 (-5.258191) | 1.615963 / 2.142072 (-0.526109) | 0.642633 / 4.805227 (-4.162594) | 0.117222 / 6.500664 (-6.383443) | 0.042590 / 0.075469 (-0.032879) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960724 / 1.841788 (-0.881064) | 11.652978 / 8.074308 (3.578670) | 10.069318 / 10.191392 (-0.122074) | 0.128161 / 0.680424 (-0.552263) | 0.014095 / 0.534201 (-0.520106) | 0.288386 / 0.579283 (-0.290897) | 0.260373 / 0.434364 (-0.173991) | 0.327443 / 0.540337 (-0.212894) | 0.419020 / 1.386936 (-0.967916) |\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) | 0.005018 / 0.011353 (-0.006335) | 0.003503 / 0.011008 (-0.007505) | 0.049718 / 0.038508 (0.011210) | 0.029311 / 0.023109 (0.006202) | 0.271097 / 0.275898 (-0.004801) | 0.297370 / 0.323480 (-0.026110) | 0.004230 / 0.007986 (-0.003755) | 0.002741 / 0.004328 (-0.001587) | 0.049686 / 0.004250 (0.045435) | 0.044171 / 0.037052 (0.007119) | 0.274851 / 0.258489 (0.016362) | 0.309554 / 0.293841 (0.015714) | 0.029488 / 0.128546 (-0.099058) | 0.010767 / 0.075646 (-0.064880) | 0.057739 / 0.419271 (-0.361532) | 0.053319 / 0.043533 (0.009786) | 0.277739 / 0.255139 (0.022600) | 0.291341 / 0.283200 (0.008142) | 0.019587 / 0.141683 (-0.122096) | 1.113823 / 1.452155 (-0.338332) | 1.169409 / 1.492716 (-0.323307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091889 / 0.018006 (0.073883) | 0.309162 / 0.000490 (0.308672) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022202 / 0.037411 (-0.015209) | 0.076113 / 0.014526 (0.061587) | 0.088416 / 0.176557 (-0.088141) | 0.126822 / 0.737135 (-0.610314) | 0.089540 / 0.296338 (-0.206798) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293697 / 0.215209 (0.078487) | 2.880680 / 2.077655 (0.803026) | 1.580122 / 1.504120 (0.076002) | 1.449492 / 1.541195 (-0.091703) | 1.478900 / 1.468490 (0.010410) | 0.563402 / 4.584777 (-4.021375) | 2.408692 / 3.745712 (-1.337020) | 2.794108 / 5.269862 (-2.475754) | 1.728549 / 4.565676 (-2.837128) | 0.063152 / 0.424275 (-0.361123) | 0.004985 / 0.007607 (-0.002622) | 0.343340 / 0.226044 (0.117295) | 3.426454 / 2.268929 (1.157525) | 1.932918 / 55.444624 (-53.511706) | 1.649533 / 6.876477 (-5.226944) | 1.673416 / 2.142072 (-0.468656) | 0.640000 / 4.805227 (-4.165227) | 0.115501 / 6.500664 (-6.385163) | 0.040756 / 0.075469 (-0.034713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992468 / 1.841788 (-0.849319) | 12.392072 / 8.074308 (4.317764) | 11.025362 / 10.191392 (0.833970) | 0.130788 / 0.680424 (-0.549635) | 0.015647 / 0.534201 (-0.518554) | 0.285914 / 0.579283 (-0.293369) | 0.277208 / 0.434364 (-0.157156) | 0.322917 / 0.540337 (-0.217420) | 0.427308 / 1.386936 (-0.959628) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#999790bcf52f883de1b5233c5632ae73395021cf \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6574). 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) | 0.005447 / 0.011353 (-0.005906) | 0.004030 / 0.011008 (-0.006978) | 0.063770 / 0.038508 (0.025262) | 0.032602 / 0.023109 (0.009493) | 0.247722 / 0.275898 (-0.028176) | 0.286507 / 0.323480 (-0.036973) | 0.003035 / 0.007986 (-0.004951) | 0.003638 / 0.004328 (-0.000690) | 0.048790 / 0.004250 (0.044540) | 0.045358 / 0.037052 (0.008306) | 0.256308 / 0.258489 (-0.002181) | 0.286601 / 0.293841 (-0.007239) | 0.028644 / 0.128546 (-0.099903) | 0.011149 / 0.075646 (-0.064497) | 0.209796 / 0.419271 (-0.209475) | 0.036737 / 0.043533 (-0.006796) | 0.247427 / 0.255139 (-0.007712) | 0.274564 / 0.283200 (-0.008636) | 0.019717 / 0.141683 (-0.121966) | 1.107423 / 1.452155 (-0.344732) | 1.167830 / 1.492716 (-0.324886) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095695 / 0.018006 (0.077688) | 0.305675 / 0.000490 (0.305185) | 0.000211 / 0.000200 (0.000011) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018969 / 0.037411 (-0.018443) | 0.063764 / 0.014526 (0.049239) | 0.075831 / 0.176557 (-0.100726) | 0.125340 / 0.737135 (-0.611795) | 0.077585 / 0.296338 (-0.218753) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280876 / 0.215209 (0.065667) | 2.748107 / 2.077655 (0.670452) | 1.452201 / 1.504120 (-0.051919) | 1.328001 / 1.541195 (-0.213194) | 1.415581 / 1.468490 (-0.052909) | 0.568228 / 4.584777 (-4.016549) | 2.410486 / 3.745712 (-1.335226) | 2.975157 / 5.269862 (-2.294704) | 1.854096 / 4.565676 (-2.711581) | 0.063275 / 0.424275 (-0.361000) | 0.005121 / 0.007607 (-0.002487) | 0.340006 / 0.226044 (0.113961) | 3.362404 / 2.268929 (1.093476) | 1.803913 / 55.444624 (-53.640711) | 1.540557 / 6.876477 (-5.335919) | 1.629240 / 2.142072 (-0.512833) | 0.653595 / 4.805227 (-4.151632) | 0.119558 / 6.500664 (-6.381107) | 0.044365 / 0.075469 (-0.031104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964557 / 1.841788 (-0.877231) | 12.550303 / 8.074308 (4.475995) | 10.261302 / 10.191392 (0.069910) | 0.130834 / 0.680424 (-0.549589) | 0.014458 / 0.534201 (-0.519743) | 0.294833 / 0.579283 (-0.284450) | 0.268141 / 0.434364 (-0.166223) | 0.332492 / 0.540337 (-0.207845) | 0.427835 / 1.386936 (-0.959101) |\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) | 0.005577 / 0.011353 (-0.005776) | 0.003823 / 0.011008 (-0.007185) | 0.050815 / 0.038508 (0.012307) | 0.031197 / 0.023109 (0.008088) | 0.269869 / 0.275898 (-0.006029) | 0.294371 / 0.323480 (-0.029109) | 0.004153 / 0.007986 (-0.003833) | 0.002884 / 0.004328 (-0.001445) | 0.048985 / 0.004250 (0.044735) | 0.047824 / 0.037052 (0.010772) | 0.270062 / 0.258489 (0.011573) | 0.306354 / 0.293841 (0.012514) | 0.030614 / 0.128546 (-0.097932) | 0.011209 / 0.075646 (-0.064438) | 0.058943 / 0.419271 (-0.360329) | 0.060824 / 0.043533 (0.017291) | 0.273580 / 0.255139 (0.018441) | 0.288375 / 0.283200 (0.005175) | 0.022097 / 0.141683 (-0.119585) | 1.159109 / 1.452155 (-0.293046) | 1.201463 / 1.492716 (-0.291253) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093024 / 0.018006 (0.075018) | 0.302838 / 0.000490 (0.302348) | 0.000223 / 0.000200 (0.000023) | 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) | 0.022991 / 0.037411 (-0.014420) | 0.081575 / 0.014526 (0.067050) | 0.090134 / 0.176557 (-0.086423) | 0.129506 / 0.737135 (-0.607629) | 0.091747 / 0.296338 (-0.204592) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294735 / 0.215209 (0.079525) | 2.857557 / 2.077655 (0.779902) | 1.590577 / 1.504120 (0.086457) | 1.479404 / 1.541195 (-0.061790) | 1.515746 / 1.468490 (0.047256) | 0.579934 / 4.584777 (-4.004843) | 2.462790 / 3.745712 (-1.282922) | 2.944498 / 5.269862 (-2.325363) | 1.836767 / 4.565676 (-2.728909) | 0.064899 / 0.424275 (-0.359376) | 0.005232 / 0.007607 (-0.002375) | 0.349708 / 0.226044 (0.123664) | 3.424801 / 2.268929 (1.155873) | 1.945331 / 55.444624 (-53.499294) | 1.688862 / 6.876477 (-5.187615) | 1.712593 / 2.142072 (-0.429480) | 0.665894 / 4.805227 (-4.139333) | 0.121356 / 6.500664 (-6.379308) | 0.046908 / 0.075469 (-0.028561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983507 / 1.841788 (-0.858280) | 13.279790 / 8.074308 (5.205482) | 11.623531 / 10.191392 (1.432139) | 0.144567 / 0.680424 (-0.535857) | 0.016253 / 0.534201 (-0.517948) | 0.291842 / 0.579283 (-0.287441) | 0.278389 / 0.434364 (-0.155975) | 0.328971 / 0.540337 (-0.211366) | 0.443204 / 1.386936 (-0.943732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9fad0c69738434aec91b61d52c0450336f7535ed \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6573). 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) | 0.005421 / 0.011353 (-0.005932) | 0.003915 / 0.011008 (-0.007094) | 0.065109 / 0.038508 (0.026601) | 0.031274 / 0.023109 (0.008165) | 0.248702 / 0.275898 (-0.027196) | 0.275688 / 0.323480 (-0.047792) | 0.003007 / 0.007986 (-0.004978) | 0.002942 / 0.004328 (-0.001387) | 0.050928 / 0.004250 (0.046678) | 0.043751 / 0.037052 (0.006699) | 0.263860 / 0.258489 (0.005371) | 0.291499 / 0.293841 (-0.002342) | 0.028268 / 0.128546 (-0.100278) | 0.011467 / 0.075646 (-0.064180) | 0.210531 / 0.419271 (-0.208740) | 0.036302 / 0.043533 (-0.007231) | 0.251565 / 0.255139 (-0.003574) | 0.272001 / 0.283200 (-0.011199) | 0.020370 / 0.141683 (-0.121313) | 1.175493 / 1.452155 (-0.276662) | 1.229167 / 1.492716 (-0.263550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095713 / 0.018006 (0.077707) | 0.308912 / 0.000490 (0.308422) | 0.000231 / 0.000200 (0.000031) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019080 / 0.037411 (-0.018332) | 0.062043 / 0.014526 (0.047517) | 0.075642 / 0.176557 (-0.100915) | 0.122789 / 0.737135 (-0.614347) | 0.077507 / 0.296338 (-0.218831) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279929 / 0.215209 (0.064720) | 2.773336 / 2.077655 (0.695682) | 1.481740 / 1.504120 (-0.022379) | 1.357207 / 1.541195 (-0.183987) | 1.414314 / 1.468490 (-0.054176) | 0.573776 / 4.584777 (-4.011000) | 2.399273 / 3.745712 (-1.346439) | 2.918885 / 5.269862 (-2.350977) | 1.798867 / 4.565676 (-2.766809) | 0.064352 / 0.424275 (-0.359923) | 0.005164 / 0.007607 (-0.002443) | 0.337141 / 0.226044 (0.111097) | 3.402291 / 2.268929 (1.133362) | 1.854308 / 55.444624 (-53.590317) | 1.555789 / 6.876477 (-5.320687) | 1.625873 / 2.142072 (-0.516199) | 0.658589 / 4.805227 (-4.146638) | 0.122273 / 6.500664 (-6.378391) | 0.043910 / 0.075469 (-0.031560) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933127 / 1.841788 (-0.908660) | 12.436657 / 8.074308 (4.362348) | 10.891750 / 10.191392 (0.700358) | 0.143236 / 0.680424 (-0.537187) | 0.014636 / 0.534201 (-0.519565) | 0.290375 / 0.579283 (-0.288908) | 0.275473 / 0.434364 (-0.158891) | 0.327007 / 0.540337 (-0.213331) | 0.425888 / 1.386936 (-0.961048) |\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) | 0.005820 / 0.011353 (-0.005533) | 0.003684 / 0.011008 (-0.007324) | 0.050234 / 0.038508 (0.011726) | 0.030744 / 0.023109 (0.007634) | 0.279560 / 0.275898 (0.003662) | 0.305829 / 0.323480 (-0.017651) | 0.004053 / 0.007986 (-0.003933) | 0.002743 / 0.004328 (-0.001585) | 0.051087 / 0.004250 (0.046836) | 0.047601 / 0.037052 (0.010549) | 0.290441 / 0.258489 (0.031951) | 0.326719 / 0.293841 (0.032878) | 0.030245 / 0.128546 (-0.098301) | 0.011508 / 0.075646 (-0.064139) | 0.058436 / 0.419271 (-0.360835) | 0.059235 / 0.043533 (0.015702) | 0.278978 / 0.255139 (0.023839) | 0.298146 / 0.283200 (0.014946) | 0.020926 / 0.141683 (-0.120757) | 1.205608 / 1.452155 (-0.246547) | 1.224920 / 1.492716 (-0.267796) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098384 / 0.018006 (0.080378) | 0.307975 / 0.000490 (0.307485) | 0.000233 / 0.000200 (0.000033) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023012 / 0.037411 (-0.014399) | 0.077450 / 0.014526 (0.062924) | 0.089314 / 0.176557 (-0.087242) | 0.128610 / 0.737135 (-0.608526) | 0.091521 / 0.296338 (-0.204818) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.310620 / 0.215209 (0.095411) | 3.030374 / 2.077655 (0.952720) | 1.653468 / 1.504120 (0.149348) | 1.526860 / 1.541195 (-0.014334) | 1.605328 / 1.468490 (0.136838) | 0.585444 / 4.584777 (-3.999333) | 2.471700 / 3.745712 (-1.274012) | 2.791268 / 5.269862 (-2.478594) | 1.815965 / 4.565676 (-2.749712) | 0.064713 / 0.424275 (-0.359562) | 0.005095 / 0.007607 (-0.002512) | 0.364843 / 0.226044 (0.138799) | 3.601633 / 2.268929 (1.332705) | 2.022642 / 55.444624 (-53.421982) | 1.737164 / 6.876477 (-5.139312) | 1.923636 / 2.142072 (-0.218437) | 0.670673 / 4.805227 (-4.134554) | 0.121547 / 6.500664 (-6.379117) | 0.042880 / 0.075469 (-0.032589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987873 / 1.841788 (-0.853914) | 13.279323 / 8.074308 (5.205015) | 11.480806 / 10.191392 (1.289414) | 0.142118 / 0.680424 (-0.538306) | 0.016486 / 0.534201 (-0.517715) | 0.291617 / 0.579283 (-0.287667) | 0.284639 / 0.434364 (-0.149725) | 0.329596 / 0.540337 (-0.210742) | 0.430168 / 1.386936 (-0.956768) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a5b7d9562231c9fbb36e30c1cf0ac54133d1e77 \"CML watermark\")\n" ]

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[ "Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 ", "Yes, I saw it. Maybe @mishig25 can give us some hint...", "fixed https://huggingface.co/docs/datasets/v2.16.0/en/index", "Still missing 2.16.1.", "> Still missing 2.16.1.\r\n\r\nre-running the doc-buld job for the missing ones should fix\r\n\r\n", "Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583", "Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index" ]

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[ "Seems like I just used the old code which did not have `keep_in_memory=True` argument, sorry.\r\n\r\nAlthough i encountered a different problem – at 97% my python process just hung for around 11 minutes with no logs (when running dataset.map without `keep_in_memory=True` over around 3 million of dataset samples)...", "Can you open a new issue and provide a bit more details ? What kind of map operations did you run ?", "Hey. I will try to find some free time to describe it.\r\n\r\n(can't do it now, cause i need to reproduce it myself to be sure about everything, which requires spinning a new Azuree VM, copying a huge dataset to drive from network disk for a long time etc...)", "@lhoestq loading dataset like this does not spawn 50 python processes:\r\n\r\n```\r\ndatasets.load_dataset(\"/preprocessed_2256k/train\", num_proc=50)\r\n```\r\n\r\nI have 64 vCPU so i hoped it could speed up the dataset loading...\r\n\r\nMy dataset onlly has images and metadata.csv with text column alongside image file path column", "now noticed\r\n```\r\n'Setting num_proc from 50 back to 1 for the train split to disable multiprocessing as it only contains one shard\r\n```\r\n\r\nAny way to work around this?", "@lhoestq thanks, [this helped](https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/arrow_dataset.py#L1053)\r\n\r\n" ]

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[ "I think you are reporting an issue with the `transformers` library. Note this is the repository of the `datasets` library. I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\n\r\nEDIT: I have not the rights to transfer the issue\r\n~~I am transferring your issue to their repository.~~", "Thanks, I hope someone from transformers library addresses this issue.\r\n\r\nOn Mon, Jan 8, 2024 at 15:29 Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> I think you are reporting an issue with the transformers library. Note\r\n> this is the repository of the datasets library. I am transferring your\r\n> issue to their repository.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6567#issuecomment-1880688586>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNOYMD6WJMXFKPMH6DLYNO7PJAVCNFSM6AAAAABBQ63HWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGY4DQNJYGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n", "@andysingal, I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\nI don't have the rights to transfer this issue to their repo." ]

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[ "I also see the same error and get passed it by casting that line to float. \r\n\r\nso `for x in obj.detach().cpu().numpy()` becomes `for x in obj.detach().to(torch.float).cpu().numpy()`\r\n\r\nI got the idea from [this ](https://github.com/kohya-ss/sd-webui-additional-networks/pull/128/files) PR where someone was facing a similar issue (in a different repository). I guess numpy doesn't support bfloat16.\r\n\r\n" ]

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[ "My current workaround this issue is to return `None` in the second element and then filter out samples which have `None` in them.\r\n\r\n```python\r\ndef merge_samples(batch):\r\n if len(batch['a']) == 1:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [None]\r\n else:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [batch['a'][1]]\r\n return batch\r\n \r\ndef filter_fn(x):\r\n return x['d'] is not None\r\n\r\n# other code...\r\nmapped = mapped.filter(filter_fn)\r\n```" ]

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[ "Thanks for reporting! I've opened a PR with a fix", "@mariosasko thank you very much :)" ]

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