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[ "Hi ! Thanks for reporting\r\n\r\nThis is a bug in 2.16.0 for some datasets when `cache_dir` is a relative path. I opened https://github.com/huggingface/datasets/pull/6543 to fix this", "We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```" ]

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[ "@sabman @pvl @kashif @vigsterkr ", "This is surely the same issue as https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/25 that comes from the `transformers` `Trainer`. You should add `remove_unused_columns=False` to `TrainingArguments`\r\n\r\nAlso check your logs: the `Trainer` should log the length of your dataset before training starts and it surely showed length=0." ]

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[ "@albertvillanova" ]

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[ "Hi ! Thanks for reporting. I opened https://github.com/huggingface/datasets/pull/6544 to fix this", "We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```" ]

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[ "You can simply use a python dict as index:\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> ds = load_dataset(\"BeIR/dbpedia-entity\", \"corpus\", split=\"corpus\")\r\n>>> index = {key: idx for idx, key in enumerate(ds[\"_id\"])}\r\n>>> ds[index[\"<dbpedia:Pikachu>\"]]\r\n{'_id': '<dbpedia:Pikachu>',\r\n 'title': 'Pikachu',\r\n 'text': 'Pikachu (Japanese: ピカチュウ) are a fictional species of Pokémon. Pokémon are fictional creatures that appear in an assortment of comic books, animated movies and television shows, video games, and trading card games licensed by The Pokémon Company, a Japanese corporation. The Pikachu design was conceived by Ken Sugimori.'}\r\n```", "Thanks for your reply. Yes, I can do that, but it is time-consuming to do that every time I launch the program (some datasets are extremely big). HF Datasets has a nice feature to support instant data loading and efficient random access via row ids. I'm curious if this beneficial feature could be further extended to custom data columns.\r\n" ]

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[ "I solved it with `train_dataset.with_format(None)`\r\nBut then faced some more issues (which i later solved too).\r\n\r\nHuggingface does not seem to care, so I do. Here is an updated training script which saves a pre-processed (mapped) dataset to your local directory if you specify `--save_precomputed_data_dir=DIR_NAME`. Then use `--train_precomputed_data_dir` with the same dir to load it instead of `--dataset_name`.\r\n\r\n[Proper SDXL trainer code](https://github.com/kopyl/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)\r\n[Notebook for pre-computing a dataset and saving locally](https://colab.research.google.com/drive/17Yo08hePx-NlHs99RecdeiNc8CQg4O7l?usp=sharing)\r\n\r\nExample:\r\n\r\n1st run (nothing is pre-computed yet):\r\n```\r\naccelerate launch train_text_to_image_sdxl.py \\\r\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\r\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\r\n --dataset_name=lambdalabs/pokemon-blip-captions \\\r\n --save_precomputed_data_dir=\"test-5\"\r\n```\r\n\r\n2nd run (the pre-computed dataset is saved to `test-5` directory):\r\n```\r\naccelerate launch train_text_to_image_sdxl.py \\\r\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\r\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\r\n --train_precomputed_data_dir test-5\r\n```\r\n\r\nThis way when you're gonna be using your pre-computed dataset you don't need to worry about re-mapping your dataset when you change an argument for your trainer script" ]

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[ "The only way right now is to load with streaming=True", "This feature has been proposed for a long time. I'm looking forward to the implementation. On clusters `streaming=True` is not an option since we do not have Internet on compute nodes. See: https://github.com/huggingface/datasets/discussions/1896#discussioncomment-2359593" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6528). 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) | 0.004875 / 0.011353 (-0.006478) | 0.003501 / 0.011008 (-0.007507) | 0.062604 / 0.038508 (0.024096) | 0.031916 / 0.023109 (0.008806) | 0.256138 / 0.275898 (-0.019760) | 0.278514 / 0.323480 (-0.044966) | 0.002917 / 0.007986 (-0.005069) | 0.002636 / 0.004328 (-0.001693) | 0.049154 / 0.004250 (0.044904) | 0.041985 / 0.037052 (0.004933) | 0.256857 / 0.258489 (-0.001632) | 0.282628 / 0.293841 (-0.011213) | 0.027506 / 0.128546 (-0.101041) | 0.010736 / 0.075646 (-0.064910) | 0.207268 / 0.419271 (-0.212003) | 0.035312 / 0.043533 (-0.008221) | 0.259274 / 0.255139 (0.004135) | 0.281463 / 0.283200 (-0.001737) | 0.019905 / 0.141683 (-0.121778) | 1.108719 / 1.452155 (-0.343435) | 1.177871 / 1.492716 (-0.314845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004435 / 0.018006 (-0.013571) | 0.310643 / 0.000490 (0.310153) | 0.000243 / 0.000200 (0.000043) | 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) | 0.018013 / 0.037411 (-0.019398) | 0.060702 / 0.014526 (0.046176) | 0.073243 / 0.176557 (-0.103314) | 0.119523 / 0.737135 (-0.617613) | 0.074204 / 0.296338 (-0.222134) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281075 / 0.215209 (0.065866) | 2.722154 / 2.077655 (0.644499) | 1.441052 / 1.504120 (-0.063068) | 1.305940 / 1.541195 (-0.235255) | 1.356752 / 1.468490 (-0.111738) | 0.570399 / 4.584777 (-4.014378) | 2.329158 / 3.745712 (-1.416554) | 2.749093 / 5.269862 (-2.520768) | 1.717752 / 4.565676 (-2.847925) | 0.063228 / 0.424275 (-0.361047) | 0.004981 / 0.007607 (-0.002626) | 0.330601 / 0.226044 (0.104557) | 3.300987 / 2.268929 (1.032059) | 1.778673 / 55.444624 (-53.665951) | 1.507841 / 6.876477 (-5.368636) | 1.520454 / 2.142072 (-0.621619) | 0.650816 / 4.805227 (-4.154412) | 0.118606 / 6.500664 (-6.382058) | 0.042199 / 0.075469 (-0.033271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.919668 / 1.841788 (-0.922119) | 11.293437 / 8.074308 (3.219129) | 9.928525 / 10.191392 (-0.262867) | 0.127142 / 0.680424 (-0.553282) | 0.013470 / 0.534201 (-0.520731) | 0.284648 / 0.579283 (-0.294636) | 0.264942 / 0.434364 (-0.169422) | 0.321866 / 0.540337 (-0.218471) | 0.414513 / 1.386936 (-0.972423) |\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) | 0.005052 / 0.011353 (-0.006301) | 0.003204 / 0.011008 (-0.007804) | 0.051102 / 0.038508 (0.012594) | 0.032105 / 0.023109 (0.008996) | 0.273923 / 0.275898 (-0.001976) | 0.297031 / 0.323480 (-0.026449) | 0.004002 / 0.007986 (-0.003984) | 0.002636 / 0.004328 (-0.001693) | 0.047696 / 0.004250 (0.043445) | 0.044086 / 0.037052 (0.007034) | 0.277779 / 0.258489 (0.019289) | 0.306678 / 0.293841 (0.012837) | 0.028557 / 0.128546 (-0.099989) | 0.010631 / 0.075646 (-0.065015) | 0.056419 / 0.419271 (-0.362852) | 0.054285 / 0.043533 (0.010752) | 0.276506 / 0.255139 (0.021367) | 0.296315 / 0.283200 (0.013116) | 0.018642 / 0.141683 (-0.123040) | 1.146926 / 1.452155 (-0.305229) | 1.257625 / 1.492716 (-0.235092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094231 / 0.018006 (0.076225) | 0.302805 / 0.000490 (0.302315) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022510 / 0.037411 (-0.014901) | 0.076092 / 0.014526 (0.061566) | 0.090642 / 0.176557 (-0.085915) | 0.127016 / 0.737135 (-0.610120) | 0.089169 / 0.296338 (-0.207169) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290812 / 0.215209 (0.075603) | 2.858528 / 2.077655 (0.780873) | 1.577555 / 1.504120 (0.073436) | 1.447810 / 1.541195 (-0.093384) | 1.447546 / 1.468490 (-0.020944) | 0.559118 / 4.584777 (-4.025659) | 2.408930 / 3.745712 (-1.336782) | 2.733761 / 5.269862 (-2.536101) | 1.700107 / 4.565676 (-2.865570) | 0.062447 / 0.424275 (-0.361828) | 0.004999 / 0.007607 (-0.002608) | 0.340207 / 0.226044 (0.114162) | 3.344131 / 2.268929 (1.075203) | 1.902289 / 55.444624 (-53.542335) | 1.628226 / 6.876477 (-5.248251) | 1.629435 / 2.142072 (-0.512637) | 0.625011 / 4.805227 (-4.180216) | 0.119929 / 6.500664 (-6.380735) | 0.041097 / 0.075469 (-0.034372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977461 / 1.841788 (-0.864327) | 12.303189 / 8.074308 (4.228881) | 11.008743 / 10.191392 (0.817351) | 0.128578 / 0.680424 (-0.551845) | 0.015305 / 0.534201 (-0.518896) | 0.286468 / 0.579283 (-0.292816) | 0.275824 / 0.434364 (-0.158540) | 0.321487 / 0.540337 (-0.218851) | 0.420591 / 1.386936 (-0.966345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6527). 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) | 0.004870 / 0.011353 (-0.006483) | 0.003606 / 0.011008 (-0.007402) | 0.062719 / 0.038508 (0.024211) | 0.031785 / 0.023109 (0.008676) | 0.238809 / 0.275898 (-0.037089) | 0.263000 / 0.323480 (-0.060480) | 0.002844 / 0.007986 (-0.005142) | 0.002698 / 0.004328 (-0.001631) | 0.048070 / 0.004250 (0.043819) | 0.042333 / 0.037052 (0.005280) | 0.243032 / 0.258489 (-0.015457) | 0.273197 / 0.293841 (-0.020644) | 0.027498 / 0.128546 (-0.101048) | 0.010592 / 0.075646 (-0.065055) | 0.204770 / 0.419271 (-0.214502) | 0.034837 / 0.043533 (-0.008696) | 0.242518 / 0.255139 (-0.012621) | 0.267461 / 0.283200 (-0.015739) | 0.018479 / 0.141683 (-0.123204) | 1.105444 / 1.452155 (-0.346710) | 1.163659 / 1.492716 (-0.329057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004717 / 0.018006 (-0.013289) | 0.303338 / 0.000490 (0.302849) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018298 / 0.037411 (-0.019113) | 0.061225 / 0.014526 (0.046699) | 0.073514 / 0.176557 (-0.103043) | 0.120230 / 0.737135 (-0.616905) | 0.076195 / 0.296338 (-0.220144) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284731 / 0.215209 (0.069522) | 2.773463 / 2.077655 (0.695809) | 1.498239 / 1.504120 (-0.005881) | 1.372143 / 1.541195 (-0.169052) | 1.448949 / 1.468490 (-0.019542) | 0.572516 / 4.584777 (-4.012261) | 2.404041 / 3.745712 (-1.341671) | 2.763047 / 5.269862 (-2.506814) | 1.722419 / 4.565676 (-2.843257) | 0.063104 / 0.424275 (-0.361172) | 0.004989 / 0.007607 (-0.002618) | 0.341864 / 0.226044 (0.115820) | 3.391635 / 2.268929 (1.122707) | 1.872694 / 55.444624 (-53.571931) | 1.594490 / 6.876477 (-5.281987) | 1.596940 / 2.142072 (-0.545132) | 0.645265 / 4.805227 (-4.159962) | 0.117408 / 6.500664 (-6.383256) | 0.042405 / 0.075469 (-0.033064) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963207 / 1.841788 (-0.878580) | 11.676551 / 8.074308 (3.602243) | 10.194287 / 10.191392 (0.002895) | 0.130329 / 0.680424 (-0.550094) | 0.015381 / 0.534201 (-0.518820) | 0.288848 / 0.579283 (-0.290435) | 0.264781 / 0.434364 (-0.169583) | 0.321212 / 0.540337 (-0.219126) | 0.418308 / 1.386936 (-0.968628) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005533 / 0.011353 (-0.005819) | 0.003733 / 0.011008 (-0.007276) | 0.048877 / 0.038508 (0.010369) | 0.030263 / 0.023109 (0.007154) | 0.281161 / 0.275898 (0.005263) | 0.302971 / 0.323480 (-0.020509) | 0.003943 / 0.007986 (-0.004043) | 0.002717 / 0.004328 (-0.001612) | 0.047845 / 0.004250 (0.043594) | 0.045809 / 0.037052 (0.008757) | 0.283337 / 0.258489 (0.024848) | 0.312914 / 0.293841 (0.019073) | 0.029074 / 0.128546 (-0.099472) | 0.010775 / 0.075646 (-0.064871) | 0.057461 / 0.419271 (-0.361810) | 0.053756 / 0.043533 (0.010223) | 0.281809 / 0.255139 (0.026670) | 0.298339 / 0.283200 (0.015139) | 0.019270 / 0.141683 (-0.122413) | 1.117575 / 1.452155 (-0.334580) | 1.191703 / 1.492716 (-0.301013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093513 / 0.018006 (0.075507) | 0.301267 / 0.000490 (0.300777) | 0.000211 / 0.000200 (0.000012) | 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) | 0.022278 / 0.037411 (-0.015133) | 0.076805 / 0.014526 (0.062279) | 0.088820 / 0.176557 (-0.087736) | 0.127903 / 0.737135 (-0.609233) | 0.092988 / 0.296338 (-0.203350) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297787 / 0.215209 (0.082578) | 2.899652 / 2.077655 (0.821997) | 1.598830 / 1.504120 (0.094710) | 1.469398 / 1.541195 (-0.071797) | 1.511099 / 1.468490 (0.042609) | 0.559785 / 4.584777 (-4.024992) | 2.426448 / 3.745712 (-1.319264) | 2.798811 / 5.269862 (-2.471051) | 1.737790 / 4.565676 (-2.827887) | 0.062219 / 0.424275 (-0.362056) | 0.005120 / 0.007607 (-0.002487) | 0.351051 / 0.226044 (0.125007) | 3.492063 / 2.268929 (1.223134) | 1.965674 / 55.444624 (-53.478950) | 1.672874 / 6.876477 (-5.203603) | 1.709700 / 2.142072 (-0.432373) | 0.639347 / 4.805227 (-4.165880) | 0.126383 / 6.500664 (-6.374281) | 0.042731 / 0.075469 (-0.032738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968619 / 1.841788 (-0.873168) | 12.671030 / 8.074308 (4.596722) | 11.125347 / 10.191392 (0.933955) | 0.142983 / 0.680424 (-0.537441) | 0.015726 / 0.534201 (-0.518475) | 0.288610 / 0.579283 (-0.290673) | 0.276473 / 0.434364 (-0.157891) | 0.326590 / 0.540337 (-0.213748) | 0.423832 / 1.386936 (-0.963104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a85fb52fc8ddb41307e61cbf6a5189f3bba27829 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6526). 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) | 0.005101 / 0.011353 (-0.006252) | 0.003471 / 0.011008 (-0.007537) | 0.062293 / 0.038508 (0.023785) | 0.032650 / 0.023109 (0.009541) | 0.249241 / 0.275898 (-0.026657) | 0.277079 / 0.323480 (-0.046400) | 0.002971 / 0.007986 (-0.005015) | 0.002637 / 0.004328 (-0.001691) | 0.048415 / 0.004250 (0.044165) | 0.042832 / 0.037052 (0.005779) | 0.247840 / 0.258489 (-0.010649) | 0.283994 / 0.293841 (-0.009847) | 0.027764 / 0.128546 (-0.100782) | 0.010544 / 0.075646 (-0.065102) | 0.208810 / 0.419271 (-0.210462) | 0.035744 / 0.043533 (-0.007789) | 0.252811 / 0.255139 (-0.002328) | 0.276163 / 0.283200 (-0.007036) | 0.018581 / 0.141683 (-0.123102) | 1.130043 / 1.452155 (-0.322112) | 1.194298 / 1.492716 (-0.298418) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004488 / 0.018006 (-0.013518) | 0.302072 / 0.000490 (0.301582) | 0.000211 / 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) | 0.017799 / 0.037411 (-0.019613) | 0.061146 / 0.014526 (0.046620) | 0.081796 / 0.176557 (-0.094761) | 0.120407 / 0.737135 (-0.616729) | 0.075211 / 0.296338 (-0.221127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295349 / 0.215209 (0.080140) | 2.953511 / 2.077655 (0.875857) | 1.495332 / 1.504120 (-0.008788) | 1.364144 / 1.541195 (-0.177051) | 1.429562 / 1.468490 (-0.038928) | 0.574325 / 4.584777 (-4.010452) | 2.384352 / 3.745712 (-1.361360) | 2.843625 / 5.269862 (-2.426236) | 1.806802 / 4.565676 (-2.758875) | 0.065076 / 0.424275 (-0.359199) | 0.004970 / 0.007607 (-0.002638) | 0.339935 / 0.226044 (0.113891) | 3.375103 / 2.268929 (1.106175) | 1.822921 / 55.444624 (-53.621703) | 1.546126 / 6.876477 (-5.330350) | 1.573630 / 2.142072 (-0.568442) | 0.655081 / 4.805227 (-4.150146) | 0.122446 / 6.500664 (-6.378218) | 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) | 0.942127 / 1.841788 (-0.899661) | 11.470401 / 8.074308 (3.396093) | 10.025961 / 10.191392 (-0.165431) | 0.129087 / 0.680424 (-0.551337) | 0.014141 / 0.534201 (-0.520060) | 0.285470 / 0.579283 (-0.293813) | 0.266755 / 0.434364 (-0.167608) | 0.323391 / 0.540337 (-0.216947) | 0.427645 / 1.386936 (-0.959291) |\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) | 0.005578 / 0.011353 (-0.005775) | 0.003734 / 0.011008 (-0.007274) | 0.049200 / 0.038508 (0.010692) | 0.030981 / 0.023109 (0.007872) | 0.281195 / 0.275898 (0.005297) | 0.309950 / 0.323480 (-0.013530) | 0.004046 / 0.007986 (-0.003939) | 0.002709 / 0.004328 (-0.001620) | 0.048505 / 0.004250 (0.044254) | 0.046245 / 0.037052 (0.009193) | 0.280130 / 0.258489 (0.021641) | 0.313739 / 0.293841 (0.019898) | 0.029828 / 0.128546 (-0.098718) | 0.011152 / 0.075646 (-0.064495) | 0.057753 / 0.419271 (-0.361518) | 0.055112 / 0.043533 (0.011580) | 0.281861 / 0.255139 (0.026722) | 0.304402 / 0.283200 (0.021203) | 0.019931 / 0.141683 (-0.121752) | 1.150585 / 1.452155 (-0.301570) | 1.217850 / 1.492716 (-0.274866) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091552 / 0.018006 (0.073546) | 0.301772 / 0.000490 (0.301282) | 0.000225 / 0.000200 (0.000025) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023189 / 0.037411 (-0.014223) | 0.078741 / 0.014526 (0.064216) | 0.092320 / 0.176557 (-0.084236) | 0.129636 / 0.737135 (-0.607500) | 0.091673 / 0.296338 (-0.204665) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298542 / 0.215209 (0.083333) | 2.899358 / 2.077655 (0.821703) | 1.673896 / 1.504120 (0.169776) | 1.489518 / 1.541195 (-0.051677) | 1.542853 / 1.468490 (0.074363) | 0.559843 / 4.584777 (-4.024934) | 2.422101 / 3.745712 (-1.323611) | 2.844592 / 5.269862 (-2.425270) | 1.794527 / 4.565676 (-2.771150) | 0.064615 / 0.424275 (-0.359660) | 0.005078 / 0.007607 (-0.002530) | 0.355112 / 0.226044 (0.129068) | 3.462129 / 2.268929 (1.193200) | 1.975393 / 55.444624 (-53.469231) | 1.706513 / 6.876477 (-5.169963) | 1.716954 / 2.142072 (-0.425118) | 0.642094 / 4.805227 (-4.163133) | 0.119215 / 6.500664 (-6.381449) | 0.041941 / 0.075469 (-0.033528) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986774 / 1.841788 (-0.855014) | 12.702049 / 8.074308 (4.627741) | 11.727663 / 10.191392 (1.536271) | 0.135008 / 0.680424 (-0.545416) | 0.016055 / 0.534201 (-0.518146) | 0.293564 / 0.579283 (-0.285719) | 0.284884 / 0.434364 (-0.149480) | 0.332524 / 0.540337 (-0.207814) | 0.425392 / 1.386936 (-0.961544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b5fc585fcaf77b92839e82d0ce2c2fbf0d9ea95 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "closing in favor of other ideas that would not involve any typing" ]

[]

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[ "Hello @FelixLabelle,\r\n\r\nAs you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15\r\n\r\nThe data has been taken down due to reported copyright infringement.\r\n\r\nFeel free to continue the discussion there." ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6523). 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) | 0.005160 / 0.011353 (-0.006192) | 0.003962 / 0.011008 (-0.007046) | 0.064952 / 0.038508 (0.026444) | 0.053291 / 0.023109 (0.030182) | 0.237182 / 0.275898 (-0.038716) | 0.263855 / 0.323480 (-0.059625) | 0.004157 / 0.007986 (-0.003829) | 0.002901 / 0.004328 (-0.001428) | 0.050679 / 0.004250 (0.046428) | 0.044885 / 0.037052 (0.007832) | 0.243806 / 0.258489 (-0.014683) | 0.273828 / 0.293841 (-0.020013) | 0.028681 / 0.128546 (-0.099866) | 0.011086 / 0.075646 (-0.064560) | 0.211987 / 0.419271 (-0.207285) | 0.035881 / 0.043533 (-0.007652) | 0.249618 / 0.255139 (-0.005521) | 0.262880 / 0.283200 (-0.020319) | 0.017788 / 0.141683 (-0.123895) | 1.209060 / 1.452155 (-0.243094) | 1.272143 / 1.492716 (-0.220574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004594 / 0.018006 (-0.013412) | 0.305188 / 0.000490 (0.304698) | 0.000213 / 0.000200 (0.000013) | 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) | 0.019526 / 0.037411 (-0.017886) | 0.062280 / 0.014526 (0.047754) | 0.074983 / 0.176557 (-0.101573) | 0.123466 / 0.737135 (-0.613670) | 0.076240 / 0.296338 (-0.220099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276001 / 0.215209 (0.060792) | 2.689614 / 2.077655 (0.611959) | 1.441092 / 1.504120 (-0.063028) | 1.319775 / 1.541195 (-0.221419) | 1.386904 / 1.468490 (-0.081587) | 0.561388 / 4.584777 (-4.023389) | 2.386718 / 3.745712 (-1.358994) | 2.813959 / 5.269862 (-2.455903) | 1.727447 / 4.565676 (-2.838230) | 0.061965 / 0.424275 (-0.362310) | 0.004977 / 0.007607 (-0.002630) | 0.335077 / 0.226044 (0.109032) | 3.313860 / 2.268929 (1.044932) | 1.814018 / 55.444624 (-53.630606) | 1.542840 / 6.876477 (-5.333637) | 1.586887 / 2.142072 (-0.555185) | 0.643225 / 4.805227 (-4.162002) | 0.117834 / 6.500664 (-6.382830) | 0.044024 / 0.075469 (-0.031445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952804 / 1.841788 (-0.888984) | 12.447378 / 8.074308 (4.373070) | 11.281734 / 10.191392 (1.090342) | 0.143407 / 0.680424 (-0.537017) | 0.014749 / 0.534201 (-0.519452) | 0.289298 / 0.579283 (-0.289985) | 0.268217 / 0.434364 (-0.166146) | 0.327995 / 0.540337 (-0.212343) | 0.430302 / 1.386936 (-0.956634) |\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) | 0.005683 / 0.011353 (-0.005670) | 0.003813 / 0.011008 (-0.007195) | 0.048943 / 0.038508 (0.010435) | 0.060730 / 0.023109 (0.037621) | 0.266925 / 0.275898 (-0.008973) | 0.292553 / 0.323480 (-0.030927) | 0.004236 / 0.007986 (-0.003750) | 0.002790 / 0.004328 (-0.001538) | 0.048962 / 0.004250 (0.044711) | 0.040354 / 0.037052 (0.003302) | 0.266353 / 0.258489 (0.007864) | 0.298397 / 0.293841 (0.004556) | 0.029977 / 0.128546 (-0.098570) | 0.010788 / 0.075646 (-0.064858) | 0.057529 / 0.419271 (-0.361743) | 0.032896 / 0.043533 (-0.010636) | 0.266696 / 0.255139 (0.011557) | 0.283422 / 0.283200 (0.000223) | 0.020939 / 0.141683 (-0.120744) | 1.169867 / 1.452155 (-0.282287) | 1.213586 / 1.492716 (-0.279130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097035 / 0.018006 (0.079029) | 0.306968 / 0.000490 (0.306478) | 0.000234 / 0.000200 (0.000034) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023343 / 0.037411 (-0.014068) | 0.078238 / 0.014526 (0.063712) | 0.091083 / 0.176557 (-0.085474) | 0.131487 / 0.737135 (-0.605649) | 0.092614 / 0.296338 (-0.203724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294454 / 0.215209 (0.079245) | 2.881053 / 2.077655 (0.803398) | 1.623934 / 1.504120 (0.119814) | 1.509001 / 1.541195 (-0.032194) | 1.567541 / 1.468490 (0.099051) | 0.574326 / 4.584777 (-4.010451) | 2.476826 / 3.745712 (-1.268886) | 2.826183 / 5.269862 (-2.443678) | 1.771949 / 4.565676 (-2.793727) | 0.063663 / 0.424275 (-0.360613) | 0.005039 / 0.007607 (-0.002568) | 0.354861 / 0.226044 (0.128816) | 3.397655 / 2.268929 (1.128727) | 1.961958 / 55.444624 (-53.482666) | 1.694795 / 6.876477 (-5.181682) | 1.719459 / 2.142072 (-0.422614) | 0.654512 / 4.805227 (-4.150715) | 0.119285 / 6.500664 (-6.381379) | 0.042146 / 0.075469 (-0.033323) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982187 / 1.841788 (-0.859601) | 12.944627 / 8.074308 (4.870319) | 11.370381 / 10.191392 (1.178989) | 0.142759 / 0.680424 (-0.537665) | 0.016319 / 0.534201 (-0.517882) | 0.291339 / 0.579283 (-0.287944) | 0.276842 / 0.434364 (-0.157522) | 0.324285 / 0.540337 (-0.216052) | 0.426234 / 1.386936 (-0.960702) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e1b82eaa75d2c610e59b463a67d685ec858c0838 \"CML watermark\")\n" ]

[]

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[ "After investigation, I think the issue was introduced by the use of the Parquet export:\r\n- #6448\r\n\r\nI am proposing a fix.\r\n\r\nCC: @lhoestq " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6520). 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) | 0.005484 / 0.011353 (-0.005869) | 0.003537 / 0.011008 (-0.007471) | 0.062631 / 0.038508 (0.024123) | 0.048037 / 0.023109 (0.024927) | 0.240342 / 0.275898 (-0.035556) | 0.268103 / 0.323480 (-0.055377) | 0.002927 / 0.007986 (-0.005059) | 0.002609 / 0.004328 (-0.001719) | 0.048112 / 0.004250 (0.043862) | 0.046111 / 0.037052 (0.009058) | 0.249249 / 0.258489 (-0.009240) | 0.277723 / 0.293841 (-0.016118) | 0.028374 / 0.128546 (-0.100172) | 0.010900 / 0.075646 (-0.064746) | 0.206252 / 0.419271 (-0.213019) | 0.035262 / 0.043533 (-0.008271) | 0.247438 / 0.255139 (-0.007701) | 0.270003 / 0.283200 (-0.013197) | 0.019157 / 0.141683 (-0.122526) | 1.116833 / 1.452155 (-0.335322) | 1.174495 / 1.492716 (-0.318221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092490 / 0.018006 (0.074484) | 0.302794 / 0.000490 (0.302304) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018669 / 0.037411 (-0.018743) | 0.061902 / 0.014526 (0.047376) | 0.073612 / 0.176557 (-0.102945) | 0.121196 / 0.737135 (-0.615940) | 0.075960 / 0.296338 (-0.220378) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286983 / 0.215209 (0.071774) | 2.836819 / 2.077655 (0.759165) | 1.506635 / 1.504120 (0.002515) | 1.387134 / 1.541195 (-0.154061) | 1.442310 / 1.468490 (-0.026180) | 0.571281 / 4.584777 (-4.013496) | 2.440220 / 3.745712 (-1.305492) | 2.775306 / 5.269862 (-2.494555) | 1.727047 / 4.565676 (-2.838630) | 0.064955 / 0.424275 (-0.359320) | 0.004982 / 0.007607 (-0.002625) | 0.343153 / 0.226044 (0.117108) | 3.388745 / 2.268929 (1.119817) | 1.878983 / 55.444624 (-53.565641) | 1.592642 / 6.876477 (-5.283835) | 1.601037 / 2.142072 (-0.541035) | 0.636882 / 4.805227 (-4.168345) | 0.117804 / 6.500664 (-6.382861) | 0.042467 / 0.075469 (-0.033002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941534 / 1.841788 (-0.900254) | 12.093230 / 8.074308 (4.018922) | 10.590854 / 10.191392 (0.399462) | 0.136636 / 0.680424 (-0.543788) | 0.015244 / 0.534201 (-0.518957) | 0.300216 / 0.579283 (-0.279067) | 0.267622 / 0.434364 (-0.166742) | 0.337526 / 0.540337 (-0.202811) | 0.426856 / 1.386936 (-0.960080) |\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) | 0.005282 / 0.011353 (-0.006071) | 0.003595 / 0.011008 (-0.007413) | 0.049237 / 0.038508 (0.010729) | 0.054057 / 0.023109 (0.030948) | 0.269781 / 0.275898 (-0.006117) | 0.293544 / 0.323480 (-0.029936) | 0.003991 / 0.007986 (-0.003995) | 0.002705 / 0.004328 (-0.001623) | 0.048755 / 0.004250 (0.044505) | 0.040425 / 0.037052 (0.003373) | 0.264753 / 0.258489 (0.006264) | 0.312773 / 0.293841 (0.018932) | 0.030011 / 0.128546 (-0.098535) | 0.010707 / 0.075646 (-0.064939) | 0.058164 / 0.419271 (-0.361107) | 0.033365 / 0.043533 (-0.010168) | 0.268854 / 0.255139 (0.013715) | 0.283618 / 0.283200 (0.000418) | 0.019571 / 0.141683 (-0.122111) | 1.114738 / 1.452155 (-0.337417) | 1.178990 / 1.492716 (-0.313726) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092183 / 0.018006 (0.074177) | 0.303797 / 0.000490 (0.303307) | 0.000218 / 0.000200 (0.000018) | 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) | 0.023088 / 0.037411 (-0.014323) | 0.079813 / 0.014526 (0.065287) | 0.089593 / 0.176557 (-0.086964) | 0.128127 / 0.737135 (-0.609008) | 0.091578 / 0.296338 (-0.204761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300153 / 0.215209 (0.084944) | 2.919532 / 2.077655 (0.841877) | 1.587870 / 1.504120 (0.083750) | 1.459031 / 1.541195 (-0.082164) | 1.483305 / 1.468490 (0.014815) | 0.555865 / 4.584777 (-4.028912) | 2.388350 / 3.745712 (-1.357362) | 2.817947 / 5.269862 (-2.451914) | 1.764446 / 4.565676 (-2.801230) | 0.067142 / 0.424275 (-0.357133) | 0.005148 / 0.007607 (-0.002460) | 0.347998 / 0.226044 (0.121953) | 3.431208 / 2.268929 (1.162280) | 1.942175 / 55.444624 (-53.502450) | 1.676606 / 6.876477 (-5.199871) | 1.692431 / 2.142072 (-0.449641) | 0.645974 / 4.805227 (-4.159253) | 0.117729 / 6.500664 (-6.382935) | 0.041670 / 0.075469 (-0.033799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981554 / 1.841788 (-0.860234) | 12.671959 / 8.074308 (4.597650) | 11.230694 / 10.191392 (1.039302) | 0.132694 / 0.680424 (-0.547730) | 0.015694 / 0.534201 (-0.518507) | 0.290271 / 0.579283 (-0.289013) | 0.279358 / 0.434364 (-0.155006) | 0.326515 / 0.540337 (-0.213823) | 0.421755 / 1.386936 (-0.965181) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b2147ac644596b66886f398012351641672ee54 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6519). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "nice catch @albertvillanova ", "@huggingface/datasets this PR is ready for review.", "<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) | 0.005306 / 0.011353 (-0.006047) | 0.003454 / 0.011008 (-0.007555) | 0.062157 / 0.038508 (0.023649) | 0.051945 / 0.023109 (0.028835) | 0.241834 / 0.275898 (-0.034064) | 0.265590 / 0.323480 (-0.057890) | 0.003149 / 0.007986 (-0.004837) | 0.002695 / 0.004328 (-0.001633) | 0.049197 / 0.004250 (0.044947) | 0.045576 / 0.037052 (0.008524) | 0.242866 / 0.258489 (-0.015623) | 0.280963 / 0.293841 (-0.012878) | 0.028466 / 0.128546 (-0.100080) | 0.010670 / 0.075646 (-0.064976) | 0.206501 / 0.419271 (-0.212771) | 0.035314 / 0.043533 (-0.008219) | 0.240893 / 0.255139 (-0.014246) | 0.264762 / 0.283200 (-0.018438) | 0.019988 / 0.141683 (-0.121695) | 1.095222 / 1.452155 (-0.356933) | 1.144051 / 1.492716 (-0.348666) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098034 / 0.018006 (0.080028) | 0.308541 / 0.000490 (0.308051) | 0.000261 / 0.000200 (0.000061) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018646 / 0.037411 (-0.018766) | 0.062881 / 0.014526 (0.048355) | 0.074062 / 0.176557 (-0.102494) | 0.120860 / 0.737135 (-0.616276) | 0.075388 / 0.296338 (-0.220951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282974 / 0.215209 (0.067765) | 2.755589 / 2.077655 (0.677934) | 1.459536 / 1.504120 (-0.044584) | 1.364543 / 1.541195 (-0.176652) | 1.429860 / 1.468490 (-0.038630) | 0.573277 / 4.584777 (-4.011500) | 2.422983 / 3.745712 (-1.322730) | 3.257258 / 5.269862 (-2.012603) | 1.930053 / 4.565676 (-2.635623) | 0.067476 / 0.424275 (-0.356799) | 0.005612 / 0.007607 (-0.001995) | 0.351538 / 0.226044 (0.125494) | 3.380356 / 2.268929 (1.111427) | 1.837887 / 55.444624 (-53.606738) | 1.537994 / 6.876477 (-5.338483) | 1.623630 / 2.142072 (-0.518442) | 0.662652 / 4.805227 (-4.142576) | 0.127074 / 6.500664 (-6.373590) | 0.049311 / 0.075469 (-0.026158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.151273 / 1.841788 (-0.690515) | 12.766622 / 8.074308 (4.692314) | 10.967610 / 10.191392 (0.776218) | 0.131305 / 0.680424 (-0.549119) | 0.014227 / 0.534201 (-0.519974) | 0.292054 / 0.579283 (-0.287229) | 0.262737 / 0.434364 (-0.171627) | 0.334360 / 0.540337 (-0.205978) | 0.446711 / 1.386936 (-0.940225) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003508 / 0.011008 (-0.007500) | 0.049287 / 0.038508 (0.010779) | 0.052109 / 0.023109 (0.029000) | 0.271501 / 0.275898 (-0.004397) | 0.290959 / 0.323480 (-0.032521) | 0.004347 / 0.007986 (-0.003638) | 0.002659 / 0.004328 (-0.001669) | 0.048769 / 0.004250 (0.044518) | 0.039388 / 0.037052 (0.002336) | 0.272811 / 0.258489 (0.014322) | 0.305632 / 0.293841 (0.011791) | 0.028419 / 0.128546 (-0.100127) | 0.010617 / 0.075646 (-0.065029) | 0.057433 / 0.419271 (-0.361838) | 0.032383 / 0.043533 (-0.011149) | 0.266566 / 0.255139 (0.011427) | 0.290993 / 0.283200 (0.007794) | 0.019939 / 0.141683 (-0.121743) | 1.157623 / 1.452155 (-0.294532) | 1.183298 / 1.492716 (-0.309419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099074 / 0.018006 (0.081068) | 0.315282 / 0.000490 (0.314792) | 0.000235 / 0.000200 (0.000035) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022692 / 0.037411 (-0.014719) | 0.076455 / 0.014526 (0.061929) | 0.089094 / 0.176557 (-0.087462) | 0.126407 / 0.737135 (-0.610728) | 0.089588 / 0.296338 (-0.206750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.338853 / 0.215209 (0.123644) | 2.809843 / 2.077655 (0.732188) | 1.538262 / 1.504120 (0.034143) | 1.418290 / 1.541195 (-0.122905) | 1.435145 / 1.468490 (-0.033345) | 0.565763 / 4.584777 (-4.019014) | 2.491525 / 3.745712 (-1.254187) | 2.944879 / 5.269862 (-2.324983) | 1.835840 / 4.565676 (-2.729837) | 0.065101 / 0.424275 (-0.359174) | 0.005196 / 0.007607 (-0.002412) | 0.345291 / 0.226044 (0.119247) | 3.399658 / 2.268929 (1.130729) | 1.892321 / 55.444624 (-53.552303) | 1.608293 / 6.876477 (-5.268184) | 1.651188 / 2.142072 (-0.490884) | 0.647806 / 4.805227 (-4.157421) | 0.119318 / 6.500664 (-6.381346) | 0.043058 / 0.075469 (-0.032412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983956 / 1.841788 (-0.857831) | 13.516125 / 8.074308 (5.441817) | 11.712571 / 10.191392 (1.521179) | 0.134253 / 0.680424 (-0.546171) | 0.015844 / 0.534201 (-0.518357) | 0.292444 / 0.579283 (-0.286839) | 0.282182 / 0.434364 (-0.152182) | 0.329327 / 0.540337 (-0.211010) | 0.419960 / 1.386936 (-0.966976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a887ee78835573f5d80f9e414e8443b4caff3541 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6518). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "hello!\r\n@albertvillanova \r\nThank you very much for your recognition。\r\nWhen can this PR 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) | 0.005205 / 0.011353 (-0.006148) | 0.003730 / 0.011008 (-0.007278) | 0.063195 / 0.038508 (0.024687) | 0.052329 / 0.023109 (0.029219) | 0.247299 / 0.275898 (-0.028599) | 0.269600 / 0.323480 (-0.053880) | 0.004801 / 0.007986 (-0.003185) | 0.002728 / 0.004328 (-0.001600) | 0.049195 / 0.004250 (0.044944) | 0.044859 / 0.037052 (0.007807) | 0.253047 / 0.258489 (-0.005442) | 0.277253 / 0.293841 (-0.016588) | 0.028370 / 0.128546 (-0.100176) | 0.011095 / 0.075646 (-0.064551) | 0.211090 / 0.419271 (-0.208182) | 0.035944 / 0.043533 (-0.007589) | 0.252755 / 0.255139 (-0.002384) | 0.269466 / 0.283200 (-0.013733) | 0.017514 / 0.141683 (-0.124169) | 1.107815 / 1.452155 (-0.344339) | 1.154989 / 1.492716 (-0.337728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093925 / 0.018006 (0.075919) | 0.300923 / 0.000490 (0.300433) | 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) | 0.018268 / 0.037411 (-0.019143) | 0.060508 / 0.014526 (0.045983) | 0.074564 / 0.176557 (-0.101992) | 0.121523 / 0.737135 (-0.615612) | 0.077394 / 0.296338 (-0.218945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275859 / 0.215209 (0.060650) | 2.707593 / 2.077655 (0.629938) | 1.419178 / 1.504120 (-0.084942) | 1.286737 / 1.541195 (-0.254458) | 1.350504 / 1.468490 (-0.117986) | 0.570461 / 4.584777 (-4.014316) | 2.400795 / 3.745712 (-1.344917) | 2.840876 / 5.269862 (-2.428986) | 1.724044 / 4.565676 (-2.841633) | 0.063819 / 0.424275 (-0.360456) | 0.004961 / 0.007607 (-0.002647) | 0.342537 / 0.226044 (0.116492) | 3.370942 / 2.268929 (1.102013) | 1.788659 / 55.444624 (-53.655966) | 1.501921 / 6.876477 (-5.374556) | 1.535352 / 2.142072 (-0.606721) | 0.651838 / 4.805227 (-4.153390) | 0.118979 / 6.500664 (-6.381685) | 0.047796 / 0.075469 (-0.027673) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949850 / 1.841788 (-0.891937) | 11.581988 / 8.074308 (3.507680) | 10.462837 / 10.191392 (0.271445) | 0.133298 / 0.680424 (-0.547125) | 0.015008 / 0.534201 (-0.519193) | 0.299265 / 0.579283 (-0.280018) | 0.268864 / 0.434364 (-0.165500) | 0.332888 / 0.540337 (-0.207450) | 0.420423 / 1.386936 (-0.966513) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003628 / 0.011008 (-0.007380) | 0.049545 / 0.038508 (0.011036) | 0.054095 / 0.023109 (0.030985) | 0.270679 / 0.275898 (-0.005219) | 0.295744 / 0.323480 (-0.027736) | 0.004131 / 0.007986 (-0.003855) | 0.002732 / 0.004328 (-0.001596) | 0.048714 / 0.004250 (0.044464) | 0.039916 / 0.037052 (0.002863) | 0.272354 / 0.258489 (0.013865) | 0.310553 / 0.293841 (0.016712) | 0.029525 / 0.128546 (-0.099021) | 0.011322 / 0.075646 (-0.064324) | 0.058007 / 0.419271 (-0.361265) | 0.032883 / 0.043533 (-0.010650) | 0.273609 / 0.255139 (0.018470) | 0.291780 / 0.283200 (0.008581) | 0.020538 / 0.141683 (-0.121145) | 1.118031 / 1.452155 (-0.334123) | 1.160777 / 1.492716 (-0.331940) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092966 / 0.018006 (0.074959) | 0.301432 / 0.000490 (0.300943) | 0.000225 / 0.000200 (0.000025) | 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) | 0.022736 / 0.037411 (-0.014676) | 0.077655 / 0.014526 (0.063129) | 0.093386 / 0.176557 (-0.083171) | 0.129694 / 0.737135 (-0.607441) | 0.092790 / 0.296338 (-0.203548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299161 / 0.215209 (0.083952) | 2.923300 / 2.077655 (0.845645) | 1.629661 / 1.504120 (0.125541) | 1.510797 / 1.541195 (-0.030398) | 1.507269 / 1.468490 (0.038778) | 0.574346 / 4.584777 (-4.010431) | 2.454396 / 3.745712 (-1.291316) | 2.843402 / 5.269862 (-2.426460) | 1.774815 / 4.565676 (-2.790861) | 0.063601 / 0.424275 (-0.360674) | 0.004977 / 0.007607 (-0.002630) | 0.347693 / 0.226044 (0.121649) | 3.430054 / 2.268929 (1.161126) | 1.987308 / 55.444624 (-53.457316) | 1.682756 / 6.876477 (-5.193721) | 1.688463 / 2.142072 (-0.453609) | 0.646449 / 4.805227 (-4.158778) | 0.117860 / 6.500664 (-6.382804) | 0.041305 / 0.075469 (-0.034164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987355 / 1.841788 (-0.854433) | 12.398721 / 8.074308 (4.324412) | 11.070442 / 10.191392 (0.879050) | 0.134946 / 0.680424 (-0.545477) | 0.016172 / 0.534201 (-0.518029) | 0.293359 / 0.579283 (-0.285924) | 0.282271 / 0.434364 (-0.152093) | 0.331919 / 0.540337 (-0.208418) | 0.432137 / 1.386936 (-0.954799) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2246d3187222ef939aa8e69cd1aa476cf9526945 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6516). 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) | 0.005309 / 0.011353 (-0.006044) | 0.003231 / 0.011008 (-0.007777) | 0.062690 / 0.038508 (0.024182) | 0.050811 / 0.023109 (0.027701) | 0.258319 / 0.275898 (-0.017579) | 0.275977 / 0.323480 (-0.047503) | 0.002842 / 0.007986 (-0.005143) | 0.002606 / 0.004328 (-0.001723) | 0.048672 / 0.004250 (0.044421) | 0.038730 / 0.037052 (0.001677) | 0.258531 / 0.258489 (0.000042) | 0.289327 / 0.293841 (-0.004514) | 0.027994 / 0.128546 (-0.100552) | 0.010446 / 0.075646 (-0.065200) | 0.207152 / 0.419271 (-0.212119) | 0.035839 / 0.043533 (-0.007693) | 0.258416 / 0.255139 (0.003277) | 0.274348 / 0.283200 (-0.008851) | 0.019661 / 0.141683 (-0.122022) | 1.103688 / 1.452155 (-0.348466) | 1.207711 / 1.492716 (-0.285006) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090693 / 0.018006 (0.072687) | 0.300648 / 0.000490 (0.300158) | 0.000215 / 0.000200 (0.000015) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018589 / 0.037411 (-0.018822) | 0.061056 / 0.014526 (0.046530) | 0.074512 / 0.176557 (-0.102044) | 0.121260 / 0.737135 (-0.615875) | 0.073111 / 0.296338 (-0.223227) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285811 / 0.215209 (0.070602) | 2.785081 / 2.077655 (0.707426) | 1.469493 / 1.504120 (-0.034627) | 1.346389 / 1.541195 (-0.194806) | 1.391866 / 1.468490 (-0.076624) | 0.567304 / 4.584777 (-4.017473) | 2.407150 / 3.745712 (-1.338562) | 2.809915 / 5.269862 (-2.459946) | 1.741185 / 4.565676 (-2.824491) | 0.063073 / 0.424275 (-0.361202) | 0.004974 / 0.007607 (-0.002633) | 0.336431 / 0.226044 (0.110386) | 3.331371 / 2.268929 (1.062443) | 1.841466 / 55.444624 (-53.603159) | 1.559065 / 6.876477 (-5.317411) | 1.585033 / 2.142072 (-0.557039) | 0.647469 / 4.805227 (-4.157759) | 0.117488 / 6.500664 (-6.383176) | 0.042535 / 0.075469 (-0.032934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936409 / 1.841788 (-0.905379) | 11.301514 / 8.074308 (3.227206) | 10.500465 / 10.191392 (0.309073) | 0.131316 / 0.680424 (-0.549107) | 0.014007 / 0.534201 (-0.520194) | 0.286932 / 0.579283 (-0.292351) | 0.263516 / 0.434364 (-0.170848) | 0.340883 / 0.540337 (-0.199454) | 0.443589 / 1.386936 (-0.943347) |\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) | 0.005204 / 0.011353 (-0.006149) | 0.003472 / 0.011008 (-0.007536) | 0.049235 / 0.038508 (0.010727) | 0.050668 / 0.023109 (0.027559) | 0.270198 / 0.275898 (-0.005700) | 0.293942 / 0.323480 (-0.029538) | 0.003964 / 0.007986 (-0.004022) | 0.002596 / 0.004328 (-0.001733) | 0.048654 / 0.004250 (0.044404) | 0.039411 / 0.037052 (0.002358) | 0.271938 / 0.258489 (0.013449) | 0.304308 / 0.293841 (0.010467) | 0.029042 / 0.128546 (-0.099504) | 0.010414 / 0.075646 (-0.065232) | 0.058273 / 0.419271 (-0.360999) | 0.032507 / 0.043533 (-0.011025) | 0.271671 / 0.255139 (0.016532) | 0.289850 / 0.283200 (0.006650) | 0.017292 / 0.141683 (-0.124391) | 1.126160 / 1.452155 (-0.325995) | 1.177365 / 1.492716 (-0.315351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091158 / 0.018006 (0.073152) | 0.299143 / 0.000490 (0.298653) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022558 / 0.037411 (-0.014853) | 0.076139 / 0.014526 (0.061613) | 0.088344 / 0.176557 (-0.088212) | 0.126640 / 0.737135 (-0.610495) | 0.089736 / 0.296338 (-0.206602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295351 / 0.215209 (0.080142) | 2.895779 / 2.077655 (0.818125) | 1.585886 / 1.504120 (0.081766) | 1.458601 / 1.541195 (-0.082594) | 1.468880 / 1.468490 (0.000390) | 0.554686 / 4.584777 (-4.030091) | 2.466276 / 3.745712 (-1.279437) | 2.741938 / 5.269862 (-2.527924) | 1.711793 / 4.565676 (-2.853883) | 0.062928 / 0.424275 (-0.361347) | 0.005177 / 0.007607 (-0.002430) | 0.343908 / 0.226044 (0.117863) | 3.393360 / 2.268929 (1.124431) | 1.928800 / 55.444624 (-53.515824) | 1.652181 / 6.876477 (-5.224296) | 1.643667 / 2.142072 (-0.498405) | 0.632829 / 4.805227 (-4.172398) | 0.114583 / 6.500664 (-6.386081) | 0.041248 / 0.075469 (-0.034221) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986196 / 1.841788 (-0.855592) | 12.006772 / 8.074308 (3.932464) | 10.522661 / 10.191392 (0.331269) | 0.133710 / 0.680424 (-0.546713) | 0.016714 / 0.534201 (-0.517487) | 0.286502 / 0.579283 (-0.292781) | 0.280090 / 0.434364 (-0.154273) | 0.326063 / 0.540337 (-0.214275) | 0.548485 / 1.386936 (-0.838452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f149204a2a5948287adcade5e90707aa5207a92 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6514). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "it's hard to tell if this works as expected without a test but i guess it's not trivial to implement such a test.\r\n\r\ni tried to reproduce locally (with this branch merged into the lazy-resolve-and-cache-reload) and it didn't work. \r\nI run:\r\n```\r\n ds = load_dataset(\"polinaeterna/audiofolder_two_configs_in_metadata\", \"v2\", data_files=\"v2/train/*\") \r\n```\r\nand i got this in the cache:\r\n```\r\nv2-374bfde4f55442bc/\r\n└── 0.0.0\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5 # - from this pr\r\n │   ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │   └── dataset_info.json\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5_builder.lock\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5.incomplete_info.lock\r\n ├── 7896925d64deea5d # from 2.15.0\r\n │   ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │   └── dataset_info.json\r\n ├── 7896925d64deea5d_builder.lock\r\n └── 7896925d64deea5d.incomplete_info.lock\r\n```\r\nso the first hash (the top-level dir v2-374bfde4f55442bc) matches but the second (after version) doesn't.\r\nmaybe i did something wrong though.\r\n\r\nalso i'm not sure if this is worth too much effort, maybe nobody notices if their datasets will be generated again :D idk", "I just pushed a fix, it should work just fine now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004798 / 0.011353 (-0.006555) | 0.003203 / 0.011008 (-0.007805) | 0.062247 / 0.038508 (0.023738) | 0.029906 / 0.023109 (0.006797) | 0.259370 / 0.275898 (-0.016528) | 0.276084 / 0.323480 (-0.047396) | 0.002910 / 0.007986 (-0.005076) | 0.002364 / 0.004328 (-0.001964) | 0.048080 / 0.004250 (0.043830) | 0.041168 / 0.037052 (0.004116) | 0.259833 / 0.258489 (0.001343) | 0.289882 / 0.293841 (-0.003959) | 0.026790 / 0.128546 (-0.101756) | 0.010336 / 0.075646 (-0.065311) | 0.209628 / 0.419271 (-0.209643) | 0.035080 / 0.043533 (-0.008452) | 0.256278 / 0.255139 (0.001139) | 0.279502 / 0.283200 (-0.003697) | 0.019755 / 0.141683 (-0.121928) | 1.121552 / 1.452155 (-0.330602) | 1.174360 / 1.492716 (-0.318356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093510 / 0.018006 (0.075504) | 0.302065 / 0.000490 (0.301575) | 0.000214 / 0.000200 (0.000014) | 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) | 0.017652 / 0.037411 (-0.019759) | 0.060512 / 0.014526 (0.045986) | 0.072441 / 0.176557 (-0.104115) | 0.118058 / 0.737135 (-0.619078) | 0.072657 / 0.296338 (-0.223682) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283949 / 0.215209 (0.068740) | 2.803275 / 2.077655 (0.725620) | 1.527353 / 1.504120 (0.023233) | 1.408176 / 1.541195 (-0.133019) | 1.375335 / 1.468490 (-0.093155) | 0.546426 / 4.584777 (-4.038351) | 2.402210 / 3.745712 (-1.343502) | 2.765879 / 5.269862 (-2.503982) | 1.703722 / 4.565676 (-2.861955) | 0.062669 / 0.424275 (-0.361606) | 0.005006 / 0.007607 (-0.002601) | 0.337941 / 0.226044 (0.111897) | 3.385494 / 2.268929 (1.116566) | 1.817360 / 55.444624 (-53.627264) | 1.548594 / 6.876477 (-5.327883) | 1.548610 / 2.142072 (-0.593463) | 0.630188 / 4.805227 (-4.175040) | 0.117079 / 6.500664 (-6.383585) | 0.042077 / 0.075469 (-0.033392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941606 / 1.841788 (-0.900182) | 11.226277 / 8.074308 (3.151969) | 10.118005 / 10.191392 (-0.073387) | 0.130408 / 0.680424 (-0.550015) | 0.014419 / 0.534201 (-0.519782) | 0.284812 / 0.579283 (-0.294471) | 0.266951 / 0.434364 (-0.167413) | 0.322251 / 0.540337 (-0.218087) | 0.415014 / 1.386936 (-0.971922) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003028 / 0.011008 (-0.007980) | 0.048322 / 0.038508 (0.009814) | 0.030550 / 0.023109 (0.007441) | 0.264360 / 0.275898 (-0.011538) | 0.289544 / 0.323480 (-0.033936) | 0.004053 / 0.007986 (-0.003933) | 0.002480 / 0.004328 (-0.001848) | 0.048215 / 0.004250 (0.043964) | 0.044208 / 0.037052 (0.007156) | 0.263943 / 0.258489 (0.005454) | 0.297648 / 0.293841 (0.003807) | 0.029315 / 0.128546 (-0.099231) | 0.010533 / 0.075646 (-0.065114) | 0.057021 / 0.419271 (-0.362251) | 0.053751 / 0.043533 (0.010218) | 0.265153 / 0.255139 (0.010014) | 0.284988 / 0.283200 (0.001788) | 0.018459 / 0.141683 (-0.123224) | 1.225657 / 1.452155 (-0.226498) | 1.195737 / 1.492716 (-0.296979) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093030 / 0.018006 (0.075024) | 0.301022 / 0.000490 (0.300533) | 0.000228 / 0.000200 (0.000028) | 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) | 0.022073 / 0.037411 (-0.015339) | 0.075912 / 0.014526 (0.061386) | 0.087628 / 0.176557 (-0.088929) | 0.125607 / 0.737135 (-0.611529) | 0.088568 / 0.296338 (-0.207770) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303482 / 0.215209 (0.088273) | 2.965987 / 2.077655 (0.888333) | 1.615273 / 1.504120 (0.111153) | 1.482851 / 1.541195 (-0.058344) | 1.562627 / 1.468490 (0.094137) | 0.563626 / 4.584777 (-4.021151) | 2.448741 / 3.745712 (-1.296971) | 2.761006 / 5.269862 (-2.508855) | 1.711242 / 4.565676 (-2.854434) | 0.064593 / 0.424275 (-0.359682) | 0.005044 / 0.007607 (-0.002563) | 0.354131 / 0.226044 (0.128087) | 3.511698 / 2.268929 (1.242770) | 1.951087 / 55.444624 (-53.493538) | 1.682171 / 6.876477 (-5.194305) | 1.666330 / 2.142072 (-0.475742) | 0.654880 / 4.805227 (-4.150347) | 0.118544 / 6.500664 (-6.382120) | 0.040753 / 0.075469 (-0.034717) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967771 / 1.841788 (-0.874017) | 12.017277 / 8.074308 (3.942969) | 10.624947 / 10.191392 (0.433555) | 0.128834 / 0.680424 (-0.551590) | 0.015739 / 0.534201 (-0.518462) | 0.285906 / 0.579283 (-0.293377) | 0.273659 / 0.434364 (-0.160705) | 0.324044 / 0.540337 (-0.216293) | 0.419469 / 1.386936 (-0.967467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2afbf785f8d0551cdd65a81c5c3228e469613724 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6512). 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) | 0.005468 / 0.011353 (-0.005885) | 0.003447 / 0.011008 (-0.007561) | 0.062569 / 0.038508 (0.024061) | 0.049427 / 0.023109 (0.026318) | 0.238463 / 0.275898 (-0.037435) | 0.268320 / 0.323480 (-0.055159) | 0.002834 / 0.007986 (-0.005151) | 0.002679 / 0.004328 (-0.001649) | 0.048613 / 0.004250 (0.044363) | 0.038793 / 0.037052 (0.001741) | 0.247710 / 0.258489 (-0.010779) | 0.277557 / 0.293841 (-0.016284) | 0.027134 / 0.128546 (-0.101412) | 0.010346 / 0.075646 (-0.065301) | 0.205782 / 0.419271 (-0.213490) | 0.035549 / 0.043533 (-0.007983) | 0.241667 / 0.255139 (-0.013472) | 0.268358 / 0.283200 (-0.014842) | 0.017119 / 0.141683 (-0.124563) | 1.108487 / 1.452155 (-0.343668) | 1.177519 / 1.492716 (-0.315197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090925 / 0.018006 (0.072919) | 0.310422 / 0.000490 (0.309932) | 0.000212 / 0.000200 (0.000012) | 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) | 0.018912 / 0.037411 (-0.018499) | 0.061534 / 0.014526 (0.047008) | 0.073608 / 0.176557 (-0.102949) | 0.119278 / 0.737135 (-0.617858) | 0.074698 / 0.296338 (-0.221640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287224 / 0.215209 (0.072014) | 2.792022 / 2.077655 (0.714367) | 1.474605 / 1.504120 (-0.029515) | 1.348714 / 1.541195 (-0.192481) | 1.381339 / 1.468490 (-0.087151) | 0.553033 / 4.584777 (-4.031744) | 2.360745 / 3.745712 (-1.384967) | 2.779281 / 5.269862 (-2.490580) | 1.743922 / 4.565676 (-2.821754) | 0.063817 / 0.424275 (-0.360458) | 0.004954 / 0.007607 (-0.002653) | 0.340039 / 0.226044 (0.113994) | 3.336771 / 2.268929 (1.067843) | 1.825573 / 55.444624 (-53.619051) | 1.525362 / 6.876477 (-5.351115) | 1.578793 / 2.142072 (-0.563280) | 0.638432 / 4.805227 (-4.166795) | 0.117601 / 6.500664 (-6.383063) | 0.041890 / 0.075469 (-0.033579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936896 / 1.841788 (-0.904892) | 11.426979 / 8.074308 (3.352671) | 10.636043 / 10.191392 (0.444651) | 0.136172 / 0.680424 (-0.544252) | 0.014249 / 0.534201 (-0.519952) | 0.287806 / 0.579283 (-0.291477) | 0.266046 / 0.434364 (-0.168318) | 0.326155 / 0.540337 (-0.214183) | 0.455508 / 1.386936 (-0.931428) |\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) | 0.005199 / 0.011353 (-0.006154) | 0.003476 / 0.011008 (-0.007532) | 0.050519 / 0.038508 (0.012011) | 0.050732 / 0.023109 (0.027623) | 0.270140 / 0.275898 (-0.005758) | 0.295539 / 0.323480 (-0.027941) | 0.004057 / 0.007986 (-0.003928) | 0.002771 / 0.004328 (-0.001558) | 0.049157 / 0.004250 (0.044906) | 0.039863 / 0.037052 (0.002811) | 0.275934 / 0.258489 (0.017445) | 0.306971 / 0.293841 (0.013130) | 0.029405 / 0.128546 (-0.099141) | 0.010746 / 0.075646 (-0.064900) | 0.058427 / 0.419271 (-0.360845) | 0.032448 / 0.043533 (-0.011085) | 0.271851 / 0.255139 (0.016712) | 0.290337 / 0.283200 (0.007138) | 0.019145 / 0.141683 (-0.122538) | 1.112232 / 1.452155 (-0.339922) | 1.215153 / 1.492716 (-0.277564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088590 / 0.018006 (0.070584) | 0.299047 / 0.000490 (0.298558) | 0.000219 / 0.000200 (0.000019) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022755 / 0.037411 (-0.014656) | 0.078720 / 0.014526 (0.064194) | 0.089051 / 0.176557 (-0.087505) | 0.129330 / 0.737135 (-0.607805) | 0.090645 / 0.296338 (-0.205693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294083 / 0.215209 (0.078874) | 2.907195 / 2.077655 (0.829540) | 1.607392 / 1.504120 (0.103272) | 1.481931 / 1.541195 (-0.059263) | 1.486934 / 1.468490 (0.018444) | 0.574093 / 4.584777 (-4.010684) | 2.439775 / 3.745712 (-1.305937) | 2.739818 / 5.269862 (-2.530044) | 1.753922 / 4.565676 (-2.811755) | 0.063738 / 0.424275 (-0.360537) | 0.005219 / 0.007607 (-0.002388) | 0.350342 / 0.226044 (0.124297) | 3.463644 / 2.268929 (1.194716) | 1.971598 / 55.444624 (-53.473026) | 1.671752 / 6.876477 (-5.204724) | 1.686504 / 2.142072 (-0.455569) | 0.655870 / 4.805227 (-4.149357) | 0.117580 / 6.500664 (-6.383084) | 0.041210 / 0.075469 (-0.034259) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996305 / 1.841788 (-0.845482) | 12.426361 / 8.074308 (4.352053) | 10.600309 / 10.191392 (0.408917) | 0.129728 / 0.680424 (-0.550695) | 0.015267 / 0.534201 (-0.518934) | 0.285444 / 0.579283 (-0.293839) | 0.272375 / 0.434364 (-0.161989) | 0.323478 / 0.540337 (-0.216860) | 0.547566 / 1.386936 (-0.839370) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a91582de288d98e94bcb5ab634ca1cfeeff544c5 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6511). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, this PR is ready for review.", "<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) | 0.005343 / 0.011353 (-0.006010) | 0.003521 / 0.011008 (-0.007487) | 0.061835 / 0.038508 (0.023327) | 0.052633 / 0.023109 (0.029524) | 0.243897 / 0.275898 (-0.032001) | 0.272961 / 0.323480 (-0.050519) | 0.003013 / 0.007986 (-0.004973) | 0.002692 / 0.004328 (-0.001636) | 0.050099 / 0.004250 (0.045848) | 0.045381 / 0.037052 (0.008329) | 0.249981 / 0.258489 (-0.008508) | 0.276789 / 0.293841 (-0.017052) | 0.027929 / 0.128546 (-0.100617) | 0.010933 / 0.075646 (-0.064714) | 0.206757 / 0.419271 (-0.212514) | 0.035334 / 0.043533 (-0.008199) | 0.249411 / 0.255139 (-0.005728) | 0.268893 / 0.283200 (-0.014306) | 0.019175 / 0.141683 (-0.122507) | 1.106932 / 1.452155 (-0.345223) | 1.177819 / 1.492716 (-0.314897) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092895 / 0.018006 (0.074889) | 0.303658 / 0.000490 (0.303169) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018978 / 0.037411 (-0.018434) | 0.060459 / 0.014526 (0.045934) | 0.072900 / 0.176557 (-0.103657) | 0.119803 / 0.737135 (-0.617332) | 0.074349 / 0.296338 (-0.221989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283715 / 0.215209 (0.068505) | 2.752394 / 2.077655 (0.674739) | 1.446619 / 1.504120 (-0.057501) | 1.319612 / 1.541195 (-0.221582) | 1.374769 / 1.468490 (-0.093721) | 0.571543 / 4.584777 (-4.013234) | 2.389106 / 3.745712 (-1.356607) | 2.797837 / 5.269862 (-2.472025) | 1.737615 / 4.565676 (-2.828062) | 0.063268 / 0.424275 (-0.361007) | 0.005118 / 0.007607 (-0.002489) | 0.340238 / 0.226044 (0.114193) | 3.366207 / 2.268929 (1.097278) | 1.845934 / 55.444624 (-53.598690) | 1.540640 / 6.876477 (-5.335837) | 1.585489 / 2.142072 (-0.556584) | 0.641178 / 4.805227 (-4.164049) | 0.118701 / 6.500664 (-6.381964) | 0.042719 / 0.075469 (-0.032750) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946706 / 1.841788 (-0.895082) | 11.846230 / 8.074308 (3.771921) | 10.459268 / 10.191392 (0.267876) | 0.130557 / 0.680424 (-0.549867) | 0.014292 / 0.534201 (-0.519909) | 0.287455 / 0.579283 (-0.291828) | 0.265213 / 0.434364 (-0.169151) | 0.325670 / 0.540337 (-0.214667) | 0.422800 / 1.386936 (-0.964136) |\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) | 0.005454 / 0.011353 (-0.005899) | 0.003567 / 0.011008 (-0.007441) | 0.048696 / 0.038508 (0.010188) | 0.058844 / 0.023109 (0.035735) | 0.277011 / 0.275898 (0.001113) | 0.302544 / 0.323480 (-0.020936) | 0.004077 / 0.007986 (-0.003908) | 0.002720 / 0.004328 (-0.001609) | 0.058251 / 0.004250 (0.054001) | 0.040946 / 0.037052 (0.003893) | 0.276261 / 0.258489 (0.017772) | 0.352827 / 0.293841 (0.058986) | 0.029915 / 0.128546 (-0.098632) | 0.010562 / 0.075646 (-0.065084) | 0.057836 / 0.419271 (-0.361436) | 0.033129 / 0.043533 (-0.010404) | 0.276053 / 0.255139 (0.020914) | 0.292045 / 0.283200 (0.008846) | 0.020504 / 0.141683 (-0.121179) | 1.129746 / 1.452155 (-0.322409) | 1.190888 / 1.492716 (-0.301829) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095202 / 0.018006 (0.077196) | 0.303956 / 0.000490 (0.303466) | 0.000226 / 0.000200 (0.000026) | 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) | 0.021960 / 0.037411 (-0.015451) | 0.076209 / 0.014526 (0.061683) | 0.088813 / 0.176557 (-0.087744) | 0.129061 / 0.737135 (-0.608074) | 0.091202 / 0.296338 (-0.205136) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301394 / 0.215209 (0.086185) | 2.948057 / 2.077655 (0.870403) | 1.591371 / 1.504120 (0.087251) | 1.463515 / 1.541195 (-0.077680) | 1.516477 / 1.468490 (0.047987) | 0.577223 / 4.584777 (-4.007554) | 2.506716 / 3.745712 (-1.238996) | 2.833385 / 5.269862 (-2.436477) | 1.808896 / 4.565676 (-2.756781) | 0.063241 / 0.424275 (-0.361034) | 0.005057 / 0.007607 (-0.002550) | 0.350108 / 0.226044 (0.124063) | 3.470252 / 2.268929 (1.201324) | 1.925689 / 55.444624 (-53.518935) | 1.667521 / 6.876477 (-5.208955) | 1.690909 / 2.142072 (-0.451164) | 0.647070 / 4.805227 (-4.158157) | 0.117596 / 6.500664 (-6.383068) | 0.042431 / 0.075469 (-0.033038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977297 / 1.841788 (-0.864490) | 12.947399 / 8.074308 (4.873091) | 10.964949 / 10.191392 (0.773557) | 0.130905 / 0.680424 (-0.549518) | 0.015207 / 0.534201 (-0.518994) | 0.288151 / 0.579283 (-0.291132) | 0.281817 / 0.434364 (-0.152547) | 0.326398 / 0.540337 (-0.213940) | 0.421354 / 1.386936 (-0.965582) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b04288f0b94c987a278c5bb8459746bc35ba367 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6510). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI errors are unrelated to the changes, so I'm merging.", "<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) | 0.005161 / 0.011353 (-0.006192) | 0.003494 / 0.011008 (-0.007515) | 0.062601 / 0.038508 (0.024093) | 0.052876 / 0.023109 (0.029767) | 0.255595 / 0.275898 (-0.020303) | 0.283108 / 0.323480 (-0.040371) | 0.003856 / 0.007986 (-0.004130) | 0.002686 / 0.004328 (-0.001642) | 0.048604 / 0.004250 (0.044353) | 0.037886 / 0.037052 (0.000834) | 0.252902 / 0.258489 (-0.005587) | 0.286906 / 0.293841 (-0.006935) | 0.028570 / 0.128546 (-0.099976) | 0.010684 / 0.075646 (-0.064962) | 0.208154 / 0.419271 (-0.211118) | 0.036169 / 0.043533 (-0.007364) | 0.276026 / 0.255139 (0.020887) | 0.272274 / 0.283200 (-0.010925) | 0.017690 / 0.141683 (-0.123993) | 1.202400 / 1.452155 (-0.249755) | 1.231223 / 1.492716 (-0.261494) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095229 / 0.018006 (0.077222) | 0.302205 / 0.000490 (0.301716) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018877 / 0.037411 (-0.018534) | 0.062286 / 0.014526 (0.047760) | 0.075191 / 0.176557 (-0.101366) | 0.121419 / 0.737135 (-0.615716) | 0.075641 / 0.296338 (-0.220697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282914 / 0.215209 (0.067705) | 2.769156 / 2.077655 (0.691501) | 1.480219 / 1.504120 (-0.023901) | 1.355742 / 1.541195 (-0.185453) | 1.399740 / 1.468490 (-0.068750) | 0.556365 / 4.584777 (-4.028412) | 2.399679 / 3.745712 (-1.346033) | 2.850510 / 5.269862 (-2.419351) | 1.781428 / 4.565676 (-2.784249) | 0.063045 / 0.424275 (-0.361230) | 0.004931 / 0.007607 (-0.002676) | 0.343743 / 0.226044 (0.117698) | 3.374907 / 2.268929 (1.105978) | 1.857774 / 55.444624 (-53.586851) | 1.577154 / 6.876477 (-5.299323) | 1.626597 / 2.142072 (-0.515475) | 0.653991 / 4.805227 (-4.151236) | 0.121306 / 6.500664 (-6.379358) | 0.042131 / 0.075469 (-0.033339) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948826 / 1.841788 (-0.892962) | 11.922497 / 8.074308 (3.848188) | 10.592334 / 10.191392 (0.400942) | 0.129145 / 0.680424 (-0.551279) | 0.014652 / 0.534201 (-0.519549) | 0.286074 / 0.579283 (-0.293210) | 0.265338 / 0.434364 (-0.169026) | 0.346872 / 0.540337 (-0.193466) | 0.450480 / 1.386936 (-0.936456) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005305 / 0.011353 (-0.006048) | 0.003583 / 0.011008 (-0.007426) | 0.049855 / 0.038508 (0.011347) | 0.052882 / 0.023109 (0.029773) | 0.268429 / 0.275898 (-0.007469) | 0.293375 / 0.323480 (-0.030105) | 0.004052 / 0.007986 (-0.003934) | 0.002685 / 0.004328 (-0.001644) | 0.049206 / 0.004250 (0.044955) | 0.040187 / 0.037052 (0.003135) | 0.270112 / 0.258489 (0.011623) | 0.306380 / 0.293841 (0.012539) | 0.029161 / 0.128546 (-0.099386) | 0.010948 / 0.075646 (-0.064698) | 0.057721 / 0.419271 (-0.361550) | 0.032628 / 0.043533 (-0.010905) | 0.267458 / 0.255139 (0.012319) | 0.291905 / 0.283200 (0.008705) | 0.018096 / 0.141683 (-0.123587) | 1.112744 / 1.452155 (-0.339410) | 1.161962 / 1.492716 (-0.330754) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097449 / 0.018006 (0.079443) | 0.304270 / 0.000490 (0.303780) | 0.000235 / 0.000200 (0.000035) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023550 / 0.037411 (-0.013861) | 0.078246 / 0.014526 (0.063720) | 0.091229 / 0.176557 (-0.085327) | 0.130624 / 0.737135 (-0.606511) | 0.092767 / 0.296338 (-0.203571) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284962 / 0.215209 (0.069753) | 2.761090 / 2.077655 (0.683435) | 1.545409 / 1.504120 (0.041289) | 1.424573 / 1.541195 (-0.116622) | 1.438869 / 1.468490 (-0.029621) | 0.571281 / 4.584777 (-4.013496) | 2.419493 / 3.745712 (-1.326219) | 2.802611 / 5.269862 (-2.467251) | 1.749880 / 4.565676 (-2.815796) | 0.062566 / 0.424275 (-0.361709) | 0.005243 / 0.007607 (-0.002364) | 0.344653 / 0.226044 (0.118608) | 3.367488 / 2.268929 (1.098559) | 1.925871 / 55.444624 (-53.518754) | 1.624258 / 6.876477 (-5.252219) | 1.663742 / 2.142072 (-0.478330) | 0.634553 / 4.805227 (-4.170675) | 0.116745 / 6.500664 (-6.383919) | 0.041734 / 0.075469 (-0.033735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.006808 / 1.841788 (-0.834980) | 12.499711 / 8.074308 (4.425403) | 10.956260 / 10.191392 (0.764868) | 0.132393 / 0.680424 (-0.548031) | 0.015924 / 0.534201 (-0.518277) | 0.289837 / 0.579283 (-0.289446) | 0.281565 / 0.434364 (-0.152799) | 0.337393 / 0.540337 (-0.202945) | 0.560385 / 1.386936 (-0.826551) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f699ab27ef2c0c23dc3a514b5bb155485ff6913 \"CML watermark\")\n" ]

[]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6509). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I created `DatatasetGenerationCastError` in `exceptions.py` that inherits from `DatasetGenerationError` (for backward compatibility) that inherits from `DatasetsError`.\r\n\r\nI also added a help message at the end of the error:\r\n\r\n```\r\nPlease either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004991 / 0.011353 (-0.006361) | 0.003362 / 0.011008 (-0.007646) | 0.062093 / 0.038508 (0.023585) | 0.051533 / 0.023109 (0.028424) | 0.247508 / 0.275898 (-0.028390) | 0.275593 / 0.323480 (-0.047886) | 0.003828 / 0.007986 (-0.004158) | 0.002573 / 0.004328 (-0.001755) | 0.047727 / 0.004250 (0.043477) | 0.037029 / 0.037052 (-0.000023) | 0.250359 / 0.258489 (-0.008130) | 0.282640 / 0.293841 (-0.011201) | 0.027853 / 0.128546 (-0.100693) | 0.010247 / 0.075646 (-0.065400) | 0.206826 / 0.419271 (-0.212445) | 0.035837 / 0.043533 (-0.007695) | 0.251795 / 0.255139 (-0.003344) | 0.275654 / 0.283200 (-0.007545) | 0.017722 / 0.141683 (-0.123960) | 1.120287 / 1.452155 (-0.331868) | 1.203087 / 1.492716 (-0.289630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092320 / 0.018006 (0.074314) | 0.300079 / 0.000490 (0.299589) | 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) | 0.018193 / 0.037411 (-0.019218) | 0.061310 / 0.014526 (0.046784) | 0.072433 / 0.176557 (-0.104124) | 0.119092 / 0.737135 (-0.618043) | 0.074044 / 0.296338 (-0.222294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297184 / 0.215209 (0.081975) | 2.805197 / 2.077655 (0.727543) | 1.521326 / 1.504120 (0.017206) | 1.374321 / 1.541195 (-0.166874) | 1.388767 / 1.468490 (-0.079723) | 0.571865 / 4.584777 (-4.012912) | 2.385213 / 3.745712 (-1.360499) | 2.726840 / 5.269862 (-2.543021) | 1.725352 / 4.565676 (-2.840325) | 0.063012 / 0.424275 (-0.361263) | 0.004911 / 0.007607 (-0.002697) | 0.336430 / 0.226044 (0.110385) | 3.390616 / 2.268929 (1.121688) | 1.846398 / 55.444624 (-53.598227) | 1.576797 / 6.876477 (-5.299680) | 1.579445 / 2.142072 (-0.562627) | 0.652515 / 4.805227 (-4.152712) | 0.118393 / 6.500664 (-6.382271) | 0.042155 / 0.075469 (-0.033314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942269 / 1.841788 (-0.899518) | 11.318258 / 8.074308 (3.243950) | 10.299948 / 10.191392 (0.108556) | 0.136088 / 0.680424 (-0.544336) | 0.013682 / 0.534201 (-0.520519) | 0.287549 / 0.579283 (-0.291734) | 0.258346 / 0.434364 (-0.176018) | 0.337146 / 0.540337 (-0.203191) | 0.443922 / 1.386936 (-0.943014) |\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) | 0.005302 / 0.011353 (-0.006051) | 0.003234 / 0.011008 (-0.007774) | 0.049159 / 0.038508 (0.010651) | 0.050459 / 0.023109 (0.027350) | 0.273718 / 0.275898 (-0.002180) | 0.296997 / 0.323480 (-0.026483) | 0.003948 / 0.007986 (-0.004038) | 0.002590 / 0.004328 (-0.001739) | 0.048129 / 0.004250 (0.043879) | 0.039369 / 0.037052 (0.002317) | 0.276469 / 0.258489 (0.017980) | 0.306359 / 0.293841 (0.012519) | 0.028864 / 0.128546 (-0.099682) | 0.010253 / 0.075646 (-0.065394) | 0.058264 / 0.419271 (-0.361008) | 0.032451 / 0.043533 (-0.011082) | 0.277336 / 0.255139 (0.022197) | 0.296137 / 0.283200 (0.012937) | 0.018094 / 0.141683 (-0.123589) | 1.119539 / 1.452155 (-0.332615) | 1.163116 / 1.492716 (-0.329600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092578 / 0.018006 (0.074572) | 0.300756 / 0.000490 (0.300267) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022333 / 0.037411 (-0.015078) | 0.076632 / 0.014526 (0.062107) | 0.087829 / 0.176557 (-0.088727) | 0.127686 / 0.737135 (-0.609449) | 0.091314 / 0.296338 (-0.205024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297499 / 0.215209 (0.082290) | 2.889775 / 2.077655 (0.812120) | 1.598976 / 1.504120 (0.094856) | 1.478805 / 1.541195 (-0.062389) | 1.481818 / 1.468490 (0.013328) | 0.557972 / 4.584777 (-4.026804) | 2.453248 / 3.745712 (-1.292464) | 2.771823 / 5.269862 (-2.498039) | 1.721527 / 4.565676 (-2.844150) | 0.062786 / 0.424275 (-0.361489) | 0.005298 / 0.007607 (-0.002309) | 0.346660 / 0.226044 (0.120615) | 3.412262 / 2.268929 (1.143334) | 1.940240 / 55.444624 (-53.504384) | 1.654015 / 6.876477 (-5.222461) | 1.652039 / 2.142072 (-0.490034) | 0.636870 / 4.805227 (-4.168357) | 0.116213 / 6.500664 (-6.384451) | 0.040937 / 0.075469 (-0.034532) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001605 / 1.841788 (-0.840183) | 11.986592 / 8.074308 (3.912284) | 10.231288 / 10.191392 (0.039896) | 0.130242 / 0.680424 (-0.550182) | 0.015764 / 0.534201 (-0.518437) | 0.289257 / 0.579283 (-0.290026) | 0.275996 / 0.434364 (-0.158368) | 0.323089 / 0.540337 (-0.217248) | 0.556383 / 1.386936 (-0.830553) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#773324159ad4afd7931588a710839b76670ddf87 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6508). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nI'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?", "> Geometry columns MUST be stored using the BYTE_ARRAY parquet type. They MUST be encoded as [WKB](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry#Well-known_binary).\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#geometry-columns\r\n\r\nIt has metadata:\r\n\r\n> File metadata indicating things like the version of this specification used\r\n> Column metadata with additional metadata for each geometry column\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#metadata", "The specification is very short by the way:\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md", "https://github.com/opengeospatial/geoparquet/blob/main/format-specs/compatible-parquet.md is also worth reading for this PR", "> Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nYep, let me do that do that later today!\r\n\r\n> I'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?\r\n\r\nGeoParquet is a Parquet file with a `geometry` column that is encoded in a Binary format (technically WKB as @severo mentioned above). It is not a pyarrow extension type (though that would be cool). Regular `parquet` readers such as `pyarrow` would thus see the column as a binary column, while libraries such as `geopandas` which implement a GeoParquet reader would look at the schema metadata.\r\n\r\nE.g. taking this [file](https://huggingface.co/datasets/weiji14/clay_vector_embeddings/resolve/862b1602f326421adc99375912c08603a9f2cc5c/32VLM_v01.gpq) as an example, this is how the 'geo' schema looks like:\r\n\r\n```python\r\nimport pyarrow.parquet as pq\r\n\r\nschema = pq.read_schema(where=\"32VLM_v01.gpq\")\r\nprint(schema.metadata[b\"geo\"])\r\n```\r\n\r\n```\r\n{\r\n \"primary_column\": \"geometry\",\r\n \"columns\": {\r\n \"geometry\": {\r\n \"encoding\": \"WKB\",\r\n \"crs\": {\r\n \"$schema\": \"https://proj.org/schemas/v0.7/projjson.schema.json\",\r\n \"type\": \"GeographicCRS\",\r\n \"name\": \"WGS 84 (CRS84)\",\r\n \"datum_ensemble\": {\r\n \"name\": \"World Geodetic System 1984 ensemble\",\r\n \"members\": [\r\n {\"name\": \"World Geodetic System 1984 (Transit)\"},\r\n {\"name\": \"World Geodetic System 1984 (G730)\"},\r\n {\"name\": \"World Geodetic System 1984 (G873)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1150)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1674)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1762)\"},\r\n {\"name\": \"World Geodetic System 1984 (G2139)\"},\r\n ],\r\n \"ellipsoid\": {\r\n \"name\": \"WGS 84\",\r\n \"semi_major_axis\": 6378137,\r\n \"inverse_flattening\": 298.257223563,\r\n },\r\n \"accuracy\": \"2.0\",\r\n \"id\": {\"authority\": \"EPSG\", \"code\": 6326},\r\n },\r\n \"coordinate_system\": {\r\n \"subtype\": \"ellipsoidal\",\r\n \"axis\": [\r\n {\r\n \"name\": \"Geodetic longitude\",\r\n \"abbreviation\": \"Lon\",\r\n \"direction\": \"east\",\r\n \"unit\": \"degree\",\r\n },\r\n {\r\n \"name\": \"Geodetic latitude\",\r\n \"abbreviation\": \"Lat\",\r\n \"direction\": \"north\",\r\n \"unit\": \"degree\",\r\n },\r\n ],\r\n },\r\n \"scope\": \"Not known.\",\r\n \"area\": \"World.\",\r\n \"bbox\": {\r\n \"south_latitude\": -90,\r\n \"west_longitude\": -180,\r\n \"north_latitude\": 90,\r\n \"east_longitude\": 180,\r\n },\r\n \"id\": {\"authority\": \"OGC\", \"code\": \"CRS84\"},\r\n },\r\n \"geometry_types\": [\"Polygon\"],\r\n \"bbox\": [\r\n 5.370542846111244,\r\n 59.42344573656881,\r\n 7.368267282586697,\r\n 60.42591328670696,\r\n ],\r\n }\r\n },\r\n \"version\": \"1.0.0\",\r\n \"creator\": {\"library\": \"geopandas\", \"version\": \"0.14.1\"},\r\n}\r\n```\r\n\r\nWe can continue the discussion on how to handle this extra 'geo' schema metadata in #6438. I'd like to keep this PR small by just piggy-backing off the default Parquet reader for now, which would just show the 'geometry' column as a binary column.", "Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n\r\nIt would also enable `push_to_hub` to save geoparquet files", "> Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n> \r\n> It would also enable `push_to_hub` to save geoparquet files\r\n\r\nHmm, it should be possible to let PyArrow save a Parquet file with a geometry WKB column, but saving the GeoParquet schema metadata won't be easy without introducing [`geopandas`](https://github.com/geopandas/geopandas) as a dependency. Does this need to be done in this PR, or can it be a separate one?", "I see, then let's keep it like this for now.\r\nI just checked and it would require to add support for keeping the schema metadata in `Features` anyway.\r\n\r\nFeel free to fix your code formatting using\r\n\r\n```\r\nmake style\r\n```\r\n\r\nand we can merge this PR :)\r\n\r\n", "Cool, linted to remove the extra blank line at 7088f585557807a63673cdc58900d7ce56146cf7. :rocket:", "The previous CI failure at https://github.com/huggingface/datasets/actions/runs/7482863299/job/20668381959#step:6:5299 says `datasets.exceptions.DefunctDatasetError: Dataset 'eli5' is defunct and no longer accessible due to unavailability of the source data` which seems unrelated, might be to do with https://github.com/huggingface/datasets/issues/6605. I've updated the PR branch with changes from `main` again, could someone re-run the tests and merge if ok? Thanks!", "sorry, it took me some time to fix the CI on the `main` branch\r\n\r\nwill merge once it's green :)", "<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) | 0.005467 / 0.011353 (-0.005886) | 0.003696 / 0.011008 (-0.007313) | 0.063298 / 0.038508 (0.024790) | 0.032209 / 0.023109 (0.009100) | 0.246307 / 0.275898 (-0.029591) | 0.276864 / 0.323480 (-0.046616) | 0.003941 / 0.007986 (-0.004044) | 0.002616 / 0.004328 (-0.001713) | 0.049543 / 0.004250 (0.045292) | 0.044886 / 0.037052 (0.007833) | 0.266502 / 0.258489 (0.008013) | 0.288401 / 0.293841 (-0.005440) | 0.027911 / 0.128546 (-0.100635) | 0.011011 / 0.075646 (-0.064636) | 0.207972 / 0.419271 (-0.211299) | 0.036324 / 0.043533 (-0.007209) | 0.259450 / 0.255139 (0.004311) | 0.267317 / 0.283200 (-0.015883) | 0.018857 / 0.141683 (-0.122826) | 1.145350 / 1.452155 (-0.306805) | 1.204204 / 1.492716 (-0.288513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103864 / 0.018006 (0.085858) | 0.306941 / 0.000490 (0.306451) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018391 / 0.037411 (-0.019020) | 0.064600 / 0.014526 (0.050074) | 0.075454 / 0.176557 (-0.101102) | 0.120913 / 0.737135 (-0.616223) | 0.076998 / 0.296338 (-0.219341) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279491 / 0.215209 (0.064282) | 2.742471 / 2.077655 (0.664816) | 1.447980 / 1.504120 (-0.056140) | 1.328202 / 1.541195 (-0.212992) | 1.397291 / 1.468490 (-0.071199) | 0.585726 / 4.584777 (-3.999051) | 2.385132 / 3.745712 (-1.360580) | 2.874888 / 5.269862 (-2.394974) | 1.820177 / 4.565676 (-2.745500) | 0.063876 / 0.424275 (-0.360399) | 0.004946 / 0.007607 (-0.002661) | 0.336445 / 0.226044 (0.110401) | 3.396813 / 2.268929 (1.127885) | 1.832644 / 55.444624 (-53.611981) | 1.581304 / 6.876477 (-5.295172) | 1.607243 / 2.142072 (-0.534829) | 0.662752 / 4.805227 (-4.142476) | 0.119494 / 6.500664 (-6.381170) | 0.042573 / 0.075469 (-0.032896) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936784 / 1.841788 (-0.905003) | 12.154288 / 8.074308 (4.079980) | 10.944835 / 10.191392 (0.753443) | 0.132856 / 0.680424 (-0.547568) | 0.015197 / 0.534201 (-0.519004) | 0.290647 / 0.579283 (-0.288636) | 0.273498 / 0.434364 (-0.160866) | 0.324893 / 0.540337 (-0.215444) | 0.427905 / 1.386936 (-0.959032) |\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) | 0.005695 / 0.011353 (-0.005658) | 0.003562 / 0.011008 (-0.007446) | 0.050117 / 0.038508 (0.011608) | 0.033876 / 0.023109 (0.010767) | 0.275514 / 0.275898 (-0.000384) | 0.298460 / 0.323480 (-0.025020) | 0.004240 / 0.007986 (-0.003745) | 0.002738 / 0.004328 (-0.001591) | 0.048518 / 0.004250 (0.044268) | 0.049064 / 0.037052 (0.012012) | 0.287094 / 0.258489 (0.028605) | 0.314281 / 0.293841 (0.020440) | 0.057861 / 0.128546 (-0.070686) | 0.010893 / 0.075646 (-0.064753) | 0.062251 / 0.419271 (-0.357020) | 0.036788 / 0.043533 (-0.006745) | 0.272431 / 0.255139 (0.017292) | 0.292022 / 0.283200 (0.008822) | 0.019874 / 0.141683 (-0.121809) | 1.156939 / 1.452155 (-0.295216) | 1.237966 / 1.492716 (-0.254751) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096156 / 0.018006 (0.078150) | 0.306652 / 0.000490 (0.306162) | 0.000230 / 0.000200 (0.000031) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022965 / 0.037411 (-0.014447) | 0.081349 / 0.014526 (0.066823) | 0.089035 / 0.176557 (-0.087521) | 0.128831 / 0.737135 (-0.608304) | 0.090321 / 0.296338 (-0.206017) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293110 / 0.215209 (0.077901) | 2.884493 / 2.077655 (0.806839) | 1.582522 / 1.504120 (0.078402) | 1.518977 / 1.541195 (-0.022218) | 1.528449 / 1.468490 (0.059959) | 0.577369 / 4.584777 (-4.007408) | 2.473060 / 3.745712 (-1.272652) | 3.104363 / 5.269862 (-2.165499) | 1.916529 / 4.565676 (-2.649147) | 0.064594 / 0.424275 (-0.359682) | 0.005386 / 0.007607 (-0.002221) | 0.353336 / 0.226044 (0.127292) | 3.471914 / 2.268929 (1.202985) | 1.959222 / 55.444624 (-53.485402) | 1.677153 / 6.876477 (-5.199324) | 1.716961 / 2.142072 (-0.425112) | 0.658355 / 4.805227 (-4.146873) | 0.117296 / 6.500664 (-6.383368) | 0.041139 / 0.075469 (-0.034330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025220 / 1.841788 (-0.816567) | 14.510987 / 8.074308 (6.436679) | 11.851428 / 10.191392 (1.660036) | 0.143759 / 0.680424 (-0.536665) | 0.015644 / 0.534201 (-0.518557) | 0.296824 / 0.579283 (-0.282459) | 0.281566 / 0.434364 (-0.152798) | 0.335094 / 0.540337 (-0.205244) | 0.425199 / 1.386936 (-0.961737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fabc2c8cee8822572115893715b76dfdabac1631 \"CML watermark\")\n" ]

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[ "As this is a specific issue of the \"glue\" dataset, I have transferred it to the dataset Discussion page: https://huggingface.co/datasets/glue/discussions/15\r\n\r\nLet's continue the discussion there!" ]

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[ "I ran into the same problem when I used a server cluster (Slurm system managed) that couldn't load any of the huggingface datasets or models, but it worked on my laptop. I suspected some system configuration-related problem, but I had no idea. \r\nMy problems are consistent with [issue #2618](https://github.com/huggingface/datasets/issues/2618). All the huggingface-related libraries I use are the latest versions.\r\n\r\n", "> I ran into the same problem when I used a server cluster (Slurm system managed) that couldn't load any of the huggingface datasets or models, but it worked on my laptop. I suspected some system configuration-related problem, but I had no idea. My problems are consistent with [issue #2618](https://github.com/huggingface/datasets/issues/2618). All the huggingface-related libraries I use are the latest versions.\r\n\r\nhave you solved this issue yet? i met the same problem on server but everything works on laptop. I think maybe the filelock repo is contradictory with file system.", "I am having the same issue on a computing cluster but this works on my laptop as well. I instead have this error:\r\n`/home/.conda/envs/py10/lib/python3.10/site-packages/filelock/_unix.py\", line 43, in _acquire\r\n fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 5] Input/output error`\r\n\r\nthe load_dataset command does not work on server for local or hosted hugging-face datasets, and I have tried for several files" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6503). 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) | 0.005003 / 0.011353 (-0.006350) | 0.003020 / 0.011008 (-0.007988) | 0.061370 / 0.038508 (0.022862) | 0.050996 / 0.023109 (0.027887) | 0.243434 / 0.275898 (-0.032464) | 0.266317 / 0.323480 (-0.057163) | 0.003888 / 0.007986 (-0.004098) | 0.002607 / 0.004328 (-0.001721) | 0.047541 / 0.004250 (0.043290) | 0.037933 / 0.037052 (0.000881) | 0.259695 / 0.258489 (0.001206) | 0.279374 / 0.293841 (-0.014467) | 0.027258 / 0.128546 (-0.101288) | 0.010184 / 0.075646 (-0.065462) | 0.207412 / 0.419271 (-0.211860) | 0.034978 / 0.043533 (-0.008554) | 0.247871 / 0.255139 (-0.007267) | 0.265273 / 0.283200 (-0.017927) | 0.017886 / 0.141683 (-0.123796) | 1.090451 / 1.452155 (-0.361704) | 1.152034 / 1.492716 (-0.340682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094383 / 0.018006 (0.076377) | 0.301151 / 0.000490 (0.300661) | 0.000211 / 0.000200 (0.000011) | 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) | 0.018927 / 0.037411 (-0.018484) | 0.062152 / 0.014526 (0.047626) | 0.072177 / 0.176557 (-0.104380) | 0.119792 / 0.737135 (-0.617343) | 0.073333 / 0.296338 (-0.223005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282671 / 0.215209 (0.067462) | 2.721148 / 2.077655 (0.643494) | 1.472689 / 1.504120 (-0.031431) | 1.355226 / 1.541195 (-0.185969) | 1.375935 / 1.468490 (-0.092556) | 0.562600 / 4.584777 (-4.022177) | 2.364046 / 3.745712 (-1.381666) | 2.714984 / 5.269862 (-2.554878) | 1.738413 / 4.565676 (-2.827263) | 0.062564 / 0.424275 (-0.361711) | 0.004964 / 0.007607 (-0.002643) | 0.341300 / 0.226044 (0.115255) | 3.345187 / 2.268929 (1.076259) | 1.857822 / 55.444624 (-53.586803) | 1.581002 / 6.876477 (-5.295475) | 1.585919 / 2.142072 (-0.556153) | 0.640105 / 4.805227 (-4.165122) | 0.117880 / 6.500664 (-6.382784) | 0.042032 / 0.075469 (-0.033437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962701 / 1.841788 (-0.879086) | 11.309251 / 8.074308 (3.234943) | 10.462520 / 10.191392 (0.271128) | 0.127399 / 0.680424 (-0.553025) | 0.014549 / 0.534201 (-0.519652) | 0.297017 / 0.579283 (-0.282266) | 0.266152 / 0.434364 (-0.168212) | 0.349252 / 0.540337 (-0.191085) | 0.457015 / 1.386936 (-0.929921) |\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) | 0.005341 / 0.011353 (-0.006012) | 0.003108 / 0.011008 (-0.007900) | 0.048862 / 0.038508 (0.010353) | 0.053354 / 0.023109 (0.030245) | 0.274499 / 0.275898 (-0.001399) | 0.296698 / 0.323480 (-0.026782) | 0.003974 / 0.007986 (-0.004012) | 0.002631 / 0.004328 (-0.001697) | 0.048013 / 0.004250 (0.043762) | 0.040416 / 0.037052 (0.003363) | 0.276581 / 0.258489 (0.018092) | 0.301296 / 0.293841 (0.007455) | 0.029049 / 0.128546 (-0.099497) | 0.010253 / 0.075646 (-0.065393) | 0.057157 / 0.419271 (-0.362114) | 0.031830 / 0.043533 (-0.011703) | 0.274341 / 0.255139 (0.019202) | 0.292583 / 0.283200 (0.009383) | 0.018449 / 0.141683 (-0.123234) | 1.145099 / 1.452155 (-0.307055) | 1.192958 / 1.492716 (-0.299758) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091596 / 0.018006 (0.073590) | 0.300917 / 0.000490 (0.300427) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021657 / 0.037411 (-0.015754) | 0.068464 / 0.014526 (0.053938) | 0.079869 / 0.176557 (-0.096687) | 0.117523 / 0.737135 (-0.619613) | 0.081257 / 0.296338 (-0.215082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294876 / 0.215209 (0.079667) | 2.879372 / 2.077655 (0.801718) | 1.619887 / 1.504120 (0.115767) | 1.482154 / 1.541195 (-0.059041) | 1.494656 / 1.468490 (0.026166) | 0.558914 / 4.584777 (-4.025862) | 2.420948 / 3.745712 (-1.324765) | 2.728992 / 5.269862 (-2.540869) | 1.722135 / 4.565676 (-2.843542) | 0.062182 / 0.424275 (-0.362093) | 0.004933 / 0.007607 (-0.002674) | 0.342759 / 0.226044 (0.116715) | 3.424083 / 2.268929 (1.155154) | 1.950673 / 55.444624 (-53.493951) | 1.683126 / 6.876477 (-5.193351) | 1.673135 / 2.142072 (-0.468937) | 0.633711 / 4.805227 (-4.171516) | 0.114898 / 6.500664 (-6.385766) | 0.040332 / 0.075469 (-0.035137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975102 / 1.841788 (-0.866685) | 11.975731 / 8.074308 (3.901423) | 10.961103 / 10.191392 (0.769711) | 0.131152 / 0.680424 (-0.549272) | 0.016268 / 0.534201 (-0.517933) | 0.285031 / 0.579283 (-0.294252) | 0.279556 / 0.434364 (-0.154808) | 0.324183 / 0.540337 (-0.216154) | 0.571404 / 1.386936 (-0.815532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f67312956fc15572b6a0ca0dfcc0ceb90fbb794 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). 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) | 0.005472 / 0.011353 (-0.005881) | 0.003715 / 0.011008 (-0.007293) | 0.063257 / 0.038508 (0.024749) | 0.060683 / 0.023109 (0.037574) | 0.250885 / 0.275898 (-0.025013) | 0.271685 / 0.323480 (-0.051795) | 0.003051 / 0.007986 (-0.004934) | 0.002799 / 0.004328 (-0.001530) | 0.049113 / 0.004250 (0.044863) | 0.038965 / 0.037052 (0.001912) | 0.252688 / 0.258489 (-0.005801) | 0.282536 / 0.293841 (-0.011305) | 0.028722 / 0.128546 (-0.099824) | 0.010586 / 0.075646 (-0.065060) | 0.205145 / 0.419271 (-0.214127) | 0.036996 / 0.043533 (-0.006537) | 0.248874 / 0.255139 (-0.006265) | 0.266148 / 0.283200 (-0.017051) | 0.018540 / 0.141683 (-0.123143) | 1.120216 / 1.452155 (-0.331938) | 1.191072 / 1.492716 (-0.301644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095721 / 0.018006 (0.077714) | 0.313401 / 0.000490 (0.312911) | 0.000234 / 0.000200 (0.000034) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018604 / 0.037411 (-0.018807) | 0.061571 / 0.014526 (0.047045) | 0.075343 / 0.176557 (-0.101213) | 0.121272 / 0.737135 (-0.615864) | 0.076448 / 0.296338 (-0.219890) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286885 / 0.215209 (0.071676) | 2.809100 / 2.077655 (0.731445) | 1.485365 / 1.504120 (-0.018755) | 1.367672 / 1.541195 (-0.173523) | 1.423570 / 1.468490 (-0.044920) | 0.571063 / 4.584777 (-4.013714) | 2.385248 / 3.745712 (-1.360464) | 2.855251 / 5.269862 (-2.414610) | 1.799371 / 4.565676 (-2.766306) | 0.063491 / 0.424275 (-0.360784) | 0.004942 / 0.007607 (-0.002665) | 0.346181 / 0.226044 (0.120137) | 3.388123 / 2.268929 (1.119195) | 1.819093 / 55.444624 (-53.625532) | 1.552998 / 6.876477 (-5.323479) | 1.627930 / 2.142072 (-0.514143) | 0.653438 / 4.805227 (-4.151789) | 0.123831 / 6.500664 (-6.376833) | 0.043340 / 0.075469 (-0.032129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952167 / 1.841788 (-0.889621) | 12.149515 / 8.074308 (4.075207) | 10.665085 / 10.191392 (0.473693) | 0.127768 / 0.680424 (-0.552656) | 0.014022 / 0.534201 (-0.520179) | 0.285959 / 0.579283 (-0.293324) | 0.269727 / 0.434364 (-0.164637) | 0.336646 / 0.540337 (-0.203692) | 0.442932 / 1.386936 (-0.944005) |\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) | 0.005351 / 0.011353 (-0.006002) | 0.003561 / 0.011008 (-0.007448) | 0.048890 / 0.038508 (0.010382) | 0.054093 / 0.023109 (0.030984) | 0.274397 / 0.275898 (-0.001501) | 0.296980 / 0.323480 (-0.026500) | 0.004126 / 0.007986 (-0.003860) | 0.002751 / 0.004328 (-0.001578) | 0.049131 / 0.004250 (0.044880) | 0.040769 / 0.037052 (0.003716) | 0.279147 / 0.258489 (0.020658) | 0.302014 / 0.293841 (0.008173) | 0.029847 / 0.128546 (-0.098699) | 0.010710 / 0.075646 (-0.064936) | 0.057626 / 0.419271 (-0.361645) | 0.032801 / 0.043533 (-0.010732) | 0.272698 / 0.255139 (0.017559) | 0.289238 / 0.283200 (0.006039) | 0.017876 / 0.141683 (-0.123807) | 1.152059 / 1.452155 (-0.300096) | 1.212289 / 1.492716 (-0.280427) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| 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 (0.074908) | 0.303092 / 0.000490 (0.302603) | 0.000214 / 0.000200 (0.000014) | 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) | 0.022074 / 0.037411 (-0.015337) | 0.070109 / 0.014526 (0.055583) | 0.083360 / 0.176557 (-0.093196) | 0.122445 / 0.737135 (-0.614690) | 0.083625 / 0.296338 (-0.212714) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282788 / 0.215209 (0.067579) | 2.789229 / 2.077655 (0.711574) | 1.571077 / 1.504120 (0.066957) | 1.452627 / 1.541195 (-0.088567) | 1.493176 / 1.468490 (0.024686) | 0.556892 / 4.584777 (-4.027885) | 2.442771 / 3.745712 (-1.302941) | 2.826316 / 5.269862 (-2.443545) | 1.758276 / 4.565676 (-2.807401) | 0.063039 / 0.424275 (-0.361236) | 0.004928 / 0.007607 (-0.002679) | 0.338247 / 0.226044 (0.112202) | 3.346344 / 2.268929 (1.077416) | 1.952520 / 55.444624 (-53.492104) | 1.664520 / 6.876477 (-5.211956) | 1.701528 / 2.142072 (-0.440544) | 0.634746 / 4.805227 (-4.170481) | 0.116879 / 6.500664 (-6.383786) | 0.040990 / 0.075469 (-0.034479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969521 / 1.841788 (-0.872267) | 12.431395 / 8.074308 (4.357087) | 10.907503 / 10.191392 (0.716111) | 0.131028 / 0.680424 (-0.549396) | 0.015239 / 0.534201 (-0.518962) | 0.290793 / 0.579283 (-0.288490) | 0.275072 / 0.434364 (-0.159292) | 0.331036 / 0.540337 (-0.209301) | 0.567858 / 1.386936 (-0.819078) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#092118fc00f7dd718ab3643739d7b23ff16c9eff \"CML watermark\")\n" ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/6502.diff", "html_url": "https://github.com/huggingface/datasets/pull/6502", "merged_at": "2023-12-15T14:58:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6502.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6502" }

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6500). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This is ready for review @huggingface/datasets. ", "Also what if the config is being overwritten and it was the default config and the user doesn't pass `set_default` ?\r\nI'd expect the config to keep being the default one but lmk what you think", "How can you unset a config as the default one? In the case you mentioned, I would expect the config not being the default one.", "Maybe by passing `set_default=False` ? (set_default can be None by default)", "I think that way we are unnecessarily complicating the logic of `push_to_hub` and as I told you, I would expect the contrary: the result of calling `push_to_hub` with a determined set of arguments should always be the same, independently of previous calls and the current state of the config on the Hub. Push to hub should be somehow stateless in that sense, and IMO the user expects that the push overwrites previous config if already present on the Hub. I find very confusing making it to partially update the config on the Hub.", "That makes sense, having it stateless is simpler and no need to do something too fancy indeed", "<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) | 0.005329 / 0.011353 (-0.006024) | 0.002998 / 0.011008 (-0.008010) | 0.063756 / 0.038508 (0.025248) | 0.051713 / 0.023109 (0.028603) | 0.248135 / 0.275898 (-0.027763) | 0.269136 / 0.323480 (-0.054344) | 0.002970 / 0.007986 (-0.005015) | 0.002566 / 0.004328 (-0.001763) | 0.048110 / 0.004250 (0.043859) | 0.038415 / 0.037052 (0.001363) | 0.254012 / 0.258489 (-0.004477) | 0.281915 / 0.293841 (-0.011926) | 0.027503 / 0.128546 (-0.101043) | 0.010370 / 0.075646 (-0.065276) | 0.208965 / 0.419271 (-0.210306) | 0.035508 / 0.043533 (-0.008024) | 0.249116 / 0.255139 (-0.006023) | 0.266350 / 0.283200 (-0.016850) | 0.018440 / 0.141683 (-0.123243) | 1.101089 / 1.452155 (-0.351066) | 1.164870 / 1.492716 (-0.327847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090909 / 0.018006 (0.072903) | 0.298041 / 0.000490 (0.297551) | 0.000211 / 0.000200 (0.000012) | 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) | 0.018137 / 0.037411 (-0.019275) | 0.059574 / 0.014526 (0.045048) | 0.071754 / 0.176557 (-0.104803) | 0.117980 / 0.737135 (-0.619155) | 0.072903 / 0.296338 (-0.223435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282844 / 0.215209 (0.067635) | 2.740916 / 2.077655 (0.663261) | 1.444546 / 1.504120 (-0.059574) | 1.321904 / 1.541195 (-0.219291) | 1.356957 / 1.468490 (-0.111533) | 0.568389 / 4.584777 (-4.016388) | 2.354042 / 3.745712 (-1.391671) | 2.719427 / 5.269862 (-2.550435) | 1.719616 / 4.565676 (-2.846061) | 0.062537 / 0.424275 (-0.361738) | 0.004915 / 0.007607 (-0.002692) | 0.334716 / 0.226044 (0.108672) | 3.299499 / 2.268929 (1.030571) | 1.814629 / 55.444624 (-53.629996) | 1.515245 / 6.876477 (-5.361232) | 1.553085 / 2.142072 (-0.588987) | 0.643859 / 4.805227 (-4.161368) | 0.116650 / 6.500664 (-6.384014) | 0.041432 / 0.075469 (-0.034037) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948227 / 1.841788 (-0.893561) | 11.331103 / 8.074308 (3.256795) | 10.209658 / 10.191392 (0.018266) | 0.126721 / 0.680424 (-0.553703) | 0.013638 / 0.534201 (-0.520563) | 0.282540 / 0.579283 (-0.296743) | 0.262635 / 0.434364 (-0.171729) | 0.335357 / 0.540337 (-0.204981) | 0.441798 / 1.386936 (-0.945138) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005200 / 0.011353 (-0.006153) | 0.003012 / 0.011008 (-0.007996) | 0.047571 / 0.038508 (0.009063) | 0.055069 / 0.023109 (0.031959) | 0.271150 / 0.275898 (-0.004748) | 0.294957 / 0.323480 (-0.028523) | 0.003922 / 0.007986 (-0.004064) | 0.002627 / 0.004328 (-0.001702) | 0.047777 / 0.004250 (0.043527) | 0.039507 / 0.037052 (0.002454) | 0.276314 / 0.258489 (0.017825) | 0.300436 / 0.293841 (0.006595) | 0.028951 / 0.128546 (-0.099595) | 0.010583 / 0.075646 (-0.065063) | 0.056535 / 0.419271 (-0.362737) | 0.032654 / 0.043533 (-0.010879) | 0.272945 / 0.255139 (0.017806) | 0.291909 / 0.283200 (0.008709) | 0.017545 / 0.141683 (-0.124138) | 1.195897 / 1.452155 (-0.256258) | 1.171855 / 1.492716 (-0.320861) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091919 / 0.018006 (0.073913) | 0.299297 / 0.000490 (0.298807) | 0.000225 / 0.000200 (0.000025) | 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) | 0.022271 / 0.037411 (-0.015140) | 0.068903 / 0.014526 (0.054377) | 0.083767 / 0.176557 (-0.092790) | 0.120239 / 0.737135 (-0.616896) | 0.083448 / 0.296338 (-0.212891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295353 / 0.215209 (0.080144) | 2.911452 / 2.077655 (0.833798) | 1.577941 / 1.504120 (0.073821) | 1.454514 / 1.541195 (-0.086681) | 1.459575 / 1.468490 (-0.008915) | 0.572475 / 4.584777 (-4.012302) | 2.443634 / 3.745712 (-1.302078) | 2.801171 / 5.269862 (-2.468691) | 1.724214 / 4.565676 (-2.841462) | 0.063539 / 0.424275 (-0.360736) | 0.004939 / 0.007607 (-0.002668) | 0.347705 / 0.226044 (0.121660) | 3.489591 / 2.268929 (1.220663) | 1.944952 / 55.444624 (-53.499672) | 1.652810 / 6.876477 (-5.223667) | 1.656361 / 2.142072 (-0.485712) | 0.647052 / 4.805227 (-4.158176) | 0.117286 / 6.500664 (-6.383379) | 0.040979 / 0.075469 (-0.034490) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971761 / 1.841788 (-0.870027) | 11.770547 / 8.074308 (3.696239) | 10.402502 / 10.191392 (0.211110) | 0.128280 / 0.680424 (-0.552144) | 0.015160 / 0.534201 (-0.519041) | 0.286706 / 0.579283 (-0.292578) | 0.274539 / 0.434364 (-0.159825) | 0.324591 / 0.540337 (-0.215747) | 0.573846 / 1.386936 (-0.813090) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3329be80b9abfe83285ef940a590a4e9f68835a3 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6499). 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) | 0.005701 / 0.011353 (-0.005652) | 0.003546 / 0.011008 (-0.007463) | 0.063335 / 0.038508 (0.024827) | 0.051987 / 0.023109 (0.028878) | 0.240429 / 0.275898 (-0.035469) | 0.260659 / 0.323480 (-0.062820) | 0.003866 / 0.007986 (-0.004120) | 0.002617 / 0.004328 (-0.001712) | 0.048653 / 0.004250 (0.044403) | 0.038176 / 0.037052 (0.001124) | 0.245496 / 0.258489 (-0.012993) | 0.277141 / 0.293841 (-0.016700) | 0.027886 / 0.128546 (-0.100660) | 0.010738 / 0.075646 (-0.064908) | 0.211255 / 0.419271 (-0.208016) | 0.045205 / 0.043533 (0.001672) | 0.243062 / 0.255139 (-0.012077) | 0.262877 / 0.283200 (-0.020323) | 0.023426 / 0.141683 (-0.118257) | 1.092247 / 1.452155 (-0.359908) | 1.161074 / 1.492716 (-0.331642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090488 / 0.018006 (0.072482) | 0.300993 / 0.000490 (0.300504) | 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) | 0.018543 / 0.037411 (-0.018868) | 0.061418 / 0.014526 (0.046892) | 0.073242 / 0.176557 (-0.103314) | 0.120757 / 0.737135 (-0.616378) | 0.073967 / 0.296338 (-0.222372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282341 / 0.215209 (0.067132) | 2.741106 / 2.077655 (0.663451) | 1.416573 / 1.504120 (-0.087547) | 1.287904 / 1.541195 (-0.253291) | 1.309425 / 1.468490 (-0.159065) | 0.582592 / 4.584777 (-4.002184) | 2.404866 / 3.745712 (-1.340846) | 2.895397 / 5.269862 (-2.374464) | 1.799864 / 4.565676 (-2.765812) | 0.064386 / 0.424275 (-0.359889) | 0.004920 / 0.007607 (-0.002687) | 0.330879 / 0.226044 (0.104835) | 3.287064 / 2.268929 (1.018135) | 1.765169 / 55.444624 (-53.679456) | 1.490442 / 6.876477 (-5.386034) | 1.530960 / 2.142072 (-0.611113) | 0.655939 / 4.805227 (-4.149288) | 0.118529 / 6.500664 (-6.382135) | 0.042350 / 0.075469 (-0.033119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959027 / 1.841788 (-0.882761) | 11.911284 / 8.074308 (3.836976) | 10.576898 / 10.191392 (0.385506) | 0.141038 / 0.680424 (-0.539386) | 0.014184 / 0.534201 (-0.520017) | 0.305335 / 0.579283 (-0.273948) | 0.267531 / 0.434364 (-0.166832) | 0.353362 / 0.540337 (-0.186975) | 0.466258 / 1.386936 (-0.920678) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003561 / 0.011008 (-0.007448) | 0.049181 / 0.038508 (0.010673) | 0.056664 / 0.023109 (0.033555) | 0.267142 / 0.275898 (-0.008756) | 0.291871 / 0.323480 (-0.031609) | 0.003996 / 0.007986 (-0.003990) | 0.003147 / 0.004328 (-0.001181) | 0.048527 / 0.004250 (0.044276) | 0.040239 / 0.037052 (0.003187) | 0.269728 / 0.258489 (0.011239) | 0.295531 / 0.293841 (0.001690) | 0.030316 / 0.128546 (-0.098231) | 0.010666 / 0.075646 (-0.064981) | 0.058176 / 0.419271 (-0.361095) | 0.033218 / 0.043533 (-0.010315) | 0.265383 / 0.255139 (0.010244) | 0.285102 / 0.283200 (0.001902) | 0.018295 / 0.141683 (-0.123388) | 1.117830 / 1.452155 (-0.334325) | 1.196919 / 1.492716 (-0.295798) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088547 / 0.018006 (0.070541) | 0.293220 / 0.000490 (0.292730) | 0.000211 / 0.000200 (0.000011) | 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) | 0.022060 / 0.037411 (-0.015351) | 0.071973 / 0.014526 (0.057448) | 0.081721 / 0.176557 (-0.094836) | 0.119990 / 0.737135 (-0.617145) | 0.081639 / 0.296338 (-0.214700) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293712 / 0.215209 (0.078503) | 2.872986 / 2.077655 (0.795331) | 1.568944 / 1.504120 (0.064824) | 1.434555 / 1.541195 (-0.106639) | 1.457747 / 1.468490 (-0.010743) | 0.559296 / 4.584777 (-4.025481) | 2.471845 / 3.745712 (-1.273867) | 2.840916 / 5.269862 (-2.428946) | 1.754909 / 4.565676 (-2.810768) | 0.064585 / 0.424275 (-0.359690) | 0.004992 / 0.007607 (-0.002615) | 0.349149 / 0.226044 (0.123104) | 3.385906 / 2.268929 (1.116977) | 1.940644 / 55.444624 (-53.503980) | 1.638300 / 6.876477 (-5.238177) | 1.649939 / 2.142072 (-0.492133) | 0.645680 / 4.805227 (-4.159547) | 0.118080 / 6.500664 (-6.382584) | 0.040643 / 0.075469 (-0.034826) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969965 / 1.841788 (-0.871822) | 12.099766 / 8.074308 (4.025457) | 10.550650 / 10.191392 (0.359258) | 0.131736 / 0.680424 (-0.548688) | 0.015483 / 0.534201 (-0.518718) | 0.289231 / 0.579283 (-0.290052) | 0.287505 / 0.434364 (-0.146858) | 0.327326 / 0.540337 (-0.213011) | 0.570364 / 1.386936 (-0.816572) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#533c38cef16111e9e8154eeb76c207f1f4936ddf \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6498). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> I was just thinking: what if the user does not pass a config name and the dataset has only a config with a name different from \"default\"?\r\n\r\nYou mean if there is a DEFAULT_CONFIG_NAME defined in the script but the dataset only has one configuration ? We can't easily get the number of configs without running the python code so I don't think we can support detect this case\r\n", "Most datasets with a script don't define DEFAULT_CONFIG_NAME if there is only one configuration anyway.\r\n\r\nSo there is no issue e.g. for `squad`", "> I was trying to mean the case where DEFAULT_CONFIG_NAME is None but there is only a single config in BUILDER_CONFIGS, with a name different from \"default\".\r\n\r\nIn this case we can detect if \"DEFAULT_CONFIG_NAME\" is not mentioned and use the Parquet export. If it is mentioned (and maybe it is set to None or to the single config) I consider that it may have multiple configs and fall back on using the script", "... but the user does not pass the config name.", "In this case we load the single configuration (this is how a DatasetBuilder works)", "see \r\n\r\nhttps://github.com/huggingface/datasets/blob/2feaa589de86dd85941301fc8c3fa091731a67c0/src/datasets/builder.py#L532-L532", "<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) | 0.005122 / 0.011353 (-0.006231) | 0.003565 / 0.011008 (-0.007443) | 0.062706 / 0.038508 (0.024198) | 0.049314 / 0.023109 (0.026205) | 0.247325 / 0.275898 (-0.028573) | 0.269788 / 0.323480 (-0.053692) | 0.003895 / 0.007986 (-0.004090) | 0.002788 / 0.004328 (-0.001540) | 0.048615 / 0.004250 (0.044365) | 0.037591 / 0.037052 (0.000539) | 0.253495 / 0.258489 (-0.004994) | 0.281200 / 0.293841 (-0.012641) | 0.027712 / 0.128546 (-0.100834) | 0.010901 / 0.075646 (-0.064745) | 0.205577 / 0.419271 (-0.213694) | 0.035989 / 0.043533 (-0.007544) | 0.252978 / 0.255139 (-0.002161) | 0.268042 / 0.283200 (-0.015157) | 0.017857 / 0.141683 (-0.123826) | 1.096633 / 1.452155 (-0.355521) | 1.147026 / 1.492716 (-0.345691) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095609 / 0.018006 (0.077603) | 0.311941 / 0.000490 (0.311451) | 0.000211 / 0.000200 (0.000011) | 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) | 0.019042 / 0.037411 (-0.018369) | 0.060549 / 0.014526 (0.046023) | 0.074761 / 0.176557 (-0.101796) | 0.121729 / 0.737135 (-0.615406) | 0.075661 / 0.296338 (-0.220677) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284774 / 0.215209 (0.069565) | 2.764576 / 2.077655 (0.686921) | 1.489926 / 1.504120 (-0.014194) | 1.387276 / 1.541195 (-0.153919) | 1.400931 / 1.468490 (-0.067559) | 0.555623 / 4.584777 (-4.029154) | 2.409488 / 3.745712 (-1.336224) | 2.781053 / 5.269862 (-2.488808) | 1.750472 / 4.565676 (-2.815204) | 0.062232 / 0.424275 (-0.362043) | 0.004974 / 0.007607 (-0.002633) | 0.336324 / 0.226044 (0.110280) | 3.286619 / 2.268929 (1.017691) | 1.825070 / 55.444624 (-53.619554) | 1.537993 / 6.876477 (-5.338484) | 1.586520 / 2.142072 (-0.555553) | 0.640090 / 4.805227 (-4.165138) | 0.117637 / 6.500664 (-6.383027) | 0.042318 / 0.075469 (-0.033151) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964051 / 1.841788 (-0.877736) | 11.706259 / 8.074308 (3.631951) | 10.752311 / 10.191392 (0.560919) | 0.128117 / 0.680424 (-0.552307) | 0.014001 / 0.534201 (-0.520200) | 0.286255 / 0.579283 (-0.293028) | 0.263810 / 0.434364 (-0.170554) | 0.329347 / 0.540337 (-0.210991) | 0.437349 / 1.386936 (-0.949587) |\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) | 0.003586 / 0.011008 (-0.007422) | 0.049339 / 0.038508 (0.010831) | 0.051287 / 0.023109 (0.028178) | 0.274397 / 0.275898 (-0.001501) | 0.292977 / 0.323480 (-0.030503) | 0.004029 / 0.007986 (-0.003957) | 0.002727 / 0.004328 (-0.001602) | 0.048779 / 0.004250 (0.044528) | 0.040075 / 0.037052 (0.003022) | 0.277676 / 0.258489 (0.019187) | 0.301963 / 0.293841 (0.008122) | 0.029340 / 0.128546 (-0.099206) | 0.010714 / 0.075646 (-0.064932) | 0.057253 / 0.419271 (-0.362018) | 0.033426 / 0.043533 (-0.010107) | 0.276673 / 0.255139 (0.021534) | 0.291053 / 0.283200 (0.007854) | 0.017660 / 0.141683 (-0.124023) | 1.122354 / 1.452155 (-0.329800) | 1.180381 / 1.492716 (-0.312335) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091903 / 0.018006 (0.073897) | 0.300720 / 0.000490 (0.300231) | 0.000288 / 0.000200 (0.000088) | 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) | 0.021521 / 0.037411 (-0.015890) | 0.068233 / 0.014526 (0.053707) | 0.081245 / 0.176557 (-0.095312) | 0.119996 / 0.737135 (-0.617139) | 0.082483 / 0.296338 (-0.213856) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302776 / 0.215209 (0.087567) | 2.950776 / 2.077655 (0.873122) | 1.631032 / 1.504120 (0.126912) | 1.502021 / 1.541195 (-0.039174) | 1.514213 / 1.468490 (0.045723) | 0.578246 / 4.584777 (-4.006531) | 2.443768 / 3.745712 (-1.301944) | 2.827811 / 5.269862 (-2.442051) | 1.771529 / 4.565676 (-2.794148) | 0.064479 / 0.424275 (-0.359797) | 0.005061 / 0.007607 (-0.002546) | 0.350966 / 0.226044 (0.124922) | 3.458616 / 2.268929 (1.189687) | 1.967917 / 55.444624 (-53.476707) | 1.704661 / 6.876477 (-5.171815) | 1.698895 / 2.142072 (-0.443178) | 0.663259 / 4.805227 (-4.141968) | 0.122140 / 6.500664 (-6.378525) | 0.041099 / 0.075469 (-0.034371) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972080 / 1.841788 (-0.869708) | 12.123286 / 8.074308 (4.048978) | 10.819854 / 10.191392 (0.628462) | 0.131486 / 0.680424 (-0.548938) | 0.015785 / 0.534201 (-0.518416) | 0.290048 / 0.579283 (-0.289235) | 0.277822 / 0.434364 (-0.156542) | 0.325949 / 0.540337 (-0.214388) | 0.577681 / 1.386936 (-0.809255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#30f6a2d9af183eba4501f0b8d90e9200bdca6bb1 \"CML watermark\")\n" ]

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[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Naive question: is there any breaking change when loading?\r\n\r\nNo breaking changes except that the cache folders are different\r\n\r\ne.g. for glue sst2 (has parquet export)\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037\r\nOn main\r\n~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\r\n```\r\n\r\ne.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9\r\nOn main (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13\r\nOn 2.15.0 (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564\r\n```\r\n\r\n\r\ne.g. for lhoestq/demo1 (no metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11\r\nOn main\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\n```", "There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory\r\nedit: fixed", "I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.\r\n\r\nAnd for local file it's a hash that takes into account the resolved files (and their last modified dates)", "I also ran the `transformers` CI on this branch and it's green", "FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday", "Merging this one, and hopefully the cache backward compatibility PR soon too :)\r\n\r\nThen it will be release time", "<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) | 0.005444 / 0.011353 (-0.005909) | 0.003562 / 0.011008 (-0.007446) | 0.063183 / 0.038508 (0.024675) | 0.048885 / 0.023109 (0.025776) | 0.248422 / 0.275898 (-0.027476) | 0.277844 / 0.323480 (-0.045636) | 0.003019 / 0.007986 (-0.004966) | 0.002660 / 0.004328 (-0.001669) | 0.048928 / 0.004250 (0.044677) | 0.044850 / 0.037052 (0.007798) | 0.248505 / 0.258489 (-0.009984) | 0.282231 / 0.293841 (-0.011610) | 0.028302 / 0.128546 (-0.100244) | 0.010829 / 0.075646 (-0.064818) | 0.206738 / 0.419271 (-0.212533) | 0.035485 / 0.043533 (-0.008048) | 0.244575 / 0.255139 (-0.010564) | 0.281411 / 0.283200 (-0.001789) | 0.019563 / 0.141683 (-0.122120) | 1.113769 / 1.452155 (-0.338386) | 1.176831 / 1.492716 (-0.315885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004718 / 0.018006 (-0.013288) | 0.304103 / 0.000490 (0.303614) | 0.000214 / 0.000200 (0.000014) | 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) | 0.019642 / 0.037411 (-0.017769) | 0.060275 / 0.014526 (0.045749) | 0.073072 / 0.176557 (-0.103484) | 0.119789 / 0.737135 (-0.617346) | 0.074535 / 0.296338 (-0.221804) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278799 / 0.215209 (0.063590) | 2.725320 / 2.077655 (0.647665) | 1.419048 / 1.504120 (-0.085071) | 1.335041 / 1.541195 (-0.206154) | 1.373029 / 1.468490 (-0.095461) | 0.566774 / 4.584777 (-4.018003) | 2.383796 / 3.745712 (-1.361916) | 2.734804 / 5.269862 (-2.535057) | 1.712277 / 4.565676 (-2.853399) | 0.062119 / 0.424275 (-0.362156) | 0.004949 / 0.007607 (-0.002658) | 0.336126 / 0.226044 (0.110082) | 3.298602 / 2.268929 (1.029674) | 1.842815 / 55.444624 (-53.601809) | 1.544028 / 6.876477 (-5.332449) | 1.566717 / 2.142072 (-0.575355) | 0.643006 / 4.805227 (-4.162221) | 0.118241 / 6.500664 (-6.382423) | 0.042453 / 0.075469 (-0.033016) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949015 / 1.841788 (-0.892773) | 11.717958 / 8.074308 (3.643649) | 10.482448 / 10.191392 (0.291056) | 0.128564 / 0.680424 (-0.551860) | 0.014792 / 0.534201 (-0.519408) | 0.288636 / 0.579283 (-0.290647) | 0.263345 / 0.434364 (-0.171019) | 0.325753 / 0.540337 (-0.214584) | 0.421294 / 1.386936 (-0.965642) |\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) | 0.005367 / 0.011353 (-0.005985) | 0.003802 / 0.011008 (-0.007206) | 0.049322 / 0.038508 (0.010814) | 0.055201 / 0.023109 (0.032092) | 0.287811 / 0.275898 (0.011913) | 0.305141 / 0.323480 (-0.018339) | 0.004095 / 0.007986 (-0.003890) | 0.002733 / 0.004328 (-0.001595) | 0.049508 / 0.004250 (0.045258) | 0.039199 / 0.037052 (0.002147) | 0.282719 / 0.258489 (0.024230) | 0.311156 / 0.293841 (0.017315) | 0.029469 / 0.128546 (-0.099077) | 0.010709 / 0.075646 (-0.064937) | 0.057646 / 0.419271 (-0.361626) | 0.032696 / 0.043533 (-0.010837) | 0.285087 / 0.255139 (0.029948) | 0.294142 / 0.283200 (0.010942) | 0.019779 / 0.141683 (-0.121904) | 1.176844 / 1.452155 (-0.275310) | 1.190925 / 1.492716 (-0.301792) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092885 / 0.018006 (0.074879) | 0.301129 / 0.000490 (0.300640) | 0.000232 / 0.000200 (0.000032) | 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) | 0.023202 / 0.037411 (-0.014210) | 0.076850 / 0.014526 (0.062325) | 0.090058 / 0.176557 (-0.086499) | 0.128091 / 0.737135 (-0.609045) | 0.091098 / 0.296338 (-0.205240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292973 / 0.215209 (0.077764) | 2.876022 / 2.077655 (0.798367) | 1.672115 / 1.504120 (0.167995) | 1.555103 / 1.541195 (0.013909) | 1.559832 / 1.468490 (0.091342) | 0.558017 / 4.584777 (-4.026760) | 2.428448 / 3.745712 (-1.317264) | 2.812024 / 5.269862 (-2.457837) | 1.738470 / 4.565676 (-2.827207) | 0.062669 / 0.424275 (-0.361607) | 0.005071 / 0.007607 (-0.002536) | 0.351804 / 0.226044 (0.125759) | 3.412207 / 2.268929 (1.143279) | 2.023478 / 55.444624 (-53.421147) | 1.761281 / 6.876477 (-5.115195) | 1.770789 / 2.142072 (-0.371283) | 0.643062 / 4.805227 (-4.162165) | 0.116616 / 6.500664 (-6.384048) | 0.041816 / 0.075469 (-0.033653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988430 / 1.841788 (-0.853357) | 12.278636 / 8.074308 (4.204328) | 11.066185 / 10.191392 (0.874793) | 0.141191 / 0.680424 (-0.539233) | 0.015547 / 0.534201 (-0.518654) | 0.288045 / 0.579283 (-0.291238) | 0.279651 / 0.434364 (-0.154713) | 0.329869 / 0.540337 (-0.210469) | 0.420391 / 1.386936 (-0.966545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6492). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This PR is ready to review @huggingface/datasets.", "<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) | 0.005093 / 0.011353 (-0.006259) | 0.003695 / 0.011008 (-0.007313) | 0.064648 / 0.038508 (0.026140) | 0.054677 / 0.023109 (0.031568) | 0.242007 / 0.275898 (-0.033891) | 0.265216 / 0.323480 (-0.058264) | 0.003847 / 0.007986 (-0.004138) | 0.003773 / 0.004328 (-0.000556) | 0.048595 / 0.004250 (0.044345) | 0.038122 / 0.037052 (0.001070) | 0.245698 / 0.258489 (-0.012791) | 0.278095 / 0.293841 (-0.015746) | 0.027488 / 0.128546 (-0.101058) | 0.011002 / 0.075646 (-0.064644) | 0.211443 / 0.419271 (-0.207829) | 0.035664 / 0.043533 (-0.007869) | 0.244754 / 0.255139 (-0.010385) | 0.261078 / 0.283200 (-0.022121) | 0.017768 / 0.141683 (-0.123915) | 1.130765 / 1.452155 (-0.321390) | 1.189825 / 1.492716 (-0.302891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093027 / 0.018006 (0.075021) | 0.302193 / 0.000490 (0.301703) | 0.000207 / 0.000200 (0.000007) | 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) | 0.018413 / 0.037411 (-0.018999) | 0.062715 / 0.014526 (0.048190) | 0.073287 / 0.176557 (-0.103269) | 0.120394 / 0.737135 (-0.616741) | 0.077573 / 0.296338 (-0.218765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284445 / 0.215209 (0.069236) | 2.780718 / 2.077655 (0.703063) | 1.460988 / 1.504120 (-0.043132) | 1.345799 / 1.541195 (-0.195395) | 1.399892 / 1.468490 (-0.068598) | 0.576051 / 4.584777 (-4.008726) | 2.418792 / 3.745712 (-1.326921) | 2.901330 / 5.269862 (-2.368532) | 1.765083 / 4.565676 (-2.800593) | 0.063555 / 0.424275 (-0.360720) | 0.004991 / 0.007607 (-0.002616) | 0.339657 / 0.226044 (0.113613) | 3.372963 / 2.268929 (1.104034) | 1.853667 / 55.444624 (-53.590958) | 1.552022 / 6.876477 (-5.324454) | 1.616452 / 2.142072 (-0.525620) | 0.652309 / 4.805227 (-4.152919) | 0.121125 / 6.500664 (-6.379539) | 0.042420 / 0.075469 (-0.033049) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.954514 / 1.841788 (-0.887274) | 11.853736 / 8.074308 (3.779428) | 10.624571 / 10.191392 (0.433179) | 0.134118 / 0.680424 (-0.546306) | 0.014200 / 0.534201 (-0.520001) | 0.290106 / 0.579283 (-0.289177) | 0.270637 / 0.434364 (-0.163727) | 0.336155 / 0.540337 (-0.204182) | 0.443962 / 1.386936 (-0.942974) |\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) | 0.005282 / 0.011353 (-0.006071) | 0.003526 / 0.011008 (-0.007482) | 0.048994 / 0.038508 (0.010486) | 0.055345 / 0.023109 (0.032236) | 0.271587 / 0.275898 (-0.004311) | 0.294676 / 0.323480 (-0.028804) | 0.003989 / 0.007986 (-0.003996) | 0.002594 / 0.004328 (-0.001735) | 0.048310 / 0.004250 (0.044059) | 0.039945 / 0.037052 (0.002893) | 0.277304 / 0.258489 (0.018815) | 0.312017 / 0.293841 (0.018176) | 0.028364 / 0.128546 (-0.100182) | 0.010683 / 0.075646 (-0.064963) | 0.057990 / 0.419271 (-0.361281) | 0.032418 / 0.043533 (-0.011115) | 0.273835 / 0.255139 (0.018697) | 0.288585 / 0.283200 (0.005385) | 0.018964 / 0.141683 (-0.122719) | 1.148863 / 1.452155 (-0.303292) | 1.195684 / 1.492716 (-0.297032) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091967 / 0.018006 (0.073960) | 0.303236 / 0.000490 (0.302747) | 0.000214 / 0.000200 (0.000015) | 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) | 0.021960 / 0.037411 (-0.015452) | 0.068744 / 0.014526 (0.054218) | 0.081167 / 0.176557 (-0.095390) | 0.119623 / 0.737135 (-0.617513) | 0.084965 / 0.296338 (-0.211373) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297740 / 0.215209 (0.082531) | 2.924856 / 2.077655 (0.847201) | 1.602080 / 1.504120 (0.097960) | 1.494083 / 1.541195 (-0.047112) | 1.544662 / 1.468490 (0.076172) | 0.581212 / 4.584777 (-4.003565) | 2.451064 / 3.745712 (-1.294648) | 2.875213 / 5.269862 (-2.394649) | 1.780777 / 4.565676 (-2.784900) | 0.063751 / 0.424275 (-0.360524) | 0.004967 / 0.007607 (-0.002641) | 0.350321 / 0.226044 (0.124276) | 3.449585 / 2.268929 (1.180657) | 1.977666 / 55.444624 (-53.466958) | 1.685125 / 6.876477 (-5.191351) | 1.734466 / 2.142072 (-0.407606) | 0.657477 / 4.805227 (-4.147750) | 0.116767 / 6.500664 (-6.383898) | 0.041400 / 0.075469 (-0.034069) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985751 / 1.841788 (-0.856037) | 12.300065 / 8.074308 (4.225756) | 10.608238 / 10.191392 (0.416846) | 0.139907 / 0.680424 (-0.540517) | 0.015379 / 0.534201 (-0.518822) | 0.283528 / 0.579283 (-0.295755) | 0.278751 / 0.434364 (-0.155613) | 0.328811 / 0.540337 (-0.211527) | 0.584041 / 1.386936 (-0.802895) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef0f986518bd252c5314a7e3a419dedcbb166630 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). 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) | 0.005193 / 0.011353 (-0.006160) | 0.003246 / 0.011008 (-0.007762) | 0.063053 / 0.038508 (0.024545) | 0.049636 / 0.023109 (0.026527) | 0.240990 / 0.275898 (-0.034908) | 0.263732 / 0.323480 (-0.059747) | 0.004062 / 0.007986 (-0.003923) | 0.002681 / 0.004328 (-0.001648) | 0.048527 / 0.004250 (0.044277) | 0.044159 / 0.037052 (0.007107) | 0.248031 / 0.258489 (-0.010458) | 0.275705 / 0.293841 (-0.018136) | 0.028210 / 0.128546 (-0.100336) | 0.010314 / 0.075646 (-0.065332) | 0.209887 / 0.419271 (-0.209384) | 0.035649 / 0.043533 (-0.007884) | 0.251321 / 0.255139 (-0.003818) | 0.266672 / 0.283200 (-0.016528) | 0.017382 / 0.141683 (-0.124301) | 1.088937 / 1.452155 (-0.363217) | 1.143692 / 1.492716 (-0.349024) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092558 / 0.018006 (0.074552) | 0.301648 / 0.000490 (0.301159) | 0.000208 / 0.000200 (0.000008) | 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) | 0.018305 / 0.037411 (-0.019106) | 0.059836 / 0.014526 (0.045310) | 0.072926 / 0.176557 (-0.103631) | 0.119826 / 0.737135 (-0.617309) | 0.074357 / 0.296338 (-0.221982) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279051 / 0.215209 (0.063842) | 2.711402 / 2.077655 (0.633747) | 1.431782 / 1.504120 (-0.072338) | 1.316592 / 1.541195 (-0.224603) | 1.352062 / 1.468490 (-0.116428) | 0.562553 / 4.584777 (-4.022224) | 2.387719 / 3.745712 (-1.357993) | 2.693330 / 5.269862 (-2.576532) | 1.682040 / 4.565676 (-2.883636) | 0.061832 / 0.424275 (-0.362443) | 0.005066 / 0.007607 (-0.002541) | 0.332730 / 0.226044 (0.106685) | 3.315503 / 2.268929 (1.046575) | 1.787129 / 55.444624 (-53.657496) | 1.508955 / 6.876477 (-5.367522) | 1.512620 / 2.142072 (-0.629453) | 0.637120 / 4.805227 (-4.168107) | 0.116005 / 6.500664 (-6.384660) | 0.041973 / 0.075469 (-0.033496) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936996 / 1.841788 (-0.904792) | 11.485975 / 8.074308 (3.411667) | 10.604481 / 10.191392 (0.413089) | 0.130803 / 0.680424 (-0.549621) | 0.014561 / 0.534201 (-0.519640) | 0.285905 / 0.579283 (-0.293378) | 0.271573 / 0.434364 (-0.162791) | 0.329206 / 0.540337 (-0.211132) | 0.411977 / 1.386936 (-0.974959) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005333 / 0.011353 (-0.006020) | 0.003519 / 0.011008 (-0.007489) | 0.050880 / 0.038508 (0.012372) | 0.053681 / 0.023109 (0.030571) | 0.269359 / 0.275898 (-0.006539) | 0.291498 / 0.323480 (-0.031982) | 0.004006 / 0.007986 (-0.003979) | 0.002676 / 0.004328 (-0.001653) | 0.049652 / 0.004250 (0.045401) | 0.040588 / 0.037052 (0.003536) | 0.271701 / 0.258489 (0.013212) | 0.308384 / 0.293841 (0.014543) | 0.028713 / 0.128546 (-0.099833) | 0.010423 / 0.075646 (-0.065223) | 0.058099 / 0.419271 (-0.361172) | 0.032372 / 0.043533 (-0.011161) | 0.269395 / 0.255139 (0.014256) | 0.292252 / 0.283200 (0.009052) | 0.020038 / 0.141683 (-0.121645) | 1.124761 / 1.452155 (-0.327393) | 1.177609 / 1.492716 (-0.315107) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092187 / 0.018006 (0.074181) | 0.301936 / 0.000490 (0.301446) | 0.000230 / 0.000200 (0.000030) | 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) | 0.022932 / 0.037411 (-0.014480) | 0.076552 / 0.014526 (0.062027) | 0.088729 / 0.176557 (-0.087827) | 0.127198 / 0.737135 (-0.609937) | 0.091902 / 0.296338 (-0.204436) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299964 / 0.215209 (0.084755) | 2.929352 / 2.077655 (0.851697) | 1.598715 / 1.504120 (0.094595) | 1.462438 / 1.541195 (-0.078756) | 1.474308 / 1.468490 (0.005818) | 0.567120 / 4.584777 (-4.017657) | 2.481757 / 3.745712 (-1.263955) | 2.795375 / 5.269862 (-2.474487) | 1.740346 / 4.565676 (-2.825331) | 0.064048 / 0.424275 (-0.360227) | 0.004995 / 0.007607 (-0.002612) | 0.349084 / 0.226044 (0.123040) | 3.417679 / 2.268929 (1.148750) | 1.910615 / 55.444624 (-53.534009) | 1.694120 / 6.876477 (-5.182356) | 1.658654 / 2.142072 (-0.483419) | 0.638158 / 4.805227 (-4.167069) | 0.115509 / 6.500664 (-6.385156) | 0.040650 / 0.075469 (-0.034819) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988081 / 1.841788 (-0.853707) | 12.210089 / 8.074308 (4.135781) | 11.090203 / 10.191392 (0.898811) | 0.131861 / 0.680424 (-0.548563) | 0.015461 / 0.534201 (-0.518740) | 0.287737 / 0.579283 (-0.291546) | 0.284170 / 0.434364 (-0.150194) | 0.324949 / 0.540337 (-0.215388) | 0.414912 / 1.386936 (-0.972024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf71653947cecd84050daf0448dc5a73c2c071f3 \"CML watermark\")\n" ]

[]

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[ "Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.\r\nThe consequence here is any other dataset won't load because of infos mismatch.\r\nTo reproduce this aspect:\r\n1. Do a `load_datasets(...,save_infos=True)` with one dataset without a loading script\r\n2. Try to load another dataset without a loading script in the same format (e.g. json) but with a different schema " ]

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[ "Transferring repos as this is a datasets issue ", "I'm getting a similar issue even though I've already downloaded the dataset 😅 \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6486). 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) | 0.005042 / 0.011353 (-0.006311) | 0.003452 / 0.011008 (-0.007557) | 0.061845 / 0.038508 (0.023337) | 0.052042 / 0.023109 (0.028933) | 0.241791 / 0.275898 (-0.034107) | 0.264639 / 0.323480 (-0.058841) | 0.003940 / 0.007986 (-0.004045) | 0.002768 / 0.004328 (-0.001560) | 0.047851 / 0.004250 (0.043600) | 0.037599 / 0.037052 (0.000547) | 0.251462 / 0.258489 (-0.007028) | 0.274737 / 0.293841 (-0.019104) | 0.027723 / 0.128546 (-0.100823) | 0.010510 / 0.075646 (-0.065137) | 0.205581 / 0.419271 (-0.213691) | 0.035504 / 0.043533 (-0.008029) | 0.242380 / 0.255139 (-0.012759) | 0.259791 / 0.283200 (-0.023409) | 0.017752 / 0.141683 (-0.123931) | 1.089289 / 1.452155 (-0.362865) | 1.161958 / 1.492716 (-0.330759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094288 / 0.018006 (0.076282) | 0.303253 / 0.000490 (0.302763) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018496 / 0.037411 (-0.018915) | 0.060411 / 0.014526 (0.045885) | 0.074294 / 0.176557 (-0.102262) | 0.122934 / 0.737135 (-0.614201) | 0.074710 / 0.296338 (-0.221629) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286394 / 0.215209 (0.071185) | 2.806145 / 2.077655 (0.728490) | 1.497071 / 1.504120 (-0.007049) | 1.362254 / 1.541195 (-0.178940) | 1.389642 / 1.468490 (-0.078848) | 0.554503 / 4.584777 (-4.030274) | 2.348029 / 3.745712 (-1.397684) | 2.780862 / 5.269862 (-2.489000) | 1.728058 / 4.565676 (-2.837619) | 0.062617 / 0.424275 (-0.361658) | 0.004901 / 0.007607 (-0.002707) | 0.346267 / 0.226044 (0.120223) | 3.363744 / 2.268929 (1.094815) | 1.826994 / 55.444624 (-53.617630) | 1.560656 / 6.876477 (-5.315820) | 1.561083 / 2.142072 (-0.580990) | 0.643395 / 4.805227 (-4.161832) | 0.116206 / 6.500664 (-6.384458) | 0.042008 / 0.075469 (-0.033461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953416 / 1.841788 (-0.888371) | 11.461665 / 8.074308 (3.387357) | 10.623865 / 10.191392 (0.432473) | 0.128071 / 0.680424 (-0.552353) | 0.014277 / 0.534201 (-0.519924) | 0.288810 / 0.579283 (-0.290474) | 0.267575 / 0.434364 (-0.166788) | 0.327422 / 0.540337 (-0.212916) | 0.435151 / 1.386936 (-0.951785) |\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) | 0.005242 / 0.011353 (-0.006111) | 0.003515 / 0.011008 (-0.007493) | 0.048483 / 0.038508 (0.009975) | 0.051684 / 0.023109 (0.028575) | 0.276564 / 0.275898 (0.000666) | 0.297582 / 0.323480 (-0.025898) | 0.004117 / 0.007986 (-0.003869) | 0.002610 / 0.004328 (-0.001719) | 0.047811 / 0.004250 (0.043561) | 0.040622 / 0.037052 (0.003569) | 0.280265 / 0.258489 (0.021776) | 0.311719 / 0.293841 (0.017878) | 0.028811 / 0.128546 (-0.099735) | 0.010600 / 0.075646 (-0.065047) | 0.056660 / 0.419271 (-0.362611) | 0.032638 / 0.043533 (-0.010894) | 0.276434 / 0.255139 (0.021295) | 0.299095 / 0.283200 (0.015896) | 0.018483 / 0.141683 (-0.123200) | 1.156382 / 1.452155 (-0.295773) | 1.252205 / 1.492716 (-0.240511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097868 / 0.018006 (0.079862) | 0.309438 / 0.000490 (0.308948) | 0.000229 / 0.000200 (0.000029) | 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) | 0.021838 / 0.037411 (-0.015573) | 0.068358 / 0.014526 (0.053832) | 0.080432 / 0.176557 (-0.096125) | 0.119788 / 0.737135 (-0.617348) | 0.081742 / 0.296338 (-0.214597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301239 / 0.215209 (0.086030) | 2.962242 / 2.077655 (0.884587) | 1.693918 / 1.504120 (0.189798) | 1.573663 / 1.541195 (0.032468) | 1.583125 / 1.468490 (0.114635) | 0.557267 / 4.584777 (-4.027510) | 2.440048 / 3.745712 (-1.305664) | 2.727572 / 5.269862 (-2.542290) | 1.713557 / 4.565676 (-2.852120) | 0.062526 / 0.424275 (-0.361749) | 0.004982 / 0.007607 (-0.002625) | 0.353850 / 0.226044 (0.127806) | 3.530887 / 2.268929 (1.261958) | 2.047864 / 55.444624 (-53.396761) | 1.770776 / 6.876477 (-5.105701) | 1.757621 / 2.142072 (-0.384451) | 0.633847 / 4.805227 (-4.171381) | 0.114055 / 6.500664 (-6.386609) | 0.040078 / 0.075469 (-0.035391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983721 / 1.841788 (-0.858066) | 11.896537 / 8.074308 (3.822229) | 10.529883 / 10.191392 (0.338491) | 0.129593 / 0.680424 (-0.550831) | 0.016213 / 0.534201 (-0.517988) | 0.289623 / 0.579283 (-0.289660) | 0.280073 / 0.434364 (-0.154291) | 0.327446 / 0.540337 (-0.212892) | 0.574847 / 1.386936 (-0.812089) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2684a98fe38e0c87bb11e050586004108e32b79d \"CML watermark\")\n" ]

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[ "Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519. " ]

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[ "Column \"text\" doesn't exist anymore so you can't remove it", "You can get the expected result by fixing typos in the snippet :)\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset.features) - COLUMNS_TO_KEEP)\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Fixed code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\ndataset_features = dataset.features.keys()\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Whoops 😅 Thanks for the swift reply both! Works like a charm!" ]

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[ "Hello @kenfus, this is meant to be possible to do yes. Let me ping @lhoestq or @mariosasko from the `datasets` team (`huggingface_hub` is only the underlying library to download files from the Hub but here it looks more like a `datasets` problem). ", "Hi! https://github.com/huggingface/datasets/pull/6459 will fix this." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting", "<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) | 0.005294 / 0.011353 (-0.006059) | 0.003562 / 0.011008 (-0.007446) | 0.062030 / 0.038508 (0.023522) | 0.053335 / 0.023109 (0.030226) | 0.233303 / 0.275898 (-0.042595) | 0.252029 / 0.323480 (-0.071451) | 0.002835 / 0.007986 (-0.005151) | 0.002732 / 0.004328 (-0.001597) | 0.047973 / 0.004250 (0.043723) | 0.038380 / 0.037052 (0.001328) | 0.235028 / 0.258489 (-0.023461) | 0.265555 / 0.293841 (-0.028286) | 0.027136 / 0.128546 (-0.101410) | 0.010806 / 0.075646 (-0.064840) | 0.205040 / 0.419271 (-0.214231) | 0.035063 / 0.043533 (-0.008470) | 0.236351 / 0.255139 (-0.018788) | 0.254556 / 0.283200 (-0.028643) | 0.019528 / 0.141683 (-0.122155) | 1.099012 / 1.452155 (-0.353142) | 1.156250 / 1.492716 (-0.336466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093952 / 0.018006 (0.075946) | 0.304181 / 0.000490 (0.303692) | 0.000227 / 0.000200 (0.000027) | 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) | 0.018568 / 0.037411 (-0.018844) | 0.060323 / 0.014526 (0.045798) | 0.073010 / 0.176557 (-0.103546) | 0.121723 / 0.737135 (-0.615412) | 0.075668 / 0.296338 (-0.220670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288429 / 0.215209 (0.073220) | 2.797834 / 2.077655 (0.720180) | 1.480957 / 1.504120 (-0.023163) | 1.360872 / 1.541195 (-0.180323) | 1.406828 / 1.468490 (-0.061663) | 0.587596 / 4.584777 (-3.997181) | 2.533997 / 3.745712 (-1.211715) | 2.906697 / 5.269862 (-2.363164) | 1.801753 / 4.565676 (-2.763923) | 0.064360 / 0.424275 (-0.359915) | 0.005016 / 0.007607 (-0.002591) | 0.347334 / 0.226044 (0.121290) | 3.426344 / 2.268929 (1.157416) | 1.856014 / 55.444624 (-53.588610) | 1.581774 / 6.876477 (-5.294703) | 1.640036 / 2.142072 (-0.502037) | 0.656096 / 4.805227 (-4.149131) | 0.120212 / 6.500664 (-6.380452) | 0.044003 / 0.075469 (-0.031466) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943933 / 1.841788 (-0.897855) | 11.846572 / 8.074308 (3.772263) | 10.330705 / 10.191392 (0.139313) | 0.129767 / 0.680424 (-0.550657) | 0.013508 / 0.534201 (-0.520693) | 0.289672 / 0.579283 (-0.289611) | 0.266427 / 0.434364 (-0.167937) | 0.342766 / 0.540337 (-0.197571) | 0.452068 / 1.386936 (-0.934868) |\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) | 0.005308 / 0.011353 (-0.006045) | 0.003712 / 0.011008 (-0.007296) | 0.048848 / 0.038508 (0.010340) | 0.055156 / 0.023109 (0.032047) | 0.271942 / 0.275898 (-0.003956) | 0.293166 / 0.323480 (-0.030314) | 0.004056 / 0.007986 (-0.003930) | 0.002722 / 0.004328 (-0.001606) | 0.048418 / 0.004250 (0.044167) | 0.039320 / 0.037052 (0.002268) | 0.277184 / 0.258489 (0.018695) | 0.312398 / 0.293841 (0.018557) | 0.029392 / 0.128546 (-0.099155) | 0.011314 / 0.075646 (-0.064332) | 0.057883 / 0.419271 (-0.361389) | 0.032603 / 0.043533 (-0.010930) | 0.273025 / 0.255139 (0.017886) | 0.289265 / 0.283200 (0.006065) | 0.017553 / 0.141683 (-0.124129) | 1.127725 / 1.452155 (-0.324430) | 1.202293 / 1.492716 (-0.290423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097179 / 0.018006 (0.079173) | 0.309712 / 0.000490 (0.309222) | 0.000269 / 0.000200 (0.000069) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024742 / 0.037411 (-0.012670) | 0.070097 / 0.014526 (0.055571) | 0.082273 / 0.176557 (-0.094283) | 0.121696 / 0.737135 (-0.615439) | 0.082983 / 0.296338 (-0.213355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292688 / 0.215209 (0.077479) | 2.853436 / 2.077655 (0.775781) | 1.588999 / 1.504120 (0.084879) | 1.454547 / 1.541195 (-0.086648) | 1.476342 / 1.468490 (0.007852) | 0.559464 / 4.584777 (-4.025313) | 2.564597 / 3.745712 (-1.181115) | 2.900460 / 5.269862 (-2.369402) | 1.782156 / 4.565676 (-2.783520) | 0.061768 / 0.424275 (-0.362507) | 0.005042 / 0.007607 (-0.002565) | 0.345168 / 0.226044 (0.119124) | 3.412273 / 2.268929 (1.143344) | 1.953154 / 55.444624 (-53.491470) | 1.667347 / 6.876477 (-5.209130) | 1.685138 / 2.142072 (-0.456934) | 0.643270 / 4.805227 (-4.161958) | 0.115955 / 6.500664 (-6.384709) | 0.041090 / 0.075469 (-0.034379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976324 / 1.841788 (-0.865464) | 12.252294 / 8.074308 (4.177986) | 10.598062 / 10.191392 (0.406670) | 0.129779 / 0.680424 (-0.550644) | 0.015697 / 0.534201 (-0.518504) | 0.287241 / 0.579283 (-0.292042) | 0.287331 / 0.434364 (-0.147033) | 0.331710 / 0.540337 (-0.208628) | 0.574571 / 1.386936 (-0.812365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6480). 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) | 0.005392 / 0.011353 (-0.005960) | 0.003120 / 0.011008 (-0.007888) | 0.062017 / 0.038508 (0.023509) | 0.048824 / 0.023109 (0.025715) | 0.232300 / 0.275898 (-0.043598) | 0.262045 / 0.323480 (-0.061435) | 0.002909 / 0.007986 (-0.005077) | 0.003916 / 0.004328 (-0.000413) | 0.049469 / 0.004250 (0.045218) | 0.038965 / 0.037052 (0.001913) | 0.247841 / 0.258489 (-0.010648) | 0.268259 / 0.293841 (-0.025582) | 0.027588 / 0.128546 (-0.100958) | 0.010334 / 0.075646 (-0.065312) | 0.205811 / 0.419271 (-0.213460) | 0.035456 / 0.043533 (-0.008077) | 0.242774 / 0.255139 (-0.012365) | 0.260377 / 0.283200 (-0.022823) | 0.017469 / 0.141683 (-0.124214) | 1.199665 / 1.452155 (-0.252489) | 1.259316 / 1.492716 (-0.233400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092357 / 0.018006 (0.074350) | 0.303745 / 0.000490 (0.303255) | 0.000212 / 0.000200 (0.000012) | 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) | 0.018820 / 0.037411 (-0.018592) | 0.061548 / 0.014526 (0.047022) | 0.072527 / 0.176557 (-0.104030) | 0.119696 / 0.737135 (-0.617440) | 0.074153 / 0.296338 (-0.222185) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283952 / 0.215209 (0.068743) | 2.769844 / 2.077655 (0.692189) | 1.526100 / 1.504120 (0.021980) | 1.417584 / 1.541195 (-0.123611) | 1.440523 / 1.468490 (-0.027967) | 0.556994 / 4.584777 (-4.027783) | 2.400392 / 3.745712 (-1.345320) | 2.727794 / 5.269862 (-2.542068) | 1.724671 / 4.565676 (-2.841006) | 0.062111 / 0.424275 (-0.362164) | 0.004925 / 0.007607 (-0.002682) | 0.342748 / 0.226044 (0.116704) | 3.376790 / 2.268929 (1.107862) | 1.856498 / 55.444624 (-53.588127) | 1.574143 / 6.876477 (-5.302334) | 1.591828 / 2.142072 (-0.550245) | 0.644416 / 4.805227 (-4.160811) | 0.116862 / 6.500664 (-6.383802) | 0.041484 / 0.075469 (-0.033985) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975704 / 1.841788 (-0.866084) | 11.196447 / 8.074308 (3.122139) | 10.567518 / 10.191392 (0.376126) | 0.126786 / 0.680424 (-0.553638) | 0.013768 / 0.534201 (-0.520433) | 0.284531 / 0.579283 (-0.294752) | 0.260855 / 0.434364 (-0.173509) | 0.328888 / 0.540337 (-0.211450) | 0.439911 / 1.386936 (-0.947025) |\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) | 0.005108 / 0.011353 (-0.006245) | 0.003006 / 0.011008 (-0.008003) | 0.048673 / 0.038508 (0.010165) | 0.051066 / 0.023109 (0.027957) | 0.279578 / 0.275898 (0.003680) | 0.298356 / 0.323480 (-0.025123) | 0.003965 / 0.007986 (-0.004020) | 0.002662 / 0.004328 (-0.001667) | 0.049037 / 0.004250 (0.044786) | 0.039385 / 0.037052 (0.002333) | 0.284545 / 0.258489 (0.026055) | 0.314240 / 0.293841 (0.020399) | 0.028493 / 0.128546 (-0.100053) | 0.010400 / 0.075646 (-0.065247) | 0.057375 / 0.419271 (-0.361896) | 0.032382 / 0.043533 (-0.011151) | 0.283163 / 0.255139 (0.028024) | 0.298967 / 0.283200 (0.015768) | 0.017564 / 0.141683 (-0.124119) | 1.172425 / 1.452155 (-0.279730) | 1.219975 / 1.492716 (-0.272742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090664 / 0.018006 (0.072658) | 0.298419 / 0.000490 (0.297929) | 0.000211 / 0.000200 (0.000011) | 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) | 0.021739 / 0.037411 (-0.015672) | 0.068274 / 0.014526 (0.053748) | 0.080820 / 0.176557 (-0.095736) | 0.119809 / 0.737135 (-0.617326) | 0.081612 / 0.296338 (-0.214727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303346 / 0.215209 (0.088137) | 2.971648 / 2.077655 (0.893993) | 1.634828 / 1.504120 (0.130708) | 1.510851 / 1.541195 (-0.030344) | 1.515236 / 1.468490 (0.046745) | 0.558487 / 4.584777 (-4.026289) | 2.436263 / 3.745712 (-1.309449) | 2.718525 / 5.269862 (-2.551336) | 1.727421 / 4.565676 (-2.838255) | 0.061396 / 0.424275 (-0.362879) | 0.004951 / 0.007607 (-0.002656) | 0.352950 / 0.226044 (0.126906) | 3.473766 / 2.268929 (1.204838) | 1.971299 / 55.444624 (-53.473325) | 1.712173 / 6.876477 (-5.164304) | 1.711334 / 2.142072 (-0.430738) | 0.627291 / 4.805227 (-4.177936) | 0.113779 / 6.500664 (-6.386885) | 0.046561 / 0.075469 (-0.028908) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989507 / 1.841788 (-0.852280) | 11.777883 / 8.074308 (3.703575) | 10.525453 / 10.191392 (0.334061) | 0.129118 / 0.680424 (-0.551306) | 0.014989 / 0.534201 (-0.519212) | 0.282324 / 0.579283 (-0.296959) | 0.280688 / 0.434364 (-0.153676) | 0.322579 / 0.540337 (-0.217758) | 0.554327 / 1.386936 (-0.832609) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79e94fcdf3d4378ddcdf7e130bb1ae23d99c6fce \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6479). 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) | 0.005669 / 0.011353 (-0.005683) | 0.003684 / 0.011008 (-0.007324) | 0.063477 / 0.038508 (0.024969) | 0.068760 / 0.023109 (0.045651) | 0.252741 / 0.275898 (-0.023157) | 0.286499 / 0.323480 (-0.036981) | 0.003311 / 0.007986 (-0.004674) | 0.003487 / 0.004328 (-0.000842) | 0.049636 / 0.004250 (0.045385) | 0.040983 / 0.037052 (0.003931) | 0.262230 / 0.258489 (0.003740) | 0.292131 / 0.293841 (-0.001710) | 0.028231 / 0.128546 (-0.100315) | 0.010912 / 0.075646 (-0.064734) | 0.211248 / 0.419271 (-0.208023) | 0.036679 / 0.043533 (-0.006854) | 0.258139 / 0.255139 (0.003000) | 0.277568 / 0.283200 (-0.005631) | 0.019576 / 0.141683 (-0.122107) | 1.102588 / 1.452155 (-0.349567) | 1.178587 / 1.492716 (-0.314130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098968 / 0.018006 (0.080962) | 0.298777 / 0.000490 (0.298287) | 0.000220 / 0.000200 (0.000020) | 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) | 0.020408 / 0.037411 (-0.017003) | 0.062832 / 0.014526 (0.048306) | 0.076047 / 0.176557 (-0.100509) | 0.125209 / 0.737135 (-0.611926) | 0.079098 / 0.296338 (-0.217240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285603 / 0.215209 (0.070394) | 2.811530 / 2.077655 (0.733875) | 1.481012 / 1.504120 (-0.023108) | 1.362740 / 1.541195 (-0.178455) | 1.448999 / 1.468490 (-0.019491) | 0.557740 / 4.584777 (-4.027037) | 2.391377 / 3.745712 (-1.354335) | 2.973181 / 5.269862 (-2.296681) | 1.837147 / 4.565676 (-2.728530) | 0.064445 / 0.424275 (-0.359831) | 0.004992 / 0.007607 (-0.002615) | 0.339207 / 0.226044 (0.113162) | 3.378508 / 2.268929 (1.109580) | 1.843969 / 55.444624 (-53.600655) | 1.597794 / 6.876477 (-5.278682) | 1.657665 / 2.142072 (-0.484407) | 0.654267 / 4.805227 (-4.150961) | 0.120408 / 6.500664 (-6.380256) | 0.045298 / 0.075469 (-0.030171) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949030 / 1.841788 (-0.892758) | 12.922161 / 8.074308 (4.847852) | 11.115660 / 10.191392 (0.924268) | 0.130556 / 0.680424 (-0.549868) | 0.016278 / 0.534201 (-0.517923) | 0.288137 / 0.579283 (-0.291146) | 0.265978 / 0.434364 (-0.168386) | 0.331491 / 0.540337 (-0.208847) | 0.437782 / 1.386936 (-0.949154) |\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) | 0.005342 / 0.011353 (-0.006010) | 0.003636 / 0.011008 (-0.007373) | 0.049527 / 0.038508 (0.011019) | 0.054856 / 0.023109 (0.031746) | 0.271922 / 0.275898 (-0.003976) | 0.295654 / 0.323480 (-0.027826) | 0.004023 / 0.007986 (-0.003963) | 0.002814 / 0.004328 (-0.001515) | 0.048963 / 0.004250 (0.044712) | 0.039936 / 0.037052 (0.002884) | 0.274336 / 0.258489 (0.015847) | 0.310100 / 0.293841 (0.016259) | 0.030006 / 0.128546 (-0.098540) | 0.010750 / 0.075646 (-0.064896) | 0.057989 / 0.419271 (-0.361283) | 0.033692 / 0.043533 (-0.009841) | 0.274084 / 0.255139 (0.018945) | 0.289428 / 0.283200 (0.006229) | 0.018739 / 0.141683 (-0.122944) | 1.126224 / 1.452155 (-0.325931) | 1.171595 / 1.492716 (-0.321121) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093983 / 0.018006 (0.075977) | 0.298516 / 0.000490 (0.298026) | 0.000221 / 0.000200 (0.000022) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022498 / 0.037411 (-0.014914) | 0.071909 / 0.014526 (0.057383) | 0.083940 / 0.176557 (-0.092617) | 0.121059 / 0.737135 (-0.616076) | 0.084141 / 0.296338 (-0.212198) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301792 / 0.215209 (0.086583) | 2.971971 / 2.077655 (0.894317) | 1.618718 / 1.504120 (0.114598) | 1.495816 / 1.541195 (-0.045379) | 1.546709 / 1.468490 (0.078219) | 0.571448 / 4.584777 (-4.013329) | 2.459182 / 3.745712 (-1.286531) | 2.937584 / 5.269862 (-2.332278) | 1.804670 / 4.565676 (-2.761007) | 0.062264 / 0.424275 (-0.362011) | 0.004915 / 0.007607 (-0.002692) | 0.355054 / 0.226044 (0.129009) | 3.490468 / 2.268929 (1.221539) | 1.978948 / 55.444624 (-53.465677) | 1.701020 / 6.876477 (-5.175457) | 1.744684 / 2.142072 (-0.397388) | 0.635880 / 4.805227 (-4.169347) | 0.115933 / 6.500664 (-6.384732) | 0.042646 / 0.075469 (-0.032823) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999486 / 1.841788 (-0.842302) | 13.373854 / 8.074308 (5.299546) | 10.959784 / 10.191392 (0.768392) | 0.131032 / 0.680424 (-0.549392) | 0.015059 / 0.534201 (-0.519142) | 0.289892 / 0.579283 (-0.289391) | 0.279383 / 0.434364 (-0.154981) | 0.337670 / 0.540337 (-0.202668) | 0.597102 / 1.386936 (-0.789834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd9044cdaabc1f9abce02c1b71bdb48fd3525d4e \"CML watermark\")\n" ]

[]

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[ "You can create a `pandas` DataFrame following [this](https://lakefs.io/data-version-control/dvc-using-python/) tutorial, and then convert this DataFrame to a `Dataset` with `datasets.Dataset.from_pandas`. For larger datasets (to memory map them), you can use `Dataset.from_generator` with a generator function that reads lakeFS files with `s3fs`.", "@mariosasko hello,\r\nThis can achieve and https://huggingface.co/datasets Does the same effect apply to the dataset? For example, downloading while using" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). 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) | 0.005383 / 0.011353 (-0.005969) | 0.003644 / 0.011008 (-0.007364) | 0.063375 / 0.038508 (0.024866) | 0.055567 / 0.023109 (0.032457) | 0.261376 / 0.275898 (-0.014522) | 0.283731 / 0.323480 (-0.039749) | 0.004022 / 0.007986 (-0.003964) | 0.002780 / 0.004328 (-0.001549) | 0.049407 / 0.004250 (0.045156) | 0.038208 / 0.037052 (0.001156) | 0.256275 / 0.258489 (-0.002214) | 0.293203 / 0.293841 (-0.000638) | 0.028411 / 0.128546 (-0.100135) | 0.010753 / 0.075646 (-0.064894) | 0.210420 / 0.419271 (-0.208851) | 0.036062 / 0.043533 (-0.007471) | 0.260455 / 0.255139 (0.005317) | 0.294991 / 0.283200 (0.011791) | 0.019020 / 0.141683 (-0.122662) | 1.118334 / 1.452155 (-0.333821) | 1.227391 / 1.492716 (-0.265325) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094700 / 0.018006 (0.076694) | 0.302378 / 0.000490 (0.301888) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018745 / 0.037411 (-0.018667) | 0.061103 / 0.014526 (0.046578) | 0.075369 / 0.176557 (-0.101188) | 0.121573 / 0.737135 (-0.615563) | 0.076898 / 0.296338 (-0.219440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284143 / 0.215209 (0.068934) | 2.774298 / 2.077655 (0.696644) | 1.483557 / 1.504120 (-0.020563) | 1.365091 / 1.541195 (-0.176104) | 1.390170 / 1.468490 (-0.078320) | 0.561179 / 4.584777 (-4.023598) | 2.401654 / 3.745712 (-1.344058) | 2.782628 / 5.269862 (-2.487233) | 1.731497 / 4.565676 (-2.834179) | 0.061798 / 0.424275 (-0.362477) | 0.004998 / 0.007607 (-0.002609) | 0.336920 / 0.226044 (0.110875) | 3.371891 / 2.268929 (1.102963) | 1.832173 / 55.444624 (-53.612452) | 1.573515 / 6.876477 (-5.302962) | 1.595609 / 2.142072 (-0.546463) | 0.647652 / 4.805227 (-4.157575) | 0.118501 / 6.500664 (-6.382164) | 0.042521 / 0.075469 (-0.032948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939310 / 1.841788 (-0.902478) | 11.459855 / 8.074308 (3.385547) | 10.677954 / 10.191392 (0.486562) | 0.141029 / 0.680424 (-0.539395) | 0.014321 / 0.534201 (-0.519880) | 0.306679 / 0.579283 (-0.272604) | 0.262303 / 0.434364 (-0.172061) | 0.327422 / 0.540337 (-0.212915) | 0.436159 / 1.386936 (-0.950777) |\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) | 0.005430 / 0.011353 (-0.005923) | 0.003646 / 0.011008 (-0.007362) | 0.049272 / 0.038508 (0.010764) | 0.075367 / 0.023109 (0.052257) | 0.275959 / 0.275898 (0.000061) | 0.296317 / 0.323480 (-0.027163) | 0.004129 / 0.007986 (-0.003857) | 0.002731 / 0.004328 (-0.001597) | 0.048475 / 0.004250 (0.044225) | 0.041571 / 0.037052 (0.004518) | 0.277993 / 0.258489 (0.019504) | 0.298709 / 0.293841 (0.004868) | 0.033117 / 0.128546 (-0.095429) | 0.010914 / 0.075646 (-0.064732) | 0.057599 / 0.419271 (-0.361673) | 0.033354 / 0.043533 (-0.010179) | 0.275669 / 0.255139 (0.020530) | 0.288451 / 0.283200 (0.005251) | 0.019953 / 0.141683 (-0.121729) | 1.148608 / 1.452155 (-0.303547) | 1.184818 / 1.492716 (-0.307898) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099566 / 0.018006 (0.081560) | 0.344935 / 0.000490 (0.344445) | 0.000221 / 0.000200 (0.000021) | 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) | 0.021925 / 0.037411 (-0.015486) | 0.068623 / 0.014526 (0.054097) | 0.081533 / 0.176557 (-0.095024) | 0.120996 / 0.737135 (-0.616139) | 0.082495 / 0.296338 (-0.213844) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294990 / 0.215209 (0.079781) | 2.892344 / 2.077655 (0.814690) | 1.611090 / 1.504120 (0.106970) | 1.496072 / 1.541195 (-0.045123) | 1.486069 / 1.468490 (0.017579) | 0.569769 / 4.584777 (-4.015008) | 2.477623 / 3.745712 (-1.268089) | 2.819576 / 5.269862 (-2.450286) | 1.745717 / 4.565676 (-2.819959) | 0.063763 / 0.424275 (-0.360512) | 0.004970 / 0.007607 (-0.002637) | 0.344879 / 0.226044 (0.118834) | 3.452795 / 2.268929 (1.183867) | 1.964468 / 55.444624 (-53.480156) | 1.674526 / 6.876477 (-5.201951) | 1.679716 / 2.142072 (-0.462356) | 0.650005 / 4.805227 (-4.155222) | 0.117019 / 6.500664 (-6.383646) | 0.048297 / 0.075469 (-0.027172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965422 / 1.841788 (-0.876366) | 11.989414 / 8.074308 (3.915106) | 10.938462 / 10.191392 (0.747070) | 0.140089 / 0.680424 (-0.540334) | 0.015533 / 0.534201 (-0.518668) | 0.292188 / 0.579283 (-0.287095) | 0.277903 / 0.434364 (-0.156461) | 0.326164 / 0.540337 (-0.214173) | 0.565674 / 1.386936 (-0.821262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d78f07091bc42c41bea068bf1b6116e2bde46a6f \"CML watermark\")\n" ]

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[ "~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~", "This is a real issue and not related to paths.", "Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.", "I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...", "Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n", "Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005270 / 0.011353 (-0.006083) | 0.003471 / 0.011008 (-0.007537) | 0.061942 / 0.038508 (0.023434) | 0.052671 / 0.023109 (0.029562) | 0.250541 / 0.275898 (-0.025357) | 0.270677 / 0.323480 (-0.052803) | 0.002933 / 0.007986 (-0.005053) | 0.003264 / 0.004328 (-0.001064) | 0.048055 / 0.004250 (0.043804) | 0.037459 / 0.037052 (0.000407) | 0.254926 / 0.258489 (-0.003563) | 0.292547 / 0.293841 (-0.001294) | 0.027959 / 0.128546 (-0.100587) | 0.010762 / 0.075646 (-0.064884) | 0.204961 / 0.419271 (-0.214310) | 0.035488 / 0.043533 (-0.008045) | 0.254102 / 0.255139 (-0.001037) | 0.273654 / 0.283200 (-0.009546) | 0.018126 / 0.141683 (-0.123556) | 1.082330 / 1.452155 (-0.369825) | 1.147179 / 1.492716 (-0.345538) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093223 / 0.018006 (0.075217) | 0.301912 / 0.000490 (0.301422) | 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) | 0.018407 / 0.037411 (-0.019004) | 0.060412 / 0.014526 (0.045886) | 0.074063 / 0.176557 (-0.102494) | 0.118743 / 0.737135 (-0.618392) | 0.076484 / 0.296338 (-0.219854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289929 / 0.215209 (0.074720) | 2.825096 / 2.077655 (0.747442) | 1.511444 / 1.504120 (0.007324) | 1.394812 / 1.541195 (-0.146383) | 1.419751 / 1.468490 (-0.048739) | 0.569995 / 4.584777 (-4.014782) | 2.402586 / 3.745712 (-1.343126) | 2.826223 / 5.269862 (-2.443639) | 1.751554 / 4.565676 (-2.814123) | 0.064266 / 0.424275 (-0.360009) | 0.005047 / 0.007607 (-0.002561) | 0.341513 / 0.226044 (0.115469) | 3.372106 / 2.268929 (1.103177) | 1.872693 / 55.444624 (-53.571931) | 1.588200 / 6.876477 (-5.288276) | 1.630800 / 2.142072 (-0.511272) | 0.654266 / 4.805227 (-4.150961) | 0.124292 / 6.500664 (-6.376372) | 0.042876 / 0.075469 (-0.032593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948406 / 1.841788 (-0.893382) | 11.652947 / 8.074308 (3.578639) | 10.218195 / 10.191392 (0.026803) | 0.128447 / 0.680424 (-0.551976) | 0.014092 / 0.534201 (-0.520109) | 0.287631 / 0.579283 (-0.291652) | 0.264843 / 0.434364 (-0.169521) | 0.329997 / 0.540337 (-0.210340) | 0.439597 / 1.386936 (-0.947339) |\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) | 0.005418 / 0.011353 (-0.005935) | 0.003589 / 0.011008 (-0.007419) | 0.050074 / 0.038508 (0.011566) | 0.052566 / 0.023109 (0.029456) | 0.293447 / 0.275898 (0.017549) | 0.320518 / 0.323480 (-0.002962) | 0.004094 / 0.007986 (-0.003892) | 0.002690 / 0.004328 (-0.001639) | 0.048200 / 0.004250 (0.043949) | 0.040692 / 0.037052 (0.003640) | 0.297086 / 0.258489 (0.038597) | 0.323827 / 0.293841 (0.029986) | 0.029511 / 0.128546 (-0.099035) | 0.011079 / 0.075646 (-0.064568) | 0.058562 / 0.419271 (-0.360709) | 0.032897 / 0.043533 (-0.010636) | 0.297244 / 0.255139 (0.042105) | 0.316812 / 0.283200 (0.033612) | 0.018468 / 0.141683 (-0.123215) | 1.140948 / 1.452155 (-0.311207) | 1.195453 / 1.492716 (-0.297263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092677 / 0.018006 (0.074671) | 0.300775 / 0.000490 (0.300285) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021617 / 0.037411 (-0.015794) | 0.077135 / 0.014526 (0.062610) | 0.079848 / 0.176557 (-0.096709) | 0.118475 / 0.737135 (-0.618661) | 0.081174 / 0.296338 (-0.215164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294424 / 0.215209 (0.079215) | 2.863989 / 2.077655 (0.786334) | 1.590604 / 1.504120 (0.086484) | 1.474345 / 1.541195 (-0.066849) | 1.482120 / 1.468490 (0.013630) | 0.567829 / 4.584777 (-4.016948) | 2.493782 / 3.745712 (-1.251930) | 2.823460 / 5.269862 (-2.446402) | 1.732677 / 4.565676 (-2.833000) | 0.065518 / 0.424275 (-0.358757) | 0.004923 / 0.007607 (-0.002684) | 0.349313 / 0.226044 (0.123268) | 3.428618 / 2.268929 (1.159689) | 1.970641 / 55.444624 (-53.473983) | 1.655884 / 6.876477 (-5.220593) | 1.657151 / 2.142072 (-0.484921) | 0.661208 / 4.805227 (-4.144019) | 0.119129 / 6.500664 (-6.381535) | 0.040770 / 0.075469 (-0.034699) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964865 / 1.841788 (-0.876923) | 12.050218 / 8.074308 (3.975910) | 10.458749 / 10.191392 (0.267357) | 0.141856 / 0.680424 (-0.538568) | 0.015091 / 0.534201 (-0.519109) | 0.288897 / 0.579283 (-0.290387) | 0.275343 / 0.434364 (-0.159021) | 0.328363 / 0.540337 (-0.211975) | 0.579243 / 1.386936 (-0.807693) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6471). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005573 / 0.011353 (-0.005780) | 0.003449 / 0.011008 (-0.007559) | 0.063323 / 0.038508 (0.024815) | 0.049369 / 0.023109 (0.026260) | 0.254280 / 0.275898 (-0.021618) | 0.267721 / 0.323480 (-0.055759) | 0.002894 / 0.007986 (-0.005092) | 0.002646 / 0.004328 (-0.001683) | 0.049284 / 0.004250 (0.045033) | 0.037947 / 0.037052 (0.000895) | 0.251654 / 0.258489 (-0.006836) | 0.279729 / 0.293841 (-0.014112) | 0.028022 / 0.128546 (-0.100525) | 0.010653 / 0.075646 (-0.064993) | 0.208567 / 0.419271 (-0.210704) | 0.035863 / 0.043533 (-0.007670) | 0.248522 / 0.255139 (-0.006617) | 0.270274 / 0.283200 (-0.012925) | 0.019683 / 0.141683 (-0.122000) | 1.136342 / 1.452155 (-0.315812) | 1.206757 / 1.492716 (-0.285960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094682 / 0.018006 (0.076676) | 0.304092 / 0.000490 (0.303602) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018606 / 0.037411 (-0.018805) | 0.060568 / 0.014526 (0.046042) | 0.074067 / 0.176557 (-0.102490) | 0.118979 / 0.737135 (-0.618156) | 0.075676 / 0.296338 (-0.220663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290452 / 0.215209 (0.075243) | 2.848868 / 2.077655 (0.771213) | 1.534932 / 1.504120 (0.030812) | 1.386717 / 1.541195 (-0.154478) | 1.416645 / 1.468490 (-0.051845) | 0.569020 / 4.584777 (-4.015757) | 2.421168 / 3.745712 (-1.324545) | 2.781358 / 5.269862 (-2.488503) | 1.758495 / 4.565676 (-2.807182) | 0.063851 / 0.424275 (-0.360424) | 0.004968 / 0.007607 (-0.002639) | 0.339198 / 0.226044 (0.113154) | 3.356392 / 2.268929 (1.087464) | 1.858145 / 55.444624 (-53.586479) | 1.589000 / 6.876477 (-5.287477) | 1.569175 / 2.142072 (-0.572897) | 0.650571 / 4.805227 (-4.154657) | 0.120288 / 6.500664 (-6.380376) | 0.042489 / 0.075469 (-0.032980) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939963 / 1.841788 (-0.901824) | 11.493612 / 8.074308 (3.419304) | 10.353780 / 10.191392 (0.162388) | 0.141945 / 0.680424 (-0.538479) | 0.014397 / 0.534201 (-0.519804) | 0.286971 / 0.579283 (-0.292312) | 0.266787 / 0.434364 (-0.167577) | 0.330385 / 0.540337 (-0.209952) | 0.438542 / 1.386936 (-0.948394) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005360 / 0.011353 (-0.005993) | 0.003720 / 0.011008 (-0.007288) | 0.048790 / 0.038508 (0.010282) | 0.050256 / 0.023109 (0.027147) | 0.275445 / 0.275898 (-0.000453) | 0.297725 / 0.323480 (-0.025755) | 0.004077 / 0.007986 (-0.003909) | 0.002759 / 0.004328 (-0.001569) | 0.047653 / 0.004250 (0.043403) | 0.040205 / 0.037052 (0.003153) | 0.281028 / 0.258489 (0.022539) | 0.304682 / 0.293841 (0.010841) | 0.030158 / 0.128546 (-0.098388) | 0.010957 / 0.075646 (-0.064689) | 0.058193 / 0.419271 (-0.361079) | 0.033277 / 0.043533 (-0.010256) | 0.279501 / 0.255139 (0.024362) | 0.295381 / 0.283200 (0.012181) | 0.017889 / 0.141683 (-0.123794) | 1.121354 / 1.452155 (-0.330801) | 1.225702 / 1.492716 (-0.267014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093385 / 0.018006 (0.075378) | 0.304642 / 0.000490 (0.304152) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021456 / 0.037411 (-0.015955) | 0.068536 / 0.014526 (0.054010) | 0.080867 / 0.176557 (-0.095689) | 0.119093 / 0.737135 (-0.618042) | 0.081875 / 0.296338 (-0.214464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304434 / 0.215209 (0.089225) | 2.990303 / 2.077655 (0.912649) | 1.616959 / 1.504120 (0.112839) | 1.493256 / 1.541195 (-0.047939) | 1.542857 / 1.468490 (0.074367) | 0.575517 / 4.584777 (-4.009260) | 2.455165 / 3.745712 (-1.290547) | 2.810089 / 5.269862 (-2.459773) | 1.756502 / 4.565676 (-2.809175) | 0.064801 / 0.424275 (-0.359475) | 0.004969 / 0.007607 (-0.002638) | 0.360227 / 0.226044 (0.134183) | 3.575029 / 2.268929 (1.306100) | 1.989955 / 55.444624 (-53.454669) | 1.705306 / 6.876477 (-5.171171) | 1.688523 / 2.142072 (-0.453550) | 0.663266 / 4.805227 (-4.141962) | 0.121852 / 6.500664 (-6.378812) | 0.041853 / 0.075469 (-0.033616) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983535 / 1.841788 (-0.858252) | 11.827656 / 8.074308 (3.753348) | 10.663265 / 10.191392 (0.471873) | 0.145942 / 0.680424 (-0.534482) | 0.016004 / 0.534201 (-0.518197) | 0.288907 / 0.579283 (-0.290376) | 0.279100 / 0.434364 (-0.155264) | 0.328061 / 0.540337 (-0.212276) | 0.570253 / 1.386936 (-0.816683) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b52cbc18919869460557e15028e7f489eae8afc7 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004998 / 0.011353 (-0.006355) | 0.003523 / 0.011008 (-0.007486) | 0.064932 / 0.038508 (0.026424) | 0.050107 / 0.023109 (0.026998) | 0.253715 / 0.275898 (-0.022183) | 0.275364 / 0.323480 (-0.048116) | 0.003902 / 0.007986 (-0.004084) | 0.002716 / 0.004328 (-0.001612) | 0.048458 / 0.004250 (0.044208) | 0.037802 / 0.037052 (0.000750) | 0.262328 / 0.258489 (0.003839) | 0.285911 / 0.293841 (-0.007930) | 0.027112 / 0.128546 (-0.101435) | 0.010780 / 0.075646 (-0.064867) | 0.206447 / 0.419271 (-0.212824) | 0.035771 / 0.043533 (-0.007761) | 0.255031 / 0.255139 (-0.000108) | 0.270530 / 0.283200 (-0.012670) | 0.017152 / 0.141683 (-0.124530) | 1.094734 / 1.452155 (-0.357421) | 1.163480 / 1.492716 (-0.329237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092944 / 0.018006 (0.074938) | 0.301042 / 0.000490 (0.300553) | 0.000238 / 0.000200 (0.000038) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019090 / 0.037411 (-0.018321) | 0.061046 / 0.014526 (0.046520) | 0.073330 / 0.176557 (-0.103227) | 0.121124 / 0.737135 (-0.616012) | 0.080544 / 0.296338 (-0.215795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.323866 / 0.215209 (0.108657) | 2.797727 / 2.077655 (0.720072) | 1.502994 / 1.504120 (-0.001126) | 1.376177 / 1.541195 (-0.165018) | 1.422741 / 1.468490 (-0.045749) | 0.562990 / 4.584777 (-4.021786) | 2.431781 / 3.745712 (-1.313931) | 2.783226 / 5.269862 (-2.486635) | 1.788055 / 4.565676 (-2.777621) | 0.064206 / 0.424275 (-0.360069) | 0.004989 / 0.007607 (-0.002618) | 0.338282 / 0.226044 (0.112237) | 3.356226 / 2.268929 (1.087297) | 1.855644 / 55.444624 (-53.588980) | 1.580876 / 6.876477 (-5.295601) | 1.617418 / 2.142072 (-0.524655) | 0.636816 / 4.805227 (-4.168411) | 0.117680 / 6.500664 (-6.382985) | 0.042560 / 0.075469 (-0.032909) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956410 / 1.841788 (-0.885377) | 11.764886 / 8.074308 (3.690578) | 10.535801 / 10.191392 (0.344409) | 0.137797 / 0.680424 (-0.542627) | 0.014368 / 0.534201 (-0.519833) | 0.286213 / 0.579283 (-0.293070) | 0.267093 / 0.434364 (-0.167271) | 0.334802 / 0.540337 (-0.205535) | 0.441866 / 1.386936 (-0.945070) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005348 / 0.011353 (-0.006005) | 0.003551 / 0.011008 (-0.007458) | 0.049226 / 0.038508 (0.010718) | 0.052072 / 0.023109 (0.028963) | 0.268025 / 0.275898 (-0.007873) | 0.289968 / 0.323480 (-0.033512) | 0.004034 / 0.007986 (-0.003952) | 0.002675 / 0.004328 (-0.001653) | 0.048099 / 0.004250 (0.043848) | 0.040141 / 0.037052 (0.003089) | 0.272974 / 0.258489 (0.014485) | 0.296097 / 0.293841 (0.002256) | 0.028972 / 0.128546 (-0.099575) | 0.010689 / 0.075646 (-0.064957) | 0.057853 / 0.419271 (-0.361418) | 0.032488 / 0.043533 (-0.011045) | 0.272018 / 0.255139 (0.016879) | 0.287179 / 0.283200 (0.003980) | 0.018446 / 0.141683 (-0.123237) | 1.140346 / 1.452155 (-0.311809) | 1.247743 / 1.492716 (-0.244974) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091987 / 0.018006 (0.073980) | 0.300527 / 0.000490 (0.300037) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021390 / 0.037411 (-0.016021) | 0.068768 / 0.014526 (0.054242) | 0.080798 / 0.176557 (-0.095759) | 0.119081 / 0.737135 (-0.618054) | 0.082461 / 0.296338 (-0.213878) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286631 / 0.215209 (0.071422) | 2.804633 / 2.077655 (0.726978) | 1.574122 / 1.504120 (0.070002) | 1.459994 / 1.541195 (-0.081201) | 1.499739 / 1.468490 (0.031249) | 0.579595 / 4.584777 (-4.005182) | 2.426407 / 3.745712 (-1.319306) | 2.917994 / 5.269862 (-2.351868) | 1.846439 / 4.565676 (-2.719238) | 0.063274 / 0.424275 (-0.361001) | 0.005028 / 0.007607 (-0.002579) | 0.341114 / 0.226044 (0.115070) | 3.402677 / 2.268929 (1.133748) | 1.940980 / 55.444624 (-53.503645) | 1.651902 / 6.876477 (-5.224575) | 1.677037 / 2.142072 (-0.465036) | 0.651576 / 4.805227 (-4.153651) | 0.116398 / 6.500664 (-6.384266) | 0.041060 / 0.075469 (-0.034409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973278 / 1.841788 (-0.868509) | 12.248332 / 8.074308 (4.174024) | 10.830627 / 10.191392 (0.639235) | 0.143146 / 0.680424 (-0.537278) | 0.016249 / 0.534201 (-0.517952) | 0.298563 / 0.579283 (-0.280720) | 0.278643 / 0.434364 (-0.155721) | 0.338206 / 0.540337 (-0.202132) | 0.589485 / 1.386936 (-0.797451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n" ]

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[ "We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)", "Thanks!" ]

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[ "Friendly bump, I would be happy to work on this issue once I get the go-ahead from the dev team. ", "Thanks for the PR!\r\n\r\nI'm struggling with this as well and would love to see this PR merged. My case is slightly different, with keys completely missing rather than being `None`:\r\n\r\n```\r\nds = Dataset.from_dict({'speaker': [{'name': 'Ben'}]})\r\nds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})\r\nprint(concatenate_datasets([ds, ds2]).features)\r\nprint(concatenate_datasets([ds, ds2]).to_dict())\r\n```\r\n\r\nI would expect this to work as well because other Dataset functions already handle this situation well. For example, this works just as expected:\r\n\r\n```\r\nds = Dataset.from_dict({'n': [1,2]})\r\nds_mapped = ds.map(lambda x: {\r\n 'speaker': {'name': 'Ben'} if x['n'] == 1 else {'name': 'Fred', 'email': 'abc@aol.com'}\r\n})\r\nprint(ds_mapped)\r\n```" ]