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[ "I get similar when dealing with a large jsonl file (6k lines), \r\n\r\n> TypeError: Couldn't cast array of type timestamp[us] to null\r\n\r\nYet when I split it into 1k lines, files, load_dataset works fine!\r\n\r\nhttps://github.com/huggingface/course/issues/692\r\n\r\n" ]

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[ "Thanks for reporting, @arame.\r\n\r\nI guess you have an old version of `transformers` (that submodule is present in `transformers` since version 3.0.1, since nearly 4 years ago). If you update it, the error should disappear:\r\n```shell\r\npip install -U transformers\r\n```\r\n\r\nOn the other hand, I am wondering: does it make sense to use `transformers` in this case, even if we don't need it to load the `go_emotions` dataset (already converted to Parquet files)?\r\n- Maybe @mariosasko can give some insight, as he included these code lines:\r\n - #6454\r\n\r\nhttps://github.com/huggingface/datasets/blob/9751fb14594d354e952f0ebdfaf31cb203b011e7/src/datasets/utils/_dill.py#L60-L63\r\n", "The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n\r\nHowever, the logic does not account for `transformers<3`, so we should add a version check to fix that.", "> The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n> \r\n> However, the logic does not account for `transformers<3`, so we should add a version check to fix that.\r\n\r\nThank you for that Mario. Would this fix solve the problem and do you have any idea when it will be done? \r\nI tried the pip install suggested by Albert and it made no difference.", "I tried running the code today and the problem appears to be fixed." ]

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[ "Hi ! This issue has been fixed by https://github.com/huggingface/datasets/pull/6283\r\n\r\nCan you try again with the new release 2.17.0 ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\n", "Amazing! It's indeed fixed now. Thanks!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6653). 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) | 0.003424 / 0.011008 (-0.007584) | 0.064195 / 0.038508 (0.025687) | 0.031742 / 0.023109 (0.008633) | 0.244774 / 0.275898 (-0.031124) | 0.268529 / 0.323480 (-0.054951) | 0.003970 / 0.007986 (-0.004016) | 0.002657 / 0.004328 (-0.001672) | 0.048847 / 0.004250 (0.044597) | 0.042196 / 0.037052 (0.005144) | 0.266044 / 0.258489 (0.007555) | 0.282400 / 0.293841 (-0.011441) | 0.027617 / 0.128546 (-0.100929) | 0.010400 / 0.075646 (-0.065246) | 0.205910 / 0.419271 (-0.213362) | 0.035820 / 0.043533 (-0.007713) | 0.247750 / 0.255139 (-0.007389) | 0.267318 / 0.283200 (-0.015882) | 0.017980 / 0.141683 (-0.123703) | 1.107263 / 1.452155 (-0.344892) | 1.173208 / 1.492716 (-0.319509) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095830 / 0.018006 (0.077824) | 0.293891 / 0.000490 (0.293401) | 0.000257 / 0.000200 (0.000057) | 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) | 0.018138 / 0.037411 (-0.019273) | 0.061631 / 0.014526 (0.047105) | 0.073038 / 0.176557 (-0.103519) | 0.118317 / 0.737135 (-0.618818) | 0.074190 / 0.296338 (-0.222148) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287026 / 0.215209 (0.071817) | 2.786137 / 2.077655 (0.708482) | 1.472575 / 1.504120 (-0.031544) | 1.346919 / 1.541195 (-0.194276) | 1.388535 / 1.468490 (-0.079955) | 0.565731 / 4.584777 (-4.019046) | 2.382573 / 3.745712 (-1.363139) | 2.736926 / 5.269862 (-2.532935) | 1.716517 / 4.565676 (-2.849159) | 0.062168 / 0.424275 (-0.362108) | 0.004924 / 0.007607 (-0.002683) | 0.341897 / 0.226044 (0.115853) | 3.355715 / 2.268929 (1.086787) | 1.837014 / 55.444624 (-53.607611) | 1.532063 / 6.876477 (-5.344414) | 1.548193 / 2.142072 (-0.593880) | 0.634995 / 4.805227 (-4.170232) | 0.115622 / 6.500664 (-6.385042) | 0.042252 / 0.075469 (-0.033217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970713 / 1.841788 (-0.871075) | 11.727576 / 8.074308 (3.653268) | 9.806524 / 10.191392 (-0.384868) | 0.127622 / 0.680424 (-0.552802) | 0.014140 / 0.534201 (-0.520061) | 0.286832 / 0.579283 (-0.292451) | 0.266556 / 0.434364 (-0.167808) | 0.325940 / 0.540337 (-0.214398) | 0.421839 / 1.386936 (-0.965097) |\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) | 0.005495 / 0.011353 (-0.005858) | 0.003676 / 0.011008 (-0.007332) | 0.054361 / 0.038508 (0.015853) | 0.030743 / 0.023109 (0.007633) | 0.277200 / 0.275898 (0.001302) | 0.313459 / 0.323480 (-0.010021) | 0.004316 / 0.007986 (-0.003670) | 0.002750 / 0.004328 (-0.001578) | 0.049491 / 0.004250 (0.045241) | 0.044268 / 0.037052 (0.007215) | 0.292529 / 0.258489 (0.034039) | 0.326524 / 0.293841 (0.032683) | 0.048040 / 0.128546 (-0.080507) | 0.010390 / 0.075646 (-0.065256) | 0.058459 / 0.419271 (-0.360813) | 0.033765 / 0.043533 (-0.009768) | 0.276003 / 0.255139 (0.020864) | 0.297299 / 0.283200 (0.014099) | 0.018532 / 0.141683 (-0.123151) | 1.157639 / 1.452155 (-0.294515) | 1.220492 / 1.492716 (-0.272225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093903 / 0.018006 (0.075897) | 0.303005 / 0.000490 (0.302515) | 0.000224 / 0.000200 (0.000024) | 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) | 0.021580 / 0.037411 (-0.015831) | 0.076176 / 0.014526 (0.061650) | 0.086998 / 0.176557 (-0.089558) | 0.124148 / 0.737135 (-0.612987) | 0.088613 / 0.296338 (-0.207725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300623 / 0.215209 (0.085414) | 2.911876 / 2.077655 (0.834221) | 1.588398 / 1.504120 (0.084278) | 1.471251 / 1.541195 (-0.069944) | 1.505528 / 1.468490 (0.037038) | 0.570635 / 4.584777 (-4.014142) | 2.485769 / 3.745712 (-1.259943) | 2.785355 / 5.269862 (-2.484507) | 1.752944 / 4.565676 (-2.812732) | 0.063146 / 0.424275 (-0.361129) | 0.004980 / 0.007607 (-0.002627) | 0.354577 / 0.226044 (0.128532) | 3.477181 / 2.268929 (1.208253) | 1.951906 / 55.444624 (-53.492718) | 1.677169 / 6.876477 (-5.199307) | 1.686338 / 2.142072 (-0.455735) | 0.637156 / 4.805227 (-4.168071) | 0.117732 / 6.500664 (-6.382932) | 0.041091 / 0.075469 (-0.034378) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010071 / 1.841788 (-0.831717) | 12.172242 / 8.074308 (4.097934) | 10.422811 / 10.191392 (0.231419) | 0.137185 / 0.680424 (-0.543239) | 0.014643 / 0.534201 (-0.519558) | 0.287248 / 0.579283 (-0.292035) | 0.272779 / 0.434364 (-0.161585) | 0.331761 / 0.540337 (-0.208576) | 0.417266 / 1.386936 (-0.969670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9751fb14594d354e952f0ebdfaf31cb203b011e7 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6652). 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) | 0.005207 / 0.011353 (-0.006145) | 0.003785 / 0.011008 (-0.007223) | 0.064221 / 0.038508 (0.025713) | 0.028981 / 0.023109 (0.005872) | 0.246215 / 0.275898 (-0.029683) | 0.268058 / 0.323480 (-0.055422) | 0.004028 / 0.007986 (-0.003958) | 0.002804 / 0.004328 (-0.001525) | 0.048878 / 0.004250 (0.044627) | 0.042641 / 0.037052 (0.005589) | 0.255590 / 0.258489 (-0.002899) | 0.287377 / 0.293841 (-0.006464) | 0.027772 / 0.128546 (-0.100774) | 0.010637 / 0.075646 (-0.065009) | 0.211526 / 0.419271 (-0.207746) | 0.035789 / 0.043533 (-0.007744) | 0.243042 / 0.255139 (-0.012097) | 0.268369 / 0.283200 (-0.014830) | 0.017907 / 0.141683 (-0.123776) | 1.138829 / 1.452155 (-0.313326) | 1.175732 / 1.492716 (-0.316984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094205 / 0.018006 (0.076199) | 0.304317 / 0.000490 (0.303827) | 0.000206 / 0.000200 (0.000006) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018424 / 0.037411 (-0.018987) | 0.061719 / 0.014526 (0.047193) | 0.073471 / 0.176557 (-0.103085) | 0.121577 / 0.737135 (-0.615558) | 0.075134 / 0.296338 (-0.221204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275178 / 0.215209 (0.059969) | 2.689222 / 2.077655 (0.611568) | 1.396680 / 1.504120 (-0.107439) | 1.278782 / 1.541195 (-0.262413) | 1.326632 / 1.468490 (-0.141858) | 0.566915 / 4.584777 (-4.017862) | 2.365928 / 3.745712 (-1.379784) | 2.785435 / 5.269862 (-2.484427) | 1.745131 / 4.565676 (-2.820546) | 0.062798 / 0.424275 (-0.361477) | 0.005107 / 0.007607 (-0.002500) | 0.330441 / 0.226044 (0.104396) | 3.266265 / 2.268929 (0.997337) | 1.792588 / 55.444624 (-53.652036) | 1.516021 / 6.876477 (-5.360455) | 1.562750 / 2.142072 (-0.579323) | 0.652964 / 4.805227 (-4.152264) | 0.117813 / 6.500664 (-6.382852) | 0.042372 / 0.075469 (-0.033097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010107 / 1.841788 (-0.831680) | 11.819910 / 8.074308 (3.745602) | 9.701673 / 10.191392 (-0.489719) | 0.178165 / 0.680424 (-0.502259) | 0.014438 / 0.534201 (-0.519763) | 0.297733 / 0.579283 (-0.281550) | 0.264914 / 0.434364 (-0.169450) | 0.324531 / 0.540337 (-0.215806) | 0.430207 / 1.386936 (-0.956729) |\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) | 0.005848 / 0.011353 (-0.005505) | 0.003870 / 0.011008 (-0.007138) | 0.050379 / 0.038508 (0.011871) | 0.031238 / 0.023109 (0.008129) | 0.276839 / 0.275898 (0.000941) | 0.299488 / 0.323480 (-0.023992) | 0.005143 / 0.007986 (-0.002842) | 0.002725 / 0.004328 (-0.001604) | 0.048184 / 0.004250 (0.043934) | 0.046232 / 0.037052 (0.009180) | 0.287058 / 0.258489 (0.028569) | 0.322659 / 0.293841 (0.028818) | 0.047598 / 0.128546 (-0.080949) | 0.011116 / 0.075646 (-0.064530) | 0.058252 / 0.419271 (-0.361019) | 0.033404 / 0.043533 (-0.010128) | 0.277650 / 0.255139 (0.022511) | 0.295610 / 0.283200 (0.012410) | 0.018124 / 0.141683 (-0.123559) | 1.135052 / 1.452155 (-0.317103) | 1.194261 / 1.492716 (-0.298456) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095595 / 0.018006 (0.077588) | 0.306408 / 0.000490 (0.305918) | 0.000216 / 0.000200 (0.000016) | 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) | 0.022027 / 0.037411 (-0.015385) | 0.076224 / 0.014526 (0.061698) | 0.087441 / 0.176557 (-0.089116) | 0.126636 / 0.737135 (-0.610499) | 0.089442 / 0.296338 (-0.206896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291315 / 0.215209 (0.076106) | 2.835304 / 2.077655 (0.757650) | 1.581102 / 1.504120 (0.076982) | 1.463046 / 1.541195 (-0.078149) | 1.481982 / 1.468490 (0.013492) | 0.559989 / 4.584777 (-4.024788) | 2.385262 / 3.745712 (-1.360450) | 2.773478 / 5.269862 (-2.496383) | 1.744427 / 4.565676 (-2.821249) | 0.062687 / 0.424275 (-0.361589) | 0.005149 / 0.007607 (-0.002458) | 0.374600 / 0.226044 (0.148555) | 3.376507 / 2.268929 (1.107579) | 1.935290 / 55.444624 (-53.509334) | 1.663227 / 6.876477 (-5.213250) | 1.678987 / 2.142072 (-0.463085) | 0.638970 / 4.805227 (-4.166258) | 0.120000 / 6.500664 (-6.380664) | 0.040862 / 0.075469 (-0.034608) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008795 / 1.841788 (-0.832993) | 12.275084 / 8.074308 (4.200776) | 10.340088 / 10.191392 (0.148696) | 0.136454 / 0.680424 (-0.543970) | 0.014404 / 0.534201 (-0.519797) | 0.289478 / 0.579283 (-0.289805) | 0.279243 / 0.434364 (-0.155121) | 0.330992 / 0.540337 (-0.209346) | 0.422043 / 1.386936 (-0.964893) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70633576ecf1f3f5e5cdfd8c9189246b3604f4b6 \"CML watermark\")\n" ]

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[ "Hi! Does running the following code also return the same error on your machine? \r\n\r\n```python\r\nimport copy\r\nimport pyarrow as pa\r\nfrom datasets.table import InMemoryTable\r\n\r\ncopy.deepcopy(InMemoryTable(pa.table({\"a\": [1, 2, 3], \"b\": [\"foo\", \"bar\", \"foobar\"]})))\r\n```", "No, it doesn't, it runs fine. But what's really strange is that the error just went away after I reran the data prep script for conversion from csv to a datasets object. I realize that's not very helpful since the problem isn't reproducible. ", "Feel free to close the issue then :)." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6649). 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) | 0.005197 / 0.011353 (-0.006156) | 0.003469 / 0.011008 (-0.007539) | 0.062306 / 0.038508 (0.023798) | 0.028417 / 0.023109 (0.005308) | 0.241147 / 0.275898 (-0.034751) | 0.270910 / 0.323480 (-0.052569) | 0.003053 / 0.007986 (-0.004933) | 0.003343 / 0.004328 (-0.000985) | 0.048044 / 0.004250 (0.043794) | 0.043738 / 0.037052 (0.006686) | 0.259274 / 0.258489 (0.000785) | 0.282522 / 0.293841 (-0.011319) | 0.027807 / 0.128546 (-0.100739) | 0.010413 / 0.075646 (-0.065234) | 0.206322 / 0.419271 (-0.212950) | 0.035770 / 0.043533 (-0.007763) | 0.243465 / 0.255139 (-0.011674) | 0.261596 / 0.283200 (-0.021604) | 0.018613 / 0.141683 (-0.123070) | 1.115509 / 1.452155 (-0.336645) | 1.189403 / 1.492716 (-0.303314) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.086075 / 0.018006 (0.068069) | 0.296140 / 0.000490 (0.295650) | 0.000198 / 0.000200 (-0.000002) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018238 / 0.037411 (-0.019173) | 0.061783 / 0.014526 (0.047257) | 0.072014 / 0.176557 (-0.104543) | 0.118746 / 0.737135 (-0.618389) | 0.073279 / 0.296338 (-0.223060) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278281 / 0.215209 (0.063072) | 2.772209 / 2.077655 (0.694555) | 1.404503 / 1.504120 (-0.099617) | 1.274753 / 1.541195 (-0.266441) | 1.304394 / 1.468490 (-0.164096) | 0.556903 / 4.584777 (-4.027874) | 2.335428 / 3.745712 (-1.410284) | 2.712255 / 5.269862 (-2.557606) | 1.722252 / 4.565676 (-2.843425) | 0.061268 / 0.424275 (-0.363007) | 0.005029 / 0.007607 (-0.002578) | 0.326112 / 0.226044 (0.100067) | 3.207917 / 2.268929 (0.938988) | 1.743513 / 55.444624 (-53.701111) | 1.476418 / 6.876477 (-5.400059) | 1.489776 / 2.142072 (-0.652297) | 0.628181 / 4.805227 (-4.177046) | 0.115959 / 6.500664 (-6.384706) | 0.041854 / 0.075469 (-0.033615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969039 / 1.841788 (-0.872749) | 11.178646 / 8.074308 (3.104338) | 9.639716 / 10.191392 (-0.551676) | 0.139750 / 0.680424 (-0.540674) | 0.014230 / 0.534201 (-0.519971) | 0.285318 / 0.579283 (-0.293965) | 0.260788 / 0.434364 (-0.173576) | 0.324183 / 0.540337 (-0.216154) | 0.416326 / 1.386936 (-0.970610) |\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) | 0.005149 / 0.011353 (-0.006204) | 0.003469 / 0.011008 (-0.007539) | 0.049761 / 0.038508 (0.011253) | 0.030723 / 0.023109 (0.007614) | 0.271562 / 0.275898 (-0.004336) | 0.297843 / 0.323480 (-0.025637) | 0.004296 / 0.007986 (-0.003690) | 0.002704 / 0.004328 (-0.001624) | 0.048890 / 0.004250 (0.044640) | 0.044776 / 0.037052 (0.007723) | 0.285490 / 0.258489 (0.027001) | 0.312888 / 0.293841 (0.019047) | 0.046239 / 0.128546 (-0.082307) | 0.010238 / 0.075646 (-0.065408) | 0.057968 / 0.419271 (-0.361304) | 0.033295 / 0.043533 (-0.010238) | 0.274320 / 0.255139 (0.019181) | 0.296199 / 0.283200 (0.012999) | 0.017856 / 0.141683 (-0.123827) | 1.147532 / 1.452155 (-0.304622) | 1.211647 / 1.492716 (-0.281070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089655 / 0.018006 (0.071649) | 0.297275 / 0.000490 (0.296785) | 0.000207 / 0.000200 (0.000007) | 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) | 0.021739 / 0.037411 (-0.015672) | 0.075041 / 0.014526 (0.060515) | 0.085754 / 0.176557 (-0.090802) | 0.124512 / 0.737135 (-0.612623) | 0.086926 / 0.296338 (-0.209412) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290306 / 0.215209 (0.075097) | 2.847404 / 2.077655 (0.769749) | 1.606175 / 1.504120 (0.102055) | 1.483220 / 1.541195 (-0.057974) | 1.514551 / 1.468490 (0.046061) | 0.559332 / 4.584777 (-4.025445) | 2.403089 / 3.745712 (-1.342624) | 2.715179 / 5.269862 (-2.554683) | 1.688340 / 4.565676 (-2.877337) | 0.062057 / 0.424275 (-0.362218) | 0.004955 / 0.007607 (-0.002652) | 0.338909 / 0.226044 (0.112865) | 3.356882 / 2.268929 (1.087954) | 1.942259 / 55.444624 (-53.502366) | 1.675195 / 6.876477 (-5.201282) | 1.688158 / 2.142072 (-0.453914) | 0.637270 / 4.805227 (-4.167957) | 0.114314 / 6.500664 (-6.386350) | 0.040677 / 0.075469 (-0.034792) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.022126 / 1.841788 (-0.819661) | 11.783359 / 8.074308 (3.709051) | 10.247652 / 10.191392 (0.056260) | 0.138188 / 0.680424 (-0.542236) | 0.014850 / 0.534201 (-0.519351) | 0.287414 / 0.579283 (-0.291869) | 0.274393 / 0.434364 (-0.159971) | 0.327255 / 0.540337 (-0.213082) | 0.416355 / 1.386936 (-0.970581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#727a952367966a98b759d54f333b1e2c28cfd4d4 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6648). 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) | 0.004951 / 0.011353 (-0.006402) | 0.003187 / 0.011008 (-0.007821) | 0.062959 / 0.038508 (0.024451) | 0.028037 / 0.023109 (0.004928) | 0.241374 / 0.275898 (-0.034524) | 0.262792 / 0.323480 (-0.060688) | 0.004132 / 0.007986 (-0.003854) | 0.002766 / 0.004328 (-0.001563) | 0.051416 / 0.004250 (0.047165) | 0.040957 / 0.037052 (0.003904) | 0.260760 / 0.258489 (0.002271) | 0.282018 / 0.293841 (-0.011823) | 0.027689 / 0.128546 (-0.100857) | 0.010433 / 0.075646 (-0.065214) | 0.211598 / 0.419271 (-0.207674) | 0.035447 / 0.043533 (-0.008086) | 0.244333 / 0.255139 (-0.010806) | 0.263192 / 0.283200 (-0.020008) | 0.016816 / 0.141683 (-0.124867) | 1.103188 / 1.452155 (-0.348967) | 1.179093 / 1.492716 (-0.313623) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092412 / 0.018006 (0.074406) | 0.301226 / 0.000490 (0.300736) | 0.000208 / 0.000200 (0.000008) | 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) | 0.018146 / 0.037411 (-0.019265) | 0.061447 / 0.014526 (0.046921) | 0.072162 / 0.176557 (-0.104394) | 0.118965 / 0.737135 (-0.618170) | 0.073756 / 0.296338 (-0.222583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285361 / 0.215209 (0.070152) | 2.776928 / 2.077655 (0.699273) | 1.506859 / 1.504120 (0.002739) | 1.379119 / 1.541195 (-0.162075) | 1.401798 / 1.468490 (-0.066692) | 0.572512 / 4.584777 (-4.012265) | 2.403793 / 3.745712 (-1.341919) | 2.740496 / 5.269862 (-2.529366) | 1.714611 / 4.565676 (-2.851065) | 0.063496 / 0.424275 (-0.360780) | 0.005009 / 0.007607 (-0.002598) | 0.342438 / 0.226044 (0.116393) | 3.368129 / 2.268929 (1.099200) | 1.831200 / 55.444624 (-53.613424) | 1.553611 / 6.876477 (-5.322866) | 1.578116 / 2.142072 (-0.563956) | 0.653034 / 4.805227 (-4.152193) | 0.117724 / 6.500664 (-6.382940) | 0.041188 / 0.075469 (-0.034282) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972520 / 1.841788 (-0.869268) | 11.186297 / 8.074308 (3.111989) | 9.485829 / 10.191392 (-0.705563) | 0.139715 / 0.680424 (-0.540708) | 0.013705 / 0.534201 (-0.520496) | 0.287384 / 0.579283 (-0.291899) | 0.266784 / 0.434364 (-0.167580) | 0.320789 / 0.540337 (-0.219548) | 0.417484 / 1.386936 (-0.969452) |\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) | 0.005570 / 0.011353 (-0.005783) | 0.003416 / 0.011008 (-0.007592) | 0.051160 / 0.038508 (0.012652) | 0.031082 / 0.023109 (0.007973) | 0.279336 / 0.275898 (0.003438) | 0.300529 / 0.323480 (-0.022951) | 0.004320 / 0.007986 (-0.003666) | 0.002781 / 0.004328 (-0.001548) | 0.049642 / 0.004250 (0.045391) | 0.044379 / 0.037052 (0.007327) | 0.293797 / 0.258489 (0.035308) | 0.317844 / 0.293841 (0.024003) | 0.049697 / 0.128546 (-0.078849) | 0.010624 / 0.075646 (-0.065023) | 0.058834 / 0.419271 (-0.360437) | 0.033869 / 0.043533 (-0.009664) | 0.280547 / 0.255139 (0.025408) | 0.300685 / 0.283200 (0.017486) | 0.017010 / 0.141683 (-0.124673) | 1.172277 / 1.452155 (-0.279878) | 1.205359 / 1.492716 (-0.287358) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092914 / 0.018006 (0.074907) | 0.303561 / 0.000490 (0.303071) | 0.000219 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022379 / 0.037411 (-0.015032) | 0.075460 / 0.014526 (0.060934) | 0.085795 / 0.176557 (-0.090762) | 0.124776 / 0.737135 (-0.612360) | 0.088260 / 0.296338 (-0.208079) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302873 / 0.215209 (0.087664) | 2.936173 / 2.077655 (0.858519) | 1.589251 / 1.504120 (0.085131) | 1.477552 / 1.541195 (-0.063643) | 1.479322 / 1.468490 (0.010832) | 0.570481 / 4.584777 (-4.014296) | 2.434137 / 3.745712 (-1.311575) | 2.774012 / 5.269862 (-2.495849) | 1.718103 / 4.565676 (-2.847574) | 0.061951 / 0.424275 (-0.362324) | 0.004992 / 0.007607 (-0.002615) | 0.352250 / 0.226044 (0.126205) | 3.457417 / 2.268929 (1.188488) | 1.934587 / 55.444624 (-53.510037) | 1.646904 / 6.876477 (-5.229573) | 1.669429 / 2.142072 (-0.472643) | 0.649665 / 4.805227 (-4.155562) | 0.116630 / 6.500664 (-6.384034) | 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) | 1.011488 / 1.841788 (-0.830300) | 11.866394 / 8.074308 (3.792086) | 10.144588 / 10.191392 (-0.046804) | 0.129931 / 0.680424 (-0.550493) | 0.014885 / 0.534201 (-0.519316) | 0.287463 / 0.579283 (-0.291821) | 0.280754 / 0.434364 (-0.153610) | 0.330139 / 0.540337 (-0.210199) | 0.414653 / 1.386936 (-0.972283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#585275b8deaebd1bdcbd3725fa63172395791c73 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6647). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thanks for adding the explicit loading command.\r\n> \r\n> However, I would move it just below, where we present the JSON-Lines example.\r\n> \r\n> * Maybe adding that this format is called JSON-Lines\r\n> * Add the example after the JSON-Lines data example\r\n> \r\n> https://github.com/huggingface/datasets/blob/14d9afbb7ae1b787c450261ca0ff374551993031/docs/source/loading.mdx#L135-L138\r\n\r\nThank you @albertvillanova for the feedback! I moved the jsonl file loading example to a more appropriate location. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6646). 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) | 0.005598 / 0.011353 (-0.005755) | 0.003640 / 0.011008 (-0.007369) | 0.064557 / 0.038508 (0.026049) | 0.029645 / 0.023109 (0.006536) | 0.243695 / 0.275898 (-0.032203) | 0.261252 / 0.323480 (-0.062228) | 0.004067 / 0.007986 (-0.003919) | 0.002883 / 0.004328 (-0.001446) | 0.049192 / 0.004250 (0.044942) | 0.045299 / 0.037052 (0.008246) | 0.273207 / 0.258489 (0.014718) | 0.288668 / 0.293841 (-0.005173) | 0.028114 / 0.128546 (-0.100432) | 0.010597 / 0.075646 (-0.065049) | 0.215345 / 0.419271 (-0.203927) | 0.036119 / 0.043533 (-0.007414) | 0.243718 / 0.255139 (-0.011421) | 0.266657 / 0.283200 (-0.016543) | 0.018176 / 0.141683 (-0.123507) | 1.127926 / 1.452155 (-0.324229) | 1.168066 / 1.492716 (-0.324650) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096001 / 0.018006 (0.077994) | 0.304317 / 0.000490 (0.303828) | 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) | 0.018241 / 0.037411 (-0.019170) | 0.061505 / 0.014526 (0.046979) | 0.072456 / 0.176557 (-0.104101) | 0.118315 / 0.737135 (-0.618821) | 0.075154 / 0.296338 (-0.221184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278748 / 0.215209 (0.063538) | 2.729923 / 2.077655 (0.652268) | 1.416835 / 1.504120 (-0.087285) | 1.294016 / 1.541195 (-0.247179) | 1.323249 / 1.468490 (-0.145241) | 0.575389 / 4.584777 (-4.009388) | 2.404923 / 3.745712 (-1.340789) | 2.769233 / 5.269862 (-2.500629) | 1.742340 / 4.565676 (-2.823336) | 0.062664 / 0.424275 (-0.361611) | 0.004951 / 0.007607 (-0.002656) | 0.335024 / 0.226044 (0.108979) | 3.291446 / 2.268929 (1.022518) | 1.797095 / 55.444624 (-53.647530) | 1.532963 / 6.876477 (-5.343513) | 1.529315 / 2.142072 (-0.612758) | 0.654922 / 4.805227 (-4.150305) | 0.118772 / 6.500664 (-6.381892) | 0.042034 / 0.075469 (-0.033435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983646 / 1.841788 (-0.858141) | 11.518625 / 8.074308 (3.444317) | 9.538781 / 10.191392 (-0.652611) | 0.140300 / 0.680424 (-0.540124) | 0.013966 / 0.534201 (-0.520235) | 0.287071 / 0.579283 (-0.292212) | 0.270201 / 0.434364 (-0.164163) | 0.323294 / 0.540337 (-0.217044) | 0.418130 / 1.386936 (-0.968806) |\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) | 0.005508 / 0.011353 (-0.005844) | 0.003714 / 0.011008 (-0.007294) | 0.050031 / 0.038508 (0.011523) | 0.031866 / 0.023109 (0.008756) | 0.272248 / 0.275898 (-0.003650) | 0.295105 / 0.323480 (-0.028375) | 0.005179 / 0.007986 (-0.002807) | 0.002820 / 0.004328 (-0.001508) | 0.048896 / 0.004250 (0.044646) | 0.045975 / 0.037052 (0.008922) | 0.287662 / 0.258489 (0.029173) | 0.321139 / 0.293841 (0.027298) | 0.049242 / 0.128546 (-0.079304) | 0.010732 / 0.075646 (-0.064914) | 0.057943 / 0.419271 (-0.361328) | 0.033527 / 0.043533 (-0.010006) | 0.271746 / 0.255139 (0.016607) | 0.291404 / 0.283200 (0.008204) | 0.019351 / 0.141683 (-0.122332) | 1.157221 / 1.452155 (-0.294934) | 1.215757 / 1.492716 (-0.276959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096950 / 0.018006 (0.078944) | 0.312002 / 0.000490 (0.311512) | 0.000223 / 0.000200 (0.000023) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022288 / 0.037411 (-0.015123) | 0.075282 / 0.014526 (0.060756) | 0.087445 / 0.176557 (-0.089112) | 0.125617 / 0.737135 (-0.611519) | 0.088878 / 0.296338 (-0.207460) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291961 / 0.215209 (0.076752) | 2.881445 / 2.077655 (0.803790) | 1.586128 / 1.504120 (0.082008) | 1.458636 / 1.541195 (-0.082558) | 1.487001 / 1.468490 (0.018511) | 0.575466 / 4.584777 (-4.009311) | 2.454941 / 3.745712 (-1.290771) | 2.878077 / 5.269862 (-2.391785) | 1.787215 / 4.565676 (-2.778462) | 0.064010 / 0.424275 (-0.360265) | 0.005092 / 0.007607 (-0.002516) | 0.360500 / 0.226044 (0.134455) | 3.465574 / 2.268929 (1.196646) | 1.957516 / 55.444624 (-53.487108) | 1.666282 / 6.876477 (-5.210195) | 1.690070 / 2.142072 (-0.452002) | 0.661323 / 4.805227 (-4.143905) | 0.117824 / 6.500664 (-6.382840) | 0.042286 / 0.075469 (-0.033183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026517 / 1.841788 (-0.815270) | 12.083347 / 8.074308 (4.009039) | 10.269319 / 10.191392 (0.077927) | 0.139253 / 0.680424 (-0.541171) | 0.016258 / 0.534201 (-0.517943) | 0.290583 / 0.579283 (-0.288700) | 0.284338 / 0.434364 (-0.150026) | 0.335865 / 0.540337 (-0.204473) | 0.416600 / 1.386936 (-0.970336) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba3cfad91e9366cda0ba203700fc745d8bcd1f17 \"CML watermark\")\n", "Thanks, I was needing this example today <3 " ]

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[ "I'd be very grateful. This upper bound banished me straight into dependency hell today. :(" ]

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[ "The pinned fsspec version range dependency conflict has been affecting several of our users in https://github.com/iterative/dvc. I've opened an initial PR that I think should resolve the glob behavior changes with using datasets + the latest fsspec release.\r\n\r\nPlease let us know if there's any other fsspec related behavior in datasets that needs to be updated to get 2024.2 supported, we'd like to get this conflict resolved as quickly as possible and we're willing to contribute any additional work that's required here.\r\n\r\ncc @dberenbaum" ]

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[ "Hi ! make sure your query embeddings are numpy arrays, not torch tensors ;)", "Hi Quentin, not sure how that solves the problem number 1. I am trying to pass on a dataset with a faiss gpu for training to the standard trainer but getting this serialisation error. What is a workaround this? I do not want to remove the faiss index, as I would want to use it to create batches of retrieved samples from the dataset. \r\nThanks in advance for your help!", "Issue number one seems to be an issue with FAISS indexes not being compatible with copy.deepcopy.\r\n\r\nMaybe you try to not remove the columns, e.g. by passing `remove_unused_columns=False`" ]

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[ "I see now, that I have to use `load_from_disk`, in order to load dataset properly, not `load_dataset`. Why is this behavior split? Why do we need both, `load_dataset` and `load_from_disk`?\r\n\r\nUnless answered, I believe this might be helpful for other hf datasets newbies.\r\n\r\nAnyway, made a `load_dataset` compatible dataset in a following way. I created a directory, and just copied jsonl there as `train.jsonl/test.jsonl`.\r\n```python\r\noutput_folder = os.path.join(args.output_folder, f\"{task_meta_type}_{task_type}\")\r\nos.makedirs(output_folder, exist_ok=True)\r\nfile = f\"{task_meta_type}_{task_type}_train.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"train.jsonl\"))\r\n# now test\r\nfile = f\"{task_meta_type}_{task_type}_test.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"test.jsonl\"))\r\n```\r\n", "Hi @MFajcik, \r\n\r\nYou can find information about save_to_disk/load_from_disk in our docs:\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/process#save\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.save_to_disk\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.load_from_disk" ]

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[ "Hi @Hughhuh. \r\n\r\nI have formatted the issue because it was not easily readable. Additionally, the environment info is incomplete: it seems you did not run the proposed CLI command `datasets-cli env` and essential information is missing: version of `datasets`, version of `pyarrow`,...\r\n\r\nWith the information you provided, it seems an issue with the specific \"samsum\" dataset. I'm transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/samsum/discussions/5" ]

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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 \"torch\" formatting is usually fast because we do zero-copy conversion from the Arrow data on your disk to Torch tensors. However IterableDataset shuffling seems to do data copies that slow down the pipeline, and it shuffles python objects instead of Arrow data.\r\n\r\nTo fix this we need to implement `BufferShuffledExamplesIterable.iter_arrow()` (same as regular `BufferShuffledExamplesIterable.__iter__()` but yields Arrow tables)\r\n\r\nhttps://github.com/huggingface/datasets/blob/b7d854b7fd3e9a330e21b76ee8421d4a7ebb4a7a/src/datasets/iterable_dataset.py#L968-L974\r\n" ]

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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?\r\n\r\nEdit: Ah just realized you can avoid the issue with inferring features altogether when you set the format to arrow (or pandas).", "<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) | 0.004990 / 0.011353 (-0.006363) | 0.003138 / 0.011008 (-0.007870) | 0.062368 / 0.038508 (0.023860) | 0.028634 / 0.023109 (0.005524) | 0.241297 / 0.275898 (-0.034601) | 0.264433 / 0.323480 (-0.059047) | 0.003133 / 0.007986 (-0.004852) | 0.003444 / 0.004328 (-0.000885) | 0.048522 / 0.004250 (0.044271) | 0.043700 / 0.037052 (0.006648) | 0.257054 / 0.258489 (-0.001435) | 0.277551 / 0.293841 (-0.016290) | 0.027132 / 0.128546 (-0.101414) | 0.010395 / 0.075646 (-0.065251) | 0.208003 / 0.419271 (-0.211269) | 0.035814 / 0.043533 (-0.007719) | 0.250098 / 0.255139 (-0.005041) | 0.266726 / 0.283200 (-0.016474) | 0.018424 / 0.141683 (-0.123259) | 1.129242 / 1.452155 (-0.322912) | 1.167674 / 1.492716 (-0.325042) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091808 / 0.018006 (0.073802) | 0.298726 / 0.000490 (0.298236) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019119 / 0.037411 (-0.018292) | 0.061969 / 0.014526 (0.047443) | 0.073392 / 0.176557 (-0.103165) | 0.119460 / 0.737135 (-0.617675) | 0.074072 / 0.296338 (-0.222266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281435 / 0.215209 (0.066226) | 2.702094 / 2.077655 (0.624439) | 1.411541 / 1.504120 (-0.092579) | 1.284084 / 1.541195 (-0.257111) | 1.302638 / 1.468490 (-0.165852) | 0.562420 / 4.584777 (-4.022357) | 2.364890 / 3.745712 (-1.380822) | 2.744033 / 5.269862 (-2.525828) | 1.699000 / 4.565676 (-2.866677) | 0.062315 / 0.424275 (-0.361961) | 0.004982 / 0.007607 (-0.002625) | 0.334385 / 0.226044 (0.108341) | 3.203268 / 2.268929 (0.934339) | 1.766998 / 55.444624 (-53.677627) | 1.497164 / 6.876477 (-5.379313) | 1.509996 / 2.142072 (-0.632077) | 0.633014 / 4.805227 (-4.172213) | 0.115317 / 6.500664 (-6.385347) | 0.041120 / 0.075469 (-0.034349) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965877 / 1.841788 (-0.875911) | 11.219909 / 8.074308 (3.145601) | 9.333822 / 10.191392 (-0.857570) | 0.136482 / 0.680424 (-0.543941) | 0.013632 / 0.534201 (-0.520569) | 0.287251 / 0.579283 (-0.292032) | 0.262786 / 0.434364 (-0.171578) | 0.322893 / 0.540337 (-0.217444) | 0.418180 / 1.386936 (-0.968756) |\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) | 0.005444 / 0.011353 (-0.005909) | 0.003147 / 0.011008 (-0.007862) | 0.049242 / 0.038508 (0.010734) | 0.030944 / 0.023109 (0.007834) | 0.281901 / 0.275898 (0.006003) | 0.303820 / 0.323480 (-0.019660) | 0.004326 / 0.007986 (-0.003659) | 0.002696 / 0.004328 (-0.001632) | 0.048306 / 0.004250 (0.044055) | 0.044145 / 0.037052 (0.007093) | 0.297253 / 0.258489 (0.038764) | 0.324062 / 0.293841 (0.030221) | 0.046724 / 0.128546 (-0.081823) | 0.010079 / 0.075646 (-0.065567) | 0.057635 / 0.419271 (-0.361636) | 0.033621 / 0.043533 (-0.009912) | 0.282303 / 0.255139 (0.027164) | 0.300761 / 0.283200 (0.017561) | 0.017116 / 0.141683 (-0.124567) | 1.156519 / 1.452155 (-0.295636) | 1.216087 / 1.492716 (-0.276630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093011 / 0.018006 (0.075005) | 0.301310 / 0.000490 (0.300820) | 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) | 0.023112 / 0.037411 (-0.014299) | 0.075192 / 0.014526 (0.060666) | 0.086213 / 0.176557 (-0.090343) | 0.125853 / 0.737135 (-0.611282) | 0.087754 / 0.296338 (-0.208585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301095 / 0.215209 (0.085886) | 2.911769 / 2.077655 (0.834114) | 1.614708 / 1.504120 (0.110588) | 1.494497 / 1.541195 (-0.046698) | 1.506978 / 1.468490 (0.038488) | 0.572743 / 4.584777 (-4.012034) | 2.417142 / 3.745712 (-1.328570) | 2.755338 / 5.269862 (-2.514523) | 1.711026 / 4.565676 (-2.854650) | 0.062732 / 0.424275 (-0.361543) | 0.005031 / 0.007607 (-0.002576) | 0.352343 / 0.226044 (0.126298) | 3.465183 / 2.268929 (1.196255) | 1.958795 / 55.444624 (-53.485829) | 1.682239 / 6.876477 (-5.194238) | 1.688897 / 2.142072 (-0.453176) | 0.643311 / 4.805227 (-4.161916) | 0.115426 / 6.500664 (-6.385238) | 0.040338 / 0.075469 (-0.035131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005322 / 1.841788 (-0.836466) | 11.779380 / 8.074308 (3.705072) | 10.041574 / 10.191392 (-0.149818) | 0.127617 / 0.680424 (-0.552807) | 0.015840 / 0.534201 (-0.518361) | 0.286905 / 0.579283 (-0.292378) | 0.275180 / 0.434364 (-0.159183) | 0.332498 / 0.540337 (-0.207840) | 0.410719 / 1.386936 (-0.976217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32b206d47f582380f9c64578dcfa6c48252db3b8 \"CML watermark\")\n" ]

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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.", "<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) | 0.005577 / 0.011353 (-0.005776) | 0.004452 / 0.011008 (-0.006556) | 0.067849 / 0.038508 (0.029341) | 0.032328 / 0.023109 (0.009219) | 0.256924 / 0.275898 (-0.018974) | 0.273410 / 0.323480 (-0.050070) | 0.004359 / 0.007986 (-0.003626) | 0.003484 / 0.004328 (-0.000845) | 0.053880 / 0.004250 (0.049630) | 0.058142 / 0.037052 (0.021089) | 0.268863 / 0.258489 (0.010374) | 0.307977 / 0.293841 (0.014136) | 0.028840 / 0.128546 (-0.099707) | 0.011808 / 0.075646 (-0.063839) | 0.216277 / 0.419271 (-0.202995) | 0.039245 / 0.043533 (-0.004288) | 0.250420 / 0.255139 (-0.004719) | 0.273642 / 0.283200 (-0.009557) | 0.019340 / 0.141683 (-0.122342) | 1.176734 / 1.452155 (-0.275421) | 1.250643 / 1.492716 (-0.242074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.181210 / 0.018006 (0.163204) | 1.070750 / 0.000490 (1.070261) | 0.000315 / 0.000200 (0.000115) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022905 / 0.037411 (-0.014507) | 0.064549 / 0.014526 (0.050023) | 0.077113 / 0.176557 (-0.099443) | 0.131976 / 0.737135 (-0.605159) | 0.081266 / 0.296338 (-0.215072) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291363 / 0.215209 (0.076154) | 2.851691 / 2.077655 (0.774036) | 1.592815 / 1.504120 (0.088695) | 1.494550 / 1.541195 (-0.046645) | 1.516464 / 1.468490 (0.047974) | 0.583244 / 4.584777 (-4.001532) | 2.504907 / 3.745712 (-1.240805) | 3.183490 / 5.269862 (-2.086371) | 1.932854 / 4.565676 (-2.632823) | 0.067564 / 0.424275 (-0.356711) | 0.006587 / 0.007607 (-0.001020) | 0.346368 / 0.226044 (0.120324) | 3.428256 / 2.268929 (1.159327) | 1.994176 / 55.444624 (-53.450448) | 1.688116 / 6.876477 (-5.188360) | 1.767653 / 2.142072 (-0.374420) | 0.673867 / 4.805227 (-4.131360) | 0.125582 / 6.500664 (-6.375082) | 0.047198 / 0.075469 (-0.028271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.002895 / 1.841788 (-0.838893) | 16.332893 / 8.074308 (8.258585) | 10.781993 / 10.191392 (0.590601) | 0.153919 / 0.680424 (-0.526505) | 0.015528 / 0.534201 (-0.518673) | 0.306182 / 0.579283 (-0.273101) | 0.296380 / 0.434364 (-0.137984) | 0.341432 / 0.540337 (-0.198905) | 0.455900 / 1.386936 (-0.931036) |\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) | 0.006442 / 0.011353 (-0.004911) | 0.004433 / 0.011008 (-0.006576) | 0.053327 / 0.038508 (0.014819) | 0.035966 / 0.023109 (0.012856) | 0.280913 / 0.275898 (0.005015) | 0.308419 / 0.323480 (-0.015061) | 0.005842 / 0.007986 (-0.002144) | 0.003789 / 0.004328 (-0.000539) | 0.053983 / 0.004250 (0.049732) | 0.069052 / 0.037052 (0.032000) | 0.299225 / 0.258489 (0.040736) | 0.336470 / 0.293841 (0.042629) | 0.068170 / 0.128546 (-0.060377) | 0.012259 / 0.075646 (-0.063388) | 0.064166 / 0.419271 (-0.355106) | 0.037291 / 0.043533 (-0.006241) | 0.281318 / 0.255139 (0.026179) | 0.297093 / 0.283200 (0.013893) | 0.021358 / 0.141683 (-0.120324) | 1.189584 / 1.452155 (-0.262571) | 1.256985 / 1.492716 (-0.235731) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216726 / 0.018006 (0.198720) | 2.496957 / 0.000490 (2.496467) | 0.000336 / 0.000200 (0.000136) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026604 / 0.037411 (-0.010807) | 0.080398 / 0.014526 (0.065873) | 0.094475 / 0.176557 (-0.082082) | 0.136263 / 0.737135 (-0.600873) | 0.097898 / 0.296338 (-0.198440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295171 / 0.215209 (0.079962) | 2.947530 / 2.077655 (0.869875) | 1.607531 / 1.504120 (0.103411) | 1.485045 / 1.541195 (-0.056150) | 1.524899 / 1.468490 (0.056409) | 0.572934 / 4.584777 (-4.011843) | 2.544320 / 3.745712 (-1.201393) | 3.292630 / 5.269862 (-1.977232) | 1.927138 / 4.565676 (-2.638539) | 0.068560 / 0.424275 (-0.355715) | 0.005982 / 0.007607 (-0.001625) | 0.345833 / 0.226044 (0.119789) | 3.424253 / 2.268929 (1.155324) | 2.195017 / 55.444624 (-53.249608) | 1.712037 / 6.876477 (-5.164440) | 1.763899 / 2.142072 (-0.378174) | 0.653776 / 4.805227 (-4.151451) | 0.123056 / 6.500664 (-6.377609) | 0.044572 / 0.075469 (-0.030897) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.033400 / 1.841788 (-0.808388) | 15.409887 / 8.074308 (7.335579) | 11.220990 / 10.191392 (1.029597) | 0.153603 / 0.680424 (-0.526821) | 0.016866 / 0.534201 (-0.517335) | 0.311945 / 0.579283 (-0.267338) | 0.307048 / 0.434364 (-0.127316) | 0.350422 / 0.540337 (-0.189915) | 0.447308 / 1.386936 (-0.939628) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14d9afbb7ae1b787c450261ca0ff374551993031 \"CML watermark\")\n" ]

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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.", "<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) | 0.005125 / 0.011353 (-0.006228) | 0.003772 / 0.011008 (-0.007236) | 0.063258 / 0.038508 (0.024750) | 0.029479 / 0.023109 (0.006370) | 0.245554 / 0.275898 (-0.030344) | 0.266395 / 0.323480 (-0.057085) | 0.003063 / 0.007986 (-0.004922) | 0.003298 / 0.004328 (-0.001031) | 0.049242 / 0.004250 (0.044991) | 0.042390 / 0.037052 (0.005338) | 0.258176 / 0.258489 (-0.000313) | 0.279935 / 0.293841 (-0.013906) | 0.027910 / 0.128546 (-0.100637) | 0.011033 / 0.075646 (-0.064613) | 0.207763 / 0.419271 (-0.211509) | 0.036127 / 0.043533 (-0.007405) | 0.247363 / 0.255139 (-0.007776) | 0.261309 / 0.283200 (-0.021890) | 0.020259 / 0.141683 (-0.121424) | 1.152760 / 1.452155 (-0.299395) | 1.194853 / 1.492716 (-0.297863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088936 / 0.018006 (0.070930) | 0.298396 / 0.000490 (0.297906) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018434 / 0.037411 (-0.018977) | 0.061991 / 0.014526 (0.047466) | 0.072786 / 0.176557 (-0.103771) | 0.120437 / 0.737135 (-0.616698) | 0.078375 / 0.296338 (-0.217964) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275821 / 0.215209 (0.060612) | 2.703358 / 2.077655 (0.625703) | 1.446783 / 1.504120 (-0.057337) | 1.333556 / 1.541195 (-0.207639) | 1.325753 / 1.468490 (-0.142737) | 0.565196 / 4.584777 (-4.019581) | 2.411193 / 3.745712 (-1.334520) | 2.702764 / 5.269862 (-2.567098) | 1.727425 / 4.565676 (-2.838252) | 0.062966 / 0.424275 (-0.361309) | 0.004985 / 0.007607 (-0.002622) | 0.333473 / 0.226044 (0.107428) | 3.270615 / 2.268929 (1.001687) | 1.822213 / 55.444624 (-53.622411) | 1.546572 / 6.876477 (-5.329905) | 1.568767 / 2.142072 (-0.573305) | 0.655907 / 4.805227 (-4.149321) | 0.117173 / 6.500664 (-6.383491) | 0.042415 / 0.075469 (-0.033054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987966 / 1.841788 (-0.853822) | 11.851206 / 8.074308 (3.776898) | 10.327751 / 10.191392 (0.136359) | 0.127929 / 0.680424 (-0.552494) | 0.013781 / 0.534201 (-0.520420) | 0.286910 / 0.579283 (-0.292373) | 0.273615 / 0.434364 (-0.160749) | 0.323373 / 0.540337 (-0.216965) | 0.426407 / 1.386936 (-0.960529) |\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) | 0.005412 / 0.011353 (-0.005941) | 0.003619 / 0.011008 (-0.007389) | 0.049603 / 0.038508 (0.011095) | 0.031246 / 0.023109 (0.008136) | 0.279723 / 0.275898 (0.003825) | 0.298557 / 0.323480 (-0.024923) | 0.004253 / 0.007986 (-0.003733) | 0.002758 / 0.004328 (-0.001570) | 0.048931 / 0.004250 (0.044680) | 0.044245 / 0.037052 (0.007193) | 0.295876 / 0.258489 (0.037387) | 0.322720 / 0.293841 (0.028879) | 0.046746 / 0.128546 (-0.081800) | 0.010841 / 0.075646 (-0.064805) | 0.058528 / 0.419271 (-0.360744) | 0.034224 / 0.043533 (-0.009308) | 0.279192 / 0.255139 (0.024053) | 0.299775 / 0.283200 (0.016576) | 0.017862 / 0.141683 (-0.123820) | 1.154478 / 1.452155 (-0.297677) | 1.190483 / 1.492716 (-0.302234) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088717 / 0.018006 (0.070710) | 0.297905 / 0.000490 (0.297415) | 0.000209 / 0.000200 (0.000009) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021458 / 0.037411 (-0.015953) | 0.075616 / 0.014526 (0.061090) | 0.087080 / 0.176557 (-0.089476) | 0.125315 / 0.737135 (-0.611821) | 0.088958 / 0.296338 (-0.207381) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287085 / 0.215209 (0.071876) | 2.807798 / 2.077655 (0.730143) | 1.552201 / 1.504120 (0.048081) | 1.422374 / 1.541195 (-0.118820) | 1.437908 / 1.468490 (-0.030582) | 0.569738 / 4.584777 (-4.015039) | 2.493921 / 3.745712 (-1.251791) | 2.648376 / 5.269862 (-2.621486) | 1.741721 / 4.565676 (-2.823955) | 0.063023 / 0.424275 (-0.361253) | 0.005166 / 0.007607 (-0.002441) | 0.336927 / 0.226044 (0.110882) | 3.384517 / 2.268929 (1.115588) | 1.909888 / 55.444624 (-53.534736) | 1.641879 / 6.876477 (-5.234597) | 1.727734 / 2.142072 (-0.414338) | 0.647127 / 4.805227 (-4.158100) | 0.115831 / 6.500664 (-6.384833) | 0.041161 / 0.075469 (-0.034309) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.016310 / 1.841788 (-0.825477) | 12.088500 / 8.074308 (4.014192) | 10.799730 / 10.191392 (0.608338) | 0.129049 / 0.680424 (-0.551375) | 0.015379 / 0.534201 (-0.518822) | 0.291352 / 0.579283 (-0.287931) | 0.284579 / 0.434364 (-0.149785) | 0.331214 / 0.540337 (-0.209124) | 0.422902 / 1.386936 (-0.964034) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#991169ed4901d129d0e0ab8d7fccd6a0728da4b8 \"CML watermark\")\n" ]

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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.", "<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) | 0.004913 / 0.011353 (-0.006440) | 0.003595 / 0.011008 (-0.007413) | 0.068385 / 0.038508 (0.029876) | 0.028612 / 0.023109 (0.005503) | 0.236590 / 0.275898 (-0.039308) | 0.261890 / 0.323480 (-0.061590) | 0.003027 / 0.007986 (-0.004958) | 0.002674 / 0.004328 (-0.001654) | 0.049255 / 0.004250 (0.045004) | 0.040500 / 0.037052 (0.003447) | 0.248759 / 0.258489 (-0.009730) | 0.280299 / 0.293841 (-0.013542) | 0.027300 / 0.128546 (-0.101247) | 0.010475 / 0.075646 (-0.065171) | 0.208744 / 0.419271 (-0.210527) | 0.035214 / 0.043533 (-0.008319) | 0.251922 / 0.255139 (-0.003217) | 0.263582 / 0.283200 (-0.019618) | 0.018738 / 0.141683 (-0.122945) | 1.150940 / 1.452155 (-0.301215) | 1.187240 / 1.492716 (-0.305476) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093505 / 0.018006 (0.075499) | 0.301101 / 0.000490 (0.300611) | 0.000232 / 0.000200 (0.000032) | 0.000067 / 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) | 0.017779 / 0.037411 (-0.019632) | 0.061412 / 0.014526 (0.046886) | 0.074353 / 0.176557 (-0.102203) | 0.118717 / 0.737135 (-0.618418) | 0.074214 / 0.296338 (-0.222125) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281722 / 0.215209 (0.066513) | 2.716867 / 2.077655 (0.639212) | 1.423379 / 1.504120 (-0.080741) | 1.315379 / 1.541195 (-0.225816) | 1.294638 / 1.468490 (-0.173852) | 0.549658 / 4.584777 (-4.035119) | 2.349889 / 3.745712 (-1.395823) | 2.722354 / 5.269862 (-2.547507) | 1.700271 / 4.565676 (-2.865406) | 0.061099 / 0.424275 (-0.363176) | 0.004931 / 0.007607 (-0.002677) | 0.339181 / 0.226044 (0.113136) | 3.242467 / 2.268929 (0.973538) | 1.777929 / 55.444624 (-53.666696) | 1.498380 / 6.876477 (-5.378097) | 1.511482 / 2.142072 (-0.630590) | 0.627076 / 4.805227 (-4.178151) | 0.115936 / 6.500664 (-6.384729) | 0.041791 / 0.075469 (-0.033678) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983132 / 1.841788 (-0.858656) | 11.431810 / 8.074308 (3.357502) | 10.298918 / 10.191392 (0.107526) | 0.139754 / 0.680424 (-0.540670) | 0.013984 / 0.534201 (-0.520217) | 0.283627 / 0.579283 (-0.295656) | 0.264970 / 0.434364 (-0.169393) | 0.323896 / 0.540337 (-0.216441) | 0.420132 / 1.386936 (-0.966804) |\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) | 0.005323 / 0.011353 (-0.006030) | 0.003725 / 0.011008 (-0.007283) | 0.050191 / 0.038508 (0.011683) | 0.032196 / 0.023109 (0.009087) | 0.265037 / 0.275898 (-0.010861) | 0.289573 / 0.323480 (-0.033907) | 0.004345 / 0.007986 (-0.003640) | 0.002794 / 0.004328 (-0.001534) | 0.048955 / 0.004250 (0.044705) | 0.045421 / 0.037052 (0.008369) | 0.279792 / 0.258489 (0.021303) | 0.307374 / 0.293841 (0.013533) | 0.046997 / 0.128546 (-0.081549) | 0.010531 / 0.075646 (-0.065115) | 0.058921 / 0.419271 (-0.360351) | 0.033620 / 0.043533 (-0.009912) | 0.268138 / 0.255139 (0.012999) | 0.285941 / 0.283200 (0.002742) | 0.018396 / 0.141683 (-0.123287) | 1.151089 / 1.452155 (-0.301066) | 1.209351 / 1.492716 (-0.283365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092258 / 0.018006 (0.074252) | 0.300893 / 0.000490 (0.300403) | 0.000212 / 0.000200 (0.000013) | 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) | 0.022233 / 0.037411 (-0.015178) | 0.075220 / 0.014526 (0.060694) | 0.085901 / 0.176557 (-0.090656) | 0.125080 / 0.737135 (-0.612056) | 0.086978 / 0.296338 (-0.209361) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292877 / 0.215209 (0.077667) | 2.841005 / 2.077655 (0.763350) | 1.555168 / 1.504120 (0.051048) | 1.420801 / 1.541195 (-0.120394) | 1.431475 / 1.468490 (-0.037015) | 0.569803 / 4.584777 (-4.014974) | 2.451731 / 3.745712 (-1.293981) | 2.662825 / 5.269862 (-2.607036) | 1.732260 / 4.565676 (-2.833416) | 0.063030 / 0.424275 (-0.361245) | 0.004971 / 0.007607 (-0.002637) | 0.345250 / 0.226044 (0.119206) | 3.390909 / 2.268929 (1.121980) | 1.908666 / 55.444624 (-53.535959) | 1.628976 / 6.876477 (-5.247501) | 1.719270 / 2.142072 (-0.422803) | 0.653712 / 4.805227 (-4.151515) | 0.116423 / 6.500664 (-6.384241) | 0.040835 / 0.075469 (-0.034634) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005538 / 1.841788 (-0.836250) | 12.105381 / 8.074308 (4.031073) | 10.656295 / 10.191392 (0.464903) | 0.131850 / 0.680424 (-0.548574) | 0.016297 / 0.534201 (-0.517904) | 0.285566 / 0.579283 (-0.293717) | 0.276086 / 0.434364 (-0.158278) | 0.326663 / 0.540337 (-0.213675) | 0.410639 / 1.386936 (-0.976297) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1dc3f04586ee65c890b74649afc42316121af689 \"CML watermark\")\n" ]

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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.", "<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) | 0.005222 / 0.011353 (-0.006131) | 0.003621 / 0.011008 (-0.007387) | 0.063091 / 0.038508 (0.024583) | 0.029395 / 0.023109 (0.006285) | 0.231445 / 0.275898 (-0.044453) | 0.256716 / 0.323480 (-0.066764) | 0.004905 / 0.007986 (-0.003081) | 0.002703 / 0.004328 (-0.001625) | 0.048526 / 0.004250 (0.044276) | 0.041382 / 0.037052 (0.004330) | 0.247468 / 0.258489 (-0.011021) | 0.270670 / 0.293841 (-0.023171) | 0.028088 / 0.128546 (-0.100458) | 0.010661 / 0.075646 (-0.064985) | 0.205812 / 0.419271 (-0.213459) | 0.035880 / 0.043533 (-0.007653) | 0.237310 / 0.255139 (-0.017829) | 0.255440 / 0.283200 (-0.027760) | 0.018334 / 0.141683 (-0.123349) | 1.128815 / 1.452155 (-0.323340) | 1.204771 / 1.492716 (-0.287945) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089175 / 0.018006 (0.071169) | 0.298584 / 0.000490 (0.298095) | 0.000206 / 0.000200 (0.000006) | 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) | 0.018532 / 0.037411 (-0.018880) | 0.061158 / 0.014526 (0.046632) | 0.074177 / 0.176557 (-0.102380) | 0.119408 / 0.737135 (-0.617728) | 0.073821 / 0.296338 (-0.222518) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277630 / 0.215209 (0.062420) | 2.735038 / 2.077655 (0.657383) | 1.437251 / 1.504120 (-0.066868) | 1.304596 / 1.541195 (-0.236598) | 1.316830 / 1.468490 (-0.151661) | 0.551057 / 4.584777 (-4.033720) | 2.337247 / 3.745712 (-1.408465) | 2.761501 / 5.269862 (-2.508361) | 1.729000 / 4.565676 (-2.836677) | 0.069398 / 0.424275 (-0.354877) | 0.005059 / 0.007607 (-0.002548) | 0.359594 / 0.226044 (0.133550) | 3.283325 / 2.268929 (1.014397) | 1.777410 / 55.444624 (-53.667214) | 1.518522 / 6.876477 (-5.357954) | 1.546712 / 2.142072 (-0.595361) | 0.627047 / 4.805227 (-4.178180) | 0.117058 / 6.500664 (-6.383606) | 0.043437 / 0.075469 (-0.032032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.056303 / 1.841788 (-0.785484) | 11.552295 / 8.074308 (3.477987) | 10.184582 / 10.191392 (-0.006810) | 0.129061 / 0.680424 (-0.551363) | 0.014093 / 0.534201 (-0.520108) | 0.292268 / 0.579283 (-0.287015) | 0.264750 / 0.434364 (-0.169614) | 0.334770 / 0.540337 (-0.205567) | 0.436749 / 1.386936 (-0.950187) |\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) | 0.005408 / 0.011353 (-0.005945) | 0.003650 / 0.011008 (-0.007358) | 0.054263 / 0.038508 (0.015755) | 0.031112 / 0.023109 (0.008003) | 0.270582 / 0.275898 (-0.005316) | 0.303506 / 0.323480 (-0.019974) | 0.004351 / 0.007986 (-0.003635) | 0.002654 / 0.004328 (-0.001674) | 0.049631 / 0.004250 (0.045381) | 0.045209 / 0.037052 (0.008156) | 0.284992 / 0.258489 (0.026503) | 0.316653 / 0.293841 (0.022812) | 0.049526 / 0.128546 (-0.079020) | 0.010696 / 0.075646 (-0.064951) | 0.057859 / 0.419271 (-0.361413) | 0.034227 / 0.043533 (-0.009306) | 0.269656 / 0.255139 (0.014517) | 0.288766 / 0.283200 (0.005567) | 0.017892 / 0.141683 (-0.123791) | 1.167492 / 1.452155 (-0.284662) | 1.217263 / 1.492716 (-0.275454) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089306 / 0.018006 (0.071299) | 0.300774 / 0.000490 (0.300284) | 0.000198 / 0.000200 (-0.000002) | 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) | 0.022050 / 0.037411 (-0.015361) | 0.076781 / 0.014526 (0.062255) | 0.086597 / 0.176557 (-0.089959) | 0.125094 / 0.737135 (-0.612042) | 0.089412 / 0.296338 (-0.206927) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287444 / 0.215209 (0.072235) | 2.830047 / 2.077655 (0.752392) | 1.567492 / 1.504120 (0.063372) | 1.439875 / 1.541195 (-0.101320) | 1.461699 / 1.468490 (-0.006791) | 0.569595 / 4.584777 (-4.015182) | 2.454391 / 3.745712 (-1.291322) | 2.655829 / 5.269862 (-2.614032) | 1.756122 / 4.565676 (-2.809554) | 0.063333 / 0.424275 (-0.360942) | 0.005086 / 0.007607 (-0.002521) | 0.351210 / 0.226044 (0.125166) | 3.375545 / 2.268929 (1.106617) | 1.945367 / 55.444624 (-53.499258) | 1.662635 / 6.876477 (-5.213841) | 1.762859 / 2.142072 (-0.379213) | 0.651889 / 4.805227 (-4.153339) | 0.118341 / 6.500664 (-6.382323) | 0.040897 / 0.075469 (-0.034572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005270 / 1.841788 (-0.836518) | 12.247847 / 8.074308 (4.173539) | 10.828131 / 10.191392 (0.636739) | 0.129741 / 0.680424 (-0.550683) | 0.015184 / 0.534201 (-0.519017) | 0.295440 / 0.579283 (-0.283843) | 0.276759 / 0.434364 (-0.157605) | 0.329046 / 0.540337 (-0.211291) | 0.421750 / 1.386936 (-0.965186) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea261ddc295527d0c1cd9f90fb61668f14135608 \"CML watermark\")\n" ]

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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.", "<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) | 0.004907 / 0.011353 (-0.006446) | 0.003200 / 0.011008 (-0.007808) | 0.062601 / 0.038508 (0.024093) | 0.028607 / 0.023109 (0.005498) | 0.242688 / 0.275898 (-0.033210) | 0.263754 / 0.323480 (-0.059726) | 0.003084 / 0.007986 (-0.004901) | 0.002744 / 0.004328 (-0.001585) | 0.048686 / 0.004250 (0.044436) | 0.040734 / 0.037052 (0.003682) | 0.262585 / 0.258489 (0.004096) | 0.282822 / 0.293841 (-0.011019) | 0.027470 / 0.128546 (-0.101076) | 0.010356 / 0.075646 (-0.065290) | 0.206397 / 0.419271 (-0.212874) | 0.035440 / 0.043533 (-0.008093) | 0.248599 / 0.255139 (-0.006540) | 0.268869 / 0.283200 (-0.014331) | 0.018542 / 0.141683 (-0.123141) | 1.128139 / 1.452155 (-0.324016) | 1.172115 / 1.492716 (-0.320602) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107939 / 0.018006 (0.089933) | 0.301801 / 0.000490 (0.301311) | 0.000207 / 0.000200 (0.000007) | 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) | 0.018505 / 0.037411 (-0.018906) | 0.061350 / 0.014526 (0.046824) | 0.072645 / 0.176557 (-0.103912) | 0.119459 / 0.737135 (-0.617676) | 0.074711 / 0.296338 (-0.221628) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275132 / 0.215209 (0.059922) | 2.714936 / 2.077655 (0.637281) | 1.434204 / 1.504120 (-0.069916) | 1.328358 / 1.541195 (-0.212837) | 1.320706 / 1.468490 (-0.147784) | 0.555723 / 4.584777 (-4.029054) | 2.401335 / 3.745712 (-1.344378) | 2.765609 / 5.269862 (-2.504253) | 1.715207 / 4.565676 (-2.850470) | 0.074990 / 0.424275 (-0.349285) | 0.004999 / 0.007607 (-0.002608) | 0.328435 / 0.226044 (0.102390) | 3.254945 / 2.268929 (0.986017) | 1.781105 / 55.444624 (-53.663519) | 1.509491 / 6.876477 (-5.366985) | 1.520670 / 2.142072 (-0.621402) | 0.636411 / 4.805227 (-4.168817) | 0.115616 / 6.500664 (-6.385048) | 0.041633 / 0.075469 (-0.033836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975462 / 1.841788 (-0.866326) | 11.480359 / 8.074308 (3.406051) | 10.528665 / 10.191392 (0.337273) | 0.141323 / 0.680424 (-0.539100) | 0.013510 / 0.534201 (-0.520691) | 0.293570 / 0.579283 (-0.285713) | 0.259956 / 0.434364 (-0.174408) | 0.331440 / 0.540337 (-0.208898) | 0.453487 / 1.386936 (-0.933449) |\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) | 0.005278 / 0.011353 (-0.006075) | 0.003400 / 0.011008 (-0.007608) | 0.049442 / 0.038508 (0.010934) | 0.031738 / 0.023109 (0.008628) | 0.292334 / 0.275898 (0.016436) | 0.308931 / 0.323480 (-0.014549) | 0.004290 / 0.007986 (-0.003696) | 0.002738 / 0.004328 (-0.001591) | 0.048944 / 0.004250 (0.044694) | 0.044273 / 0.037052 (0.007221) | 0.301434 / 0.258489 (0.042945) | 0.333067 / 0.293841 (0.039226) | 0.048741 / 0.128546 (-0.079805) | 0.010357 / 0.075646 (-0.065289) | 0.057777 / 0.419271 (-0.361495) | 0.033892 / 0.043533 (-0.009641) | 0.286921 / 0.255139 (0.031782) | 0.306204 / 0.283200 (0.023005) | 0.018764 / 0.141683 (-0.122919) | 1.142000 / 1.452155 (-0.310155) | 1.206728 / 1.492716 (-0.285988) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094233 / 0.018006 (0.076227) | 0.302553 / 0.000490 (0.302063) | 0.000213 / 0.000200 (0.000013) | 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) | 0.021814 / 0.037411 (-0.015598) | 0.075143 / 0.014526 (0.060617) | 0.087717 / 0.176557 (-0.088840) | 0.126079 / 0.737135 (-0.611056) | 0.089083 / 0.296338 (-0.207255) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293844 / 0.215209 (0.078635) | 2.859481 / 2.077655 (0.781827) | 1.580366 / 1.504120 (0.076246) | 1.462633 / 1.541195 (-0.078562) | 1.471052 / 1.468490 (0.002562) | 0.574755 / 4.584777 (-4.010022) | 2.408925 / 3.745712 (-1.336787) | 2.673618 / 5.269862 (-2.596243) | 1.746218 / 4.565676 (-2.819459) | 0.063435 / 0.424275 (-0.360840) | 0.005023 / 0.007607 (-0.002584) | 0.341990 / 0.226044 (0.115946) | 3.430862 / 2.268929 (1.161933) | 1.953869 / 55.444624 (-53.490755) | 1.661276 / 6.876477 (-5.215201) | 1.761575 / 2.142072 (-0.380498) | 0.656388 / 4.805227 (-4.148839) | 0.117774 / 6.500664 (-6.382890) | 0.040290 / 0.075469 (-0.035179) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.004315 / 1.841788 (-0.837473) | 12.249719 / 8.074308 (4.175411) | 10.942703 / 10.191392 (0.751311) | 0.128552 / 0.680424 (-0.551872) | 0.015958 / 0.534201 (-0.518242) | 0.287330 / 0.579283 (-0.291953) | 0.274336 / 0.434364 (-0.160028) | 0.326233 / 0.540337 (-0.214104) | 0.414548 / 1.386936 (-0.972388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db47d6d95c5346368710d3c852f20ffc1b0f1c1c \"CML watermark\")\n" ]

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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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[ "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.", "> 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\n> In 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.\r\n\r\nIs there any method to modify one dataset's n_shard? modify the number of files is ok? one file == one shard?", "> modify the number of files is ok? one file == one shard?\r\n\r\nYep, one file == one shard :)" ]

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[ "This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)" ]

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[ "Thanks for reporting, @kiehls90.\r\n\r\nAs this seems an issue with the specific \"wiki_dpr\" dataset, I am transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/wiki_dpr/discussions/13" ]

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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?", "We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...\r\n\r\nI am closing the issue. Feel free to reopen it (with additional information) if the problem persists.", "Yeah 👍\r\n\r\nOn Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> We cannot reproduce the issue and we do not have enough information:\r\n> environment info (need to run datasets-cli env), stack trace,...\r\n>\r\n> I am closing the issue. Feel free to reopen it (with additional\r\n> information) if the problem persists.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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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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[ "I think not all torch tensors should be converted to float, what if it's a tensor of integers for example ?\r\nMaybe you can check for the tensor dtype before converting", "@lhoestq Please could this be merged? 🙏", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005552 / 0.011353 (-0.005801) | 0.003707 / 0.011008 (-0.007301) | 0.063794 / 0.038508 (0.025286) | 0.031897 / 0.023109 (0.008788) | 0.263086 / 0.275898 (-0.012812) | 0.281184 / 0.323480 (-0.042296) | 0.003183 / 0.007986 (-0.004802) | 0.002681 / 0.004328 (-0.001648) | 0.050259 / 0.004250 (0.046009) | 0.048395 / 0.037052 (0.011342) | 0.266925 / 0.258489 (0.008436) | 0.298146 / 0.293841 (0.004305) | 0.027995 / 0.128546 (-0.100551) | 0.010689 / 0.075646 (-0.064957) | 0.204956 / 0.419271 (-0.214316) | 0.036453 / 0.043533 (-0.007080) | 0.255406 / 0.255139 (0.000267) | 0.271388 / 0.283200 (-0.011811) | 0.019748 / 0.141683 (-0.121935) | 1.103926 / 1.452155 (-0.348228) | 1.167250 / 1.492716 (-0.325466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100483 / 0.018006 (0.082477) | 0.307331 / 0.000490 (0.306841) | 0.000216 / 0.000200 (0.000016) | 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) | 0.018918 / 0.037411 (-0.018493) | 0.062569 / 0.014526 (0.048044) | 0.074935 / 0.176557 (-0.101621) | 0.122590 / 0.737135 (-0.614545) | 0.076475 / 0.296338 (-0.219864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279001 / 0.215209 (0.063792) | 2.771630 / 2.077655 (0.693975) | 1.439666 / 1.504120 (-0.064454) | 1.303422 / 1.541195 (-0.237773) | 1.355670 / 1.468490 (-0.112820) | 0.576264 / 4.584777 (-4.008513) | 2.394868 / 3.745712 (-1.350844) | 2.941487 / 5.269862 (-2.328375) | 1.808733 / 4.565676 (-2.756943) | 0.063691 / 0.424275 (-0.360584) | 0.005399 / 0.007607 (-0.002208) | 0.335610 / 0.226044 (0.109566) | 3.295903 / 2.268929 (1.026974) | 1.771836 / 55.444624 (-53.672788) | 1.511246 / 6.876477 (-5.365231) | 1.535926 / 2.142072 (-0.606147) | 0.649020 / 4.805227 (-4.156207) | 0.119754 / 6.500664 (-6.380910) | 0.043319 / 0.075469 (-0.032150) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967275 / 1.841788 (-0.874513) | 12.358482 / 8.074308 (4.284174) | 9.933324 / 10.191392 (-0.258068) | 0.133565 / 0.680424 (-0.546859) | 0.015650 / 0.534201 (-0.518551) | 0.286978 / 0.579283 (-0.292305) | 0.262912 / 0.434364 (-0.171451) | 0.330335 / 0.540337 (-0.210002) | 0.427671 / 1.386936 (-0.959265) |\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) | 0.005660 / 0.011353 (-0.005693) | 0.003908 / 0.011008 (-0.007101) | 0.051874 / 0.038508 (0.013366) | 0.033141 / 0.023109 (0.010032) | 0.270512 / 0.275898 (-0.005386) | 0.296790 / 0.323480 (-0.026690) | 0.004335 / 0.007986 (-0.003651) | 0.002842 / 0.004328 (-0.001487) | 0.078264 / 0.004250 (0.074014) | 0.044436 / 0.037052 (0.007384) | 0.283230 / 0.258489 (0.024741) | 0.318026 / 0.293841 (0.024185) | 0.031459 / 0.128546 (-0.097087) | 0.010710 / 0.075646 (-0.064937) | 0.058152 / 0.419271 (-0.361119) | 0.034021 / 0.043533 (-0.009512) | 0.269956 / 0.255139 (0.014817) | 0.288783 / 0.283200 (0.005583) | 0.019246 / 0.141683 (-0.122436) | 1.127264 / 1.452155 (-0.324891) | 1.169777 / 1.492716 (-0.322939) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101523 / 0.018006 (0.083516) | 0.315120 / 0.000490 (0.314630) | 0.000218 / 0.000200 (0.000018) | 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) | 0.023078 / 0.037411 (-0.014333) | 0.080021 / 0.014526 (0.065495) | 0.089574 / 0.176557 (-0.086982) | 0.131258 / 0.737135 (-0.605877) | 0.090604 / 0.296338 (-0.205734) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302197 / 0.215209 (0.086988) | 2.980071 / 2.077655 (0.902416) | 1.585480 / 1.504120 (0.081360) | 1.462904 / 1.541195 (-0.078291) | 1.501102 / 1.468490 (0.032612) | 0.580342 / 4.584777 (-4.004435) | 0.972118 / 3.745712 (-2.773594) | 2.930530 / 5.269862 (-2.339331) | 1.824132 / 4.565676 (-2.741545) | 0.064711 / 0.424275 (-0.359564) | 0.005084 / 0.007607 (-0.002523) | 0.352693 / 0.226044 (0.126649) | 3.522775 / 2.268929 (1.253847) | 1.965063 / 55.444624 (-53.479561) | 1.679250 / 6.876477 (-5.197226) | 1.711691 / 2.142072 (-0.430382) | 0.663719 / 4.805227 (-4.141509) | 0.119858 / 6.500664 (-6.380806) | 0.041744 / 0.075469 (-0.033725) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.017970 / 1.841788 (-0.823817) | 12.898917 / 8.074308 (4.824609) | 10.244728 / 10.191392 (0.053336) | 0.133860 / 0.680424 (-0.546564) | 0.016044 / 0.534201 (-0.518157) | 0.287543 / 0.579283 (-0.291740) | 0.126418 / 0.434364 (-0.307946) | 0.394970 / 0.540337 (-0.145368) | 0.420455 / 1.386936 (-0.966481) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d71ffeb10bc129f6f923cfadb5ccd9383b8033 \"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 ! The metrics in `datasets` are deprecated in favor of https://github.com/huggingface/evaluate\r\n\r\nYou can open a PR here instead: https://huggingface.co/spaces/evaluate-metric/squad_v2/tree/main" ]

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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", "I am facing similar issue while reading a parquet file from s3.\r\ni try with every version between 2.14 to 2.16.1 but it dosen't work ", "Re-define the DownloadConfig might work:\r\n\r\n```\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\n def copy(self):\r\n return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})\r\n\r\ndownloadconfig = ReviseDownloadConfig()\r\n```\r\n", "> Re-define the DownloadConfig might work:\r\n> \r\n> ```\r\n> class ReviseDownloadConfig(DownloadConfig):\r\n> def __post_init__(self, use_auth_token):\r\n> if use_auth_token != \"deprecated\":\r\n> warnings.warn(\r\n> \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n> f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n> FutureWarning,\r\n> )\r\n> self.token = use_auth_token\r\n> ```\r\nThis seemed to work for me.\r\n", "use pandas and then convert to `Dataset`", "I am currently facing the same issue while using a custom loading script with files located in a remote S3 instance. I was using the `download_custom` functionality but now it is deprecated mentioning that I should use the native S3 loading, which is not working. \r\n\r\nAs stated before, the library forces the existence of a `hf` key in the `storage_options` variable, which is **not** accepted by `s3fs` : \r\n\r\n```python\r\n.../site-packages/s3fs/core.py\", line 516, in set_session\r\n self.session = aiobotocore.session.AioSession(**self.kwargs)\r\nTypeError: __init__() got an unexpected keyword argument 'hf'.\r\n````\r\n\r\nMeanwhile, if my `storage_options` var stays like:\r\n```python\r\n{'key': '...',\r\n 'secret': '...',\r\n 'client_kwargs': {'endpoint_url': '...'}}\r\n```\r\nit works alright. " ]

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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", "<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) | 0.005403 / 0.011353 (-0.005950) | 0.003807 / 0.011008 (-0.007201) | 0.063850 / 0.038508 (0.025342) | 0.028242 / 0.023109 (0.005132) | 0.242866 / 0.275898 (-0.033032) | 0.266015 / 0.323480 (-0.057464) | 0.004111 / 0.007986 (-0.003875) | 0.002816 / 0.004328 (-0.001513) | 0.048862 / 0.004250 (0.044611) | 0.043036 / 0.037052 (0.005984) | 0.255149 / 0.258489 (-0.003340) | 0.280105 / 0.293841 (-0.013736) | 0.028182 / 0.128546 (-0.100365) | 0.010997 / 0.075646 (-0.064649) | 0.208131 / 0.419271 (-0.211141) | 0.036030 / 0.043533 (-0.007502) | 0.241551 / 0.255139 (-0.013588) | 0.260741 / 0.283200 (-0.022459) | 0.018045 / 0.141683 (-0.123638) | 1.175308 / 1.452155 (-0.276847) | 1.192160 / 1.492716 (-0.300556) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094579 / 0.018006 (0.076573) | 0.309850 / 0.000490 (0.309360) | 0.000232 / 0.000200 (0.000032) | 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) | 0.019519 / 0.037411 (-0.017892) | 0.062201 / 0.014526 (0.047675) | 0.074017 / 0.176557 (-0.102539) | 0.121987 / 0.737135 (-0.615148) | 0.078958 / 0.296338 (-0.217380) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286306 / 0.215209 (0.071097) | 2.777004 / 2.077655 (0.699350) | 1.481445 / 1.504120 (-0.022675) | 1.348643 / 1.541195 (-0.192552) | 1.382257 / 1.468490 (-0.086234) | 0.571436 / 4.584777 (-4.013341) | 2.373279 / 3.745712 (-1.372433) | 2.749366 / 5.269862 (-2.520496) | 1.724937 / 4.565676 (-2.840739) | 0.062233 / 0.424275 (-0.362042) | 0.005013 / 0.007607 (-0.002594) | 0.339623 / 0.226044 (0.113579) | 3.385770 / 2.268929 (1.116842) | 1.832023 / 55.444624 (-53.612601) | 1.556172 / 6.876477 (-5.320305) | 1.573301 / 2.142072 (-0.568772) | 0.648866 / 4.805227 (-4.156361) | 0.121228 / 6.500664 (-6.379436) | 0.041684 / 0.075469 (-0.033786) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974595 / 1.841788 (-0.867192) | 11.519692 / 8.074308 (3.445383) | 9.773075 / 10.191392 (-0.418317) | 0.138149 / 0.680424 (-0.542274) | 0.014068 / 0.534201 (-0.520133) | 0.288161 / 0.579283 (-0.291122) | 0.272832 / 0.434364 (-0.161532) | 0.324476 / 0.540337 (-0.215862) | 0.419962 / 1.386936 (-0.966974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005668 / 0.011353 (-0.005685) | 0.003637 / 0.011008 (-0.007371) | 0.049582 / 0.038508 (0.011074) | 0.030982 / 0.023109 (0.007872) | 0.273036 / 0.275898 (-0.002862) | 0.297562 / 0.323480 (-0.025918) | 0.004382 / 0.007986 (-0.003603) | 0.002763 / 0.004328 (-0.001566) | 0.050807 / 0.004250 (0.046556) | 0.046914 / 0.037052 (0.009862) | 0.287443 / 0.258489 (0.028954) | 0.319694 / 0.293841 (0.025853) | 0.051110 / 0.128546 (-0.077436) | 0.010650 / 0.075646 (-0.064997) | 0.058254 / 0.419271 (-0.361018) | 0.033419 / 0.043533 (-0.010114) | 0.275634 / 0.255139 (0.020495) | 0.288618 / 0.283200 (0.005419) | 0.018004 / 0.141683 (-0.123678) | 1.134166 / 1.452155 (-0.317989) | 1.192533 / 1.492716 (-0.300183) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098573 / 0.018006 (0.080566) | 0.308152 / 0.000490 (0.307662) | 0.000249 / 0.000200 (0.000049) | 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) | 0.022443 / 0.037411 (-0.014968) | 0.075628 / 0.014526 (0.061103) | 0.088807 / 0.176557 (-0.087750) | 0.127519 / 0.737135 (-0.609617) | 0.090156 / 0.296338 (-0.206182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294493 / 0.215209 (0.079284) | 2.862084 / 2.077655 (0.784429) | 1.585962 / 1.504120 (0.081842) | 1.466366 / 1.541195 (-0.074829) | 1.503306 / 1.468490 (0.034816) | 0.581524 / 4.584777 (-4.003253) | 2.475593 / 3.745712 (-1.270120) | 2.852014 / 5.269862 (-2.417847) | 1.834047 / 4.565676 (-2.731630) | 0.064009 / 0.424275 (-0.360266) | 0.005094 / 0.007607 (-0.002514) | 0.355960 / 0.226044 (0.129916) | 3.428849 / 2.268929 (1.159920) | 1.958501 / 55.444624 (-53.486124) | 1.675448 / 6.876477 (-5.201029) | 1.719960 / 2.142072 (-0.422113) | 0.659609 / 4.805227 (-4.145618) | 0.119036 / 6.500664 (-6.381628) | 0.041800 / 0.075469 (-0.033669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025955 / 1.841788 (-0.815833) | 12.432417 / 8.074308 (4.358108) | 10.444854 / 10.191392 (0.253462) | 0.130106 / 0.680424 (-0.550318) | 0.015655 / 0.534201 (-0.518546) | 0.288184 / 0.579283 (-0.291099) | 0.285023 / 0.434364 (-0.149340) | 0.329244 / 0.540337 (-0.211093) | 0.415484 / 1.386936 (-0.971452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262b060525efd973cac3f2073ba3944f3ddd7e3 \"CML watermark\")\n" ]

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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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