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[ "Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c \"import dill; print(dill.__version__)\"` in your environment?", "`0.3.6` I feel like that's bad news, because it's probably not the issue.\r\n\r\nMy mistake, about the wrong path guess. I think I didn't notice that the first `dill` in the path isn't supposed to be included in the path specification in python.\r\n<img width=\"146\" alt=\"Screen Shot 2023-01-31 at 12 58 32 PM\" src=\"https://user-images.githubusercontent.com/35349273/215844209-74af6a8f-9bff-4c75-9495-44c658c8e9f7.png\">\r\n", "Hi, @avivbrokman, this issue you report appeared only with old versions of dill. See:\r\n- #288\r\n\r\nAre you sure you are in the right Python environment?\r\n- Please note that Jupyter (where I guess you get the error) may have multiple execution backends (IPython kernels) that might be different from the Python environment your are using to get the dill version\r\n - Have you run `import dill; print(dill.__version__)` in the same Jupyter/IPython that you were using when you got the error while executing `import datasets`?", "I'm using spyder, and I am still getting `0.3.6` for `dill`, so unfortunately #288 isn't applicable, I think. However, I found something odd that I believe is a clue: \r\n\r\n```\r\nimport inspect\r\nimport dill\r\n\r\ninspect.getfile(dill)\r\n>>> '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill/__init__.py'\r\n```\r\n\r\nI checked out the directory, and there is no `dill` subdirectory within '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill`, as there should be. Rather, `_dill.py` is in '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill` itself. \r\n\r\n If I run `pip install dill` or `pip install --upgrade dill`, I get the message `Requirement already satisfied: dill in ./opt/anaconda3/lib/python3.9/site-packages (0.3.6)`. If I run `conda upgrade dill`, I get the message `Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.` a couple of times, followed by\r\n\r\n```\r\nSolving environment: failed\r\nSolving environment: / \r\nFound conflicts! Looking for incompatible packages.\r\n```\r\n\r\nAnd then terminal proceeds to list conflicts between different packages I have.\r\n\r\nThis is all very strange to me because I recently uninstalled and reinstalled `anaconda`.\r\n", "As I said above, I guess this is not a problem with `datasets`. I think you have different Python environments: one with the new dill version (the one you get while using pip) and other with the old dill version (the one where you get the AttributeError).\r\n\r\nYou should update `dill` in the Python environment you are using within spyder.\r\n\r\nPlease note that the `_dill` module is present in the `dill` package since their 2.8.0 version." ]

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[ "Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.", "Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`\r\n\r\nFor instance, the below code\r\n\r\n```python\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\r\n path='csv',\r\n data_files={'train': TRAINING_SET_PATH},\r\n sep='\\t',\r\n header=None,\r\n column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']\r\n)\r\n```\r\n\r\nleads to \r\n\r\n```python\r\ndataset\r\n>>> DatasetDict({\r\n train: Dataset({\r\n features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 62543\r\n })\r\n})\r\n\r\ndataset['train'][0]\r\n>>> {'tokens': 'Distribution',\r\n 'pos_tags': 'NN',\r\n 'chunk_tags': 'O',\r\n 'ner_tags': 'O'\r\n}\r\n```\r\nIs there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008492 / 0.011353 (-0.002861) | 0.004717 / 0.011008 (-0.006292) | 0.101111 / 0.038508 (0.062602) | 0.029129 / 0.023109 (0.006019) | 0.307564 / 0.275898 (0.031666) | 0.367038 / 0.323480 (0.043558) | 0.007105 / 0.007986 (-0.000881) | 0.003622 / 0.004328 (-0.000706) | 0.078370 / 0.004250 (0.074120) | 0.036960 / 0.037052 (-0.000093) | 0.315612 / 0.258489 (0.057123) | 0.353601 / 0.293841 (0.059760) | 0.032900 / 0.128546 (-0.095647) | 0.011405 / 0.075646 (-0.064241) | 0.322331 / 0.419271 (-0.096940) | 0.040823 / 0.043533 (-0.002710) | 0.306734 / 0.255139 (0.051595) | 0.328155 / 0.283200 (0.044955) | 0.087169 / 0.141683 (-0.054514) | 1.460543 / 1.452155 (0.008389) | 1.498094 / 1.492716 (0.005378) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011863 / 0.018006 (-0.006143) | 0.416315 / 0.000490 (0.415826) | 0.003463 / 0.000200 (0.003263) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023219 / 0.037411 (-0.014192) | 0.096469 / 0.014526 (0.081943) | 0.105960 / 0.176557 (-0.070596) | 0.148993 / 0.737135 (-0.588142) | 0.108112 / 0.296338 (-0.188226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415662 / 0.215209 (0.200453) | 4.155111 / 2.077655 (2.077456) | 1.834943 / 1.504120 (0.330823) | 1.622752 / 1.541195 (0.081557) | 1.701630 / 1.468490 (0.233140) | 0.690596 / 4.584777 (-3.894181) | 3.399385 / 3.745712 (-0.346327) | 3.140521 / 5.269862 (-2.129341) | 1.609152 / 4.565676 (-2.956524) | 0.082132 / 0.424275 (-0.342143) | 0.012343 / 0.007607 (0.004735) | 0.532715 / 0.226044 (0.306670) | 5.323032 / 2.268929 (3.054104) | 2.326625 / 55.444624 (-53.118000) | 1.944263 / 6.876477 (-4.932213) | 1.994015 / 2.142072 (-0.148058) | 0.813805 / 4.805227 (-3.991422) | 0.149233 / 6.500664 (-6.351431) | 0.065318 / 0.075469 (-0.010151) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212441 / 1.841788 (-0.629347) | 13.979069 / 8.074308 (5.904761) | 14.003998 / 10.191392 (3.812606) | 0.146956 / 0.680424 (-0.533468) | 0.028564 / 0.534201 (-0.505637) | 0.392370 / 0.579283 (-0.186913) | 0.399695 / 0.434364 (-0.034669) | 0.473481 / 0.540337 (-0.066856) | 0.562625 / 1.386936 (-0.824311) |\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) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076217 / 0.038508 (0.037709) | 0.028888 / 0.023109 (0.005779) | 0.345431 / 0.275898 (0.069533) | 0.389246 / 0.323480 (0.065766) | 0.005939 / 0.007986 (-0.002046) | 0.003356 / 0.004328 (-0.000973) | 0.075880 / 0.004250 (0.071629) | 0.041427 / 0.037052 (0.004374) | 0.344481 / 0.258489 (0.085992) | 0.398508 / 0.293841 (0.104667) | 0.031801 / 0.128546 (-0.096745) | 0.011763 / 0.075646 (-0.063884) | 0.085600 / 0.419271 (-0.333672) | 0.042656 / 0.043533 (-0.000876) | 0.345893 / 0.255139 (0.090754) | 0.376910 / 0.283200 (0.093711) | 0.092451 / 0.141683 (-0.049232) | 1.461222 / 1.452155 (0.009068) | 1.555822 / 1.492716 (0.063106) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235781 / 0.018006 (0.217774) | 0.418485 / 0.000490 (0.417995) | 0.005560 / 0.000200 (0.005360) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025410 / 0.037411 (-0.012001) | 0.103780 / 0.014526 (0.089254) | 0.110183 / 0.176557 (-0.066374) | 0.151097 / 0.737135 (-0.586039) | 0.112539 / 0.296338 (-0.183799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436686 / 0.215209 (0.221477) | 4.341594 / 2.077655 (2.263940) | 2.062309 / 1.504120 (0.558190) | 1.857461 / 1.541195 (0.316267) | 1.947204 / 1.468490 (0.478713) | 0.699641 / 4.584777 (-3.885136) | 3.406983 / 3.745712 (-0.338729) | 3.294705 / 5.269862 (-1.975157) | 1.360582 / 4.565676 (-3.205095) | 0.083025 / 0.424275 (-0.341250) | 0.012461 / 0.007607 (0.004854) | 0.537767 / 0.226044 (0.311722) | 5.393316 / 2.268929 (3.124387) | 2.516692 / 55.444624 (-52.927932) | 2.163987 / 6.876477 (-4.712490) | 2.220480 / 2.142072 (0.078408) | 0.810648 / 4.805227 (-3.994579) | 0.151820 / 6.500664 (-6.348844) | 0.068080 / 0.075469 (-0.007389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279382 / 1.841788 (-0.562405) | 13.989947 / 8.074308 (5.915638) | 14.039229 / 10.191392 (3.847836) | 0.141071 / 0.680424 (-0.539352) | 0.017118 / 0.534201 (-0.517083) | 0.381558 / 0.579283 (-0.197725) | 0.390407 / 0.434364 (-0.043957) | 0.440920 / 0.540337 (-0.099418) | 0.525478 / 1.386936 (-0.861458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeedb5167d150888a640cd70ca63d6d72bbe1043 \"CML watermark\")\n" ]

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[ "I think I need to create another PR on https://huggingface.co/datasets/huggingface/documentation-images/tree/main/datasets for hosting the images there?", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the update @awsaf49 !", "> Thanks a lot for the updates!\r\n> \r\n> Just some minor things remain and the we should be good to ship this 🚀\r\n\r\n@sayakpaul I have updated the minor things. Please approve the workflows", "I think this PR is good to go..\r\n@sayakpaul @lhoestq ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009064 / 0.011353 (-0.002289) | 0.005262 / 0.011008 (-0.005746) | 0.099608 / 0.038508 (0.061100) | 0.035015 / 0.023109 (0.011906) | 0.296501 / 0.275898 (0.020602) | 0.353619 / 0.323480 (0.030139) | 0.007903 / 0.007986 (-0.000083) | 0.004093 / 0.004328 (-0.000235) | 0.075260 / 0.004250 (0.071009) | 0.043142 / 0.037052 (0.006089) | 0.307755 / 0.258489 (0.049266) | 0.336340 / 0.293841 (0.042499) | 0.038596 / 0.128546 (-0.089950) | 0.011861 / 0.075646 (-0.063786) | 0.334226 / 0.419271 (-0.085045) | 0.051472 / 0.043533 (0.007940) | 0.298539 / 0.255139 (0.043400) | 0.316856 / 0.283200 (0.033656) | 0.108620 / 0.141683 (-0.033063) | 1.434901 / 1.452155 (-0.017254) | 1.468368 / 1.492716 (-0.024348) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208402 / 0.018006 (0.190395) | 0.445799 / 0.000490 (0.445309) | 0.003704 / 0.000200 (0.003504) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025435 / 0.037411 (-0.011976) | 0.105874 / 0.014526 (0.091348) | 0.115652 / 0.176557 (-0.060905) | 0.150872 / 0.737135 (-0.586263) | 0.121705 / 0.296338 (-0.174633) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397816 / 0.215209 (0.182607) | 3.977766 / 2.077655 (1.900111) | 1.850848 / 1.504120 (0.346728) | 1.686062 / 1.541195 (0.144867) | 1.786277 / 1.468490 (0.317787) | 0.696250 / 4.584777 (-3.888527) | 3.785255 / 3.745712 (0.039543) | 3.355013 / 5.269862 (-1.914849) | 1.818232 / 4.565676 (-2.747444) | 0.085408 / 0.424275 (-0.338867) | 0.012567 / 0.007607 (0.004960) | 0.524185 / 0.226044 (0.298140) | 5.061975 / 2.268929 (2.793047) | 2.299866 / 55.444624 (-53.144758) | 1.966709 / 6.876477 (-4.909768) | 2.018760 / 2.142072 (-0.123313) | 0.841341 / 4.805227 (-3.963886) | 0.166374 / 6.500664 (-6.334290) | 0.061854 / 0.075469 (-0.013615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221666 / 1.841788 (-0.620122) | 14.373194 / 8.074308 (6.298886) | 14.253614 / 10.191392 (4.062222) | 0.172979 / 0.680424 (-0.507445) | 0.029176 / 0.534201 (-0.505025) | 0.447399 / 0.579283 (-0.131884) | 0.443663 / 0.434364 (0.009299) | 0.537071 / 0.540337 (-0.003267) | 0.640539 / 1.386936 (-0.746397) |\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) | 0.007019 / 0.011353 (-0.004334) | 0.005091 / 0.011008 (-0.005917) | 0.074588 / 0.038508 (0.036080) | 0.032391 / 0.023109 (0.009282) | 0.340548 / 0.275898 (0.064650) | 0.367159 / 0.323480 (0.043679) | 0.005594 / 0.007986 (-0.002392) | 0.004003 / 0.004328 (-0.000325) | 0.073946 / 0.004250 (0.069695) | 0.045921 / 0.037052 (0.008868) | 0.340245 / 0.258489 (0.081756) | 0.397958 / 0.293841 (0.104117) | 0.036539 / 0.128546 (-0.092007) | 0.012258 / 0.075646 (-0.063388) | 0.087406 / 0.419271 (-0.331865) | 0.049276 / 0.043533 (0.005743) | 0.345235 / 0.255139 (0.090096) | 0.361250 / 0.283200 (0.078050) | 0.100757 / 0.141683 (-0.040926) | 1.464644 / 1.452155 (0.012489) | 1.545852 / 1.492716 (0.053136) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222952 / 0.018006 (0.204945) | 0.434607 / 0.000490 (0.434117) | 0.000438 / 0.000200 (0.000238) | 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) | 0.028834 / 0.037411 (-0.008577) | 0.107523 / 0.014526 (0.092997) | 0.122077 / 0.176557 (-0.054479) | 0.156574 / 0.737135 (-0.580561) | 0.122917 / 0.296338 (-0.173421) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417292 / 0.215209 (0.202083) | 4.165980 / 2.077655 (2.088325) | 1.996731 / 1.504120 (0.492611) | 1.802946 / 1.541195 (0.261751) | 1.878456 / 1.468490 (0.409966) | 0.711035 / 4.584777 (-3.873742) | 3.847357 / 3.745712 (0.101644) | 2.088354 / 5.269862 (-3.181508) | 1.344763 / 4.565676 (-3.220913) | 0.086356 / 0.424275 (-0.337919) | 0.012530 / 0.007607 (0.004923) | 0.511693 / 0.226044 (0.285648) | 5.126093 / 2.268929 (2.857165) | 2.490023 / 55.444624 (-52.954602) | 2.180274 / 6.876477 (-4.696202) | 2.221511 / 2.142072 (0.079438) | 0.836348 / 4.805227 (-3.968879) | 0.169554 / 6.500664 (-6.331110) | 0.064555 / 0.075469 (-0.010914) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293466 / 1.841788 (-0.548321) | 14.785700 / 8.074308 (6.711392) | 13.858493 / 10.191392 (3.667101) | 0.161777 / 0.680424 (-0.518646) | 0.017794 / 0.534201 (-0.516407) | 0.426286 / 0.579283 (-0.152997) | 0.422517 / 0.434364 (-0.011847) | 0.530777 / 0.540337 (-0.009560) | 0.634822 / 1.386936 (-0.752114) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c6e08fcfc3a04e53430c26fa7c07da4cb18d977d \"CML watermark\")\n" ]

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[ "Seems to work now, perhaps it was something internal with our university's network." ]

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[ "I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. \r\n\r\nIt seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412) and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). \r\n\r\nThis will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).\r\n\r\nAny other important considerations?\r\n", "Thanks @MFreidank ! That's correct :)\r\n\r\nReading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema", "#self-assign" ]

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[]

[ "Can I work on this issue? I am pretty new to this.", "Hi ! Sure :) you can comment `#self-assign` to assign yourself to this issue.\r\n\r\nI can give you some pointers to get started:\r\n\r\n`load_dataset` works roughly this way:\r\n1. it instantiate a dataset builder using `load_dataset_builder()`\r\n2. the builder download and prepare the dataset as Arrow files in the cache using `download_and_prepare()`\r\n3. the builder returns a Dataset object with `as_dataset()`\r\n\r\nOne way to approach this would be to implement `as_iterable_dataset()` in `builder.py`.\r\n\r\nAnd similarly to `as_dataset()`, you can use the `ArrowReader`. It has a `get_file_instructions()` method that can be helpful. It gives you the files to read as list of dictionaries with those keys: `filename`, `skip` and `take`.\r\n\r\nThe `skip` and `take` arguments are used in case the user wants to load a subset of the dataset, e.g.\r\n```python\r\nload_dataset(..., split=\"train[:10]\")\r\n```\r\n\r\nLet me know if you have questions or if I can help :)", "This use-case is a bit specific, and `load_dataset` already has enough parameters (plus, `streaming=True` also returns an iterable dataset, so we would have to explain the difference), so I think it would be better to add `IterableDataset.from_file` to the API (more flexible and aligned with the goal from https://github.com/huggingface/datasets/issues/3444) instead.", "> This use-case is a bit specific\r\n\r\nThis allows to use `datasets` for large scale training where map-style datasets are too slow and use too much memory in PyTorch. So I would still consider adding it.\r\n\r\nAlternatively we could add this feature one level bellow:\r\n```python\r\nbuilder = load_dataset_builder(...)\r\nbuilder.download_and_prepare()\r\nids = builder.as_iterable_dataset()\r\n```", "Yes, I see how this can be useful. Still, I think `Dataset.to_iterable` + `IterableDataset.from_file` would be much cleaner in terms of the API design (and more flexible since `load_dataset` can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nAnd since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe `load_dataset` could return an iterable dataset streamed from the cache if `streaming=True` and the cache is up-to-date. ", "> This allows to use datasets for large scale training where map-style datasets are too slow and use too much memory in PyTorch.\r\n\r\nI second that. e.g. In my last experiment Oscar-en uses 16GB RSS RAM per process and when using multiple processes the host quickly runs out cpu memory. ", ">And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThis is exactly the need on JeanZay (HPC) - I have the dataset cache ready, but the compute node is offline, so making streaming work off a local cache would address that need.\r\n\r\nIf you will have a working POC I can be the tester. ", "> Yes, I see how this can be useful. Still, I think Dataset.to_iterable + IterableDataset.from_file would be much cleaner in terms of the API design (and more flexible since load_dataset can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nI like `IterableDataset.from_file` as well. On the other hand `Dataset.to_iterable` first requires to load a Dataset object, which can take time depending on your hardware and your dataset size (sometimes 1h+).\r\n\r\n> And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThat would definitely do the job. I was suggesting a different parameter just to make explicit the difference between\r\n- streaming from the raw data\r\n- streaming from the local cache\r\n\r\nBut I'd be fine with streaming from cache is the cache is up-to-date since it's always faster. We could log a message as usual to make it explicit that the cache is used", "> I was suggesting a different parameter just to make explicit the difference between\r\n\r\nMosaicML's `streaming` library does the same (tries to stream from the local cache if possible), so logging a message should be explicit enough :).", "Ok ! Sounds good then :)", "Hi Both! It has been a while since my first issue so I am gonna go for this one ! #self-assign", "#self-assign", "I like idea of `IterableDataset.from_file`. ", "https://github.com/huggingface/datasets/pull/5821 should be helpful to implement `IterableDataset.from_file`, since it defines a new ArrowExamplesIterable that takes an Arrow tables generator function (e.g. from a file) and can be used in an IterableDataset", "@lhoestq I have just started working on this issue. ", "@lhoestq Thank you for taking over.", "So what's recommanded usage of `IterableDataset.from_file` and `load_dataset`? How about I have multiple arrow files and `load_dataset` is often convenient to handle that.", "If you have multiple Arrow files you can load them using\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndata_files = {\"train\": [\"path/to/0.arrow\", \"path/to/1.arrow\", ..., \"path/to/n.arrow\"]}\r\n\r\nds = load_dataset(\"arrow\", data_files=data_files, streaming=True)\r\n```\r\n\r\nThis is equivalent to calling `IterableDataset.from_file` and `concatenate_datasets`." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009963 / 0.011353 (-0.001390) | 0.005512 / 0.011008 (-0.005496) | 0.100495 / 0.038508 (0.061987) | 0.039929 / 0.023109 (0.016820) | 0.299749 / 0.275898 (0.023850) | 0.372330 / 0.323480 (0.048850) | 0.008689 / 0.007986 (0.000703) | 0.004334 / 0.004328 (0.000006) | 0.076469 / 0.004250 (0.072218) | 0.048091 / 0.037052 (0.011039) | 0.303884 / 0.258489 (0.045395) | 0.352747 / 0.293841 (0.058906) | 0.038941 / 0.128546 (-0.089605) | 0.012541 / 0.075646 (-0.063105) | 0.334227 / 0.419271 (-0.085044) | 0.048802 / 0.043533 (0.005269) | 0.295800 / 0.255139 (0.040661) | 0.316222 / 0.283200 (0.033022) | 0.108246 / 0.141683 (-0.033437) | 1.452735 / 1.452155 (0.000580) | 1.466293 / 1.492716 (-0.026423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010497 / 0.018006 (-0.007510) | 0.507427 / 0.000490 (0.506937) | 0.003054 / 0.000200 (0.002854) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029529 / 0.037411 (-0.007883) | 0.114151 / 0.014526 (0.099625) | 0.120599 / 0.176557 (-0.055957) | 0.161881 / 0.737135 (-0.575255) | 0.127669 / 0.296338 (-0.168669) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399631 / 0.215209 (0.184421) | 3.992997 / 2.077655 (1.915343) | 1.803770 / 1.504120 (0.299650) | 1.612301 / 1.541195 (0.071106) | 1.717846 / 1.468490 (0.249356) | 0.706753 / 4.584777 (-3.878024) | 3.798224 / 3.745712 (0.052512) | 2.169733 / 5.269862 (-3.100128) | 1.358264 / 4.565676 (-3.207413) | 0.086828 / 0.424275 (-0.337447) | 0.012606 / 0.007607 (0.004999) | 0.512085 / 0.226044 (0.286041) | 5.101491 / 2.268929 (2.832563) | 2.285688 / 55.444624 (-53.158936) | 1.955160 / 6.876477 (-4.921317) | 2.045887 / 2.142072 (-0.096186) | 0.878836 / 4.805227 (-3.926392) | 0.166483 / 6.500664 (-6.334181) | 0.062656 / 0.075469 (-0.012814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215152 / 1.841788 (-0.626636) | 15.436187 / 8.074308 (7.361879) | 14.489951 / 10.191392 (4.298559) | 0.199019 / 0.680424 (-0.481404) | 0.029148 / 0.534201 (-0.505053) | 0.440309 / 0.579283 (-0.138974) | 0.452041 / 0.434364 (0.017677) | 0.527102 / 0.540337 (-0.013236) | 0.634302 / 1.386936 (-0.752634) |\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) | 0.007814 / 0.011353 (-0.003539) | 0.005582 / 0.011008 (-0.005427) | 0.075466 / 0.038508 (0.036958) | 0.034421 / 0.023109 (0.011312) | 0.342345 / 0.275898 (0.066447) | 0.389943 / 0.323480 (0.066463) | 0.006346 / 0.007986 (-0.001639) | 0.004442 / 0.004328 (0.000113) | 0.074440 / 0.004250 (0.070190) | 0.056383 / 0.037052 (0.019331) | 0.340293 / 0.258489 (0.081804) | 0.394416 / 0.293841 (0.100575) | 0.037217 / 0.128546 (-0.091330) | 0.012597 / 0.075646 (-0.063050) | 0.087005 / 0.419271 (-0.332267) | 0.051626 / 0.043533 (0.008094) | 0.336690 / 0.255139 (0.081551) | 0.369143 / 0.283200 (0.085943) | 0.110764 / 0.141683 (-0.030919) | 1.459003 / 1.452155 (0.006849) | 1.557333 / 1.492716 (0.064617) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319596 / 0.018006 (0.301590) | 0.514697 / 0.000490 (0.514207) | 0.005286 / 0.000200 (0.005086) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032579 / 0.037411 (-0.004832) | 0.111094 / 0.014526 (0.096568) | 0.127827 / 0.176557 (-0.048730) | 0.169967 / 0.737135 (-0.567168) | 0.133149 / 0.296338 (-0.163189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424637 / 0.215209 (0.209428) | 4.217889 / 2.077655 (2.140235) | 2.044844 / 1.504120 (0.540724) | 1.863513 / 1.541195 (0.322319) | 1.975674 / 1.468490 (0.507184) | 0.695493 / 4.584777 (-3.889284) | 3.815562 / 3.745712 (0.069850) | 3.534427 / 5.269862 (-1.735435) | 1.684874 / 4.565676 (-2.880802) | 0.085560 / 0.424275 (-0.338715) | 0.012439 / 0.007607 (0.004832) | 0.541231 / 0.226044 (0.315187) | 5.287166 / 2.268929 (3.018237) | 2.596622 / 55.444624 (-52.848002) | 2.315913 / 6.876477 (-4.560564) | 2.418454 / 2.142072 (0.276381) | 0.838947 / 4.805227 (-3.966281) | 0.168149 / 6.500664 (-6.332515) | 0.066439 / 0.075469 (-0.009030) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264814 / 1.841788 (-0.576974) | 15.861324 / 8.074308 (7.787016) | 14.352515 / 10.191392 (4.161123) | 0.167032 / 0.680424 (-0.513391) | 0.017766 / 0.534201 (-0.516435) | 0.421821 / 0.579283 (-0.157462) | 0.426657 / 0.434364 (-0.007707) | 0.526742 / 0.540337 (-0.013595) | 0.623851 / 1.386936 (-0.763085) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69b19755e9e37b746ef56780a62d21ef20c574d5 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008167 / 0.011353 (-0.003186) | 0.004404 / 0.011008 (-0.006605) | 0.100462 / 0.038508 (0.061954) | 0.028835 / 0.023109 (0.005726) | 0.326759 / 0.275898 (0.050861) | 0.355150 / 0.323480 (0.031670) | 0.007200 / 0.007986 (-0.000786) | 0.003293 / 0.004328 (-0.001035) | 0.078006 / 0.004250 (0.073756) | 0.033298 / 0.037052 (-0.003754) | 0.307119 / 0.258489 (0.048630) | 0.337689 / 0.293841 (0.043848) | 0.033016 / 0.128546 (-0.095530) | 0.011383 / 0.075646 (-0.064263) | 0.321989 / 0.419271 (-0.097283) | 0.039793 / 0.043533 (-0.003740) | 0.295388 / 0.255139 (0.040249) | 0.322694 / 0.283200 (0.039494) | 0.082989 / 0.141683 (-0.058694) | 1.496701 / 1.452155 (0.044546) | 1.548861 / 1.492716 (0.056145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.176587 / 0.018006 (0.158580) | 0.397660 / 0.000490 (0.397170) | 0.001063 / 0.000200 (0.000863) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022386 / 0.037411 (-0.015025) | 0.096380 / 0.014526 (0.081854) | 0.103032 / 0.176557 (-0.073525) | 0.135050 / 0.737135 (-0.602086) | 0.105941 / 0.296338 (-0.190397) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430989 / 0.215209 (0.215780) | 4.310309 / 2.077655 (2.232654) | 2.142596 / 1.504120 (0.638477) | 1.952043 / 1.541195 (0.410848) | 1.817803 / 1.468490 (0.349312) | 0.690026 / 4.584777 (-3.894751) | 3.315413 / 3.745712 (-0.430299) | 3.370336 / 5.269862 (-1.899525) | 1.668707 / 4.565676 (-2.896970) | 0.081860 / 0.424275 (-0.342415) | 0.012493 / 0.007607 (0.004886) | 0.527779 / 0.226044 (0.301735) | 5.318732 / 2.268929 (3.049804) | 2.467029 / 55.444624 (-52.977596) | 2.247171 / 6.876477 (-4.629306) | 2.270825 / 2.142072 (0.128752) | 0.802288 / 4.805227 (-4.002939) | 0.148895 / 6.500664 (-6.351770) | 0.064967 / 0.075469 (-0.010503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259304 / 1.841788 (-0.582484) | 13.662441 / 8.074308 (5.588133) | 14.074662 / 10.191392 (3.883270) | 0.152907 / 0.680424 (-0.527516) | 0.028340 / 0.534201 (-0.505861) | 0.397356 / 0.579283 (-0.181927) | 0.392600 / 0.434364 (-0.041764) | 0.467935 / 0.540337 (-0.072402) | 0.539890 / 1.386936 (-0.847046) |\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) | 0.006156 / 0.011353 (-0.005197) | 0.004371 / 0.011008 (-0.006637) | 0.076391 / 0.038508 (0.037883) | 0.026455 / 0.023109 (0.003346) | 0.339816 / 0.275898 (0.063917) | 0.370032 / 0.323480 (0.046552) | 0.004614 / 0.007986 (-0.003372) | 0.003200 / 0.004328 (-0.001129) | 0.075408 / 0.004250 (0.071157) | 0.034100 / 0.037052 (-0.002953) | 0.341232 / 0.258489 (0.082743) | 0.380290 / 0.293841 (0.086449) | 0.031021 / 0.128546 (-0.097525) | 0.011562 / 0.075646 (-0.064084) | 0.085564 / 0.419271 (-0.333708) | 0.041431 / 0.043533 (-0.002102) | 0.359570 / 0.255139 (0.104431) | 0.366919 / 0.283200 (0.083719) | 0.088242 / 0.141683 (-0.053441) | 1.460703 / 1.452155 (0.008548) | 1.534351 / 1.492716 (0.041635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225703 / 0.018006 (0.207697) | 0.395014 / 0.000490 (0.394524) | 0.000385 / 0.000200 (0.000185) | 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) | 0.023975 / 0.037411 (-0.013436) | 0.098658 / 0.014526 (0.084132) | 0.105043 / 0.176557 (-0.071513) | 0.139988 / 0.737135 (-0.597148) | 0.106854 / 0.296338 (-0.189484) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442454 / 0.215209 (0.227245) | 4.430860 / 2.077655 (2.353205) | 2.084823 / 1.504120 (0.580704) | 1.870421 / 1.541195 (0.329226) | 1.901618 / 1.468490 (0.433128) | 0.699214 / 4.584777 (-3.885563) | 3.336911 / 3.745712 (-0.408801) | 1.856479 / 5.269862 (-3.413383) | 1.166496 / 4.565676 (-3.399180) | 0.083189 / 0.424275 (-0.341086) | 0.012293 / 0.007607 (0.004686) | 0.543147 / 0.226044 (0.317102) | 5.452030 / 2.268929 (3.183101) | 2.506689 / 55.444624 (-52.937936) | 2.168186 / 6.876477 (-4.708291) | 2.172277 / 2.142072 (0.030205) | 0.813554 / 4.805227 (-3.991673) | 0.152074 / 6.500664 (-6.348590) | 0.066891 / 0.075469 (-0.008579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278635 / 1.841788 (-0.563153) | 13.690232 / 8.074308 (5.615924) | 13.403201 / 10.191392 (3.211809) | 0.128171 / 0.680424 (-0.552253) | 0.016687 / 0.534201 (-0.517514) | 0.378645 / 0.579283 (-0.200638) | 0.382922 / 0.434364 (-0.051442) | 0.467483 / 0.540337 (-0.072854) | 0.559026 / 1.386936 (-0.827910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262d411ec0e252615a140c4e3e60e7dbd38eef1 \"CML watermark\")\n" ]

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[ "@albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! ", "The source issue:\r\n- https://github.com/pandas-dev/pandas/issues/40686\r\n\r\nhas been fixed:\r\n- https://github.com/pandas-dev/pandas/pull/48576\r\n\r\nThe fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:\r\n- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0\r\n\r\nbut it will not be back-ported to `pandas-1`:\r\n- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159\r\n\r\nAlso note that `pandas-2.0.0` dropped support for Python 3.7:\r\n- https://github.com/pandas-dev/pandas/issues/41678\r\n- https://github.com/pandas-dev/pandas/pull/41989\r\n\r\nTherefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012442 / 0.011353 (0.001089) | 0.006274 / 0.011008 (-0.004734) | 0.128249 / 0.038508 (0.089741) | 0.040117 / 0.023109 (0.017008) | 0.383725 / 0.275898 (0.107827) | 0.510494 / 0.323480 (0.187014) | 0.009037 / 0.007986 (0.001051) | 0.008256 / 0.004328 (0.003927) | 0.105329 / 0.004250 (0.101079) | 0.046909 / 0.037052 (0.009857) | 0.401980 / 0.258489 (0.143491) | 0.461332 / 0.293841 (0.167491) | 0.065629 / 0.128546 (-0.062917) | 0.020043 / 0.075646 (-0.055604) | 0.453773 / 0.419271 (0.034501) | 0.063456 / 0.043533 (0.019923) | 0.384458 / 0.255139 (0.129319) | 0.449699 / 0.283200 (0.166499) | 0.118197 / 0.141683 (-0.023486) | 1.915080 / 1.452155 (0.462925) | 1.957132 / 1.492716 (0.464416) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209657 / 0.018006 (0.191651) | 0.592478 / 0.000490 (0.591988) | 0.004137 / 0.000200 (0.003937) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029607 / 0.037411 (-0.007804) | 0.129559 / 0.014526 (0.115033) | 0.148326 / 0.176557 (-0.028231) | 0.190506 / 0.737135 (-0.546629) | 0.143177 / 0.296338 (-0.153162) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626166 / 0.215209 (0.410957) | 6.612680 / 2.077655 (4.535026) | 2.432354 / 1.504120 (0.928234) | 2.051482 / 1.541195 (0.510287) | 2.055822 / 1.468490 (0.587332) | 1.210099 / 4.584777 (-3.374678) | 5.498117 / 3.745712 (1.752405) | 3.054838 / 5.269862 (-2.215024) | 2.182875 / 4.565676 (-2.382802) | 0.144518 / 0.424275 (-0.279757) | 0.014132 / 0.007607 (0.006525) | 0.801805 / 0.226044 (0.575761) | 7.911235 / 2.268929 (5.642307) | 3.372762 / 55.444624 (-52.071862) | 2.517266 / 6.876477 (-4.359210) | 2.515329 / 2.142072 (0.373256) | 1.501731 / 4.805227 (-3.303497) | 0.252569 / 6.500664 (-6.248096) | 0.080987 / 0.075469 (0.005518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.709880 / 1.841788 (-0.131907) | 18.640340 / 8.074308 (10.566032) | 23.560908 / 10.191392 (13.369516) | 0.265680 / 0.680424 (-0.414744) | 0.046438 / 0.534201 (-0.487763) | 0.571973 / 0.579283 (-0.007310) | 0.642425 / 0.434364 (0.208061) | 0.698167 / 0.540337 (0.157830) | 0.842132 / 1.386936 (-0.544804) |\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) | 0.009268 / 0.011353 (-0.002085) | 0.006052 / 0.011008 (-0.004956) | 0.133448 / 0.038508 (0.094939) | 0.034417 / 0.023109 (0.011308) | 0.435573 / 0.275898 (0.159675) | 0.479642 / 0.323480 (0.156162) | 0.008016 / 0.007986 (0.000030) | 0.006616 / 0.004328 (0.002288) | 0.106256 / 0.004250 (0.102005) | 0.048995 / 0.037052 (0.011942) | 0.450056 / 0.258489 (0.191567) | 0.511027 / 0.293841 (0.217187) | 0.052928 / 0.128546 (-0.075618) | 0.020824 / 0.075646 (-0.054822) | 0.450105 / 0.419271 (0.030834) | 0.062729 / 0.043533 (0.019196) | 0.438887 / 0.255139 (0.183748) | 0.468732 / 0.283200 (0.185532) | 0.116101 / 0.141683 (-0.025582) | 1.909689 / 1.452155 (0.457534) | 2.042007 / 1.492716 (0.549291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198265 / 0.018006 (0.180259) | 0.541799 / 0.000490 (0.541309) | 0.003938 / 0.000200 (0.003738) | 0.000116 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035933 / 0.037411 (-0.001478) | 0.130754 / 0.014526 (0.116229) | 0.146143 / 0.176557 (-0.030414) | 0.202042 / 0.737135 (-0.535094) | 0.155648 / 0.296338 (-0.140691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.691123 / 0.215209 (0.475914) | 6.708370 / 2.077655 (4.630715) | 2.957120 / 1.504120 (1.453000) | 2.558350 / 1.541195 (1.017155) | 2.611271 / 1.468490 (1.142781) | 1.327355 / 4.584777 (-3.257422) | 5.755975 / 3.745712 (2.010263) | 3.295556 / 5.269862 (-1.974305) | 2.159831 / 4.565676 (-2.405845) | 0.161409 / 0.424275 (-0.262866) | 0.015470 / 0.007607 (0.007863) | 0.840611 / 0.226044 (0.614567) | 8.550064 / 2.268929 (6.281136) | 3.832013 / 55.444624 (-51.612612) | 3.032909 / 6.876477 (-3.843568) | 3.155651 / 2.142072 (1.013578) | 1.612486 / 4.805227 (-3.192741) | 0.273789 / 6.500664 (-6.226875) | 0.085618 / 0.075469 (0.010149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.808376 / 1.841788 (-0.033412) | 18.267614 / 8.074308 (10.193306) | 21.047679 / 10.191392 (10.856286) | 0.259089 / 0.680424 (-0.421335) | 0.029211 / 0.534201 (-0.504990) | 0.556303 / 0.579283 (-0.022980) | 0.625264 / 0.434364 (0.190900) | 0.680814 / 0.540337 (0.140476) | 0.810146 / 1.386936 (-0.576790) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#20ea76c80e07acad78cf67198a4046a982feda21 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008779 / 0.011353 (-0.002574) | 0.004644 / 0.011008 (-0.006364) | 0.099814 / 0.038508 (0.061306) | 0.029830 / 0.023109 (0.006721) | 0.299159 / 0.275898 (0.023261) | 0.354815 / 0.323480 (0.031335) | 0.006968 / 0.007986 (-0.001018) | 0.003521 / 0.004328 (-0.000808) | 0.077687 / 0.004250 (0.073437) | 0.035019 / 0.037052 (-0.002034) | 0.309548 / 0.258489 (0.051059) | 0.345228 / 0.293841 (0.051387) | 0.033644 / 0.128546 (-0.094902) | 0.011564 / 0.075646 (-0.064083) | 0.321835 / 0.419271 (-0.097437) | 0.041798 / 0.043533 (-0.001735) | 0.298190 / 0.255139 (0.043051) | 0.328874 / 0.283200 (0.045674) | 0.088175 / 0.141683 (-0.053508) | 1.481755 / 1.452155 (0.029600) | 1.503085 / 1.492716 (0.010369) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.170930 / 0.018006 (0.152924) | 0.422155 / 0.000490 (0.421666) | 0.001708 / 0.000200 (0.001509) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022588 / 0.037411 (-0.014824) | 0.095775 / 0.014526 (0.081249) | 0.103939 / 0.176557 (-0.072618) | 0.138441 / 0.737135 (-0.598694) | 0.107896 / 0.296338 (-0.188442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418243 / 0.215209 (0.203034) | 4.171432 / 2.077655 (2.093777) | 1.906029 / 1.504120 (0.401909) | 1.698174 / 1.541195 (0.156979) | 1.748339 / 1.468490 (0.279849) | 0.691026 / 4.584777 (-3.893751) | 3.393354 / 3.745712 (-0.352358) | 2.722412 / 5.269862 (-2.547450) | 1.462439 / 4.565676 (-3.103238) | 0.084713 / 0.424275 (-0.339562) | 0.012131 / 0.007607 (0.004524) | 0.522153 / 0.226044 (0.296109) | 5.197916 / 2.268929 (2.928988) | 2.314270 / 55.444624 (-53.130354) | 1.986599 / 6.876477 (-4.889878) | 2.012757 / 2.142072 (-0.129315) | 0.802540 / 4.805227 (-4.002687) | 0.148673 / 6.500664 (-6.351991) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263790 / 1.841788 (-0.577998) | 13.874784 / 8.074308 (5.800476) | 13.842276 / 10.191392 (3.650884) | 0.149002 / 0.680424 (-0.531422) | 0.028550 / 0.534201 (-0.505651) | 0.396913 / 0.579283 (-0.182370) | 0.401543 / 0.434364 (-0.032821) | 0.473754 / 0.540337 (-0.066583) | 0.560455 / 1.386936 (-0.826481) |\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) | 0.006724 / 0.011353 (-0.004629) | 0.004507 / 0.011008 (-0.006502) | 0.098447 / 0.038508 (0.059939) | 0.027888 / 0.023109 (0.004779) | 0.428956 / 0.275898 (0.153058) | 0.451557 / 0.323480 (0.128077) | 0.005056 / 0.007986 (-0.002929) | 0.003363 / 0.004328 (-0.000965) | 0.075990 / 0.004250 (0.071740) | 0.038688 / 0.037052 (0.001635) | 0.421550 / 0.258489 (0.163061) | 0.459480 / 0.293841 (0.165639) | 0.031408 / 0.128546 (-0.097138) | 0.011559 / 0.075646 (-0.064088) | 0.320054 / 0.419271 (-0.099217) | 0.041917 / 0.043533 (-0.001616) | 0.420878 / 0.255139 (0.165739) | 0.444813 / 0.283200 (0.161613) | 0.090409 / 0.141683 (-0.051274) | 1.490058 / 1.452155 (0.037904) | 1.645206 / 1.492716 (0.152489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221105 / 0.018006 (0.203099) | 0.407537 / 0.000490 (0.407047) | 0.000410 / 0.000200 (0.000210) | 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) | 0.024658 / 0.037411 (-0.012754) | 0.099230 / 0.014526 (0.084705) | 0.107788 / 0.176557 (-0.068769) | 0.143040 / 0.737135 (-0.594096) | 0.109440 / 0.296338 (-0.186899) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453303 / 0.215209 (0.238094) | 4.520376 / 2.077655 (2.442722) | 2.133909 / 1.504120 (0.629789) | 1.926996 / 1.541195 (0.385801) | 2.019870 / 1.468490 (0.551380) | 0.707423 / 4.584777 (-3.877354) | 3.391903 / 3.745712 (-0.353809) | 1.860661 / 5.269862 (-3.409201) | 1.159940 / 4.565676 (-3.405736) | 0.083773 / 0.424275 (-0.340502) | 0.012228 / 0.007607 (0.004621) | 0.554666 / 0.226044 (0.328622) | 5.567564 / 2.268929 (3.298636) | 2.636718 / 55.444624 (-52.807907) | 2.240215 / 6.876477 (-4.636262) | 2.218951 / 2.142072 (0.076879) | 0.817167 / 4.805227 (-3.988060) | 0.151633 / 6.500664 (-6.349032) | 0.066515 / 0.075469 (-0.008954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296665 / 1.841788 (-0.545123) | 13.997898 / 8.074308 (5.923590) | 13.286607 / 10.191392 (3.095215) | 0.148906 / 0.680424 (-0.531518) | 0.016600 / 0.534201 (-0.517601) | 0.377459 / 0.579283 (-0.201824) | 0.379938 / 0.434364 (-0.054426) | 0.461628 / 0.540337 (-0.078709) | 0.550592 / 1.386936 (-0.836344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#053f51a3e2adb762236eb29dd02791307f45f02f \"CML watermark\")\n" ]

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[ "Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table), bsz):\r\n+ _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}\r\n```\r\n\r\nI re-ran your code and got a speed ratio of 1.00x and 1.02x", "Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!\r\n\r\nIf it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).", "This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?" ]

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[ "Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468\r\n\r\n> Not sure. Some of my PRs are still open and some do not have any discussions.\r\n\r\nSorry to hear that, feel free to ping me on those PRs" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008959 / 0.011353 (-0.002394) | 0.004549 / 0.011008 (-0.006460) | 0.102012 / 0.038508 (0.063504) | 0.030122 / 0.023109 (0.007013) | 0.303731 / 0.275898 (0.027833) | 0.344418 / 0.323480 (0.020938) | 0.007199 / 0.007986 (-0.000787) | 0.003415 / 0.004328 (-0.000913) | 0.079784 / 0.004250 (0.075534) | 0.034894 / 0.037052 (-0.002158) | 0.304739 / 0.258489 (0.046250) | 0.359457 / 0.293841 (0.065616) | 0.034194 / 0.128546 (-0.094352) | 0.011348 / 0.075646 (-0.064298) | 0.324340 / 0.419271 (-0.094931) | 0.041071 / 0.043533 (-0.002461) | 0.304437 / 0.255139 (0.049298) | 0.335517 / 0.283200 (0.052317) | 0.087787 / 0.141683 (-0.053895) | 1.467293 / 1.452155 (0.015138) | 1.543529 / 1.492716 (0.050813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187654 / 0.018006 (0.169648) | 0.426558 / 0.000490 (0.426068) | 0.003585 / 0.000200 (0.003385) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023410 / 0.037411 (-0.014001) | 0.097065 / 0.014526 (0.082539) | 0.105358 / 0.176557 (-0.071198) | 0.140941 / 0.737135 (-0.596195) | 0.109484 / 0.296338 (-0.186855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420334 / 0.215209 (0.205125) | 4.223235 / 2.077655 (2.145581) | 1.866213 / 1.504120 (0.362093) | 1.673829 / 1.541195 (0.132634) | 1.757828 / 1.468490 (0.289337) | 0.702203 / 4.584777 (-3.882574) | 3.426192 / 3.745712 (-0.319521) | 1.950392 / 5.269862 (-3.319470) | 1.286139 / 4.565676 (-3.279538) | 0.082858 / 0.424275 (-0.341417) | 0.012587 / 0.007607 (0.004980) | 0.531920 / 0.226044 (0.305876) | 5.344425 / 2.268929 (3.075497) | 2.337875 / 55.444624 (-53.106749) | 1.967713 / 6.876477 (-4.908764) | 2.022075 / 2.142072 (-0.119997) | 0.829267 / 4.805227 (-3.975961) | 0.151712 / 6.500664 (-6.348952) | 0.066617 / 0.075469 (-0.008852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251867 / 1.841788 (-0.589921) | 13.861756 / 8.074308 (5.787448) | 14.236309 / 10.191392 (4.044917) | 0.138215 / 0.680424 (-0.542209) | 0.028600 / 0.534201 (-0.505601) | 0.395890 / 0.579283 (-0.183393) | 0.403971 / 0.434364 (-0.030393) | 0.479033 / 0.540337 (-0.061305) | 0.564019 / 1.386936 (-0.822917) |\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) | 0.006845 / 0.011353 (-0.004508) | 0.004544 / 0.011008 (-0.006464) | 0.098719 / 0.038508 (0.060211) | 0.029082 / 0.023109 (0.005973) | 0.426011 / 0.275898 (0.150113) | 0.447185 / 0.323480 (0.123705) | 0.005203 / 0.007986 (-0.002783) | 0.004790 / 0.004328 (0.000462) | 0.076446 / 0.004250 (0.072196) | 0.040649 / 0.037052 (0.003596) | 0.414810 / 0.258489 (0.156321) | 0.452082 / 0.293841 (0.158241) | 0.031842 / 0.128546 (-0.096704) | 0.011575 / 0.075646 (-0.064071) | 0.320710 / 0.419271 (-0.098561) | 0.044994 / 0.043533 (0.001461) | 0.415645 / 0.255139 (0.160506) | 0.435235 / 0.283200 (0.152035) | 0.091756 / 0.141683 (-0.049927) | 1.493900 / 1.452155 (0.041746) | 1.592353 / 1.492716 (0.099637) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264710 / 0.018006 (0.246703) | 0.410553 / 0.000490 (0.410064) | 0.024497 / 0.000200 (0.024297) | 0.000232 / 0.000054 (0.000178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024452 / 0.037411 (-0.012959) | 0.102673 / 0.014526 (0.088147) | 0.107787 / 0.176557 (-0.068770) | 0.147368 / 0.737135 (-0.589767) | 0.112127 / 0.296338 (-0.184211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471294 / 0.215209 (0.256085) | 4.711638 / 2.077655 (2.633983) | 2.436819 / 1.504120 (0.932699) | 2.238540 / 1.541195 (0.697345) | 2.334134 / 1.468490 (0.865644) | 0.697668 / 4.584777 (-3.887108) | 3.414332 / 3.745712 (-0.331380) | 2.783248 / 5.269862 (-2.486614) | 1.529599 / 4.565676 (-3.036078) | 0.082626 / 0.424275 (-0.341649) | 0.012385 / 0.007607 (0.004778) | 0.580486 / 0.226044 (0.354441) | 5.837914 / 2.268929 (3.568986) | 2.915129 / 55.444624 (-52.529495) | 2.606254 / 6.876477 (-4.270223) | 2.659031 / 2.142072 (0.516958) | 0.810431 / 4.805227 (-3.994796) | 0.151666 / 6.500664 (-6.348998) | 0.066873 / 0.075469 (-0.008596) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259933 / 1.841788 (-0.581855) | 14.052388 / 8.074308 (5.978080) | 13.356141 / 10.191392 (3.164749) | 0.138416 / 0.680424 (-0.542008) | 0.016582 / 0.534201 (-0.517619) | 0.378110 / 0.579283 (-0.201173) | 0.385089 / 0.434364 (-0.049275) | 0.465299 / 0.540337 (-0.075038) | 0.559780 / 1.386936 (-0.827156) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d2859fd4d4beca33f21539a6e1df9a7f012cbd10 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011945 / 0.011353 (0.000592) | 0.006128 / 0.011008 (-0.004880) | 0.128926 / 0.038508 (0.090418) | 0.037708 / 0.023109 (0.014599) | 0.373449 / 0.275898 (0.097551) | 0.423567 / 0.323480 (0.100088) | 0.009848 / 0.007986 (0.001863) | 0.006097 / 0.004328 (0.001769) | 0.098275 / 0.004250 (0.094024) | 0.043199 / 0.037052 (0.006147) | 0.376848 / 0.258489 (0.118359) | 0.441819 / 0.293841 (0.147978) | 0.055094 / 0.128546 (-0.073453) | 0.019704 / 0.075646 (-0.055942) | 0.422746 / 0.419271 (0.003474) | 0.061764 / 0.043533 (0.018231) | 0.381056 / 0.255139 (0.125917) | 0.419343 / 0.283200 (0.136144) | 0.116720 / 0.141683 (-0.024963) | 1.763913 / 1.452155 (0.311759) | 1.872306 / 1.492716 (0.379589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198651 / 0.018006 (0.180645) | 0.560565 / 0.000490 (0.560075) | 0.004269 / 0.000200 (0.004069) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027307 / 0.037411 (-0.010104) | 0.128276 / 0.014526 (0.113750) | 0.129015 / 0.176557 (-0.047542) | 0.167269 / 0.737135 (-0.569866) | 0.143955 / 0.296338 (-0.152384) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.564954 / 0.215209 (0.349745) | 5.810570 / 2.077655 (3.732916) | 2.456382 / 1.504120 (0.952262) | 2.115809 / 1.541195 (0.574614) | 2.097363 / 1.468490 (0.628873) | 1.189712 / 4.584777 (-3.395065) | 5.318287 / 3.745712 (1.572575) | 2.965763 / 5.269862 (-2.304099) | 2.177958 / 4.565676 (-2.387719) | 0.144135 / 0.424275 (-0.280140) | 0.014348 / 0.007607 (0.006741) | 0.781715 / 0.226044 (0.555670) | 7.688349 / 2.268929 (5.419421) | 3.189260 / 55.444624 (-52.255365) | 2.552340 / 6.876477 (-4.324137) | 2.559312 / 2.142072 (0.417240) | 1.490755 / 4.805227 (-3.314473) | 0.257908 / 6.500664 (-6.242756) | 0.082016 / 0.075469 (0.006547) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565735 / 1.841788 (-0.276053) | 17.660338 / 8.074308 (9.586030) | 19.493573 / 10.191392 (9.302181) | 0.241310 / 0.680424 (-0.439114) | 0.043485 / 0.534201 (-0.490716) | 0.557397 / 0.579283 (-0.021886) | 0.624385 / 0.434364 (0.190021) | 0.634601 / 0.540337 (0.094264) | 0.743140 / 1.386936 (-0.643796) |\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) | 0.010134 / 0.011353 (-0.001219) | 0.005858 / 0.011008 (-0.005150) | 0.128741 / 0.038508 (0.090232) | 0.036769 / 0.023109 (0.013660) | 0.470894 / 0.275898 (0.194996) | 0.524302 / 0.323480 (0.200822) | 0.006830 / 0.007986 (-0.001156) | 0.006166 / 0.004328 (0.001838) | 0.094875 / 0.004250 (0.090625) | 0.051201 / 0.037052 (0.014148) | 0.493992 / 0.258489 (0.235503) | 0.510540 / 0.293841 (0.216699) | 0.056354 / 0.128546 (-0.072192) | 0.020512 / 0.075646 (-0.055134) | 0.417809 / 0.419271 (-0.001463) | 0.061941 / 0.043533 (0.018408) | 0.498883 / 0.255139 (0.243744) | 0.480762 / 0.283200 (0.197563) | 0.110753 / 0.141683 (-0.030930) | 1.914096 / 1.452155 (0.461941) | 1.941338 / 1.492716 (0.448622) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237955 / 0.018006 (0.219949) | 0.518136 / 0.000490 (0.517647) | 0.000475 / 0.000200 (0.000275) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032947 / 0.037411 (-0.004465) | 0.127857 / 0.014526 (0.113331) | 0.133911 / 0.176557 (-0.042646) | 0.188406 / 0.737135 (-0.548729) | 0.143939 / 0.296338 (-0.152400) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.787553 / 0.215209 (0.572344) | 6.976572 / 2.077655 (4.898918) | 2.897964 / 1.504120 (1.393844) | 2.545906 / 1.541195 (1.004711) | 2.622111 / 1.468490 (1.153620) | 1.278283 / 4.584777 (-3.306494) | 5.650447 / 3.745712 (1.904734) | 4.955835 / 5.269862 (-0.314027) | 2.767946 / 4.565676 (-1.797731) | 0.149385 / 0.424275 (-0.274890) | 0.014340 / 0.007607 (0.006733) | 0.861774 / 0.226044 (0.635730) | 8.660985 / 2.268929 (6.392057) | 3.685611 / 55.444624 (-51.759014) | 2.963087 / 6.876477 (-3.913390) | 3.020746 / 2.142072 (0.878673) | 1.538908 / 4.805227 (-3.266319) | 0.285875 / 6.500664 (-6.214789) | 0.080337 / 0.075469 (0.004867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.575155 / 1.841788 (-0.266633) | 17.548946 / 8.074308 (9.474638) | 19.954104 / 10.191392 (9.762712) | 0.242025 / 0.680424 (-0.438398) | 0.025586 / 0.534201 (-0.508615) | 0.515676 / 0.579283 (-0.063607) | 0.607035 / 0.434364 (0.172671) | 0.633597 / 0.540337 (0.093259) | 0.744577 / 1.386936 (-0.642359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6529cada7879496bf18dd686e4d281de81d6203c \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008578 / 0.011353 (-0.002775) | 0.004535 / 0.011008 (-0.006473) | 0.100694 / 0.038508 (0.062186) | 0.029570 / 0.023109 (0.006460) | 0.296384 / 0.275898 (0.020486) | 0.354405 / 0.323480 (0.030925) | 0.006962 / 0.007986 (-0.001024) | 0.003405 / 0.004328 (-0.000924) | 0.077275 / 0.004250 (0.073025) | 0.036623 / 0.037052 (-0.000429) | 0.309844 / 0.258489 (0.051355) | 0.340343 / 0.293841 (0.046502) | 0.033626 / 0.128546 (-0.094920) | 0.011433 / 0.075646 (-0.064214) | 0.322659 / 0.419271 (-0.096612) | 0.040509 / 0.043533 (-0.003024) | 0.294002 / 0.255139 (0.038863) | 0.323259 / 0.283200 (0.040059) | 0.088023 / 0.141683 (-0.053660) | 1.462039 / 1.452155 (0.009885) | 1.495401 / 1.492716 (0.002684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218614 / 0.018006 (0.200608) | 0.482359 / 0.000490 (0.481869) | 0.001216 / 0.000200 (0.001016) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023167 / 0.037411 (-0.014245) | 0.098468 / 0.014526 (0.083942) | 0.108273 / 0.176557 (-0.068284) | 0.139991 / 0.737135 (-0.597144) | 0.109032 / 0.296338 (-0.187307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421526 / 0.215209 (0.206317) | 4.216808 / 2.077655 (2.139153) | 1.860550 / 1.504120 (0.356431) | 1.654518 / 1.541195 (0.113323) | 1.699064 / 1.468490 (0.230574) | 0.691489 / 4.584777 (-3.893287) | 3.401885 / 3.745712 (-0.343827) | 2.792860 / 5.269862 (-2.477001) | 1.516269 / 4.565676 (-3.049408) | 0.081627 / 0.424275 (-0.342648) | 0.012556 / 0.007607 (0.004949) | 0.531535 / 0.226044 (0.305491) | 5.320752 / 2.268929 (3.051823) | 2.314502 / 55.444624 (-53.130123) | 1.967118 / 6.876477 (-4.909359) | 2.008252 / 2.142072 (-0.133821) | 0.809730 / 4.805227 (-3.995497) | 0.148112 / 6.500664 (-6.352552) | 0.064821 / 0.075469 (-0.010648) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269754 / 1.841788 (-0.572033) | 13.884200 / 8.074308 (5.809892) | 13.914390 / 10.191392 (3.722998) | 0.150176 / 0.680424 (-0.530248) | 0.028463 / 0.534201 (-0.505738) | 0.398723 / 0.579283 (-0.180561) | 0.400433 / 0.434364 (-0.033931) | 0.485169 / 0.540337 (-0.055169) | 0.565995 / 1.386936 (-0.820941) |\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) | 0.006479 / 0.011353 (-0.004874) | 0.004504 / 0.011008 (-0.006504) | 0.097905 / 0.038508 (0.059397) | 0.027140 / 0.023109 (0.004031) | 0.408742 / 0.275898 (0.132844) | 0.448707 / 0.323480 (0.125228) | 0.004819 / 0.007986 (-0.003166) | 0.004761 / 0.004328 (0.000433) | 0.075456 / 0.004250 (0.071205) | 0.036282 / 0.037052 (-0.000771) | 0.405961 / 0.258489 (0.147472) | 0.449411 / 0.293841 (0.155570) | 0.031159 / 0.128546 (-0.097387) | 0.011693 / 0.075646 (-0.063954) | 0.321124 / 0.419271 (-0.098147) | 0.041369 / 0.043533 (-0.002164) | 0.408070 / 0.255139 (0.152931) | 0.428704 / 0.283200 (0.145504) | 0.086839 / 0.141683 (-0.054844) | 1.477772 / 1.452155 (0.025617) | 1.555913 / 1.492716 (0.063197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239494 / 0.018006 (0.221488) | 0.410785 / 0.000490 (0.410295) | 0.000989 / 0.000200 (0.000789) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023805 / 0.037411 (-0.013607) | 0.097904 / 0.014526 (0.083378) | 0.106437 / 0.176557 (-0.070120) | 0.140555 / 0.737135 (-0.596580) | 0.107169 / 0.296338 (-0.189170) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470233 / 0.215209 (0.255024) | 4.700451 / 2.077655 (2.622797) | 2.391712 / 1.504120 (0.887592) | 2.191125 / 1.541195 (0.649930) | 2.268924 / 1.468490 (0.800434) | 0.692421 / 4.584777 (-3.892356) | 3.387117 / 3.745712 (-0.358595) | 1.881731 / 5.269862 (-3.388130) | 1.155759 / 4.565676 (-3.409917) | 0.082040 / 0.424275 (-0.342236) | 0.012687 / 0.007607 (0.005080) | 0.567556 / 0.226044 (0.341511) | 5.701408 / 2.268929 (3.432480) | 2.864368 / 55.444624 (-52.580256) | 2.512073 / 6.876477 (-4.364404) | 2.546078 / 2.142072 (0.404005) | 0.795939 / 4.805227 (-4.009288) | 0.150078 / 6.500664 (-6.350586) | 0.067644 / 0.075469 (-0.007825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281681 / 1.841788 (-0.560107) | 13.967107 / 8.074308 (5.892799) | 13.293648 / 10.191392 (3.102256) | 0.128027 / 0.680424 (-0.552397) | 0.016791 / 0.534201 (-0.517410) | 0.379400 / 0.579283 (-0.199884) | 0.386847 / 0.434364 (-0.047517) | 0.469859 / 0.540337 (-0.070478) | 0.564203 / 1.386936 (-0.822733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90832b5e33774ea8ec35ccb92ac14649a345bdbe \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008701 / 0.011353 (-0.002652) | 0.004564 / 0.011008 (-0.006444) | 0.100578 / 0.038508 (0.062070) | 0.029209 / 0.023109 (0.006100) | 0.315308 / 0.275898 (0.039410) | 0.381022 / 0.323480 (0.057542) | 0.007152 / 0.007986 (-0.000834) | 0.003511 / 0.004328 (-0.000817) | 0.078361 / 0.004250 (0.074110) | 0.035394 / 0.037052 (-0.001658) | 0.331076 / 0.258489 (0.072586) | 0.366613 / 0.293841 (0.072772) | 0.033466 / 0.128546 (-0.095080) | 0.011521 / 0.075646 (-0.064126) | 0.322178 / 0.419271 (-0.097093) | 0.040891 / 0.043533 (-0.002641) | 0.320418 / 0.255139 (0.065279) | 0.345199 / 0.283200 (0.062000) | 0.087906 / 0.141683 (-0.053777) | 1.476801 / 1.452155 (0.024646) | 1.497738 / 1.492716 (0.005022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178094 / 0.018006 (0.160087) | 0.408317 / 0.000490 (0.407827) | 0.001825 / 0.000200 (0.001625) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022402 / 0.037411 (-0.015010) | 0.097104 / 0.014526 (0.082578) | 0.105361 / 0.176557 (-0.071196) | 0.139728 / 0.737135 (-0.597407) | 0.109613 / 0.296338 (-0.186725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418245 / 0.215209 (0.203036) | 4.155655 / 2.077655 (2.078000) | 1.865892 / 1.504120 (0.361772) | 1.659003 / 1.541195 (0.117809) | 1.725649 / 1.468490 (0.257159) | 0.688733 / 4.584777 (-3.896044) | 3.323529 / 3.745712 (-0.422184) | 1.867807 / 5.269862 (-3.402054) | 1.157740 / 4.565676 (-3.407936) | 0.081947 / 0.424275 (-0.342329) | 0.012471 / 0.007607 (0.004864) | 0.529333 / 0.226044 (0.303288) | 5.284898 / 2.268929 (3.015970) | 2.321741 / 55.444624 (-53.122883) | 1.975683 / 6.876477 (-4.900794) | 2.029691 / 2.142072 (-0.112381) | 0.810212 / 4.805227 (-3.995015) | 0.148185 / 6.500664 (-6.352479) | 0.064594 / 0.075469 (-0.010875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183391 / 1.841788 (-0.658396) | 13.574760 / 8.074308 (5.500452) | 14.215015 / 10.191392 (4.023623) | 0.150776 / 0.680424 (-0.529648) | 0.029058 / 0.534201 (-0.505143) | 0.404071 / 0.579283 (-0.175212) | 0.401289 / 0.434364 (-0.033075) | 0.490946 / 0.540337 (-0.049392) | 0.582292 / 1.386936 (-0.804644) |\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) | 0.006695 / 0.011353 (-0.004658) | 0.004499 / 0.011008 (-0.006510) | 0.097633 / 0.038508 (0.059125) | 0.027606 / 0.023109 (0.004496) | 0.413191 / 0.275898 (0.137293) | 0.441896 / 0.323480 (0.118416) | 0.005703 / 0.007986 (-0.002283) | 0.004608 / 0.004328 (0.000280) | 0.074392 / 0.004250 (0.070141) | 0.037966 / 0.037052 (0.000913) | 0.410736 / 0.258489 (0.152247) | 0.448581 / 0.293841 (0.154740) | 0.031594 / 0.128546 (-0.096952) | 0.011597 / 0.075646 (-0.064049) | 0.319632 / 0.419271 (-0.099639) | 0.041189 / 0.043533 (-0.002343) | 0.407120 / 0.255139 (0.151981) | 0.433416 / 0.283200 (0.150216) | 0.089932 / 0.141683 (-0.051751) | 1.453919 / 1.452155 (0.001764) | 1.545892 / 1.492716 (0.053176) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224302 / 0.018006 (0.206296) | 0.415519 / 0.000490 (0.415029) | 0.000407 / 0.000200 (0.000207) | 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) | 0.024104 / 0.037411 (-0.013307) | 0.098202 / 0.014526 (0.083676) | 0.106416 / 0.176557 (-0.070140) | 0.141090 / 0.737135 (-0.596045) | 0.110188 / 0.296338 (-0.186150) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478252 / 0.215209 (0.263043) | 4.739684 / 2.077655 (2.662029) | 2.419040 / 1.504120 (0.914920) | 2.217705 / 1.541195 (0.676510) | 2.303288 / 1.468490 (0.834798) | 0.696682 / 4.584777 (-3.888095) | 3.401962 / 3.745712 (-0.343750) | 1.886015 / 5.269862 (-3.383846) | 1.175084 / 4.565676 (-3.390592) | 0.083064 / 0.424275 (-0.341211) | 0.012613 / 0.007607 (0.005006) | 0.579105 / 0.226044 (0.353060) | 5.792119 / 2.268929 (3.523191) | 2.889778 / 55.444624 (-52.554846) | 2.537438 / 6.876477 (-4.339039) | 2.574814 / 2.142072 (0.432741) | 0.803438 / 4.805227 (-4.001789) | 0.151912 / 6.500664 (-6.348752) | 0.068291 / 0.075469 (-0.007178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286002 / 1.841788 (-0.555786) | 14.179443 / 8.074308 (6.105135) | 13.443939 / 10.191392 (3.252547) | 0.152427 / 0.680424 (-0.527996) | 0.017248 / 0.534201 (-0.516953) | 0.378734 / 0.579283 (-0.200549) | 0.382276 / 0.434364 (-0.052087) | 0.465323 / 0.540337 (-0.075014) | 0.556454 / 1.386936 (-0.830482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008675 / 0.011353 (-0.002678) | 0.004537 / 0.011008 (-0.006471) | 0.100179 / 0.038508 (0.061671) | 0.029307 / 0.023109 (0.006198) | 0.294687 / 0.275898 (0.018789) | 0.356868 / 0.323480 (0.033388) | 0.006992 / 0.007986 (-0.000994) | 0.003380 / 0.004328 (-0.000949) | 0.076961 / 0.004250 (0.072710) | 0.036047 / 0.037052 (-0.001005) | 0.308037 / 0.258489 (0.049548) | 0.341089 / 0.293841 (0.047248) | 0.033416 / 0.128546 (-0.095131) | 0.011534 / 0.075646 (-0.064112) | 0.322976 / 0.419271 (-0.096296) | 0.040894 / 0.043533 (-0.002639) | 0.296501 / 0.255139 (0.041362) | 0.324605 / 0.283200 (0.041405) | 0.086713 / 0.141683 (-0.054970) | 1.502784 / 1.452155 (0.050630) | 1.535013 / 1.492716 (0.042297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186647 / 0.018006 (0.168641) | 0.411003 / 0.000490 (0.410514) | 0.003594 / 0.000200 (0.003394) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023704 / 0.037411 (-0.013707) | 0.096154 / 0.014526 (0.081629) | 0.103671 / 0.176557 (-0.072885) | 0.138878 / 0.737135 (-0.598258) | 0.106947 / 0.296338 (-0.189391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417180 / 0.215209 (0.201970) | 4.149579 / 2.077655 (2.071925) | 1.865763 / 1.504120 (0.361643) | 1.669722 / 1.541195 (0.128527) | 1.722345 / 1.468490 (0.253855) | 0.695910 / 4.584777 (-3.888867) | 3.342266 / 3.745712 (-0.403446) | 1.884568 / 5.269862 (-3.385294) | 1.265013 / 4.565676 (-3.300664) | 0.081836 / 0.424275 (-0.342439) | 0.012371 / 0.007607 (0.004764) | 0.522997 / 0.226044 (0.296953) | 5.225434 / 2.268929 (2.956506) | 2.304701 / 55.444624 (-53.139924) | 1.949067 / 6.876477 (-4.927410) | 2.016347 / 2.142072 (-0.125725) | 0.809850 / 4.805227 (-3.995377) | 0.148396 / 6.500664 (-6.352268) | 0.063340 / 0.075469 (-0.012129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224621 / 1.841788 (-0.617167) | 13.814223 / 8.074308 (5.739915) | 13.879728 / 10.191392 (3.688336) | 0.149530 / 0.680424 (-0.530894) | 0.028439 / 0.534201 (-0.505762) | 0.392726 / 0.579283 (-0.186557) | 0.396894 / 0.434364 (-0.037469) | 0.474395 / 0.540337 (-0.065943) | 0.569090 / 1.386936 (-0.817847) |\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) | 0.006483 / 0.011353 (-0.004870) | 0.004527 / 0.011008 (-0.006481) | 0.098038 / 0.038508 (0.059530) | 0.027239 / 0.023109 (0.004130) | 0.441773 / 0.275898 (0.165875) | 0.471448 / 0.323480 (0.147968) | 0.005034 / 0.007986 (-0.002951) | 0.004732 / 0.004328 (0.000403) | 0.075036 / 0.004250 (0.070785) | 0.036711 / 0.037052 (-0.000341) | 0.442634 / 0.258489 (0.184145) | 0.476479 / 0.293841 (0.182638) | 0.031303 / 0.128546 (-0.097243) | 0.011642 / 0.075646 (-0.064005) | 0.320750 / 0.419271 (-0.098521) | 0.048698 / 0.043533 (0.005165) | 0.441205 / 0.255139 (0.186066) | 0.464845 / 0.283200 (0.181645) | 0.092716 / 0.141683 (-0.048967) | 1.510028 / 1.452155 (0.057874) | 1.574065 / 1.492716 (0.081349) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220756 / 0.018006 (0.202750) | 0.393971 / 0.000490 (0.393482) | 0.002506 / 0.000200 (0.002306) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024455 / 0.037411 (-0.012956) | 0.100164 / 0.014526 (0.085638) | 0.108053 / 0.176557 (-0.068504) | 0.142973 / 0.737135 (-0.594163) | 0.110108 / 0.296338 (-0.186231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473639 / 0.215209 (0.258430) | 4.737521 / 2.077655 (2.659866) | 2.466208 / 1.504120 (0.962088) | 2.272608 / 1.541195 (0.731413) | 2.349255 / 1.468490 (0.880764) | 0.699928 / 4.584777 (-3.884849) | 3.348443 / 3.745712 (-0.397269) | 2.604611 / 5.269862 (-2.665250) | 1.543080 / 4.565676 (-3.022597) | 0.082627 / 0.424275 (-0.341648) | 0.012251 / 0.007607 (0.004644) | 0.569949 / 0.226044 (0.343905) | 5.732316 / 2.268929 (3.463388) | 2.913541 / 55.444624 (-52.531084) | 2.560584 / 6.876477 (-4.315892) | 2.615192 / 2.142072 (0.473120) | 0.803822 / 4.805227 (-4.001406) | 0.150821 / 6.500664 (-6.349843) | 0.067128 / 0.075469 (-0.008341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272278 / 1.841788 (-0.569510) | 13.783339 / 8.074308 (5.709030) | 13.243601 / 10.191392 (3.052209) | 0.136421 / 0.680424 (-0.544003) | 0.016565 / 0.534201 (-0.517636) | 0.381102 / 0.579283 (-0.198181) | 0.386166 / 0.434364 (-0.048197) | 0.474249 / 0.540337 (-0.066089) | 0.566826 / 1.386936 (-0.820110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I thought this issue was resolved in my parallel `to_tf_dataset` PR! I changed the default `num_test_batches` in `_get_output_signature` to 20 and used a test batch size of 1 to maximize variance to detect shorter samples. I think it's still okay to have this PR, though - but I'd use the new value of 20 as the default!", "@Rocketknight1 You're right - I didn't have the most recent changes to the default values. Updated now to 20! I still think it would be good to have it configurable from the `to_tf_dataset` call so the user has the option to either make it more robust if many samples are needed, or faster if only one is needed. That, and I selfishly want it for faster tests. ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010441 / 0.011353 (-0.000912) | 0.005605 / 0.011008 (-0.005404) | 0.115712 / 0.038508 (0.077204) | 0.040907 / 0.023109 (0.017797) | 0.357673 / 0.275898 (0.081775) | 0.415427 / 0.323480 (0.091947) | 0.008827 / 0.007986 (0.000842) | 0.006069 / 0.004328 (0.001740) | 0.088985 / 0.004250 (0.084735) | 0.048461 / 0.037052 (0.011409) | 0.362065 / 0.258489 (0.103576) | 0.393643 / 0.293841 (0.099802) | 0.043844 / 0.128546 (-0.084703) | 0.013757 / 0.075646 (-0.061889) | 0.390993 / 0.419271 (-0.028278) | 0.053612 / 0.043533 (0.010079) | 0.348688 / 0.255139 (0.093549) | 0.377818 / 0.283200 (0.094619) | 0.115762 / 0.141683 (-0.025920) | 1.751826 / 1.452155 (0.299672) | 1.773326 / 1.492716 (0.280609) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220668 / 0.018006 (0.202662) | 0.536830 / 0.000490 (0.536340) | 0.000467 / 0.000200 (0.000267) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031500 / 0.037411 (-0.005911) | 0.125796 / 0.014526 (0.111270) | 0.137539 / 0.176557 (-0.039017) | 0.184651 / 0.737135 (-0.552484) | 0.145707 / 0.296338 (-0.150632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465876 / 0.215209 (0.250667) | 4.637711 / 2.077655 (2.560056) | 2.132335 / 1.504120 (0.628215) | 1.862593 / 1.541195 (0.321398) | 1.961701 / 1.468490 (0.493211) | 0.800551 / 4.584777 (-3.784226) | 4.453321 / 3.745712 (0.707608) | 4.291030 / 5.269862 (-0.978832) | 2.256685 / 4.565676 (-2.308991) | 0.097787 / 0.424275 (-0.326488) | 0.014116 / 0.007607 (0.006509) | 0.593395 / 0.226044 (0.367351) | 5.885774 / 2.268929 (3.616845) | 2.666224 / 55.444624 (-52.778400) | 2.276673 / 6.876477 (-4.599803) | 2.358190 / 2.142072 (0.216117) | 0.981398 / 4.805227 (-3.823829) | 0.196997 / 6.500664 (-6.303668) | 0.077020 / 0.075469 (0.001550) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365646 / 1.841788 (-0.476142) | 17.418157 / 8.074308 (9.343849) | 15.838749 / 10.191392 (5.647357) | 0.172749 / 0.680424 (-0.507675) | 0.033711 / 0.534201 (-0.500490) | 0.513306 / 0.579283 (-0.065978) | 0.503201 / 0.434364 (0.068837) | 0.608954 / 0.540337 (0.068616) | 0.734697 / 1.386936 (-0.652239) |\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) | 0.008749 / 0.011353 (-0.002604) | 0.005738 / 0.011008 (-0.005270) | 0.084946 / 0.038508 (0.046438) | 0.040386 / 0.023109 (0.017277) | 0.398698 / 0.275898 (0.122800) | 0.435843 / 0.323480 (0.112363) | 0.006812 / 0.007986 (-0.001174) | 0.004567 / 0.004328 (0.000239) | 0.085857 / 0.004250 (0.081607) | 0.054791 / 0.037052 (0.017738) | 0.400381 / 0.258489 (0.141892) | 0.460313 / 0.293841 (0.166472) | 0.042299 / 0.128546 (-0.086247) | 0.014128 / 0.075646 (-0.061519) | 0.100497 / 0.419271 (-0.318775) | 0.058356 / 0.043533 (0.014823) | 0.399774 / 0.255139 (0.144635) | 0.428210 / 0.283200 (0.145011) | 0.122084 / 0.141683 (-0.019598) | 1.683519 / 1.452155 (0.231365) | 1.798024 / 1.492716 (0.305307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255058 / 0.018006 (0.237051) | 0.488831 / 0.000490 (0.488342) | 0.008349 / 0.000200 (0.008149) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034870 / 0.037411 (-0.002541) | 0.131818 / 0.014526 (0.117292) | 0.143607 / 0.176557 (-0.032949) | 0.197413 / 0.737135 (-0.539722) | 0.148970 / 0.296338 (-0.147368) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492831 / 0.215209 (0.277622) | 4.963085 / 2.077655 (2.885430) | 2.367803 / 1.504120 (0.863683) | 2.145535 / 1.541195 (0.604340) | 2.289452 / 1.468490 (0.820962) | 0.812691 / 4.584777 (-3.772086) | 4.554068 / 3.745712 (0.808356) | 2.377126 / 5.269862 (-2.892735) | 1.537243 / 4.565676 (-3.028433) | 0.099742 / 0.424275 (-0.324534) | 0.014757 / 0.007607 (0.007149) | 0.628714 / 0.226044 (0.402670) | 6.240197 / 2.268929 (3.971268) | 2.961929 / 55.444624 (-52.482696) | 2.533436 / 6.876477 (-4.343040) | 2.642619 / 2.142072 (0.500547) | 0.976002 / 4.805227 (-3.829225) | 0.197912 / 6.500664 (-6.302752) | 0.078767 / 0.075469 (0.003297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.522863 / 1.841788 (-0.318925) | 18.210504 / 8.074308 (10.136196) | 15.664172 / 10.191392 (5.472780) | 0.178510 / 0.680424 (-0.501914) | 0.020852 / 0.534201 (-0.513349) | 0.501757 / 0.579283 (-0.077526) | 0.496542 / 0.434364 (0.062178) | 0.624958 / 0.540337 (0.084620) | 0.746960 / 1.386936 (-0.639976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7f09ed65411c5941de45c372a8aa8d5e55b431 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The CI failure is unrelated to your PR - feel free to merge :)", "Haha thanks, you read my mind :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008332 / 0.011353 (-0.003021) | 0.004556 / 0.011008 (-0.006452) | 0.102239 / 0.038508 (0.063731) | 0.029332 / 0.023109 (0.006222) | 0.296189 / 0.275898 (0.020291) | 0.355746 / 0.323480 (0.032266) | 0.007705 / 0.007986 (-0.000281) | 0.003488 / 0.004328 (-0.000840) | 0.079142 / 0.004250 (0.074891) | 0.034980 / 0.037052 (-0.002073) | 0.307460 / 0.258489 (0.048971) | 0.345944 / 0.293841 (0.052103) | 0.033815 / 0.128546 (-0.094731) | 0.011603 / 0.075646 (-0.064044) | 0.322097 / 0.419271 (-0.097175) | 0.043753 / 0.043533 (0.000220) | 0.296706 / 0.255139 (0.041567) | 0.323195 / 0.283200 (0.039996) | 0.092295 / 0.141683 (-0.049388) | 1.542556 / 1.452155 (0.090401) | 1.571896 / 1.492716 (0.079180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191075 / 0.018006 (0.173069) | 0.407394 / 0.000490 (0.406905) | 0.002033 / 0.000200 (0.001833) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023175 / 0.037411 (-0.014236) | 0.094774 / 0.014526 (0.080248) | 0.105782 / 0.176557 (-0.070775) | 0.146608 / 0.737135 (-0.590528) | 0.107519 / 0.296338 (-0.188819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421516 / 0.215209 (0.206306) | 4.201091 / 2.077655 (2.123436) | 1.880285 / 1.504120 (0.376165) | 1.676333 / 1.541195 (0.135139) | 1.734301 / 1.468490 (0.265811) | 0.688504 / 4.584777 (-3.896273) | 3.370289 / 3.745712 (-0.375423) | 3.127661 / 5.269862 (-2.142201) | 1.562570 / 4.565676 (-3.003106) | 0.081687 / 0.424275 (-0.342588) | 0.012334 / 0.007607 (0.004727) | 0.524125 / 0.226044 (0.298080) | 5.245595 / 2.268929 (2.976667) | 2.332622 / 55.444624 (-53.112002) | 1.973212 / 6.876477 (-4.903265) | 2.006507 / 2.142072 (-0.135565) | 0.807126 / 4.805227 (-3.998101) | 0.148254 / 6.500664 (-6.352411) | 0.064240 / 0.075469 (-0.011229) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206880 / 1.841788 (-0.634907) | 13.854877 / 8.074308 (5.780569) | 13.806772 / 10.191392 (3.615380) | 0.144380 / 0.680424 (-0.536044) | 0.028492 / 0.534201 (-0.505709) | 0.393854 / 0.579283 (-0.185429) | 0.402210 / 0.434364 (-0.032154) | 0.462138 / 0.540337 (-0.078199) | 0.537480 / 1.386936 (-0.849456) |\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) | 0.006692 / 0.011353 (-0.004661) | 0.004529 / 0.011008 (-0.006479) | 0.077925 / 0.038508 (0.039417) | 0.027824 / 0.023109 (0.004715) | 0.342288 / 0.275898 (0.066390) | 0.375071 / 0.323480 (0.051591) | 0.004889 / 0.007986 (-0.003097) | 0.003353 / 0.004328 (-0.000975) | 0.076198 / 0.004250 (0.071947) | 0.037797 / 0.037052 (0.000744) | 0.347834 / 0.258489 (0.089345) | 0.384200 / 0.293841 (0.090359) | 0.032184 / 0.128546 (-0.096362) | 0.011674 / 0.075646 (-0.063972) | 0.086242 / 0.419271 (-0.333029) | 0.044465 / 0.043533 (0.000932) | 0.341712 / 0.255139 (0.086573) | 0.366908 / 0.283200 (0.083709) | 0.091526 / 0.141683 (-0.050156) | 1.495798 / 1.452155 (0.043643) | 1.571700 / 1.492716 (0.078984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221962 / 0.018006 (0.203955) | 0.393095 / 0.000490 (0.392605) | 0.000385 / 0.000200 (0.000185) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024365 / 0.037411 (-0.013046) | 0.099278 / 0.014526 (0.084753) | 0.105940 / 0.176557 (-0.070617) | 0.141334 / 0.737135 (-0.595802) | 0.110898 / 0.296338 (-0.185440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446150 / 0.215209 (0.230941) | 4.471441 / 2.077655 (2.393786) | 2.124864 / 1.504120 (0.620744) | 1.909950 / 1.541195 (0.368755) | 1.970085 / 1.468490 (0.501595) | 0.706711 / 4.584777 (-3.878066) | 3.380336 / 3.745712 (-0.365376) | 1.866106 / 5.269862 (-3.403756) | 1.160657 / 4.565676 (-3.405019) | 0.082786 / 0.424275 (-0.341489) | 0.012470 / 0.007607 (0.004862) | 0.537620 / 0.226044 (0.311575) | 5.390588 / 2.268929 (3.121659) | 2.539137 / 55.444624 (-52.905488) | 2.191867 / 6.876477 (-4.684610) | 2.236212 / 2.142072 (0.094139) | 0.810756 / 4.805227 (-3.994471) | 0.150933 / 6.500664 (-6.349731) | 0.066141 / 0.075469 (-0.009328) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271595 / 1.841788 (-0.570193) | 13.840013 / 8.074308 (5.765705) | 13.334443 / 10.191392 (3.143051) | 0.150096 / 0.680424 (-0.530328) | 0.016919 / 0.534201 (-0.517282) | 0.375534 / 0.579283 (-0.203749) | 0.387203 / 0.434364 (-0.047161) | 0.463500 / 0.540337 (-0.076838) | 0.553496 / 1.386936 (-0.833440) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f2e47230c13f977bcebdc4380623f59da67a75f \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008272 / 0.011353 (-0.003081) | 0.004494 / 0.011008 (-0.006515) | 0.100764 / 0.038508 (0.062256) | 0.028741 / 0.023109 (0.005632) | 0.309020 / 0.275898 (0.033122) | 0.354184 / 0.323480 (0.030704) | 0.007455 / 0.007986 (-0.000531) | 0.003377 / 0.004328 (-0.000951) | 0.078472 / 0.004250 (0.074222) | 0.034719 / 0.037052 (-0.002333) | 0.312787 / 0.258489 (0.054298) | 0.342878 / 0.293841 (0.049037) | 0.033326 / 0.128546 (-0.095221) | 0.011519 / 0.075646 (-0.064127) | 0.323556 / 0.419271 (-0.095716) | 0.039929 / 0.043533 (-0.003604) | 0.304627 / 0.255139 (0.049488) | 0.322876 / 0.283200 (0.039677) | 0.086410 / 0.141683 (-0.055273) | 1.502607 / 1.452155 (0.050453) | 1.577953 / 1.492716 (0.085237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192861 / 0.018006 (0.174855) | 0.406008 / 0.000490 (0.405519) | 0.001075 / 0.000200 (0.000875) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023351 / 0.037411 (-0.014060) | 0.096086 / 0.014526 (0.081561) | 0.104641 / 0.176557 (-0.071915) | 0.141940 / 0.737135 (-0.595195) | 0.109266 / 0.296338 (-0.187073) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416496 / 0.215209 (0.201287) | 4.161581 / 2.077655 (2.083926) | 1.815357 / 1.504120 (0.311238) | 1.609536 / 1.541195 (0.068341) | 1.654105 / 1.468490 (0.185615) | 0.693947 / 4.584777 (-3.890830) | 3.349029 / 3.745712 (-0.396683) | 1.883968 / 5.269862 (-3.385893) | 1.287988 / 4.565676 (-3.277688) | 0.081765 / 0.424275 (-0.342511) | 0.012373 / 0.007607 (0.004766) | 0.517186 / 0.226044 (0.291142) | 5.200892 / 2.268929 (2.931964) | 2.247414 / 55.444624 (-53.197211) | 1.910601 / 6.876477 (-4.965876) | 1.965407 / 2.142072 (-0.176666) | 0.814386 / 4.805227 (-3.990841) | 0.149295 / 6.500664 (-6.351369) | 0.064667 / 0.075469 (-0.010802) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247258 / 1.841788 (-0.594530) | 13.837355 / 8.074308 (5.763047) | 13.850454 / 10.191392 (3.659062) | 0.136078 / 0.680424 (-0.544346) | 0.028322 / 0.534201 (-0.505878) | 0.391394 / 0.579283 (-0.187889) | 0.407494 / 0.434364 (-0.026870) | 0.473784 / 0.540337 (-0.066554) | 0.562953 / 1.386936 (-0.823983) |\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) | 0.006559 / 0.011353 (-0.004794) | 0.004546 / 0.011008 (-0.006462) | 0.099527 / 0.038508 (0.061019) | 0.027428 / 0.023109 (0.004319) | 0.344276 / 0.275898 (0.068377) | 0.377897 / 0.323480 (0.054417) | 0.004913 / 0.007986 (-0.003072) | 0.003338 / 0.004328 (-0.000990) | 0.077589 / 0.004250 (0.073339) | 0.038819 / 0.037052 (0.001766) | 0.343165 / 0.258489 (0.084676) | 0.386228 / 0.293841 (0.092387) | 0.031753 / 0.128546 (-0.096794) | 0.011756 / 0.075646 (-0.063890) | 0.322537 / 0.419271 (-0.096735) | 0.049865 / 0.043533 (0.006332) | 0.340493 / 0.255139 (0.085354) | 0.372179 / 0.283200 (0.088980) | 0.099669 / 0.141683 (-0.042013) | 1.487841 / 1.452155 (0.035686) | 1.527400 / 1.492716 (0.034683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180782 / 0.018006 (0.162776) | 0.393494 / 0.000490 (0.393004) | 0.003004 / 0.000200 (0.002804) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024997 / 0.037411 (-0.012415) | 0.098232 / 0.014526 (0.083707) | 0.107869 / 0.176557 (-0.068688) | 0.141042 / 0.737135 (-0.596093) | 0.109551 / 0.296338 (-0.186787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477115 / 0.215209 (0.261906) | 4.783928 / 2.077655 (2.706273) | 2.435725 / 1.504120 (0.931605) | 2.233111 / 1.541195 (0.691916) | 2.341097 / 1.468490 (0.872607) | 0.694304 / 4.584777 (-3.890473) | 3.345687 / 3.745712 (-0.400025) | 1.886932 / 5.269862 (-3.382929) | 1.155585 / 4.565676 (-3.410092) | 0.082867 / 0.424275 (-0.341408) | 0.012420 / 0.007607 (0.004813) | 0.576575 / 0.226044 (0.350530) | 5.777691 / 2.268929 (3.508762) | 2.882219 / 55.444624 (-52.562405) | 2.543613 / 6.876477 (-4.332864) | 2.578939 / 2.142072 (0.436866) | 0.803143 / 4.805227 (-4.002084) | 0.151929 / 6.500664 (-6.348735) | 0.067777 / 0.075469 (-0.007693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282711 / 1.841788 (-0.559077) | 13.942771 / 8.074308 (5.868463) | 13.376206 / 10.191392 (3.184814) | 0.152916 / 0.680424 (-0.527508) | 0.016619 / 0.534201 (-0.517582) | 0.375141 / 0.579283 (-0.204142) | 0.381660 / 0.434364 (-0.052704) | 0.465090 / 0.540337 (-0.075247) | 0.555068 / 1.386936 (-0.831868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10a6a638e0feb955f7b607b4433ee715c30acccf \"CML watermark\")\n" ]

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[ "Hi! I agree it would be nice to have a method like that. Instead of `keep_columns`, we can name it `select_columns` to be more aligned with PyArrow's naming convention (`pa.Table.select`).", "Hi, I am a newbie to open source and would like to contribute. @mariosasko can I take up this issue ?", "Hey, I also want to work on this issue I am a newbie to open source. ", "This sounds related to https://github.com/huggingface/datasets/issues/5474\r\n\r\nI'm fine with `select_columns`, or we could also override `select` to also accept a list of columns maybe ?", "@lhoestq, I am planning to add a member function to the dataset class to perform the selection operation. Do you think its the right way to proceed? or there is a better option ?", "Unless @mariosasko thinks otherwise, I think it can go in `Dataset.select()` :)\r\nThough some parameters like keep_in_memory, indices_cache_file_name or writer_batch_size wouldn't when selecting columns, so we would need to update the docstring as well", "If someone wants to give it a shot, feel free to comment `#self-assign` and it will assign the issue to you.\r\n\r\nFeel free to ping us here if you have questions or if we can help :)", "I would rather have this functionality as a separate method. IMO it's always better to be explicit than to have an API where a single method can do different/uncorrelated things (somewhat reminds me of Pandas, and there is probably a good reason why PyArrow is more rigid in this aspect).", "In the end I also think it would be nice to have it as a separate method, this way we can also have it for `IterableDataset` (which can't have `select` for indices)" ]

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[ "ah didn't read well - it's all good", "or maybe it isn't ? `-c huggingface -c conda-forge` installs from HF or from conda-forge ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010196 / 0.011353 (-0.001157) | 0.005531 / 0.011008 (-0.005477) | 0.104601 / 0.038508 (0.066093) | 0.041322 / 0.023109 (0.018213) | 0.302080 / 0.275898 (0.026182) | 0.396579 / 0.323480 (0.073099) | 0.008874 / 0.007986 (0.000888) | 0.004482 / 0.004328 (0.000153) | 0.077487 / 0.004250 (0.073236) | 0.051113 / 0.037052 (0.014061) | 0.321850 / 0.258489 (0.063361) | 0.354946 / 0.293841 (0.061105) | 0.039822 / 0.128546 (-0.088724) | 0.012622 / 0.075646 (-0.063024) | 0.337898 / 0.419271 (-0.081374) | 0.048372 / 0.043533 (0.004839) | 0.299646 / 0.255139 (0.044507) | 0.321113 / 0.283200 (0.037914) | 0.114780 / 0.141683 (-0.026903) | 1.475750 / 1.452155 (0.023595) | 1.496307 / 1.492716 (0.003590) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311443 / 0.018006 (0.293437) | 0.567268 / 0.000490 (0.566778) | 0.006149 / 0.000200 (0.005950) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029407 / 0.037411 (-0.008004) | 0.118611 / 0.014526 (0.104085) | 0.122247 / 0.176557 (-0.054309) | 0.164770 / 0.737135 (-0.572365) | 0.128561 / 0.296338 (-0.167778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399185 / 0.215209 (0.183976) | 3.972995 / 2.077655 (1.895340) | 1.764638 / 1.504120 (0.260518) | 1.574058 / 1.541195 (0.032863) | 1.741695 / 1.468490 (0.273205) | 0.705664 / 4.584777 (-3.879113) | 3.915399 / 3.745712 (0.169686) | 2.310154 / 5.269862 (-2.959707) | 1.554067 / 4.565676 (-3.011610) | 0.087133 / 0.424275 (-0.337142) | 0.012393 / 0.007607 (0.004786) | 0.510758 / 0.226044 (0.284713) | 5.114906 / 2.268929 (2.845977) | 2.304473 / 55.444624 (-53.140152) | 1.960768 / 6.876477 (-4.915709) | 2.092263 / 2.142072 (-0.049810) | 0.867973 / 4.805227 (-3.937255) | 0.170000 / 6.500664 (-6.330664) | 0.068358 / 0.075469 (-0.007111) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.211022 / 1.841788 (-0.630765) | 16.777269 / 8.074308 (8.702961) | 15.272659 / 10.191392 (5.081267) | 0.182149 / 0.680424 (-0.498274) | 0.029577 / 0.534201 (-0.504624) | 0.446590 / 0.579283 (-0.132693) | 0.454724 / 0.434364 (0.020360) | 0.541938 / 0.540337 (0.001601) | 0.640886 / 1.386936 (-0.746050) |\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) | 0.008441 / 0.011353 (-0.002912) | 0.006105 / 0.011008 (-0.004904) | 0.100349 / 0.038508 (0.061841) | 0.040675 / 0.023109 (0.017565) | 0.381775 / 0.275898 (0.105877) | 0.425246 / 0.323480 (0.101767) | 0.007197 / 0.007986 (-0.000789) | 0.004972 / 0.004328 (0.000644) | 0.075346 / 0.004250 (0.071096) | 0.065339 / 0.037052 (0.028286) | 0.379340 / 0.258489 (0.120851) | 0.435646 / 0.293841 (0.141805) | 0.038891 / 0.128546 (-0.089656) | 0.013079 / 0.075646 (-0.062568) | 0.339273 / 0.419271 (-0.079999) | 0.057478 / 0.043533 (0.013945) | 0.373516 / 0.255139 (0.118377) | 0.402388 / 0.283200 (0.119189) | 0.123145 / 0.141683 (-0.018538) | 1.503765 / 1.452155 (0.051610) | 1.609797 / 1.492716 (0.117081) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.420354 / 0.018006 (0.402348) | 0.589272 / 0.000490 (0.588782) | 0.045861 / 0.000200 (0.045662) | 0.000527 / 0.000054 (0.000473) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033918 / 0.037411 (-0.003493) | 0.128041 / 0.014526 (0.113515) | 0.130274 / 0.176557 (-0.046283) | 0.180605 / 0.737135 (-0.556530) | 0.136377 / 0.296338 (-0.159962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440343 / 0.215209 (0.225133) | 4.390264 / 2.077655 (2.312610) | 2.218738 / 1.504120 (0.714618) | 2.052399 / 1.541195 (0.511204) | 2.231912 / 1.468490 (0.763422) | 0.716805 / 4.584777 (-3.867972) | 3.909277 / 3.745712 (0.163565) | 2.302121 / 5.269862 (-2.967740) | 1.419454 / 4.565676 (-3.146222) | 0.088067 / 0.424275 (-0.336208) | 0.012994 / 0.007607 (0.005387) | 0.548267 / 0.226044 (0.322223) | 5.462973 / 2.268929 (3.194044) | 2.768414 / 55.444624 (-52.676210) | 2.489320 / 6.876477 (-4.387157) | 2.569546 / 2.142072 (0.427474) | 0.853135 / 4.805227 (-3.952092) | 0.170618 / 6.500664 (-6.330046) | 0.069908 / 0.075469 (-0.005562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304726 / 1.841788 (-0.537062) | 17.335977 / 8.074308 (9.261669) | 15.088319 / 10.191392 (4.896927) | 0.190893 / 0.680424 (-0.489531) | 0.018133 / 0.534201 (-0.516068) | 0.429324 / 0.579283 (-0.149959) | 0.439212 / 0.434364 (0.004848) | 0.545312 / 0.540337 (0.004975) | 0.663972 / 1.386936 (-0.722964) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7505adc37498f5e0cb3dd4c13bbb06696afdda5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks !\r\n`os.path.join` will use a backslash `\\` on windows which will also fail. You can use this instead in `load_from_disk`:\r\n```python\r\nfrom .filesystems import is_remote_filesystem\r\n\r\nis_local = not is_remote_filesystem(fs)\r\npath_join = os.path.join if is_local else posixpath.join\r\n```", "Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)", "_The documentation is not available anymore as the PR was closed or merged._", "> \r\n\r\n\r\n\r\n> Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)\r\n\r\nlol it's a battle of +1 imports or +1 functions. LGTM, I was editing fast and swapped which branch gets os vs Path. Should be ok now 🤙", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012043 / 0.011353 (0.000690) | 0.006585 / 0.011008 (-0.004423) | 0.149007 / 0.038508 (0.110499) | 0.039514 / 0.023109 (0.016405) | 0.403893 / 0.275898 (0.127995) | 0.431252 / 0.323480 (0.107772) | 0.009218 / 0.007986 (0.001233) | 0.006108 / 0.004328 (0.001779) | 0.114666 / 0.004250 (0.110416) | 0.044962 / 0.037052 (0.007910) | 0.411592 / 0.258489 (0.153103) | 0.461561 / 0.293841 (0.167721) | 0.059958 / 0.128546 (-0.068589) | 0.029047 / 0.075646 (-0.046599) | 0.456000 / 0.419271 (0.036728) | 0.060744 / 0.043533 (0.017211) | 0.415816 / 0.255139 (0.160677) | 0.430488 / 0.283200 (0.147289) | 0.122477 / 0.141683 (-0.019205) | 1.862910 / 1.452155 (0.410755) | 1.974698 / 1.492716 (0.481981) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257230 / 0.018006 (0.239224) | 0.606854 / 0.000490 (0.606364) | 0.006175 / 0.000200 (0.005975) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030533 / 0.037411 (-0.006879) | 0.130702 / 0.014526 (0.116177) | 0.143781 / 0.176557 (-0.032775) | 0.183272 / 0.737135 (-0.553863) | 0.151267 / 0.296338 (-0.145071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.637422 / 0.215209 (0.422213) | 6.503535 / 2.077655 (4.425880) | 2.630387 / 1.504120 (1.126267) | 2.281180 / 1.541195 (0.739985) | 2.354341 / 1.468490 (0.885851) | 1.306497 / 4.584777 (-3.278280) | 5.837184 / 3.745712 (2.091472) | 3.257198 / 5.269862 (-2.012663) | 2.050681 / 4.565676 (-2.514995) | 0.146415 / 0.424275 (-0.277860) | 0.015386 / 0.007607 (0.007779) | 0.790146 / 0.226044 (0.564102) | 8.056137 / 2.268929 (5.787209) | 3.383566 / 55.444624 (-52.061059) | 2.707620 / 6.876477 (-4.168856) | 2.714857 / 2.142072 (0.572785) | 1.520847 / 4.805227 (-3.284380) | 0.266028 / 6.500664 (-6.234636) | 0.091422 / 0.075469 (0.015953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.656148 / 1.841788 (-0.185640) | 18.833393 / 8.074308 (10.759085) | 21.360824 / 10.191392 (11.169432) | 0.227608 / 0.680424 (-0.452816) | 0.049018 / 0.534201 (-0.485183) | 0.593418 / 0.579283 (0.014135) | 0.656690 / 0.434364 (0.222326) | 0.709171 / 0.540337 (0.168833) | 0.828226 / 1.386936 (-0.558710) |\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) | 0.010112 / 0.011353 (-0.001241) | 0.006761 / 0.011008 (-0.004247) | 0.146723 / 0.038508 (0.108215) | 0.038451 / 0.023109 (0.015342) | 0.524267 / 0.275898 (0.248369) | 0.609484 / 0.323480 (0.286004) | 0.008502 / 0.007986 (0.000516) | 0.006964 / 0.004328 (0.002635) | 0.111396 / 0.004250 (0.107146) | 0.056839 / 0.037052 (0.019787) | 0.514649 / 0.258489 (0.256160) | 0.604212 / 0.293841 (0.310372) | 0.061410 / 0.128546 (-0.067137) | 0.020396 / 0.075646 (-0.055250) | 0.505026 / 0.419271 (0.085754) | 0.067280 / 0.043533 (0.023747) | 0.522249 / 0.255139 (0.267110) | 0.559484 / 0.283200 (0.276284) | 0.120943 / 0.141683 (-0.020740) | 2.124323 / 1.452155 (0.672169) | 2.153397 / 1.492716 (0.660681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216614 / 0.018006 (0.198608) | 0.594181 / 0.000490 (0.593692) | 0.004079 / 0.000200 (0.003879) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036925 / 0.037411 (-0.000486) | 0.131322 / 0.014526 (0.116797) | 0.148542 / 0.176557 (-0.028015) | 0.196045 / 0.737135 (-0.541090) | 0.156867 / 0.296338 (-0.139472) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669722 / 0.215209 (0.454513) | 6.858856 / 2.077655 (4.781202) | 3.093969 / 1.504120 (1.589849) | 2.667385 / 1.541195 (1.126190) | 2.797192 / 1.468490 (1.328702) | 1.334759 / 4.584777 (-3.250018) | 6.024861 / 3.745712 (2.279149) | 3.257779 / 5.269862 (-2.012083) | 2.202816 / 4.565676 (-2.362860) | 0.147617 / 0.424275 (-0.276658) | 0.015451 / 0.007607 (0.007844) | 0.887015 / 0.226044 (0.660970) | 8.371288 / 2.268929 (6.102360) | 3.807451 / 55.444624 (-51.637173) | 3.079483 / 6.876477 (-3.796994) | 3.103321 / 2.142072 (0.961249) | 1.520272 / 4.805227 (-3.284955) | 0.273079 / 6.500664 (-6.227585) | 0.088613 / 0.075469 (0.013143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.818913 / 1.841788 (-0.022875) | 19.274269 / 8.074308 (11.199960) | 19.871784 / 10.191392 (9.680392) | 0.250388 / 0.680424 (-0.430036) | 0.030562 / 0.534201 (-0.503638) | 0.560566 / 0.579283 (-0.018717) | 0.664701 / 0.434364 (0.230337) | 0.714513 / 0.540337 (0.174176) | 0.827227 / 1.386936 (-0.559710) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7a9bf823ea41b85313c0392388ec68b3033ef29 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note this issue was already fixed in `datasets` 2.6.0 version:\r\n- #5040\r\n\r\nIf you update your `datasets` version, you will be able to load the dataset:\r\n```\r\npip install -U datasets\r\n```", "Oops, missed that I needed to upgrade. Thanks!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009559 / 0.011353 (-0.001794) | 0.006425 / 0.011008 (-0.004583) | 0.112951 / 0.038508 (0.074443) | 0.030835 / 0.023109 (0.007725) | 0.313846 / 0.275898 (0.037948) | 0.352780 / 0.323480 (0.029301) | 0.007740 / 0.007986 (-0.000246) | 0.006843 / 0.004328 (0.002515) | 0.082632 / 0.004250 (0.078382) | 0.039704 / 0.037052 (0.002652) | 0.328526 / 0.258489 (0.070037) | 0.369162 / 0.293841 (0.075321) | 0.047603 / 0.128546 (-0.080943) | 0.015834 / 0.075646 (-0.059812) | 0.385912 / 0.419271 (-0.033360) | 0.053838 / 0.043533 (0.010306) | 0.325778 / 0.255139 (0.070639) | 0.361863 / 0.283200 (0.078663) | 0.097388 / 0.141683 (-0.044295) | 1.510132 / 1.452155 (0.057978) | 1.555980 / 1.492716 (0.063264) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210792 / 0.018006 (0.192786) | 0.507270 / 0.000490 (0.506780) | 0.002383 / 0.000200 (0.002183) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023057 / 0.037411 (-0.014355) | 0.103471 / 0.014526 (0.088945) | 0.111671 / 0.176557 (-0.064885) | 0.145665 / 0.737135 (-0.591470) | 0.131447 / 0.296338 (-0.164891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502979 / 0.215209 (0.287770) | 5.111471 / 2.077655 (3.033816) | 2.093604 / 1.504120 (0.589484) | 1.761342 / 1.541195 (0.220148) | 1.919485 / 1.468490 (0.450995) | 1.065672 / 4.584777 (-3.519105) | 5.109746 / 3.745712 (1.364034) | 4.694027 / 5.269862 (-0.575835) | 2.438401 / 4.565676 (-2.127275) | 0.133579 / 0.424275 (-0.290696) | 0.012355 / 0.007607 (0.004748) | 0.669077 / 0.226044 (0.443033) | 6.533905 / 2.268929 (4.264976) | 2.698832 / 55.444624 (-52.745792) | 2.146377 / 6.876477 (-4.730100) | 2.220563 / 2.142072 (0.078491) | 1.287855 / 4.805227 (-3.517372) | 0.238221 / 6.500664 (-6.262443) | 0.071426 / 0.075469 (-0.004043) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.332659 / 1.841788 (-0.509129) | 15.610100 / 8.074308 (7.535791) | 16.691117 / 10.191392 (6.499725) | 0.226338 / 0.680424 (-0.454086) | 0.039964 / 0.534201 (-0.494237) | 0.462911 / 0.579283 (-0.116372) | 0.575923 / 0.434364 (0.141560) | 0.592583 / 0.540337 (0.052245) | 0.658552 / 1.386936 (-0.728384) |\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) | 0.008388 / 0.011353 (-0.002965) | 0.005360 / 0.011008 (-0.005648) | 0.104574 / 0.038508 (0.066066) | 0.030109 / 0.023109 (0.007000) | 0.389294 / 0.275898 (0.113396) | 0.424813 / 0.323480 (0.101333) | 0.006629 / 0.007986 (-0.001356) | 0.005222 / 0.004328 (0.000893) | 0.080157 / 0.004250 (0.075907) | 0.045811 / 0.037052 (0.008759) | 0.398708 / 0.258489 (0.140219) | 0.429449 / 0.293841 (0.135608) | 0.052242 / 0.128546 (-0.076304) | 0.017439 / 0.075646 (-0.058207) | 0.362678 / 0.419271 (-0.056593) | 0.054151 / 0.043533 (0.010618) | 0.387932 / 0.255139 (0.132793) | 0.410544 / 0.283200 (0.127344) | 0.101210 / 0.141683 (-0.040473) | 1.486496 / 1.452155 (0.034341) | 1.576404 / 1.492716 (0.083687) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259468 / 0.018006 (0.241461) | 0.521661 / 0.000490 (0.521172) | 0.000456 / 0.000200 (0.000256) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027045 / 0.037411 (-0.010366) | 0.107615 / 0.014526 (0.093089) | 0.133228 / 0.176557 (-0.043329) | 0.156807 / 0.737135 (-0.580328) | 0.125226 / 0.296338 (-0.171113) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528804 / 0.215209 (0.313595) | 5.516402 / 2.077655 (3.438748) | 2.387531 / 1.504120 (0.883412) | 2.084734 / 1.541195 (0.543539) | 2.091894 / 1.468490 (0.623404) | 1.089761 / 4.584777 (-3.495016) | 5.093067 / 3.745712 (1.347355) | 2.670349 / 5.269862 (-2.599512) | 1.784723 / 4.565676 (-2.780953) | 0.125528 / 0.424275 (-0.298747) | 0.013702 / 0.007607 (0.006095) | 0.667755 / 0.226044 (0.441710) | 6.653900 / 2.268929 (4.384972) | 3.006058 / 55.444624 (-52.438567) | 2.512919 / 6.876477 (-4.363558) | 2.546824 / 2.142072 (0.404751) | 1.269008 / 4.805227 (-3.536219) | 0.234388 / 6.500664 (-6.266276) | 0.065675 / 0.075469 (-0.009795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372222 / 1.841788 (-0.469566) | 15.565156 / 8.074308 (7.490848) | 16.800666 / 10.191392 (6.609274) | 0.220656 / 0.680424 (-0.459768) | 0.023690 / 0.534201 (-0.510511) | 0.450049 / 0.579283 (-0.129234) | 0.580433 / 0.434364 (0.146069) | 0.558899 / 0.540337 (0.018561) | 0.676799 / 1.386936 (-0.710137) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cc5dcacecf41efc566385b323a3ca72ab44db36 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009440 / 0.011353 (-0.001913) | 0.005159 / 0.011008 (-0.005849) | 0.099152 / 0.038508 (0.060644) | 0.035939 / 0.023109 (0.012830) | 0.300968 / 0.275898 (0.025070) | 0.365676 / 0.323480 (0.042196) | 0.008220 / 0.007986 (0.000235) | 0.004071 / 0.004328 (-0.000257) | 0.075216 / 0.004250 (0.070965) | 0.042173 / 0.037052 (0.005121) | 0.315055 / 0.258489 (0.056566) | 0.338287 / 0.293841 (0.044446) | 0.037789 / 0.128546 (-0.090758) | 0.011856 / 0.075646 (-0.063791) | 0.332975 / 0.419271 (-0.086297) | 0.047087 / 0.043533 (0.003554) | 0.295107 / 0.255139 (0.039968) | 0.315416 / 0.283200 (0.032217) | 0.102273 / 0.141683 (-0.039410) | 1.464908 / 1.452155 (0.012754) | 1.500281 / 1.492716 (0.007565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208522 / 0.018006 (0.190516) | 0.446576 / 0.000490 (0.446086) | 0.005766 / 0.000200 (0.005566) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027924 / 0.037411 (-0.009487) | 0.111296 / 0.014526 (0.096771) | 0.119055 / 0.176557 (-0.057502) | 0.157755 / 0.737135 (-0.579381) | 0.125539 / 0.296338 (-0.170799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395683 / 0.215209 (0.180474) | 3.962696 / 2.077655 (1.885042) | 1.789511 / 1.504120 (0.285391) | 1.591541 / 1.541195 (0.050346) | 1.661276 / 1.468490 (0.192786) | 0.693524 / 4.584777 (-3.891253) | 3.836526 / 3.745712 (0.090813) | 2.187284 / 5.269862 (-3.082578) | 1.521420 / 4.565676 (-3.044257) | 0.084370 / 0.424275 (-0.339905) | 0.012083 / 0.007607 (0.004476) | 0.498017 / 0.226044 (0.271972) | 4.982356 / 2.268929 (2.713428) | 2.235881 / 55.444624 (-53.208743) | 1.912067 / 6.876477 (-4.964410) | 2.052172 / 2.142072 (-0.089900) | 0.836232 / 4.805227 (-3.968995) | 0.165234 / 6.500664 (-6.335431) | 0.062933 / 0.075469 (-0.012536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197785 / 1.841788 (-0.644003) | 15.233655 / 8.074308 (7.159347) | 14.254450 / 10.191392 (4.063058) | 0.169149 / 0.680424 (-0.511274) | 0.028794 / 0.534201 (-0.505407) | 0.437214 / 0.579283 (-0.142069) | 0.434836 / 0.434364 (0.000472) | 0.531594 / 0.540337 (-0.008744) | 0.626266 / 1.386936 (-0.760670) |\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) | 0.007394 / 0.011353 (-0.003959) | 0.005305 / 0.011008 (-0.005703) | 0.098888 / 0.038508 (0.060380) | 0.033069 / 0.023109 (0.009959) | 0.388427 / 0.275898 (0.112529) | 0.415216 / 0.323480 (0.091736) | 0.005610 / 0.007986 (-0.002375) | 0.004922 / 0.004328 (0.000593) | 0.073694 / 0.004250 (0.069443) | 0.047368 / 0.037052 (0.010315) | 0.379604 / 0.258489 (0.121115) | 0.424876 / 0.293841 (0.131035) | 0.039471 / 0.128546 (-0.089075) | 0.012219 / 0.075646 (-0.063427) | 0.345925 / 0.419271 (-0.073346) | 0.048981 / 0.043533 (0.005448) | 0.379303 / 0.255139 (0.124164) | 0.404682 / 0.283200 (0.121483) | 0.103932 / 0.141683 (-0.037751) | 1.490852 / 1.452155 (0.038697) | 1.578900 / 1.492716 (0.086183) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201393 / 0.018006 (0.183387) | 0.452484 / 0.000490 (0.451994) | 0.005627 / 0.000200 (0.005428) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029317 / 0.037411 (-0.008094) | 0.114904 / 0.014526 (0.100378) | 0.126678 / 0.176557 (-0.049878) | 0.178315 / 0.737135 (-0.558820) | 0.131603 / 0.296338 (-0.164736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459830 / 0.215209 (0.244621) | 4.595358 / 2.077655 (2.517703) | 2.383582 / 1.504120 (0.879462) | 2.181945 / 1.541195 (0.640750) | 2.309517 / 1.468490 (0.841027) | 0.704803 / 4.584777 (-3.879974) | 3.820411 / 3.745712 (0.074698) | 4.872173 / 5.269862 (-0.397689) | 2.266090 / 4.565676 (-2.299586) | 0.085805 / 0.424275 (-0.338470) | 0.012488 / 0.007607 (0.004881) | 0.557500 / 0.226044 (0.331456) | 5.570830 / 2.268929 (3.301901) | 2.836202 / 55.444624 (-52.608422) | 2.530534 / 6.876477 (-4.345943) | 2.599792 / 2.142072 (0.457720) | 0.843852 / 4.805227 (-3.961376) | 0.169427 / 6.500664 (-6.331237) | 0.065521 / 0.075469 (-0.009948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246014 / 1.841788 (-0.595774) | 15.455336 / 8.074308 (7.381028) | 13.559111 / 10.191392 (3.367719) | 0.169131 / 0.680424 (-0.511293) | 0.017812 / 0.534201 (-0.516389) | 0.421161 / 0.579283 (-0.158122) | 0.458286 / 0.434364 (0.023922) | 0.534692 / 0.540337 (-0.005645) | 0.639299 / 1.386936 (-0.747637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b7558953b5a071194356bbe4c596a2890a3b847 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008860 / 0.011353 (-0.002493) | 0.004564 / 0.011008 (-0.006444) | 0.101556 / 0.038508 (0.063048) | 0.030000 / 0.023109 (0.006891) | 0.304404 / 0.275898 (0.028506) | 0.366247 / 0.323480 (0.042767) | 0.007182 / 0.007986 (-0.000804) | 0.003583 / 0.004328 (-0.000746) | 0.079665 / 0.004250 (0.075415) | 0.036529 / 0.037052 (-0.000523) | 0.310998 / 0.258489 (0.052509) | 0.346954 / 0.293841 (0.053113) | 0.034098 / 0.128546 (-0.094448) | 0.011576 / 0.075646 (-0.064070) | 0.320448 / 0.419271 (-0.098824) | 0.043328 / 0.043533 (-0.000205) | 0.307317 / 0.255139 (0.052178) | 0.325071 / 0.283200 (0.041871) | 0.096406 / 0.141683 (-0.045277) | 1.540331 / 1.452155 (0.088176) | 1.589533 / 1.492716 (0.096817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011034 / 0.018006 (-0.006972) | 0.422066 / 0.000490 (0.421577) | 0.002409 / 0.000200 (0.002209) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023703 / 0.037411 (-0.013708) | 0.099935 / 0.014526 (0.085409) | 0.105966 / 0.176557 (-0.070591) | 0.142259 / 0.737135 (-0.594876) | 0.109327 / 0.296338 (-0.187011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418381 / 0.215209 (0.203172) | 4.177564 / 2.077655 (2.099909) | 1.880196 / 1.504120 (0.376076) | 1.669169 / 1.541195 (0.127974) | 1.725989 / 1.468490 (0.257499) | 0.689384 / 4.584777 (-3.895393) | 3.380963 / 3.745712 (-0.364749) | 1.884192 / 5.269862 (-3.385670) | 1.162409 / 4.565676 (-3.403268) | 0.082045 / 0.424275 (-0.342230) | 0.012575 / 0.007607 (0.004968) | 0.525824 / 0.226044 (0.299779) | 5.272574 / 2.268929 (3.003646) | 2.283492 / 55.444624 (-53.161132) | 1.947390 / 6.876477 (-4.929087) | 2.013790 / 2.142072 (-0.128283) | 0.806280 / 4.805227 (-3.998948) | 0.149267 / 6.500664 (-6.351397) | 0.066967 / 0.075469 (-0.008502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.216511 / 1.841788 (-0.625277) | 13.869829 / 8.074308 (5.795521) | 14.189967 / 10.191392 (3.998575) | 0.148716 / 0.680424 (-0.531708) | 0.028324 / 0.534201 (-0.505877) | 0.390856 / 0.579283 (-0.188427) | 0.404389 / 0.434364 (-0.029975) | 0.456050 / 0.540337 (-0.084287) | 0.544139 / 1.386936 (-0.842797) |\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) | 0.006727 / 0.011353 (-0.004626) | 0.004515 / 0.011008 (-0.006494) | 0.098791 / 0.038508 (0.060283) | 0.027596 / 0.023109 (0.004487) | 0.439066 / 0.275898 (0.163168) | 0.480555 / 0.323480 (0.157076) | 0.005066 / 0.007986 (-0.002920) | 0.004669 / 0.004328 (0.000341) | 0.075334 / 0.004250 (0.071084) | 0.039779 / 0.037052 (0.002726) | 0.439860 / 0.258489 (0.181371) | 0.480787 / 0.293841 (0.186946) | 0.031550 / 0.128546 (-0.096996) | 0.011668 / 0.075646 (-0.063978) | 0.317348 / 0.419271 (-0.101923) | 0.041312 / 0.043533 (-0.002220) | 0.442934 / 0.255139 (0.187795) | 0.463677 / 0.283200 (0.180478) | 0.090066 / 0.141683 (-0.051617) | 1.544152 / 1.452155 (0.091998) | 1.584455 / 1.492716 (0.091738) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224284 / 0.018006 (0.206278) | 0.406982 / 0.000490 (0.406492) | 0.000427 / 0.000200 (0.000227) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024914 / 0.037411 (-0.012497) | 0.102608 / 0.014526 (0.088082) | 0.106931 / 0.176557 (-0.069626) | 0.140828 / 0.737135 (-0.596308) | 0.112015 / 0.296338 (-0.184324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471078 / 0.215209 (0.255869) | 4.705742 / 2.077655 (2.628088) | 2.437442 / 1.504120 (0.933322) | 2.242768 / 1.541195 (0.701573) | 2.302158 / 1.468490 (0.833668) | 0.697314 / 4.584777 (-3.887462) | 3.357730 / 3.745712 (-0.387982) | 1.913306 / 5.269862 (-3.356556) | 1.173879 / 4.565676 (-3.391798) | 0.083257 / 0.424275 (-0.341018) | 0.012480 / 0.007607 (0.004873) | 0.573407 / 0.226044 (0.347362) | 5.728650 / 2.268929 (3.459721) | 2.868863 / 55.444624 (-52.575761) | 2.548640 / 6.876477 (-4.327837) | 2.596622 / 2.142072 (0.454549) | 0.805563 / 4.805227 (-3.999664) | 0.150860 / 6.500664 (-6.349804) | 0.068344 / 0.075469 (-0.007125) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300368 / 1.841788 (-0.541420) | 13.920451 / 8.074308 (5.846143) | 14.222430 / 10.191392 (4.031038) | 0.152497 / 0.680424 (-0.527927) | 0.017415 / 0.534201 (-0.516786) | 0.378827 / 0.579283 (-0.200456) | 0.384165 / 0.434364 (-0.050199) | 0.439364 / 0.540337 (-0.100973) | 0.525710 / 1.386936 (-0.861226) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cd22277fa87e02ad9970483f5b75aacdfbf9a70 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008482 / 0.011353 (-0.002871) | 0.004405 / 0.011008 (-0.006604) | 0.099662 / 0.038508 (0.061154) | 0.029062 / 0.023109 (0.005953) | 0.298329 / 0.275898 (0.022431) | 0.332837 / 0.323480 (0.009357) | 0.006760 / 0.007986 (-0.001225) | 0.003290 / 0.004328 (-0.001039) | 0.077659 / 0.004250 (0.073409) | 0.034745 / 0.037052 (-0.002307) | 0.303134 / 0.258489 (0.044644) | 0.346402 / 0.293841 (0.052561) | 0.033511 / 0.128546 (-0.095035) | 0.011464 / 0.075646 (-0.064183) | 0.322932 / 0.419271 (-0.096340) | 0.040697 / 0.043533 (-0.002836) | 0.301951 / 0.255139 (0.046812) | 0.328961 / 0.283200 (0.045761) | 0.084802 / 0.141683 (-0.056881) | 1.506247 / 1.452155 (0.054092) | 1.547631 / 1.492716 (0.054915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190370 / 0.018006 (0.172363) | 0.405786 / 0.000490 (0.405297) | 0.002196 / 0.000200 (0.001997) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022958 / 0.037411 (-0.014453) | 0.095736 / 0.014526 (0.081210) | 0.103684 / 0.176557 (-0.072872) | 0.138200 / 0.737135 (-0.598936) | 0.105618 / 0.296338 (-0.190721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415239 / 0.215209 (0.200030) | 4.147223 / 2.077655 (2.069569) | 1.850322 / 1.504120 (0.346202) | 1.662815 / 1.541195 (0.121620) | 1.671563 / 1.468490 (0.203073) | 0.693806 / 4.584777 (-3.890971) | 3.352938 / 3.745712 (-0.392774) | 1.849257 / 5.269862 (-3.420604) | 1.161603 / 4.565676 (-3.404074) | 0.081884 / 0.424275 (-0.342391) | 0.012726 / 0.007607 (0.005119) | 0.521105 / 0.226044 (0.295061) | 5.231910 / 2.268929 (2.962981) | 2.306073 / 55.444624 (-53.138551) | 1.950449 / 6.876477 (-4.926028) | 1.988433 / 2.142072 (-0.153640) | 0.811168 / 4.805227 (-3.994059) | 0.149960 / 6.500664 (-6.350704) | 0.064845 / 0.075469 (-0.010624) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221487 / 1.841788 (-0.620301) | 13.756534 / 8.074308 (5.682226) | 13.825369 / 10.191392 (3.633977) | 0.155641 / 0.680424 (-0.524783) | 0.028444 / 0.534201 (-0.505757) | 0.390364 / 0.579283 (-0.188919) | 0.397592 / 0.434364 (-0.036772) | 0.455905 / 0.540337 (-0.084433) | 0.534606 / 1.386936 (-0.852330) |\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) | 0.006281 / 0.011353 (-0.005071) | 0.004533 / 0.011008 (-0.006475) | 0.098328 / 0.038508 (0.059820) | 0.026998 / 0.023109 (0.003889) | 0.424814 / 0.275898 (0.148915) | 0.457653 / 0.323480 (0.134173) | 0.004617 / 0.007986 (-0.003368) | 0.003320 / 0.004328 (-0.001009) | 0.075884 / 0.004250 (0.071634) | 0.035865 / 0.037052 (-0.001187) | 0.431674 / 0.258489 (0.173185) | 0.468286 / 0.293841 (0.174445) | 0.031915 / 0.128546 (-0.096631) | 0.011680 / 0.075646 (-0.063967) | 0.319575 / 0.419271 (-0.099696) | 0.047792 / 0.043533 (0.004259) | 0.428191 / 0.255139 (0.173052) | 0.445657 / 0.283200 (0.162458) | 0.090464 / 0.141683 (-0.051218) | 1.465480 / 1.452155 (0.013326) | 1.548985 / 1.492716 (0.056268) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185671 / 0.018006 (0.167664) | 0.399274 / 0.000490 (0.398784) | 0.002822 / 0.000200 (0.002622) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025934 / 0.037411 (-0.011477) | 0.099480 / 0.014526 (0.084954) | 0.110264 / 0.176557 (-0.066293) | 0.140558 / 0.737135 (-0.596577) | 0.110832 / 0.296338 (-0.185507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473491 / 0.215209 (0.258282) | 4.722507 / 2.077655 (2.644852) | 2.456242 / 1.504120 (0.952122) | 2.255999 / 1.541195 (0.714804) | 2.300816 / 1.468490 (0.832326) | 0.698226 / 4.584777 (-3.886551) | 3.397296 / 3.745712 (-0.348416) | 2.741674 / 5.269862 (-2.528187) | 1.462103 / 4.565676 (-3.103573) | 0.082736 / 0.424275 (-0.341539) | 0.012183 / 0.007607 (0.004576) | 0.580144 / 0.226044 (0.354099) | 5.794351 / 2.268929 (3.525422) | 2.881201 / 55.444624 (-52.563423) | 2.544384 / 6.876477 (-4.332093) | 2.555227 / 2.142072 (0.413154) | 0.805849 / 4.805227 (-3.999378) | 0.151822 / 6.500664 (-6.348842) | 0.067477 / 0.075469 (-0.007992) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300224 / 1.841788 (-0.541564) | 13.595361 / 8.074308 (5.521053) | 13.967622 / 10.191392 (3.776230) | 0.129222 / 0.680424 (-0.551202) | 0.016939 / 0.534201 (-0.517262) | 0.375190 / 0.579283 (-0.204094) | 0.383511 / 0.434364 (-0.050853) | 0.437179 / 0.540337 (-0.103158) | 0.525674 / 1.386936 (-0.861262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ed52db3d67cc8d0f2adfe53b2ec8d1124a174b8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012364 / 0.011353 (0.001011) | 0.006098 / 0.011008 (-0.004911) | 0.158908 / 0.038508 (0.120400) | 0.039798 / 0.023109 (0.016689) | 0.383786 / 0.275898 (0.107888) | 0.533961 / 0.323480 (0.210481) | 0.012079 / 0.007986 (0.004094) | 0.006483 / 0.004328 (0.002155) | 0.109660 / 0.004250 (0.105410) | 0.048391 / 0.037052 (0.011339) | 0.447426 / 0.258489 (0.188937) | 0.477292 / 0.293841 (0.183451) | 0.066492 / 0.128546 (-0.062054) | 0.021155 / 0.075646 (-0.054492) | 0.474473 / 0.419271 (0.055202) | 0.063520 / 0.043533 (0.019987) | 0.444941 / 0.255139 (0.189802) | 0.450675 / 0.283200 (0.167475) | 0.129236 / 0.141683 (-0.012447) | 2.009362 / 1.452155 (0.557207) | 1.912067 / 1.492716 (0.419350) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260384 / 0.018006 (0.242378) | 0.577654 / 0.000490 (0.577165) | 0.004977 / 0.000200 (0.004777) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028101 / 0.037411 (-0.009310) | 0.161680 / 0.014526 (0.147154) | 0.146107 / 0.176557 (-0.030450) | 0.173878 / 0.737135 (-0.563257) | 0.186149 / 0.296338 (-0.110190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.689835 / 0.215209 (0.474626) | 6.775888 / 2.077655 (4.698234) | 2.885499 / 1.504120 (1.381379) | 2.486855 / 1.541195 (0.945660) | 2.540831 / 1.468490 (1.072341) | 1.328135 / 4.584777 (-3.256642) | 5.964983 / 3.745712 (2.219271) | 3.400713 / 5.269862 (-1.869149) | 2.423257 / 4.565676 (-2.142419) | 0.129767 / 0.424275 (-0.294508) | 0.017936 / 0.007607 (0.010328) | 0.909284 / 0.226044 (0.683239) | 8.778791 / 2.268929 (6.509863) | 3.890757 / 55.444624 (-51.553867) | 3.072116 / 6.876477 (-3.804360) | 3.085390 / 2.142072 (0.943318) | 1.571710 / 4.805227 (-3.233517) | 0.279290 / 6.500664 (-6.221374) | 0.087775 / 0.075469 (0.012306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.751223 / 1.841788 (-0.090564) | 20.313135 / 8.074308 (12.238827) | 22.793800 / 10.191392 (12.602408) | 0.296052 / 0.680424 (-0.384372) | 0.053420 / 0.534201 (-0.480781) | 0.600626 / 0.579283 (0.021343) | 0.634505 / 0.434364 (0.200142) | 0.724000 / 0.540337 (0.183663) | 0.869283 / 1.386936 (-0.517653) |\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) | 0.014876 / 0.011353 (0.003523) | 0.008113 / 0.011008 (-0.002895) | 0.177038 / 0.038508 (0.138530) | 0.050825 / 0.023109 (0.027716) | 0.473989 / 0.275898 (0.198091) | 0.601058 / 0.323480 (0.277578) | 0.007536 / 0.007986 (-0.000450) | 0.006761 / 0.004328 (0.002432) | 0.105260 / 0.004250 (0.101010) | 0.073960 / 0.037052 (0.036908) | 0.447711 / 0.258489 (0.189222) | 0.609998 / 0.293841 (0.316157) | 0.061280 / 0.128546 (-0.067267) | 0.019370 / 0.075646 (-0.056276) | 0.510466 / 0.419271 (0.091194) | 0.062695 / 0.043533 (0.019162) | 0.436778 / 0.255139 (0.181639) | 0.489916 / 0.283200 (0.206717) | 0.137305 / 0.141683 (-0.004378) | 1.801554 / 1.452155 (0.349399) | 2.082409 / 1.492716 (0.589692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291304 / 0.018006 (0.273298) | 0.599041 / 0.000490 (0.598551) | 0.008017 / 0.000200 (0.007817) | 0.000127 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031243 / 0.037411 (-0.006169) | 0.139689 / 0.014526 (0.125163) | 0.138678 / 0.176557 (-0.037878) | 0.180458 / 0.737135 (-0.556677) | 0.149753 / 0.296338 (-0.146585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.699692 / 0.215209 (0.484482) | 7.273327 / 2.077655 (5.195672) | 3.222650 / 1.504120 (1.718530) | 2.679424 / 1.541195 (1.138229) | 2.842378 / 1.468490 (1.373888) | 1.394633 / 4.584777 (-3.190143) | 6.379970 / 3.745712 (2.634258) | 5.944663 / 5.269862 (0.674801) | 3.105214 / 4.565676 (-1.460462) | 0.138790 / 0.424275 (-0.285485) | 0.014211 / 0.007607 (0.006604) | 0.815275 / 0.226044 (0.589230) | 8.549334 / 2.268929 (6.280405) | 3.754795 / 55.444624 (-51.689829) | 3.125222 / 6.876477 (-3.751255) | 3.269639 / 2.142072 (1.127566) | 1.464187 / 4.805227 (-3.341040) | 0.314557 / 6.500664 (-6.186107) | 0.107354 / 0.075469 (0.031885) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.480793 / 1.841788 (-0.360995) | 16.770328 / 8.074308 (8.696019) | 18.054861 / 10.191392 (7.863469) | 0.198257 / 0.680424 (-0.482167) | 0.026493 / 0.534201 (-0.507708) | 0.489701 / 0.579283 (-0.089582) | 0.540890 / 0.434364 (0.106526) | 0.566675 / 0.540337 (0.026337) | 0.661918 / 1.386936 (-0.725018) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4b839b50e9a81693e065f5299990026b97f6580 \"CML watermark\")\n" ]

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[ "Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) ", "Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply", "> Ccing @dwofk (the author of `fast-depth`).\r\n> \r\n> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.\r\n> \r\n> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)\r\n\r\n@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.\r\n\r\nNeed a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?\r\n1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise\r\n2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise\r\n3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts): Size is big 400GB+, requires **MatLab** code for fixing **projection** and **sync**, DataType: `pgm` and `dump` hence can't be used directly.\r\n\r\ncc: @lhoestq\r\n\r\n", "I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:\r\n\r\n* https://github.com/huggingface/blog/pull/718\r\n* https://huggingface.co/docs/datasets/main/en/depth_estimation\r\n\r\nDon't worry about it if it seems overwhelming. We will work it out together :) \r\n\r\n@lhoestq what do you think? ", "@sayakpaul If I get this right I have to,\r\n1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2\r\n2. Create a PR on https://github.com/huggingface/blog\r\n3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "The last two are low-hanging fruits. Don't worry about them. ", "Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !", "Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the **discretized** depth map.\r\nhttps://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93\r\n\r\n## Solutions:\r\n\r\n#### 1. Array2D\r\nUse `Array2D` feature with `float32` for depth_map \r\n\r\n* Code:\r\n```py\r\nFeatures({'depth_map': Array2D(shape=(480, 640), dtype='float32')})\r\n```\r\n* Pros:\r\nNo precision loss.\r\n\r\n* Cons:\r\nAs depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.\r\n\r\n#### 2. Uint16\r\nUse `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.\r\n\r\n* Code\r\n```py\r\ndepth = np.array(h5f[\"depth\"])\r\ndepth /= 10.0 # [0, max_depth] -> [0, 1]\r\ndepth *= (2**16 -1) # transform from [0, 1] -> [0, 2^16 - 1]\r\ndepth = depth.astype('uint16')\r\n```\r\n* Pros:\r\n * We can visualize depth map in hf.co/datasets page like segmentation mask.\r\n * No need for post-processing.\r\n\r\n* Cons:\r\n * We need to make two change\r\n * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.\r\n * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value\r\n * Precision loss due to `float32` to `uint16`\r\n * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]` before feeding them to model.", "Thanks so much for digging into this. \r\n\r\nSince the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. \r\n\r\n@lhoestq WDYT?", "@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.\r\n\r\nPerhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?", "Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.\r\n\r\nThere is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files\r\n\r\nwe can probably merge it today and do a release right away", "Fantastic, @lhoestq! \r\n\r\n@awsaf49 then let's wait for the PR to get merged and then take the next steps? ", "Sure", "The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?", "If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. ", "I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.", "We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users", "A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?", "Yes in theory :)", "actually float32 also seems to work in this PR (it just doesn't work for multi-channel)", "In that case, a new PR isn't necessary, right?", "Yep. I just tested from the PR and it works:\r\n```python\r\n>>> train_dataset = load_dataset(\"sayakpaul/nyu_depth_v2\", split=\"train\", streaming=True) \r\nDownloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]\r\n>>> next(iter(train_dataset))\r\n{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,\r\n 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}\r\n>>> x = next(iter(train_dataset))\r\n>>> np.asarray(x[\"depth_map\"]) \r\narray([[0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n ...,\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ]], dtype=float32)\r\n```", "Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️‍♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.\r\n\r\nOn another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "Haha thanks Mr Holmes :p\r\n\r\nmaybe let's close this issue when we're done updating the blog post and the documentation", "@awsaf49 thank you for your hard work! \r\n\r\nI am a little unsure why the other links need to be updated, though. They all rely on datasets internally. ", "I think depth_map still shows discretized version. It would be nice to have corrected one.\r\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png\" width = 300>", "Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8", "Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)", "Yes I think that would be nice :)", "I'll make the changes tomorrow. I hope it's okay..." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011812 / 0.011353 (0.000459) | 0.006878 / 0.011008 (-0.004130) | 0.128720 / 0.038508 (0.090212) | 0.038506 / 0.023109 (0.015397) | 0.359670 / 0.275898 (0.083772) | 0.422908 / 0.323480 (0.099428) | 0.010115 / 0.007986 (0.002129) | 0.004332 / 0.004328 (0.000004) | 0.096281 / 0.004250 (0.092031) | 0.048850 / 0.037052 (0.011798) | 0.373795 / 0.258489 (0.115306) | 0.414643 / 0.293841 (0.120802) | 0.057568 / 0.128546 (-0.070978) | 0.024135 / 0.075646 (-0.051512) | 0.411764 / 0.419271 (-0.007507) | 0.060167 / 0.043533 (0.016634) | 0.367119 / 0.255139 (0.111980) | 0.391813 / 0.283200 (0.108613) | 0.112125 / 0.141683 (-0.029558) | 1.869560 / 1.452155 (0.417406) | 1.845649 / 1.492716 (0.352932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211449 / 0.018006 (0.193443) | 0.522453 / 0.000490 (0.521963) | 0.003984 / 0.000200 (0.003784) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026015 / 0.037411 (-0.011397) | 0.117747 / 0.014526 (0.103221) | 0.125037 / 0.176557 (-0.051520) | 0.168351 / 0.737135 (-0.568785) | 0.132390 / 0.296338 (-0.163949) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605653 / 0.215209 (0.390444) | 5.883452 / 2.077655 (3.805798) | 2.367052 / 1.504120 (0.862932) | 2.137671 / 1.541195 (0.596476) | 2.042370 / 1.468490 (0.573880) | 1.168442 / 4.584777 (-3.416335) | 5.205236 / 3.745712 (1.459524) | 2.992514 / 5.269862 (-2.277348) | 2.191829 / 4.565676 (-2.373847) | 0.137702 / 0.424275 (-0.286574) | 0.015898 / 0.007607 (0.008291) | 0.783987 / 0.226044 (0.557942) | 7.768965 / 2.268929 (5.500036) | 3.249149 / 55.444624 (-52.195476) | 2.530687 / 6.876477 (-4.345790) | 2.675212 / 2.142072 (0.533140) | 1.482804 / 4.805227 (-3.322423) | 0.276845 / 6.500664 (-6.223819) | 0.080597 / 0.075469 (0.005128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.519086 / 1.841788 (-0.322701) | 17.394093 / 8.074308 (9.319785) | 19.613554 / 10.191392 (9.422162) | 0.253291 / 0.680424 (-0.427133) | 0.047746 / 0.534201 (-0.486455) | 0.547114 / 0.579283 (-0.032170) | 0.623873 / 0.434364 (0.189509) | 0.631924 / 0.540337 (0.091586) | 0.744390 / 1.386936 (-0.642546) |\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) | 0.009229 / 0.011353 (-0.002124) | 0.006206 / 0.011008 (-0.004802) | 0.121866 / 0.038508 (0.083357) | 0.033629 / 0.023109 (0.010519) | 0.435172 / 0.275898 (0.159274) | 0.472093 / 0.323480 (0.148613) | 0.006946 / 0.007986 (-0.001039) | 0.004848 / 0.004328 (0.000519) | 0.097289 / 0.004250 (0.093038) | 0.046982 / 0.037052 (0.009930) | 0.447365 / 0.258489 (0.188876) | 0.491213 / 0.293841 (0.197372) | 0.055486 / 0.128546 (-0.073060) | 0.019788 / 0.075646 (-0.055858) | 0.399830 / 0.419271 (-0.019441) | 0.058943 / 0.043533 (0.015411) | 0.447658 / 0.255139 (0.192519) | 0.465752 / 0.283200 (0.182552) | 0.110441 / 0.141683 (-0.031242) | 1.773155 / 1.452155 (0.321001) | 1.899370 / 1.492716 (0.406653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191188 / 0.018006 (0.173181) | 0.523721 / 0.000490 (0.523232) | 0.004008 / 0.000200 (0.003808) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032579 / 0.037411 (-0.004833) | 0.120870 / 0.014526 (0.106344) | 0.154991 / 0.176557 (-0.021565) | 0.175450 / 0.737135 (-0.561685) | 0.136526 / 0.296338 (-0.159813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627262 / 0.215209 (0.412052) | 6.457989 / 2.077655 (4.380334) | 2.935188 / 1.504120 (1.431068) | 2.558705 / 1.541195 (1.017510) | 2.669455 / 1.468490 (1.200965) | 1.228791 / 4.584777 (-3.355985) | 5.621262 / 3.745712 (1.875549) | 3.181775 / 5.269862 (-2.088086) | 2.115116 / 4.565676 (-2.450560) | 0.159348 / 0.424275 (-0.264927) | 0.013598 / 0.007607 (0.005991) | 0.834732 / 0.226044 (0.608687) | 8.051097 / 2.268929 (5.782168) | 3.761681 / 55.444624 (-51.682943) | 2.898158 / 6.876477 (-3.978319) | 2.936289 / 2.142072 (0.794217) | 1.476307 / 4.805227 (-3.328920) | 0.269845 / 6.500664 (-6.230819) | 0.087225 / 0.075469 (0.011756) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632522 / 1.841788 (-0.209266) | 17.615297 / 8.074308 (9.540989) | 20.501172 / 10.191392 (10.309780) | 0.248845 / 0.680424 (-0.431579) | 0.024852 / 0.534201 (-0.509349) | 0.498957 / 0.579283 (-0.080326) | 0.588566 / 0.434364 (0.154202) | 0.611051 / 0.540337 (0.070714) | 0.726321 / 1.386936 (-0.660615) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adaaf0b5ad596538c744d41bb56ce472834b6573 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008920 / 0.011353 (-0.002433) | 0.004666 / 0.011008 (-0.006342) | 0.098584 / 0.038508 (0.060076) | 0.030213 / 0.023109 (0.007103) | 0.298180 / 0.275898 (0.022282) | 0.358932 / 0.323480 (0.035452) | 0.007182 / 0.007986 (-0.000804) | 0.005430 / 0.004328 (0.001102) | 0.077962 / 0.004250 (0.073712) | 0.038516 / 0.037052 (0.001463) | 0.308840 / 0.258489 (0.050351) | 0.343678 / 0.293841 (0.049837) | 0.033701 / 0.128546 (-0.094845) | 0.011460 / 0.075646 (-0.064186) | 0.319809 / 0.419271 (-0.099462) | 0.040731 / 0.043533 (-0.002802) | 0.299772 / 0.255139 (0.044633) | 0.324292 / 0.283200 (0.041092) | 0.087755 / 0.141683 (-0.053928) | 1.493077 / 1.452155 (0.040922) | 1.527462 / 1.492716 (0.034746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187927 / 0.018006 (0.169921) | 0.412785 / 0.000490 (0.412296) | 0.003235 / 0.000200 (0.003035) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023313 / 0.037411 (-0.014098) | 0.095663 / 0.014526 (0.081137) | 0.105094 / 0.176557 (-0.071463) | 0.140389 / 0.737135 (-0.596746) | 0.108477 / 0.296338 (-0.187861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410680 / 0.215209 (0.195471) | 4.109287 / 2.077655 (2.031632) | 1.833214 / 1.504120 (0.329094) | 1.622837 / 1.541195 (0.081642) | 1.679899 / 1.468490 (0.211409) | 0.686920 / 4.584777 (-3.897857) | 3.463267 / 3.745712 (-0.282445) | 1.867035 / 5.269862 (-3.402826) | 1.150631 / 4.565676 (-3.415046) | 0.081209 / 0.424275 (-0.343066) | 0.012384 / 0.007607 (0.004777) | 0.521070 / 0.226044 (0.295026) | 5.208829 / 2.268929 (2.939900) | 2.289032 / 55.444624 (-53.155592) | 1.942976 / 6.876477 (-4.933501) | 1.990660 / 2.142072 (-0.151413) | 0.802976 / 4.805227 (-4.002252) | 0.148199 / 6.500664 (-6.352465) | 0.064644 / 0.075469 (-0.010825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277029 / 1.841788 (-0.564759) | 13.915489 / 8.074308 (5.841181) | 14.035486 / 10.191392 (3.844094) | 0.138205 / 0.680424 (-0.542219) | 0.028968 / 0.534201 (-0.505232) | 0.394275 / 0.579283 (-0.185008) | 0.399967 / 0.434364 (-0.034397) | 0.460595 / 0.540337 (-0.079742) | 0.537625 / 1.386936 (-0.849311) |\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) | 0.006485 / 0.011353 (-0.004868) | 0.004534 / 0.011008 (-0.006474) | 0.097742 / 0.038508 (0.059234) | 0.027231 / 0.023109 (0.004122) | 0.431321 / 0.275898 (0.155423) | 0.469212 / 0.323480 (0.145732) | 0.004894 / 0.007986 (-0.003092) | 0.004147 / 0.004328 (-0.000181) | 0.073650 / 0.004250 (0.069400) | 0.037052 / 0.037052 (-0.000000) | 0.434196 / 0.258489 (0.175707) | 0.480539 / 0.293841 (0.186698) | 0.031923 / 0.128546 (-0.096623) | 0.011522 / 0.075646 (-0.064124) | 0.317062 / 0.419271 (-0.102209) | 0.041124 / 0.043533 (-0.002409) | 0.432013 / 0.255139 (0.176874) | 0.456760 / 0.283200 (0.173560) | 0.089757 / 0.141683 (-0.051925) | 1.497752 / 1.452155 (0.045597) | 1.585342 / 1.492716 (0.092626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227784 / 0.018006 (0.209778) | 0.404570 / 0.000490 (0.404080) | 0.000556 / 0.000200 (0.000356) | 0.000065 / 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) | 0.025201 / 0.037411 (-0.012210) | 0.099348 / 0.014526 (0.084822) | 0.114984 / 0.176557 (-0.061573) | 0.147039 / 0.737135 (-0.590097) | 0.109727 / 0.296338 (-0.186611) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468415 / 0.215209 (0.253206) | 4.692228 / 2.077655 (2.614573) | 2.403382 / 1.504120 (0.899262) | 2.196026 / 1.541195 (0.654832) | 2.234736 / 1.468490 (0.766246) | 0.703011 / 4.584777 (-3.881766) | 3.451513 / 3.745712 (-0.294199) | 2.596811 / 5.269862 (-2.673051) | 1.544079 / 4.565676 (-3.021598) | 0.083153 / 0.424275 (-0.341123) | 0.012605 / 0.007607 (0.004998) | 0.570265 / 0.226044 (0.344220) | 5.735996 / 2.268929 (3.467067) | 2.865336 / 55.444624 (-52.579288) | 2.508340 / 6.876477 (-4.368137) | 2.547144 / 2.142072 (0.405072) | 0.813018 / 4.805227 (-3.992210) | 0.150327 / 6.500664 (-6.350337) | 0.065837 / 0.075469 (-0.009632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268941 / 1.841788 (-0.572847) | 13.835698 / 8.074308 (5.761390) | 13.992726 / 10.191392 (3.801334) | 0.127751 / 0.680424 (-0.552673) | 0.016673 / 0.534201 (-0.517528) | 0.381921 / 0.579283 (-0.197362) | 0.390688 / 0.434364 (-0.043676) | 0.446234 / 0.540337 (-0.094103) | 0.532631 / 1.386936 (-0.854305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1492df3311bfeac55aaedf34c93c014630c4403e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008486 / 0.011353 (-0.002867) | 0.004573 / 0.011008 (-0.006435) | 0.100096 / 0.038508 (0.061588) | 0.029449 / 0.023109 (0.006340) | 0.298384 / 0.275898 (0.022486) | 0.361886 / 0.323480 (0.038406) | 0.006813 / 0.007986 (-0.001173) | 0.003394 / 0.004328 (-0.000935) | 0.077563 / 0.004250 (0.073312) | 0.035605 / 0.037052 (-0.001447) | 0.306864 / 0.258489 (0.048375) | 0.346438 / 0.293841 (0.052597) | 0.033156 / 0.128546 (-0.095390) | 0.011567 / 0.075646 (-0.064079) | 0.322189 / 0.419271 (-0.097083) | 0.040161 / 0.043533 (-0.003372) | 0.299329 / 0.255139 (0.044190) | 0.326375 / 0.283200 (0.043175) | 0.086572 / 0.141683 (-0.055111) | 1.502473 / 1.452155 (0.050319) | 1.528539 / 1.492716 (0.035823) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008502 / 0.018006 (-0.009505) | 0.411045 / 0.000490 (0.410555) | 0.003179 / 0.000200 (0.002980) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023177 / 0.037411 (-0.014234) | 0.096948 / 0.014526 (0.082422) | 0.104068 / 0.176557 (-0.072489) | 0.138739 / 0.737135 (-0.598396) | 0.108241 / 0.296338 (-0.188097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411156 / 0.215209 (0.195947) | 4.092992 / 2.077655 (2.015337) | 1.841903 / 1.504120 (0.337783) | 1.637449 / 1.541195 (0.096254) | 1.670968 / 1.468490 (0.202478) | 0.697301 / 4.584777 (-3.887476) | 3.354717 / 3.745712 (-0.390995) | 1.851518 / 5.269862 (-3.418344) | 1.160367 / 4.565676 (-3.405309) | 0.082613 / 0.424275 (-0.341662) | 0.012477 / 0.007607 (0.004870) | 0.524839 / 0.226044 (0.298795) | 5.264173 / 2.268929 (2.995245) | 2.294530 / 55.444624 (-53.150094) | 1.933233 / 6.876477 (-4.943244) | 1.968959 / 2.142072 (-0.173113) | 0.817104 / 4.805227 (-3.988123) | 0.149072 / 6.500664 (-6.351592) | 0.064911 / 0.075469 (-0.010558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.222215 / 1.841788 (-0.619573) | 13.607545 / 8.074308 (5.533237) | 13.990230 / 10.191392 (3.798838) | 0.150855 / 0.680424 (-0.529568) | 0.028844 / 0.534201 (-0.505357) | 0.396169 / 0.579283 (-0.183114) | 0.406957 / 0.434364 (-0.027407) | 0.464069 / 0.540337 (-0.076268) | 0.554027 / 1.386936 (-0.832909) |\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) | 0.006296 / 0.011353 (-0.005057) | 0.004563 / 0.011008 (-0.006445) | 0.097719 / 0.038508 (0.059211) | 0.027106 / 0.023109 (0.003996) | 0.409333 / 0.275898 (0.133435) | 0.445397 / 0.323480 (0.121917) | 0.004906 / 0.007986 (-0.003080) | 0.003316 / 0.004328 (-0.001012) | 0.075363 / 0.004250 (0.071112) | 0.039366 / 0.037052 (0.002314) | 0.412710 / 0.258489 (0.154221) | 0.451789 / 0.293841 (0.157948) | 0.031810 / 0.128546 (-0.096736) | 0.011681 / 0.075646 (-0.063965) | 0.318484 / 0.419271 (-0.100788) | 0.046741 / 0.043533 (0.003208) | 0.411631 / 0.255139 (0.156492) | 0.435274 / 0.283200 (0.152074) | 0.092366 / 0.141683 (-0.049317) | 1.492243 / 1.452155 (0.040089) | 1.617603 / 1.492716 (0.124887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217376 / 0.018006 (0.199369) | 0.400940 / 0.000490 (0.400450) | 0.003700 / 0.000200 (0.003500) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023733 / 0.037411 (-0.013678) | 0.098553 / 0.014526 (0.084027) | 0.105790 / 0.176557 (-0.070767) | 0.139537 / 0.737135 (-0.597598) | 0.109862 / 0.296338 (-0.186477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476562 / 0.215209 (0.261353) | 4.773469 / 2.077655 (2.695814) | 2.447302 / 1.504120 (0.943182) | 2.240596 / 1.541195 (0.699401) | 2.271370 / 1.468490 (0.802880) | 0.698913 / 4.584777 (-3.885864) | 3.345648 / 3.745712 (-0.400064) | 1.845008 / 5.269862 (-3.424854) | 1.163213 / 4.565676 (-3.402464) | 0.082456 / 0.424275 (-0.341819) | 0.012315 / 0.007607 (0.004708) | 0.575881 / 0.226044 (0.349836) | 5.769575 / 2.268929 (3.500647) | 2.909759 / 55.444624 (-52.534865) | 2.580259 / 6.876477 (-4.296218) | 2.590473 / 2.142072 (0.448401) | 0.802765 / 4.805227 (-4.002462) | 0.151514 / 6.500664 (-6.349150) | 0.067718 / 0.075469 (-0.007751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293014 / 1.841788 (-0.548773) | 13.934072 / 8.074308 (5.859763) | 13.538760 / 10.191392 (3.347368) | 0.126490 / 0.680424 (-0.553934) | 0.016653 / 0.534201 (-0.517548) | 0.381220 / 0.579283 (-0.198064) | 0.387571 / 0.434364 (-0.046793) | 0.444674 / 0.540337 (-0.095663) | 0.550802 / 1.386936 (-0.836134) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bed576f2205c96f6cb26b5c6522345cb8b06ecfc \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ", "The lib `requests` does not perform that requote on redirect URLs.", "Indeed, the `requests` library does perform a requoting, but this does not unquote `%27`:\r\n```python\r\nIn [1]: from requests.utils import requote_uri\r\n\r\nIn [2]: url = \"https://netloc/path?param=param%27%27value\"\r\n\r\nIn [3]: url\r\nOut[3]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [4]: requote_uri(url)\r\nOut[4]: 'https://netloc/path?param=param%27%27value'\r\n```\r\n\r\nHowever, the `aiohttp` library uses `yarl.ULR` and this does unquote `%27`:\r\n```python\r\nIn [5]: from yarl import URL\r\n\r\nIn [6]: url\r\nOut[6]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [7]: str(URL(url))\r\nOut[7]: \"https://netloc/path?param=param''value\"\r\n```\r\n\r\nIf we pass `requote_redirect_url=False` to `aiohttp`, then it passes `encoded=True` to `yarl.ULR`: https://github.com/aio-libs/aiohttp/blob/4635161ee8e7ad321cca46e01ce5bfeb1ad8bf26/aiohttp/client.py#L578-L580\r\n```python\r\nparsed_url = URL(\r\n r_url, encoded=not self._requote_redirect_url\r\n)\r\n```\r\nwhich does not unquote `%27`:\r\n```python\r\nIn [8]: url\r\nOut[8]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [9]: str(URL(url, encoded=True))\r\nOut[9]: 'https://netloc/path?param=param%27%27value'\r\n```", "See the issues we opened in the respective libraries:\r\n- aiohttp\r\n - aio-libs/aiohttp#7183\r\n- requests\r\n - psf/requests#6341", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012399 / 0.011353 (0.001047) | 0.006388 / 0.011008 (-0.004620) | 0.134173 / 0.038508 (0.095665) | 0.037059 / 0.023109 (0.013949) | 0.420697 / 0.275898 (0.144799) | 0.473981 / 0.323480 (0.150502) | 0.009857 / 0.007986 (0.001871) | 0.004791 / 0.004328 (0.000463) | 0.106886 / 0.004250 (0.102636) | 0.044871 / 0.037052 (0.007818) | 0.429843 / 0.258489 (0.171354) | 0.461569 / 0.293841 (0.167728) | 0.057285 / 0.128546 (-0.071261) | 0.018809 / 0.075646 (-0.056837) | 0.432613 / 0.419271 (0.013342) | 0.058086 / 0.043533 (0.014553) | 0.413064 / 0.255139 (0.157925) | 0.444407 / 0.283200 (0.161207) | 0.119102 / 0.141683 (-0.022581) | 1.875954 / 1.452155 (0.423799) | 1.916392 / 1.492716 (0.423676) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267489 / 0.018006 (0.249483) | 0.567554 / 0.000490 (0.567064) | 0.005901 / 0.000200 (0.005701) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031248 / 0.037411 (-0.006164) | 0.123014 / 0.014526 (0.108489) | 0.140001 / 0.176557 (-0.036556) | 0.191476 / 0.737135 (-0.545659) | 0.141687 / 0.296338 (-0.154652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.637481 / 0.215209 (0.422272) | 6.255969 / 2.077655 (4.178314) | 2.559811 / 1.504120 (1.055691) | 2.118154 / 1.541195 (0.576960) | 2.079487 / 1.468490 (0.610997) | 1.201079 / 4.584777 (-3.383698) | 5.592625 / 3.745712 (1.846913) | 5.143344 / 5.269862 (-0.126517) | 2.764716 / 4.565676 (-1.800960) | 0.142539 / 0.424275 (-0.281736) | 0.015541 / 0.007607 (0.007934) | 0.771407 / 0.226044 (0.545363) | 7.631657 / 2.268929 (5.362728) | 3.279684 / 55.444624 (-52.164940) | 2.587566 / 6.876477 (-4.288911) | 2.624622 / 2.142072 (0.482549) | 1.427878 / 4.805227 (-3.377350) | 0.257759 / 6.500664 (-6.242906) | 0.078616 / 0.075469 (0.003147) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609305 / 1.841788 (-0.232483) | 18.258792 / 8.074308 (10.184484) | 20.345242 / 10.191392 (10.153850) | 0.267366 / 0.680424 (-0.413058) | 0.047035 / 0.534201 (-0.487166) | 0.568881 / 0.579283 (-0.010402) | 0.662763 / 0.434364 (0.228399) | 0.668927 / 0.540337 (0.128590) | 0.755766 / 1.386936 (-0.631170) |\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) | 0.010017 / 0.011353 (-0.001336) | 0.006816 / 0.011008 (-0.004192) | 0.105038 / 0.038508 (0.066529) | 0.038689 / 0.023109 (0.015580) | 0.482113 / 0.275898 (0.206215) | 0.540072 / 0.323480 (0.216592) | 0.007738 / 0.007986 (-0.000248) | 0.005134 / 0.004328 (0.000806) | 0.102203 / 0.004250 (0.097953) | 0.054080 / 0.037052 (0.017028) | 0.501057 / 0.258489 (0.242568) | 0.567186 / 0.293841 (0.273345) | 0.060330 / 0.128546 (-0.068217) | 0.020059 / 0.075646 (-0.055587) | 0.123102 / 0.419271 (-0.296170) | 0.063426 / 0.043533 (0.019893) | 0.494171 / 0.255139 (0.239032) | 0.538238 / 0.283200 (0.255039) | 0.119613 / 0.141683 (-0.022069) | 1.853728 / 1.452155 (0.401574) | 1.984621 / 1.492716 (0.491904) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282511 / 0.018006 (0.264505) | 0.563190 / 0.000490 (0.562700) | 0.000465 / 0.000200 (0.000265) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029267 / 0.037411 (-0.008144) | 0.135618 / 0.014526 (0.121093) | 0.146286 / 0.176557 (-0.030271) | 0.188570 / 0.737135 (-0.548565) | 0.155839 / 0.296338 (-0.140499) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671660 / 0.215209 (0.456451) | 6.718775 / 2.077655 (4.641120) | 3.004601 / 1.504120 (1.500481) | 2.640504 / 1.541195 (1.099309) | 2.666788 / 1.468490 (1.198298) | 1.242655 / 4.584777 (-3.342122) | 5.780119 / 3.745712 (2.034407) | 3.247935 / 5.269862 (-2.021927) | 2.114007 / 4.565676 (-2.451669) | 0.147546 / 0.424275 (-0.276729) | 0.014408 / 0.007607 (0.006801) | 0.824407 / 0.226044 (0.598362) | 8.278185 / 2.268929 (6.009257) | 3.733463 / 55.444624 (-51.711161) | 2.976732 / 6.876477 (-3.899745) | 3.132758 / 2.142072 (0.990686) | 1.446095 / 4.805227 (-3.359132) | 0.258628 / 6.500664 (-6.242036) | 0.085513 / 0.075469 (0.010043) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.702681 / 1.841788 (-0.139106) | 18.725123 / 8.074308 (10.650815) | 19.622808 / 10.191392 (9.431416) | 0.215845 / 0.680424 (-0.464579) | 0.029246 / 0.534201 (-0.504955) | 0.554819 / 0.579283 (-0.024464) | 0.630926 / 0.434364 (0.196562) | 0.637663 / 0.540337 (0.097325) | 0.837948 / 1.386936 (-0.548988) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008540 / 0.011353 (-0.002813) | 0.004538 / 0.011008 (-0.006470) | 0.101507 / 0.038508 (0.062999) | 0.029751 / 0.023109 (0.006641) | 0.292608 / 0.275898 (0.016710) | 0.354734 / 0.323480 (0.031254) | 0.007430 / 0.007986 (-0.000556) | 0.003365 / 0.004328 (-0.000964) | 0.078703 / 0.004250 (0.074452) | 0.034858 / 0.037052 (-0.002194) | 0.303518 / 0.258489 (0.045029) | 0.336523 / 0.293841 (0.042682) | 0.033741 / 0.128546 (-0.094805) | 0.011460 / 0.075646 (-0.064186) | 0.319551 / 0.419271 (-0.099721) | 0.041102 / 0.043533 (-0.002431) | 0.295914 / 0.255139 (0.040775) | 0.322142 / 0.283200 (0.038943) | 0.084694 / 0.141683 (-0.056989) | 1.481308 / 1.452155 (0.029153) | 1.530271 / 1.492716 (0.037554) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180516 / 0.018006 (0.162510) | 0.405741 / 0.000490 (0.405251) | 0.002806 / 0.000200 (0.002606) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023359 / 0.037411 (-0.014052) | 0.096950 / 0.014526 (0.082424) | 0.103991 / 0.176557 (-0.072566) | 0.143700 / 0.737135 (-0.593435) | 0.106764 / 0.296338 (-0.189575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416966 / 0.215209 (0.201757) | 4.145601 / 2.077655 (2.067946) | 1.838258 / 1.504120 (0.334139) | 1.629396 / 1.541195 (0.088201) | 1.649707 / 1.468490 (0.181217) | 0.689624 / 4.584777 (-3.895153) | 3.414584 / 3.745712 (-0.331129) | 1.874295 / 5.269862 (-3.395566) | 1.251930 / 4.565676 (-3.313746) | 0.081782 / 0.424275 (-0.342493) | 0.012868 / 0.007607 (0.005261) | 0.523904 / 0.226044 (0.297859) | 5.251032 / 2.268929 (2.982104) | 2.301549 / 55.444624 (-53.143075) | 1.942110 / 6.876477 (-4.934367) | 2.023014 / 2.142072 (-0.119058) | 0.816492 / 4.805227 (-3.988736) | 0.150107 / 6.500664 (-6.350558) | 0.065118 / 0.075469 (-0.010351) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226433 / 1.841788 (-0.615355) | 13.852569 / 8.074308 (5.778261) | 13.862779 / 10.191392 (3.671387) | 0.146361 / 0.680424 (-0.534062) | 0.028652 / 0.534201 (-0.505549) | 0.398251 / 0.579283 (-0.181032) | 0.403590 / 0.434364 (-0.030774) | 0.492184 / 0.540337 (-0.048154) | 0.581040 / 1.386936 (-0.805896) |\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) | 0.006859 / 0.011353 (-0.004494) | 0.004632 / 0.011008 (-0.006376) | 0.076653 / 0.038508 (0.038145) | 0.027865 / 0.023109 (0.004755) | 0.354472 / 0.275898 (0.078573) | 0.385462 / 0.323480 (0.061982) | 0.005125 / 0.007986 (-0.002861) | 0.003420 / 0.004328 (-0.000909) | 0.076018 / 0.004250 (0.071768) | 0.040197 / 0.037052 (0.003144) | 0.353675 / 0.258489 (0.095186) | 0.394911 / 0.293841 (0.101070) | 0.032909 / 0.128546 (-0.095637) | 0.011713 / 0.075646 (-0.063933) | 0.085921 / 0.419271 (-0.333350) | 0.044462 / 0.043533 (0.000929) | 0.349997 / 0.255139 (0.094858) | 0.375207 / 0.283200 (0.092008) | 0.091288 / 0.141683 (-0.050394) | 1.536515 / 1.452155 (0.084361) | 1.581878 / 1.492716 (0.089162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273284 / 0.018006 (0.255277) | 0.424457 / 0.000490 (0.423967) | 0.044659 / 0.000200 (0.044459) | 0.000247 / 0.000054 (0.000192) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025473 / 0.037411 (-0.011938) | 0.100014 / 0.014526 (0.085488) | 0.108551 / 0.176557 (-0.068006) | 0.147913 / 0.737135 (-0.589223) | 0.112729 / 0.296338 (-0.183610) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448162 / 0.215209 (0.232953) | 4.472701 / 2.077655 (2.395046) | 2.078384 / 1.504120 (0.574264) | 1.861292 / 1.541195 (0.320097) | 1.920482 / 1.468490 (0.451991) | 0.706968 / 4.584777 (-3.877809) | 3.433109 / 3.745712 (-0.312603) | 1.898684 / 5.269862 (-3.371178) | 1.174375 / 4.565676 (-3.391302) | 0.083666 / 0.424275 (-0.340609) | 0.012388 / 0.007607 (0.004781) | 0.546011 / 0.226044 (0.319966) | 5.487514 / 2.268929 (3.218585) | 2.534124 / 55.444624 (-52.910500) | 2.168441 / 6.876477 (-4.708036) | 2.203458 / 2.142072 (0.061386) | 0.813333 / 4.805227 (-3.991894) | 0.153169 / 6.500664 (-6.347495) | 0.067151 / 0.075469 (-0.008318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277815 / 1.841788 (-0.563972) | 13.920545 / 8.074308 (5.846237) | 13.473801 / 10.191392 (3.282409) | 0.129035 / 0.680424 (-0.551389) | 0.016737 / 0.534201 (-0.517464) | 0.388413 / 0.579283 (-0.190870) | 0.388785 / 0.434364 (-0.045579) | 0.481735 / 0.540337 (-0.058602) | 0.576390 / 1.386936 (-0.810546) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n" ]

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[ "Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train\").take(3)`\r\n\r\n\r\n", "Thank you for your quick response!" ]

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[ "Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to ensure your dataset is self-contained:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset ...\r\ndset = dset.with_format(\"arrow\")\r\ndset.map(embed_table_storage, batched=True)\r\ndset = dset.with_format(\"python\")\r\n```\r\n", "Understood. Thank you, Mario.\r\n\r\nPerhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.\r\n\r\nBut regardless if X relies on Y - it could check if Y is still there when loading X. so not checking full consistency but just the top-level directory it relies on." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012395 / 0.011353 (0.001042) | 0.006466 / 0.011008 (-0.004542) | 0.127605 / 0.038508 (0.089097) | 0.044929 / 0.023109 (0.021820) | 0.399856 / 0.275898 (0.123958) | 0.491341 / 0.323480 (0.167861) | 0.009193 / 0.007986 (0.001207) | 0.005419 / 0.004328 (0.001090) | 0.100577 / 0.004250 (0.096327) | 0.045338 / 0.037052 (0.008286) | 0.409970 / 0.258489 (0.151481) | 0.452941 / 0.293841 (0.159100) | 0.054350 / 0.128546 (-0.074197) | 0.019069 / 0.075646 (-0.056578) | 0.427036 / 0.419271 (0.007765) | 0.073616 / 0.043533 (0.030083) | 0.395384 / 0.255139 (0.140245) | 0.442381 / 0.283200 (0.159181) | 0.123185 / 0.141683 (-0.018498) | 1.797640 / 1.452155 (0.345485) | 1.888860 / 1.492716 (0.396143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211041 / 0.018006 (0.193035) | 0.539350 / 0.000490 (0.538860) | 0.001683 / 0.000200 (0.001483) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031699 / 0.037411 (-0.005712) | 0.132696 / 0.014526 (0.118170) | 0.133710 / 0.176557 (-0.042846) | 0.190074 / 0.737135 (-0.547061) | 0.142919 / 0.296338 (-0.153420) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643521 / 0.215209 (0.428312) | 6.137350 / 2.077655 (4.059695) | 2.463894 / 1.504120 (0.959774) | 2.120043 / 1.541195 (0.578848) | 2.121898 / 1.468490 (0.653408) | 1.287319 / 4.584777 (-3.297458) | 5.517864 / 3.745712 (1.772151) | 5.070820 / 5.269862 (-0.199042) | 2.948967 / 4.565676 (-1.616710) | 0.175861 / 0.424275 (-0.248415) | 0.015292 / 0.007607 (0.007685) | 0.843195 / 0.226044 (0.617150) | 7.884275 / 2.268929 (5.615347) | 3.182821 / 55.444624 (-52.261803) | 2.576093 / 6.876477 (-4.300384) | 2.537160 / 2.142072 (0.395088) | 1.510029 / 4.805227 (-3.295198) | 0.249404 / 6.500664 (-6.251260) | 0.080434 / 0.075469 (0.004965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.618695 / 1.841788 (-0.223093) | 18.879207 / 8.074308 (10.804899) | 21.075272 / 10.191392 (10.883880) | 0.260781 / 0.680424 (-0.419643) | 0.046387 / 0.534201 (-0.487813) | 0.570709 / 0.579283 (-0.008574) | 0.619050 / 0.434364 (0.184686) | 0.642295 / 0.540337 (0.101958) | 0.780070 / 1.386936 (-0.606866) |\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) | 0.010418 / 0.011353 (-0.000935) | 0.006104 / 0.011008 (-0.004905) | 0.133609 / 0.038508 (0.095101) | 0.035101 / 0.023109 (0.011992) | 0.471931 / 0.275898 (0.196033) | 0.504498 / 0.323480 (0.181018) | 0.007388 / 0.007986 (-0.000598) | 0.004852 / 0.004328 (0.000523) | 0.094535 / 0.004250 (0.090284) | 0.056832 / 0.037052 (0.019779) | 0.470513 / 0.258489 (0.212024) | 0.531285 / 0.293841 (0.237444) | 0.058271 / 0.128546 (-0.070276) | 0.020523 / 0.075646 (-0.055123) | 0.437398 / 0.419271 (0.018126) | 0.065390 / 0.043533 (0.021857) | 0.503702 / 0.255139 (0.248563) | 0.515876 / 0.283200 (0.232677) | 0.118615 / 0.141683 (-0.023068) | 1.865380 / 1.452155 (0.413225) | 1.990316 / 1.492716 (0.497600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246772 / 0.018006 (0.228766) | 0.560607 / 0.000490 (0.560118) | 0.005675 / 0.000200 (0.005475) | 0.000142 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034692 / 0.037411 (-0.002719) | 0.174016 / 0.014526 (0.159490) | 0.179838 / 0.176557 (0.003282) | 0.217118 / 0.737135 (-0.520018) | 0.184811 / 0.296338 (-0.111527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675970 / 0.215209 (0.460760) | 6.787039 / 2.077655 (4.709384) | 2.932619 / 1.504120 (1.428499) | 2.545076 / 1.541195 (1.003882) | 2.566705 / 1.468490 (1.098215) | 1.287365 / 4.584777 (-3.297412) | 5.468441 / 3.745712 (1.722729) | 5.227726 / 5.269862 (-0.042136) | 2.868970 / 4.565676 (-1.696706) | 0.153535 / 0.424275 (-0.270740) | 0.020087 / 0.007607 (0.012480) | 0.860562 / 0.226044 (0.634518) | 8.656109 / 2.268929 (6.387180) | 3.749424 / 55.444624 (-51.695200) | 3.011337 / 6.876477 (-3.865139) | 3.119045 / 2.142072 (0.976973) | 1.562174 / 4.805227 (-3.243053) | 0.279161 / 6.500664 (-6.221504) | 0.084905 / 0.075469 (0.009436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.638684 / 1.841788 (-0.203104) | 18.834760 / 8.074308 (10.760452) | 21.554310 / 10.191392 (11.362918) | 0.274518 / 0.680424 (-0.405906) | 0.030343 / 0.534201 (-0.503858) | 0.539094 / 0.579283 (-0.040189) | 0.627258 / 0.434364 (0.192895) | 0.624638 / 0.540337 (0.084301) | 0.742776 / 1.386936 (-0.644160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98c9b27be45e1f5bc8c18d8bb2414478efe68055 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008372 / 0.011353 (-0.002981) | 0.004658 / 0.011008 (-0.006350) | 0.102005 / 0.038508 (0.063497) | 0.029030 / 0.023109 (0.005920) | 0.296968 / 0.275898 (0.021070) | 0.364898 / 0.323480 (0.041418) | 0.006899 / 0.007986 (-0.001087) | 0.003410 / 0.004328 (-0.000919) | 0.079705 / 0.004250 (0.075455) | 0.034265 / 0.037052 (-0.002787) | 0.305695 / 0.258489 (0.047206) | 0.343275 / 0.293841 (0.049434) | 0.033783 / 0.128546 (-0.094763) | 0.011604 / 0.075646 (-0.064042) | 0.322577 / 0.419271 (-0.096694) | 0.040540 / 0.043533 (-0.002993) | 0.299176 / 0.255139 (0.044037) | 0.333157 / 0.283200 (0.049957) | 0.087460 / 0.141683 (-0.054223) | 1.494392 / 1.452155 (0.042237) | 1.539580 / 1.492716 (0.046863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.176206 / 0.018006 (0.158200) | 0.413702 / 0.000490 (0.413212) | 0.002625 / 0.000200 (0.002425) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023886 / 0.037411 (-0.013525) | 0.099758 / 0.014526 (0.085232) | 0.104349 / 0.176557 (-0.072208) | 0.147138 / 0.737135 (-0.589998) | 0.108682 / 0.296338 (-0.187657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411957 / 0.215209 (0.196748) | 4.110004 / 2.077655 (2.032349) | 1.820951 / 1.504120 (0.316831) | 1.629726 / 1.541195 (0.088532) | 1.672573 / 1.468490 (0.204083) | 0.686627 / 4.584777 (-3.898150) | 3.382665 / 3.745712 (-0.363047) | 2.875908 / 5.269862 (-2.393954) | 1.475331 / 4.565676 (-3.090345) | 0.081353 / 0.424275 (-0.342922) | 0.012521 / 0.007607 (0.004914) | 0.516226 / 0.226044 (0.290182) | 5.157658 / 2.268929 (2.888729) | 2.302012 / 55.444624 (-53.142612) | 1.950831 / 6.876477 (-4.925646) | 1.962081 / 2.142072 (-0.179992) | 0.800007 / 4.805227 (-4.005221) | 0.148462 / 6.500664 (-6.352202) | 0.064448 / 0.075469 (-0.011021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227977 / 1.841788 (-0.613810) | 13.776087 / 8.074308 (5.701779) | 13.749825 / 10.191392 (3.558433) | 0.137034 / 0.680424 (-0.543390) | 0.028461 / 0.534201 (-0.505740) | 0.392335 / 0.579283 (-0.186948) | 0.397404 / 0.434364 (-0.036960) | 0.450831 / 0.540337 (-0.089507) | 0.533716 / 1.386936 (-0.853220) |\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) | 0.006883 / 0.011353 (-0.004470) | 0.004625 / 0.011008 (-0.006383) | 0.099039 / 0.038508 (0.060531) | 0.028068 / 0.023109 (0.004958) | 0.419988 / 0.275898 (0.144090) | 0.449543 / 0.323480 (0.126063) | 0.005232 / 0.007986 (-0.002753) | 0.003527 / 0.004328 (-0.000801) | 0.076308 / 0.004250 (0.072057) | 0.040523 / 0.037052 (0.003471) | 0.420165 / 0.258489 (0.161676) | 0.463220 / 0.293841 (0.169379) | 0.032368 / 0.128546 (-0.096178) | 0.011784 / 0.075646 (-0.063863) | 0.320675 / 0.419271 (-0.098597) | 0.041861 / 0.043533 (-0.001672) | 0.424903 / 0.255139 (0.169764) | 0.443528 / 0.283200 (0.160328) | 0.090869 / 0.141683 (-0.050814) | 1.504757 / 1.452155 (0.052602) | 1.557824 / 1.492716 (0.065108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224020 / 0.018006 (0.206014) | 0.404090 / 0.000490 (0.403601) | 0.000403 / 0.000200 (0.000203) | 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) | 0.024556 / 0.037411 (-0.012855) | 0.101280 / 0.014526 (0.086754) | 0.108017 / 0.176557 (-0.068540) | 0.146679 / 0.737135 (-0.590456) | 0.111468 / 0.296338 (-0.184870) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478955 / 0.215209 (0.263746) | 4.769628 / 2.077655 (2.691973) | 2.473238 / 1.504120 (0.969118) | 2.263588 / 1.541195 (0.722393) | 2.285425 / 1.468490 (0.816935) | 0.699051 / 4.584777 (-3.885726) | 3.390495 / 3.745712 (-0.355217) | 1.858569 / 5.269862 (-3.411293) | 1.162081 / 4.565676 (-3.403596) | 0.083294 / 0.424275 (-0.340981) | 0.012410 / 0.007607 (0.004803) | 0.580786 / 0.226044 (0.354741) | 5.866868 / 2.268929 (3.597940) | 2.944358 / 55.444624 (-52.500266) | 2.596241 / 6.876477 (-4.280235) | 2.664464 / 2.142072 (0.522392) | 0.806751 / 4.805227 (-3.998476) | 0.152389 / 6.500664 (-6.348275) | 0.066945 / 0.075469 (-0.008524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290545 / 1.841788 (-0.551243) | 14.005727 / 8.074308 (5.931419) | 14.478951 / 10.191392 (4.287559) | 0.127488 / 0.680424 (-0.552935) | 0.016929 / 0.534201 (-0.517272) | 0.378380 / 0.579283 (-0.200904) | 0.387499 / 0.434364 (-0.046865) | 0.440816 / 0.540337 (-0.099522) | 0.525794 / 1.386936 (-0.861142) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07549c6fcb2dced59d7614b4b8264d54ef573407 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008704 / 0.011353 (-0.002649) | 0.004474 / 0.011008 (-0.006534) | 0.101720 / 0.038508 (0.063212) | 0.030426 / 0.023109 (0.007317) | 0.298944 / 0.275898 (0.023046) | 0.371491 / 0.323480 (0.048011) | 0.007042 / 0.007986 (-0.000944) | 0.003479 / 0.004328 (-0.000850) | 0.078086 / 0.004250 (0.073835) | 0.037014 / 0.037052 (-0.000038) | 0.312964 / 0.258489 (0.054475) | 0.351251 / 0.293841 (0.057410) | 0.033286 / 0.128546 (-0.095260) | 0.011468 / 0.075646 (-0.064179) | 0.321784 / 0.419271 (-0.097488) | 0.040700 / 0.043533 (-0.002832) | 0.303799 / 0.255139 (0.048660) | 0.336982 / 0.283200 (0.053782) | 0.089448 / 0.141683 (-0.052235) | 1.462430 / 1.452155 (0.010275) | 1.524448 / 1.492716 (0.031732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178390 / 0.018006 (0.160384) | 0.402474 / 0.000490 (0.401984) | 0.002697 / 0.000200 (0.002497) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022679 / 0.037411 (-0.014733) | 0.097759 / 0.014526 (0.083234) | 0.105102 / 0.176557 (-0.071454) | 0.140720 / 0.737135 (-0.596415) | 0.109119 / 0.296338 (-0.187219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414153 / 0.215209 (0.198944) | 4.131799 / 2.077655 (2.054144) | 1.852325 / 1.504120 (0.348205) | 1.646955 / 1.541195 (0.105760) | 1.662880 / 1.468490 (0.194390) | 0.693823 / 4.584777 (-3.890954) | 3.378843 / 3.745712 (-0.366869) | 1.861324 / 5.269862 (-3.408538) | 1.156916 / 4.565676 (-3.408761) | 0.082385 / 0.424275 (-0.341890) | 0.012166 / 0.007607 (0.004559) | 0.528690 / 0.226044 (0.302646) | 5.286388 / 2.268929 (3.017459) | 2.319941 / 55.444624 (-53.124684) | 1.959462 / 6.876477 (-4.917014) | 1.995102 / 2.142072 (-0.146970) | 0.817158 / 4.805227 (-3.988069) | 0.149479 / 6.500664 (-6.351185) | 0.065668 / 0.075469 (-0.009801) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240228 / 1.841788 (-0.601560) | 13.770357 / 8.074308 (5.696048) | 13.940638 / 10.191392 (3.749246) | 0.152589 / 0.680424 (-0.527835) | 0.028498 / 0.534201 (-0.505703) | 0.392579 / 0.579283 (-0.186704) | 0.402843 / 0.434364 (-0.031521) | 0.455429 / 0.540337 (-0.084909) | 0.541090 / 1.386936 (-0.845846) |\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) | 0.006692 / 0.011353 (-0.004661) | 0.004514 / 0.011008 (-0.006495) | 0.097058 / 0.038508 (0.058550) | 0.027780 / 0.023109 (0.004671) | 0.415806 / 0.275898 (0.139908) | 0.443079 / 0.323480 (0.119599) | 0.005181 / 0.007986 (-0.002805) | 0.003408 / 0.004328 (-0.000921) | 0.075263 / 0.004250 (0.071013) | 0.038169 / 0.037052 (0.001116) | 0.417292 / 0.258489 (0.158803) | 0.461875 / 0.293841 (0.168034) | 0.032280 / 0.128546 (-0.096266) | 0.011571 / 0.075646 (-0.064075) | 0.319091 / 0.419271 (-0.100181) | 0.048295 / 0.043533 (0.004762) | 0.423619 / 0.255139 (0.168480) | 0.435064 / 0.283200 (0.151864) | 0.094869 / 0.141683 (-0.046814) | 1.523000 / 1.452155 (0.070846) | 1.583097 / 1.492716 (0.090381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214326 / 0.018006 (0.196320) | 0.391623 / 0.000490 (0.391134) | 0.004602 / 0.000200 (0.004403) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024306 / 0.037411 (-0.013106) | 0.101178 / 0.014526 (0.086652) | 0.108504 / 0.176557 (-0.068053) | 0.144114 / 0.737135 (-0.593022) | 0.111088 / 0.296338 (-0.185250) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472573 / 0.215209 (0.257364) | 4.748929 / 2.077655 (2.671274) | 2.441602 / 1.504120 (0.937482) | 2.238841 / 1.541195 (0.697647) | 2.303303 / 1.468490 (0.834813) | 0.696618 / 4.584777 (-3.888159) | 3.373867 / 3.745712 (-0.371845) | 2.809009 / 5.269862 (-2.460852) | 1.337240 / 4.565676 (-3.228437) | 0.082682 / 0.424275 (-0.341593) | 0.012834 / 0.007607 (0.005227) | 0.569686 / 0.226044 (0.343642) | 5.723407 / 2.268929 (3.454478) | 2.882944 / 55.444624 (-52.561680) | 2.543530 / 6.876477 (-4.332947) | 2.581856 / 2.142072 (0.439784) | 0.802353 / 4.805227 (-4.002874) | 0.149947 / 6.500664 (-6.350717) | 0.065865 / 0.075469 (-0.009604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282146 / 1.841788 (-0.559642) | 13.831344 / 8.074308 (5.757036) | 14.081550 / 10.191392 (3.890157) | 0.141735 / 0.680424 (-0.538689) | 0.016677 / 0.534201 (-0.517524) | 0.378967 / 0.579283 (-0.200316) | 0.383775 / 0.434364 (-0.050589) | 0.432892 / 0.540337 (-0.107446) | 0.518042 / 1.386936 (-0.868894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01b4a5a18b56fa7b648b0f131f6b5568b1fd436a \"CML watermark\")\n", "Omg I love this ! cc @TevenLeScao @thomasw21 this will save your terminals from infinite streams of progress bars", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008680 / 0.011353 (-0.002673) | 0.004597 / 0.011008 (-0.006411) | 0.101154 / 0.038508 (0.062646) | 0.029831 / 0.023109 (0.006722) | 0.300619 / 0.275898 (0.024721) | 0.358259 / 0.323480 (0.034779) | 0.007284 / 0.007986 (-0.000701) | 0.003511 / 0.004328 (-0.000817) | 0.078805 / 0.004250 (0.074555) | 0.037192 / 0.037052 (0.000140) | 0.307241 / 0.258489 (0.048752) | 0.354648 / 0.293841 (0.060807) | 0.033696 / 0.128546 (-0.094851) | 0.011660 / 0.075646 (-0.063986) | 0.324266 / 0.419271 (-0.095006) | 0.043393 / 0.043533 (-0.000140) | 0.297503 / 0.255139 (0.042364) | 0.326037 / 0.283200 (0.042838) | 0.091165 / 0.141683 (-0.050517) | 1.479970 / 1.452155 (0.027816) | 1.508507 / 1.492716 (0.015791) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179995 / 0.018006 (0.161989) | 0.464282 / 0.000490 (0.463793) | 0.003953 / 0.000200 (0.003753) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022696 / 0.037411 (-0.014715) | 0.099510 / 0.014526 (0.084984) | 0.103741 / 0.176557 (-0.072816) | 0.137837 / 0.737135 (-0.599299) | 0.108776 / 0.296338 (-0.187563) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417034 / 0.215209 (0.201825) | 4.183479 / 2.077655 (2.105824) | 1.855329 / 1.504120 (0.351209) | 1.660675 / 1.541195 (0.119481) | 1.723936 / 1.468490 (0.255446) | 0.687815 / 4.584777 (-3.896962) | 3.331280 / 3.745712 (-0.414432) | 2.821430 / 5.269862 (-2.448432) | 1.542394 / 4.565676 (-3.023283) | 0.081665 / 0.424275 (-0.342610) | 0.012483 / 0.007607 (0.004875) | 0.524758 / 0.226044 (0.298713) | 5.277285 / 2.268929 (3.008357) | 2.278067 / 55.444624 (-53.166557) | 1.923232 / 6.876477 (-4.953245) | 1.978645 / 2.142072 (-0.163428) | 0.806225 / 4.805227 (-3.999002) | 0.147568 / 6.500664 (-6.353096) | 0.064206 / 0.075469 (-0.011263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.175079 / 1.841788 (-0.666708) | 13.677443 / 8.074308 (5.603135) | 14.064103 / 10.191392 (3.872711) | 0.167462 / 0.680424 (-0.512962) | 0.028677 / 0.534201 (-0.505524) | 0.399090 / 0.579283 (-0.180193) | 0.398930 / 0.434364 (-0.035433) | 0.461604 / 0.540337 (-0.078733) | 0.540978 / 1.386936 (-0.845958) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006846 / 0.011353 (-0.004507) | 0.004452 / 0.011008 (-0.006556) | 0.076169 / 0.038508 (0.037661) | 0.028290 / 0.023109 (0.005181) | 0.341105 / 0.275898 (0.065207) | 0.381465 / 0.323480 (0.057986) | 0.005038 / 0.007986 (-0.002948) | 0.003298 / 0.004328 (-0.001031) | 0.075794 / 0.004250 (0.071544) | 0.039225 / 0.037052 (0.002173) | 0.342995 / 0.258489 (0.084506) | 0.384878 / 0.293841 (0.091037) | 0.031766 / 0.128546 (-0.096780) | 0.011597 / 0.075646 (-0.064049) | 0.084849 / 0.419271 (-0.334423) | 0.041795 / 0.043533 (-0.001737) | 0.341770 / 0.255139 (0.086631) | 0.383142 / 0.283200 (0.099942) | 0.088854 / 0.141683 (-0.052829) | 1.465116 / 1.452155 (0.012961) | 1.566888 / 1.492716 (0.074171) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225129 / 0.018006 (0.207123) | 0.394290 / 0.000490 (0.393801) | 0.000397 / 0.000200 (0.000197) | 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) | 0.025492 / 0.037411 (-0.011919) | 0.100494 / 0.014526 (0.085968) | 0.110587 / 0.176557 (-0.065969) | 0.142715 / 0.737135 (-0.594420) | 0.110962 / 0.296338 (-0.185376) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437240 / 0.215209 (0.222031) | 4.379191 / 2.077655 (2.301536) | 2.055059 / 1.504120 (0.550939) | 1.844643 / 1.541195 (0.303448) | 1.914678 / 1.468490 (0.446188) | 0.695607 / 4.584777 (-3.889170) | 3.353845 / 3.745712 (-0.391867) | 1.837403 / 5.269862 (-3.432459) | 1.155518 / 4.565676 (-3.410158) | 0.082753 / 0.424275 (-0.341523) | 0.012812 / 0.007607 (0.005205) | 0.537304 / 0.226044 (0.311260) | 5.387425 / 2.268929 (3.118497) | 2.506986 / 55.444624 (-52.937638) | 2.159031 / 6.876477 (-4.717445) | 2.187844 / 2.142072 (0.045772) | 0.796880 / 4.805227 (-4.008347) | 0.151850 / 6.500664 (-6.348815) | 0.067577 / 0.075469 (-0.007892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257779 / 1.841788 (-0.584009) | 13.968842 / 8.074308 (5.894534) | 13.544220 / 10.191392 (3.352828) | 0.149962 / 0.680424 (-0.530462) | 0.016875 / 0.534201 (-0.517326) | 0.394714 / 0.579283 (-0.184570) | 0.387845 / 0.434364 (-0.046519) | 0.481674 / 0.540337 (-0.058664) | 0.569820 / 1.386936 (-0.817116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71e50283422a93e805ea76722ce2520d1aae39c2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009745 / 0.011353 (-0.001607) | 0.005307 / 0.011008 (-0.005702) | 0.104230 / 0.038508 (0.065722) | 0.039745 / 0.023109 (0.016635) | 0.306102 / 0.275898 (0.030204) | 0.384390 / 0.323480 (0.060910) | 0.008265 / 0.007986 (0.000279) | 0.005516 / 0.004328 (0.001187) | 0.076023 / 0.004250 (0.071772) | 0.048266 / 0.037052 (0.011213) | 0.315380 / 0.258489 (0.056891) | 0.365735 / 0.293841 (0.071895) | 0.038222 / 0.128546 (-0.090324) | 0.012397 / 0.075646 (-0.063249) | 0.348964 / 0.419271 (-0.070307) | 0.047668 / 0.043533 (0.004135) | 0.301037 / 0.255139 (0.045898) | 0.322982 / 0.283200 (0.039783) | 0.109307 / 0.141683 (-0.032376) | 1.420777 / 1.452155 (-0.031378) | 1.468290 / 1.492716 (-0.024426) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262386 / 0.018006 (0.244380) | 0.557151 / 0.000490 (0.556661) | 0.000352 / 0.000200 (0.000152) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029508 / 0.037411 (-0.007903) | 0.113960 / 0.014526 (0.099434) | 0.123176 / 0.176557 (-0.053381) | 0.161928 / 0.737135 (-0.575207) | 0.129196 / 0.296338 (-0.167142) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407051 / 0.215209 (0.191842) | 4.072550 / 2.077655 (1.994895) | 1.899809 / 1.504120 (0.395689) | 1.751981 / 1.541195 (0.210786) | 1.841361 / 1.468490 (0.372871) | 0.713908 / 4.584777 (-3.870869) | 3.703339 / 3.745712 (-0.042373) | 2.091283 / 5.269862 (-3.178578) | 1.323810 / 4.565676 (-3.241866) | 0.084691 / 0.424275 (-0.339584) | 0.012685 / 0.007607 (0.005078) | 0.511301 / 0.226044 (0.285257) | 5.109741 / 2.268929 (2.840813) | 2.315073 / 55.444624 (-53.129551) | 2.012746 / 6.876477 (-4.863731) | 2.160074 / 2.142072 (0.018002) | 0.853025 / 4.805227 (-3.952202) | 0.165301 / 6.500664 (-6.335363) | 0.062244 / 0.075469 (-0.013225) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219727 / 1.841788 (-0.622061) | 15.319675 / 8.074308 (7.245367) | 13.100883 / 10.191392 (2.909491) | 0.173451 / 0.680424 (-0.506973) | 0.029173 / 0.534201 (-0.505028) | 0.440162 / 0.579283 (-0.139122) | 0.429771 / 0.434364 (-0.004593) | 0.518689 / 0.540337 (-0.021648) | 0.608590 / 1.386936 (-0.778346) |\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) | 0.007839 / 0.011353 (-0.003514) | 0.005409 / 0.011008 (-0.005599) | 0.076468 / 0.038508 (0.037960) | 0.036568 / 0.023109 (0.013459) | 0.337568 / 0.275898 (0.061670) | 0.379353 / 0.323480 (0.055873) | 0.006208 / 0.007986 (-0.001778) | 0.005971 / 0.004328 (0.001643) | 0.073765 / 0.004250 (0.069514) | 0.056609 / 0.037052 (0.019556) | 0.344578 / 0.258489 (0.086089) | 0.405249 / 0.293841 (0.111408) | 0.037652 / 0.128546 (-0.090894) | 0.012549 / 0.075646 (-0.063097) | 0.087086 / 0.419271 (-0.332186) | 0.056669 / 0.043533 (0.013136) | 0.334121 / 0.255139 (0.078983) | 0.354582 / 0.283200 (0.071383) | 0.113293 / 0.141683 (-0.028390) | 1.437327 / 1.452155 (-0.014828) | 1.574400 / 1.492716 (0.081684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325235 / 0.018006 (0.307229) | 0.535405 / 0.000490 (0.534915) | 0.014119 / 0.000200 (0.013919) | 0.000278 / 0.000054 (0.000224) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006585) | 0.114077 / 0.014526 (0.099552) | 0.128799 / 0.176557 (-0.047758) | 0.172164 / 0.737135 (-0.564971) | 0.133665 / 0.296338 (-0.162673) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430898 / 0.215209 (0.215689) | 4.285507 / 2.077655 (2.207853) | 2.089767 / 1.504120 (0.585647) | 1.899457 / 1.541195 (0.358262) | 2.042875 / 1.468490 (0.574385) | 0.690575 / 4.584777 (-3.894202) | 3.815905 / 3.745712 (0.070192) | 3.371085 / 5.269862 (-1.898776) | 1.865748 / 4.565676 (-2.699929) | 0.086678 / 0.424275 (-0.337597) | 0.013172 / 0.007607 (0.005565) | 0.552038 / 0.226044 (0.325994) | 5.275093 / 2.268929 (3.006165) | 2.561102 / 55.444624 (-52.883522) | 2.224235 / 6.876477 (-4.652242) | 2.330315 / 2.142072 (0.188243) | 0.845163 / 4.805227 (-3.960064) | 0.170675 / 6.500664 (-6.329989) | 0.068446 / 0.075469 (-0.007023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261213 / 1.841788 (-0.580575) | 15.354959 / 8.074308 (7.280651) | 15.034302 / 10.191392 (4.842910) | 0.146704 / 0.680424 (-0.533720) | 0.017986 / 0.534201 (-0.516215) | 0.425978 / 0.579283 (-0.153305) | 0.421806 / 0.434364 (-0.012558) | 0.494844 / 0.540337 (-0.045493) | 0.587870 / 1.386936 (-0.799066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0933901bb757e9a386095aef0fb11de9f9a04085 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012765 / 0.011353 (0.001412) | 0.006429 / 0.011008 (-0.004579) | 0.133669 / 0.038508 (0.095161) | 0.041420 / 0.023109 (0.018311) | 0.419990 / 0.275898 (0.144092) | 0.505218 / 0.323480 (0.181738) | 0.010189 / 0.007986 (0.002204) | 0.005134 / 0.004328 (0.000805) | 0.100890 / 0.004250 (0.096640) | 0.045639 / 0.037052 (0.008587) | 0.440593 / 0.258489 (0.182103) | 0.476966 / 0.293841 (0.183125) | 0.059270 / 0.128546 (-0.069276) | 0.018625 / 0.075646 (-0.057021) | 0.444957 / 0.419271 (0.025686) | 0.060669 / 0.043533 (0.017136) | 0.415373 / 0.255139 (0.160234) | 0.461810 / 0.283200 (0.178610) | 0.116119 / 0.141683 (-0.025564) | 1.873691 / 1.452155 (0.421536) | 1.939891 / 1.492716 (0.447175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259529 / 0.018006 (0.241523) | 0.587213 / 0.000490 (0.586723) | 0.003729 / 0.000200 (0.003529) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032064 / 0.037411 (-0.005347) | 0.140228 / 0.014526 (0.125702) | 0.147139 / 0.176557 (-0.029417) | 0.193731 / 0.737135 (-0.543405) | 0.162126 / 0.296338 (-0.134213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639262 / 0.215209 (0.424053) | 6.496491 / 2.077655 (4.418836) | 2.602044 / 1.504120 (1.097924) | 2.245891 / 1.541195 (0.704696) | 2.301321 / 1.468490 (0.832831) | 1.234088 / 4.584777 (-3.350689) | 5.883315 / 3.745712 (2.137603) | 3.166902 / 5.269862 (-2.102959) | 2.258279 / 4.565676 (-2.307398) | 0.146203 / 0.424275 (-0.278072) | 0.015490 / 0.007607 (0.007883) | 0.800188 / 0.226044 (0.574144) | 8.150866 / 2.268929 (5.881938) | 3.419508 / 55.444624 (-52.025117) | 2.712174 / 6.876477 (-4.164302) | 2.805059 / 2.142072 (0.662987) | 1.421047 / 4.805227 (-3.384180) | 0.254274 / 6.500664 (-6.246390) | 0.083886 / 0.075469 (0.008417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651962 / 1.841788 (-0.189826) | 19.453202 / 8.074308 (11.378894) | 24.643881 / 10.191392 (14.452489) | 0.263612 / 0.680424 (-0.416812) | 0.046913 / 0.534201 (-0.487288) | 0.579861 / 0.579283 (0.000578) | 0.695137 / 0.434364 (0.260773) | 0.705479 / 0.540337 (0.165142) | 0.806073 / 1.386936 (-0.580863) |\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) | 0.010384 / 0.011353 (-0.000969) | 0.007460 / 0.011008 (-0.003548) | 0.107830 / 0.038508 (0.069322) | 0.036792 / 0.023109 (0.013682) | 0.469585 / 0.275898 (0.193687) | 0.521278 / 0.323480 (0.197798) | 0.007472 / 0.007986 (-0.000513) | 0.007774 / 0.004328 (0.003446) | 0.105405 / 0.004250 (0.101154) | 0.053732 / 0.037052 (0.016680) | 0.486299 / 0.258489 (0.227810) | 0.537067 / 0.293841 (0.243226) | 0.053378 / 0.128546 (-0.075168) | 0.022018 / 0.075646 (-0.053628) | 0.127765 / 0.419271 (-0.291507) | 0.063844 / 0.043533 (0.020311) | 0.479724 / 0.255139 (0.224585) | 0.511243 / 0.283200 (0.228043) | 0.123223 / 0.141683 (-0.018460) | 1.934167 / 1.452155 (0.482013) | 2.003168 / 1.492716 (0.510451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227670 / 0.018006 (0.209664) | 0.609125 / 0.000490 (0.608635) | 0.004408 / 0.000200 (0.004208) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035905 / 0.037411 (-0.001506) | 0.142207 / 0.014526 (0.127681) | 0.154749 / 0.176557 (-0.021808) | 0.216191 / 0.737135 (-0.520944) | 0.156577 / 0.296338 (-0.139761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665085 / 0.215209 (0.449876) | 6.510923 / 2.077655 (4.433269) | 2.902438 / 1.504120 (1.398318) | 2.561427 / 1.541195 (1.020232) | 2.669556 / 1.468490 (1.201066) | 1.190340 / 4.584777 (-3.394437) | 5.933066 / 3.745712 (2.187354) | 5.627784 / 5.269862 (0.357922) | 2.971922 / 4.565676 (-1.593755) | 0.140884 / 0.424275 (-0.283391) | 0.015382 / 0.007607 (0.007775) | 0.810441 / 0.226044 (0.584396) | 8.255538 / 2.268929 (5.986609) | 3.819014 / 55.444624 (-51.625611) | 3.222479 / 6.876477 (-3.653998) | 3.181700 / 2.142072 (1.039627) | 1.483403 / 4.805227 (-3.321824) | 0.262726 / 6.500664 (-6.237939) | 0.090252 / 0.075469 (0.014783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748566 / 1.841788 (-0.093222) | 19.566894 / 8.074308 (11.492586) | 24.382155 / 10.191392 (14.190763) | 0.260118 / 0.680424 (-0.420305) | 0.028725 / 0.534201 (-0.505476) | 0.564875 / 0.579283 (-0.014408) | 0.666708 / 0.434364 (0.232344) | 0.691165 / 0.540337 (0.150827) | 0.837061 / 1.386936 (-0.549875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fe6bf908e9f12e0b69b4059c392da8264881525d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010098 / 0.011353 (-0.001255) | 0.005797 / 0.011008 (-0.005211) | 0.111262 / 0.038508 (0.072754) | 0.039687 / 0.023109 (0.016578) | 0.331081 / 0.275898 (0.055183) | 0.395878 / 0.323480 (0.072398) | 0.009244 / 0.007986 (0.001259) | 0.004498 / 0.004328 (0.000170) | 0.086129 / 0.004250 (0.081879) | 0.046662 / 0.037052 (0.009610) | 0.361926 / 0.258489 (0.103437) | 0.386155 / 0.293841 (0.092314) | 0.043657 / 0.128546 (-0.084889) | 0.013545 / 0.075646 (-0.062101) | 0.383735 / 0.419271 (-0.035537) | 0.055727 / 0.043533 (0.012194) | 0.355356 / 0.255139 (0.100217) | 0.358749 / 0.283200 (0.075550) | 0.123219 / 0.141683 (-0.018463) | 1.707982 / 1.452155 (0.255828) | 1.773342 / 1.492716 (0.280626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238902 / 0.018006 (0.220896) | 0.495525 / 0.000490 (0.495036) | 0.001742 / 0.000200 (0.001542) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031276 / 0.037411 (-0.006135) | 0.124286 / 0.014526 (0.109760) | 0.136236 / 0.176557 (-0.040321) | 0.180257 / 0.737135 (-0.556879) | 0.141047 / 0.296338 (-0.155292) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465075 / 0.215209 (0.249865) | 4.543997 / 2.077655 (2.466342) | 2.036632 / 1.504120 (0.532512) | 1.820356 / 1.541195 (0.279161) | 1.860692 / 1.468490 (0.392202) | 0.807549 / 4.584777 (-3.777227) | 4.400369 / 3.745712 (0.654657) | 2.423372 / 5.269862 (-2.846490) | 1.741338 / 4.565676 (-2.824339) | 0.099457 / 0.424275 (-0.324818) | 0.014464 / 0.007607 (0.006857) | 0.599442 / 0.226044 (0.373398) | 5.867798 / 2.268929 (3.598870) | 2.641859 / 55.444624 (-52.802766) | 2.294246 / 6.876477 (-4.582231) | 2.329639 / 2.142072 (0.187567) | 0.981897 / 4.805227 (-3.823331) | 0.189278 / 6.500664 (-6.311386) | 0.071868 / 0.075469 (-0.003601) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471800 / 1.841788 (-0.369988) | 17.149150 / 8.074308 (9.074841) | 15.818942 / 10.191392 (5.627550) | 0.174760 / 0.680424 (-0.505664) | 0.033507 / 0.534201 (-0.500694) | 0.511055 / 0.579283 (-0.068228) | 0.517107 / 0.434364 (0.082743) | 0.650813 / 0.540337 (0.110476) | 0.752515 / 1.386936 (-0.634421) |\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) | 0.008651 / 0.011353 (-0.002702) | 0.005935 / 0.011008 (-0.005073) | 0.088589 / 0.038508 (0.050081) | 0.038796 / 0.023109 (0.015687) | 0.415430 / 0.275898 (0.139532) | 0.443693 / 0.323480 (0.120213) | 0.006631 / 0.007986 (-0.001354) | 0.004638 / 0.004328 (0.000309) | 0.085779 / 0.004250 (0.081529) | 0.053994 / 0.037052 (0.016942) | 0.408349 / 0.258489 (0.149860) | 0.475441 / 0.293841 (0.181600) | 0.042792 / 0.128546 (-0.085754) | 0.013938 / 0.075646 (-0.061709) | 0.102173 / 0.419271 (-0.317098) | 0.057940 / 0.043533 (0.014407) | 0.408967 / 0.255139 (0.153828) | 0.422741 / 0.283200 (0.139541) | 0.121844 / 0.141683 (-0.019839) | 1.772779 / 1.452155 (0.320625) | 1.837706 / 1.492716 (0.344989) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228896 / 0.018006 (0.210890) | 0.497964 / 0.000490 (0.497475) | 0.004402 / 0.000200 (0.004202) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035626 / 0.037411 (-0.001786) | 0.132021 / 0.014526 (0.117495) | 0.145599 / 0.176557 (-0.030957) | 0.192317 / 0.737135 (-0.544818) | 0.150165 / 0.296338 (-0.146174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.500216 / 0.215209 (0.285007) | 5.002916 / 2.077655 (2.925262) | 2.502439 / 1.504120 (0.998319) | 2.353019 / 1.541195 (0.811825) | 2.485082 / 1.468490 (1.016592) | 0.827694 / 4.584777 (-3.757083) | 4.569319 / 3.745712 (0.823607) | 3.739820 / 5.269862 (-1.530042) | 2.097857 / 4.565676 (-2.467819) | 0.098636 / 0.424275 (-0.325639) | 0.014608 / 0.007607 (0.007001) | 0.604411 / 0.226044 (0.378366) | 6.131702 / 2.268929 (3.862774) | 3.043988 / 55.444624 (-52.400637) | 2.642427 / 6.876477 (-4.234050) | 2.687223 / 2.142072 (0.545151) | 0.968808 / 4.805227 (-3.836419) | 0.193876 / 6.500664 (-6.306788) | 0.076931 / 0.075469 (0.001462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.511820 / 1.841788 (-0.329968) | 17.971574 / 8.074308 (9.897265) | 16.512738 / 10.191392 (6.321346) | 0.223702 / 0.680424 (-0.456722) | 0.020191 / 0.534201 (-0.514010) | 0.511045 / 0.579283 (-0.068238) | 0.499813 / 0.434364 (0.065449) | 0.642147 / 0.540337 (0.101810) | 0.756029 / 1.386936 (-0.630907) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f6c7b9eb4bca89ec90c465623f7a2e6f5251062 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008909 / 0.011353 (-0.002444) | 0.005096 / 0.011008 (-0.005912) | 0.098568 / 0.038508 (0.060060) | 0.034548 / 0.023109 (0.011438) | 0.294762 / 0.275898 (0.018864) | 0.366093 / 0.323480 (0.042613) | 0.007476 / 0.007986 (-0.000510) | 0.003982 / 0.004328 (-0.000347) | 0.075975 / 0.004250 (0.071725) | 0.040499 / 0.037052 (0.003446) | 0.315050 / 0.258489 (0.056561) | 0.351273 / 0.293841 (0.057433) | 0.038327 / 0.128546 (-0.090219) | 0.011943 / 0.075646 (-0.063703) | 0.332148 / 0.419271 (-0.087124) | 0.047648 / 0.043533 (0.004115) | 0.295817 / 0.255139 (0.040678) | 0.322704 / 0.283200 (0.039504) | 0.100830 / 0.141683 (-0.040853) | 1.422162 / 1.452155 (-0.029993) | 1.468972 / 1.492716 (-0.023744) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201164 / 0.018006 (0.183158) | 0.435425 / 0.000490 (0.434935) | 0.001576 / 0.000200 (0.001376) | 0.000218 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026667 / 0.037411 (-0.010744) | 0.106161 / 0.014526 (0.091636) | 0.115836 / 0.176557 (-0.060720) | 0.151511 / 0.737135 (-0.585624) | 0.122248 / 0.296338 (-0.174091) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395974 / 0.215209 (0.180765) | 3.952958 / 2.077655 (1.875303) | 1.772111 / 1.504120 (0.267991) | 1.581370 / 1.541195 (0.040175) | 1.602811 / 1.468490 (0.134321) | 0.694072 / 4.584777 (-3.890705) | 3.640238 / 3.745712 (-0.105474) | 2.028865 / 5.269862 (-3.240997) | 1.419182 / 4.565676 (-3.146495) | 0.084078 / 0.424275 (-0.340197) | 0.012248 / 0.007607 (0.004641) | 0.499768 / 0.226044 (0.273723) | 4.997449 / 2.268929 (2.728521) | 2.280711 / 55.444624 (-53.163913) | 1.971701 / 6.876477 (-4.904776) | 1.983248 / 2.142072 (-0.158824) | 0.831030 / 4.805227 (-3.974198) | 0.163008 / 6.500664 (-6.337656) | 0.061887 / 0.075469 (-0.013582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.191744 / 1.841788 (-0.650043) | 14.424546 / 8.074308 (6.350238) | 14.530127 / 10.191392 (4.338735) | 0.165793 / 0.680424 (-0.514631) | 0.029099 / 0.534201 (-0.505102) | 0.447830 / 0.579283 (-0.131453) | 0.441036 / 0.434364 (0.006672) | 0.554697 / 0.540337 (0.014360) | 0.668854 / 1.386936 (-0.718082) |\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) | 0.006825 / 0.011353 (-0.004528) | 0.004998 / 0.011008 (-0.006010) | 0.074197 / 0.038508 (0.035689) | 0.032381 / 0.023109 (0.009272) | 0.335745 / 0.275898 (0.059847) | 0.360474 / 0.323480 (0.036994) | 0.005420 / 0.007986 (-0.002566) | 0.005121 / 0.004328 (0.000792) | 0.074980 / 0.004250 (0.070730) | 0.046392 / 0.037052 (0.009340) | 0.338693 / 0.258489 (0.080204) | 0.383679 / 0.293841 (0.089838) | 0.035380 / 0.128546 (-0.093166) | 0.012197 / 0.075646 (-0.063449) | 0.085738 / 0.419271 (-0.333533) | 0.049990 / 0.043533 (0.006458) | 0.342640 / 0.255139 (0.087501) | 0.355139 / 0.283200 (0.071939) | 0.102992 / 0.141683 (-0.038690) | 1.451900 / 1.452155 (-0.000254) | 1.550919 / 1.492716 (0.058202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223241 / 0.018006 (0.205235) | 0.436954 / 0.000490 (0.436464) | 0.003319 / 0.000200 (0.003120) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028042 / 0.037411 (-0.009370) | 0.106079 / 0.014526 (0.091554) | 0.122713 / 0.176557 (-0.053843) | 0.156543 / 0.737135 (-0.580593) | 0.122424 / 0.296338 (-0.173914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439482 / 0.215209 (0.224273) | 4.283112 / 2.077655 (2.205457) | 2.139705 / 1.504120 (0.635585) | 1.940898 / 1.541195 (0.399703) | 2.003906 / 1.468490 (0.535416) | 0.703269 / 4.584777 (-3.881508) | 3.780391 / 3.745712 (0.034679) | 2.079963 / 5.269862 (-3.189898) | 1.330669 / 4.565676 (-3.235007) | 0.086582 / 0.424275 (-0.337693) | 0.012497 / 0.007607 (0.004890) | 0.519329 / 0.226044 (0.293284) | 5.218117 / 2.268929 (2.949189) | 2.635982 / 55.444624 (-52.808643) | 2.301111 / 6.876477 (-4.575366) | 2.341312 / 2.142072 (0.199239) | 0.840157 / 4.805227 (-3.965070) | 0.166174 / 6.500664 (-6.334490) | 0.062890 / 0.075469 (-0.012579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257672 / 1.841788 (-0.584116) | 14.983374 / 8.074308 (6.909066) | 14.284441 / 10.191392 (4.093049) | 0.176077 / 0.680424 (-0.504347) | 0.017544 / 0.534201 (-0.516657) | 0.429619 / 0.579283 (-0.149664) | 0.426371 / 0.434364 (-0.007993) | 0.534832 / 0.540337 (-0.005506) | 0.643322 / 1.386936 (-0.743614) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0409b1435876fa97b3674b0275285e84b49d83f8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010622 / 0.011353 (-0.000731) | 0.005856 / 0.011008 (-0.005152) | 0.108608 / 0.038508 (0.070100) | 0.039868 / 0.023109 (0.016759) | 0.327853 / 0.275898 (0.051955) | 0.396721 / 0.323480 (0.073241) | 0.008916 / 0.007986 (0.000930) | 0.004590 / 0.004328 (0.000261) | 0.085020 / 0.004250 (0.080770) | 0.046608 / 0.037052 (0.009555) | 0.356369 / 0.258489 (0.097880) | 0.391142 / 0.293841 (0.097301) | 0.040579 / 0.128546 (-0.087967) | 0.012249 / 0.075646 (-0.063397) | 0.387740 / 0.419271 (-0.031532) | 0.057794 / 0.043533 (0.014262) | 0.335763 / 0.255139 (0.080624) | 0.369847 / 0.283200 (0.086647) | 0.121276 / 0.141683 (-0.020407) | 1.605406 / 1.452155 (0.153251) | 1.709524 / 1.492716 (0.216808) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226688 / 0.018006 (0.208681) | 0.493320 / 0.000490 (0.492831) | 0.002825 / 0.000200 (0.002626) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031874 / 0.037411 (-0.005538) | 0.117365 / 0.014526 (0.102840) | 0.127697 / 0.176557 (-0.048859) | 0.175589 / 0.737135 (-0.561546) | 0.137731 / 0.296338 (-0.158608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472563 / 0.215209 (0.257354) | 4.744383 / 2.077655 (2.666728) | 2.152015 / 1.504120 (0.647895) | 1.925398 / 1.541195 (0.384203) | 2.054613 / 1.468490 (0.586123) | 0.821703 / 4.584777 (-3.763074) | 4.468177 / 3.745712 (0.722465) | 4.687682 / 5.269862 (-0.582179) | 2.379674 / 4.565676 (-2.186003) | 0.101325 / 0.424275 (-0.322950) | 0.014891 / 0.007607 (0.007284) | 0.593161 / 0.226044 (0.367117) | 5.641670 / 2.268929 (3.372741) | 2.460206 / 55.444624 (-52.984419) | 2.131148 / 6.876477 (-4.745329) | 2.351067 / 2.142072 (0.208994) | 0.997634 / 4.805227 (-3.807593) | 0.195338 / 6.500664 (-6.305326) | 0.075540 / 0.075469 (0.000071) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.411585 / 1.841788 (-0.430203) | 17.055689 / 8.074308 (8.981381) | 16.544028 / 10.191392 (6.352636) | 0.180840 / 0.680424 (-0.499584) | 0.034549 / 0.534201 (-0.499652) | 0.510256 / 0.579283 (-0.069027) | 0.525632 / 0.434364 (0.091268) | 0.601206 / 0.540337 (0.060868) | 0.668468 / 1.386936 (-0.718469) |\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) | 0.008989 / 0.011353 (-0.002364) | 0.006065 / 0.011008 (-0.004943) | 0.088294 / 0.038508 (0.049786) | 0.040404 / 0.023109 (0.017295) | 0.405622 / 0.275898 (0.129724) | 0.454519 / 0.323480 (0.131039) | 0.006919 / 0.007986 (-0.001067) | 0.004545 / 0.004328 (0.000217) | 0.087023 / 0.004250 (0.082772) | 0.055962 / 0.037052 (0.018910) | 0.400942 / 0.258489 (0.142453) | 0.490670 / 0.293841 (0.196829) | 0.044086 / 0.128546 (-0.084461) | 0.014485 / 0.075646 (-0.061162) | 0.103333 / 0.419271 (-0.315938) | 0.059663 / 0.043533 (0.016130) | 0.404944 / 0.255139 (0.149805) | 0.425763 / 0.283200 (0.142563) | 0.123989 / 0.141683 (-0.017694) | 1.777244 / 1.452155 (0.325089) | 1.879884 / 1.492716 (0.387167) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226440 / 0.018006 (0.208434) | 0.492688 / 0.000490 (0.492198) | 0.004691 / 0.000200 (0.004491) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035123 / 0.037411 (-0.002288) | 0.134288 / 0.014526 (0.119762) | 0.145542 / 0.176557 (-0.031015) | 0.195372 / 0.737135 (-0.541764) | 0.152551 / 0.296338 (-0.143787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468615 / 0.215209 (0.253406) | 4.813363 / 2.077655 (2.735708) | 2.333606 / 1.504120 (0.829486) | 2.107344 / 1.541195 (0.566149) | 2.109109 / 1.468490 (0.640619) | 0.783779 / 4.584777 (-3.800998) | 4.521448 / 3.745712 (0.775736) | 2.290532 / 5.269862 (-2.979329) | 1.553488 / 4.565676 (-3.012189) | 0.088786 / 0.424275 (-0.335489) | 0.013091 / 0.007607 (0.005484) | 0.567165 / 0.226044 (0.341120) | 5.974315 / 2.268929 (3.705386) | 2.815018 / 55.444624 (-52.629606) | 2.488954 / 6.876477 (-4.387522) | 2.461849 / 2.142072 (0.319776) | 0.934487 / 4.805227 (-3.870740) | 0.190209 / 6.500664 (-6.310455) | 0.074811 / 0.075469 (-0.000658) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.513476 / 1.841788 (-0.328311) | 17.902599 / 8.074308 (9.828291) | 14.308027 / 10.191392 (4.116635) | 0.201992 / 0.680424 (-0.478432) | 0.018678 / 0.534201 (-0.515523) | 0.454707 / 0.579283 (-0.124576) | 0.470643 / 0.434364 (0.036279) | 0.612534 / 0.540337 (0.072197) | 0.685773 / 1.386936 (-0.701163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a66da3633a811eb8ea01d23469c41dfec0ffb8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009385 / 0.011353 (-0.001968) | 0.005220 / 0.011008 (-0.005788) | 0.098722 / 0.038508 (0.060214) | 0.035382 / 0.023109 (0.012273) | 0.297114 / 0.275898 (0.021216) | 0.371443 / 0.323480 (0.047963) | 0.008070 / 0.007986 (0.000084) | 0.004204 / 0.004328 (-0.000125) | 0.075621 / 0.004250 (0.071370) | 0.046015 / 0.037052 (0.008963) | 0.304569 / 0.258489 (0.046080) | 0.345598 / 0.293841 (0.051757) | 0.037946 / 0.128546 (-0.090600) | 0.011972 / 0.075646 (-0.063674) | 0.331993 / 0.419271 (-0.087279) | 0.047250 / 0.043533 (0.003717) | 0.296588 / 0.255139 (0.041449) | 0.316070 / 0.283200 (0.032870) | 0.108211 / 0.141683 (-0.033472) | 1.447619 / 1.452155 (-0.004535) | 1.481243 / 1.492716 (-0.011473) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274860 / 0.018006 (0.256854) | 0.503139 / 0.000490 (0.502649) | 0.003598 / 0.000200 (0.003398) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026752 / 0.037411 (-0.010660) | 0.109008 / 0.014526 (0.094482) | 0.119109 / 0.176557 (-0.057448) | 0.158462 / 0.737135 (-0.578673) | 0.126171 / 0.296338 (-0.170168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396396 / 0.215209 (0.181187) | 3.963055 / 2.077655 (1.885400) | 1.796308 / 1.504120 (0.292188) | 1.600565 / 1.541195 (0.059370) | 1.742409 / 1.468490 (0.273919) | 0.690942 / 4.584777 (-3.893835) | 3.713343 / 3.745712 (-0.032369) | 2.066804 / 5.269862 (-3.203058) | 1.292946 / 4.565676 (-3.272730) | 0.084344 / 0.424275 (-0.339931) | 0.012473 / 0.007607 (0.004865) | 0.513109 / 0.226044 (0.287065) | 5.175141 / 2.268929 (2.906213) | 2.266559 / 55.444624 (-53.178066) | 1.935737 / 6.876477 (-4.940740) | 2.028911 / 2.142072 (-0.113161) | 0.831191 / 4.805227 (-3.974036) | 0.163155 / 6.500664 (-6.337509) | 0.063414 / 0.075469 (-0.012055) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195429 / 1.841788 (-0.646358) | 15.257933 / 8.074308 (7.183625) | 14.358815 / 10.191392 (4.167423) | 0.152677 / 0.680424 (-0.527747) | 0.028890 / 0.534201 (-0.505311) | 0.455342 / 0.579283 (-0.123941) | 0.442602 / 0.434364 (0.008238) | 0.526833 / 0.540337 (-0.013505) | 0.618296 / 1.386936 (-0.768640) |\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) | 0.007613 / 0.011353 (-0.003740) | 0.005515 / 0.011008 (-0.005493) | 0.073759 / 0.038508 (0.035251) | 0.033944 / 0.023109 (0.010835) | 0.347764 / 0.275898 (0.071866) | 0.371143 / 0.323480 (0.047664) | 0.005997 / 0.007986 (-0.001988) | 0.004322 / 0.004328 (-0.000006) | 0.073002 / 0.004250 (0.068751) | 0.053051 / 0.037052 (0.015999) | 0.340345 / 0.258489 (0.081856) | 0.383761 / 0.293841 (0.089920) | 0.037734 / 0.128546 (-0.090813) | 0.012815 / 0.075646 (-0.062831) | 0.086998 / 0.419271 (-0.332273) | 0.050165 / 0.043533 (0.006632) | 0.343864 / 0.255139 (0.088725) | 0.356734 / 0.283200 (0.073534) | 0.108955 / 0.141683 (-0.032728) | 1.464558 / 1.452155 (0.012403) | 1.560084 / 1.492716 (0.067368) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327885 / 0.018006 (0.309878) | 0.515515 / 0.000490 (0.515025) | 0.000439 / 0.000200 (0.000239) | 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) | 0.030741 / 0.037411 (-0.006670) | 0.107634 / 0.014526 (0.093108) | 0.127121 / 0.176557 (-0.049436) | 0.164044 / 0.737135 (-0.573092) | 0.129097 / 0.296338 (-0.167242) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435690 / 0.215209 (0.220481) | 4.350705 / 2.077655 (2.273050) | 2.199597 / 1.504120 (0.695477) | 2.022715 / 1.541195 (0.481521) | 2.265907 / 1.468490 (0.797417) | 0.695817 / 4.584777 (-3.888960) | 3.795207 / 3.745712 (0.049494) | 3.061587 / 5.269862 (-2.208274) | 1.872213 / 4.565676 (-2.693463) | 0.085265 / 0.424275 (-0.339010) | 0.012243 / 0.007607 (0.004636) | 0.547209 / 0.226044 (0.321164) | 5.383626 / 2.268929 (3.114698) | 2.707439 / 55.444624 (-52.737185) | 2.393773 / 6.876477 (-4.482703) | 2.481385 / 2.142072 (0.339312) | 0.826169 / 4.805227 (-3.979059) | 0.166643 / 6.500664 (-6.334021) | 0.065817 / 0.075469 (-0.009652) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274469 / 1.841788 (-0.567318) | 15.565025 / 8.074308 (7.490717) | 14.254192 / 10.191392 (4.062800) | 0.166785 / 0.680424 (-0.513639) | 0.017830 / 0.534201 (-0.516371) | 0.430406 / 0.579283 (-0.148877) | 0.435655 / 0.434364 (0.001292) | 0.530605 / 0.540337 (-0.009732) | 0.636355 / 1.386936 (-0.750581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#146983fdc70b9fe2cc38109368e185b6ffa7a05e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008466 / 0.011353 (-0.002887) | 0.004679 / 0.011008 (-0.006329) | 0.100534 / 0.038508 (0.062025) | 0.029513 / 0.023109 (0.006403) | 0.302866 / 0.275898 (0.026968) | 0.352816 / 0.323480 (0.029336) | 0.006912 / 0.007986 (-0.001074) | 0.003513 / 0.004328 (-0.000815) | 0.078625 / 0.004250 (0.074375) | 0.036725 / 0.037052 (-0.000327) | 0.312135 / 0.258489 (0.053646) | 0.344579 / 0.293841 (0.050738) | 0.033870 / 0.128546 (-0.094677) | 0.011563 / 0.075646 (-0.064083) | 0.318982 / 0.419271 (-0.100290) | 0.043002 / 0.043533 (-0.000531) | 0.301956 / 0.255139 (0.046817) | 0.330798 / 0.283200 (0.047599) | 0.091755 / 0.141683 (-0.049927) | 1.458577 / 1.452155 (0.006422) | 1.532642 / 1.492716 (0.039926) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194853 / 0.018006 (0.176847) | 0.396844 / 0.000490 (0.396354) | 0.004401 / 0.000200 (0.004201) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022971 / 0.037411 (-0.014441) | 0.096595 / 0.014526 (0.082069) | 0.106104 / 0.176557 (-0.070452) | 0.144815 / 0.737135 (-0.592320) | 0.110036 / 0.296338 (-0.186303) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415025 / 0.215209 (0.199816) | 4.138136 / 2.077655 (2.060481) | 1.861253 / 1.504120 (0.357133) | 1.653420 / 1.541195 (0.112226) | 1.703784 / 1.468490 (0.235294) | 0.698261 / 4.584777 (-3.886516) | 3.357240 / 3.745712 (-0.388472) | 3.025790 / 5.269862 (-2.244072) | 1.637191 / 4.565676 (-2.928485) | 0.085620 / 0.424275 (-0.338655) | 0.012454 / 0.007607 (0.004846) | 0.524708 / 0.226044 (0.298663) | 5.269234 / 2.268929 (3.000306) | 2.290612 / 55.444624 (-53.154012) | 1.936107 / 6.876477 (-4.940370) | 1.968216 / 2.142072 (-0.173856) | 0.810438 / 4.805227 (-3.994789) | 0.154133 / 6.500664 (-6.346531) | 0.064978 / 0.075469 (-0.010491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231782 / 1.841788 (-0.610006) | 13.545573 / 8.074308 (5.471264) | 14.558765 / 10.191392 (4.367373) | 0.140763 / 0.680424 (-0.539661) | 0.029259 / 0.534201 (-0.504942) | 0.407776 / 0.579283 (-0.171507) | 0.410244 / 0.434364 (-0.024120) | 0.477313 / 0.540337 (-0.063024) | 0.551465 / 1.386936 (-0.835471) |\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) | 0.006272 / 0.011353 (-0.005081) | 0.004397 / 0.011008 (-0.006611) | 0.077496 / 0.038508 (0.038988) | 0.026946 / 0.023109 (0.003837) | 0.342992 / 0.275898 (0.067094) | 0.374407 / 0.323480 (0.050927) | 0.004849 / 0.007986 (-0.003136) | 0.004549 / 0.004328 (0.000220) | 0.076439 / 0.004250 (0.072189) | 0.035829 / 0.037052 (-0.001224) | 0.343483 / 0.258489 (0.084994) | 0.385581 / 0.293841 (0.091740) | 0.031745 / 0.128546 (-0.096801) | 0.011617 / 0.075646 (-0.064030) | 0.087207 / 0.419271 (-0.332064) | 0.042252 / 0.043533 (-0.001281) | 0.343223 / 0.255139 (0.088084) | 0.368707 / 0.283200 (0.085508) | 0.093259 / 0.141683 (-0.048424) | 1.506904 / 1.452155 (0.054750) | 1.567583 / 1.492716 (0.074867) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.158962 / 0.018006 (0.140955) | 0.395982 / 0.000490 (0.395492) | 0.003604 / 0.000200 (0.003404) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025003 / 0.037411 (-0.012408) | 0.101176 / 0.014526 (0.086650) | 0.104494 / 0.176557 (-0.072062) | 0.140414 / 0.737135 (-0.596722) | 0.108398 / 0.296338 (-0.187941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436849 / 0.215209 (0.221640) | 4.369428 / 2.077655 (2.291774) | 2.070613 / 1.504120 (0.566493) | 1.867511 / 1.541195 (0.326317) | 1.866589 / 1.468490 (0.398099) | 0.700036 / 4.584777 (-3.884741) | 3.407513 / 3.745712 (-0.338199) | 3.022409 / 5.269862 (-2.247453) | 1.581423 / 4.565676 (-2.984253) | 0.083425 / 0.424275 (-0.340850) | 0.012380 / 0.007607 (0.004773) | 0.535087 / 0.226044 (0.309043) | 5.374814 / 2.268929 (3.105886) | 2.504841 / 55.444624 (-52.939784) | 2.166484 / 6.876477 (-4.709993) | 2.166363 / 2.142072 (0.024291) | 0.803692 / 4.805227 (-4.001535) | 0.150873 / 6.500664 (-6.349791) | 0.066253 / 0.075469 (-0.009216) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291256 / 1.841788 (-0.550532) | 13.827843 / 8.074308 (5.753535) | 13.839334 / 10.191392 (3.647942) | 0.153530 / 0.680424 (-0.526894) | 0.016896 / 0.534201 (-0.517305) | 0.379937 / 0.579283 (-0.199346) | 0.396241 / 0.434364 (-0.038123) | 0.461808 / 0.540337 (-0.078530) | 0.553023 / 1.386936 (-0.833913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#779ddc5c7ebbd406b2a6c9092c3f455a2cc7f5e7 \"CML watermark\")\n" ]

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[]

[ "Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.", "Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008215 / 0.011353 (-0.003138) | 0.004510 / 0.011008 (-0.006498) | 0.099270 / 0.038508 (0.060761) | 0.028682 / 0.023109 (0.005573) | 0.332726 / 0.275898 (0.056827) | 0.371025 / 0.323480 (0.047545) | 0.006665 / 0.007986 (-0.001320) | 0.003329 / 0.004328 (-0.001000) | 0.078509 / 0.004250 (0.074259) | 0.032388 / 0.037052 (-0.004664) | 0.348540 / 0.258489 (0.090051) | 0.382212 / 0.293841 (0.088371) | 0.033307 / 0.128546 (-0.095239) | 0.011642 / 0.075646 (-0.064004) | 0.322573 / 0.419271 (-0.096699) | 0.041297 / 0.043533 (-0.002236) | 0.322710 / 0.255139 (0.067571) | 0.361593 / 0.283200 (0.078394) | 0.082276 / 0.141683 (-0.059407) | 1.481932 / 1.452155 (0.029777) | 1.531677 / 1.492716 (0.038961) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194964 / 0.018006 (0.176958) | 0.406002 / 0.000490 (0.405512) | 0.001015 / 0.000200 (0.000815) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023317 / 0.037411 (-0.014095) | 0.097231 / 0.014526 (0.082705) | 0.103898 / 0.176557 (-0.072659) | 0.139864 / 0.737135 (-0.597271) | 0.106785 / 0.296338 (-0.189554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419036 / 0.215209 (0.203827) | 4.193985 / 2.077655 (2.116330) | 1.879069 / 1.504120 (0.374949) | 1.675384 / 1.541195 (0.134190) | 1.696225 / 1.468490 (0.227735) | 0.695257 / 4.584777 (-3.889520) | 3.437971 / 3.745712 (-0.307741) | 2.656037 / 5.269862 (-2.613824) | 1.463320 / 4.565676 (-3.102356) | 0.082575 / 0.424275 (-0.341700) | 0.012593 / 0.007607 (0.004986) | 0.526643 / 0.226044 (0.300599) | 5.278366 / 2.268929 (3.009437) | 2.288106 / 55.444624 (-53.156518) | 1.954875 / 6.876477 (-4.921602) | 1.950641 / 2.142072 (-0.191431) | 0.808289 / 4.805227 (-3.996938) | 0.148790 / 6.500664 (-6.351875) | 0.064775 / 0.075469 (-0.010694) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215219 / 1.841788 (-0.626569) | 13.551467 / 8.074308 (5.477159) | 13.841547 / 10.191392 (3.650155) | 0.153610 / 0.680424 (-0.526814) | 0.028308 / 0.534201 (-0.505893) | 0.397087 / 0.579283 (-0.182196) | 0.401724 / 0.434364 (-0.032640) | 0.458042 / 0.540337 (-0.082296) | 0.544955 / 1.386936 (-0.841981) |\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) | 0.006321 / 0.011353 (-0.005032) | 0.004336 / 0.011008 (-0.006673) | 0.097196 / 0.038508 (0.058688) | 0.026933 / 0.023109 (0.003824) | 0.416520 / 0.275898 (0.140622) | 0.450703 / 0.323480 (0.127223) | 0.004831 / 0.007986 (-0.003155) | 0.003252 / 0.004328 (-0.001076) | 0.074981 / 0.004250 (0.070730) | 0.036136 / 0.037052 (-0.000917) | 0.423166 / 0.258489 (0.164677) | 0.460936 / 0.293841 (0.167095) | 0.031859 / 0.128546 (-0.096687) | 0.011500 / 0.075646 (-0.064146) | 0.318197 / 0.419271 (-0.101074) | 0.041472 / 0.043533 (-0.002061) | 0.419227 / 0.255139 (0.164088) | 0.444712 / 0.283200 (0.161512) | 0.088841 / 0.141683 (-0.052841) | 1.497237 / 1.452155 (0.045083) | 1.572111 / 1.492716 (0.079395) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239261 / 0.018006 (0.221255) | 0.400358 / 0.000490 (0.399868) | 0.003460 / 0.000200 (0.003261) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024016 / 0.037411 (-0.013395) | 0.098414 / 0.014526 (0.083888) | 0.107220 / 0.176557 (-0.069337) | 0.143538 / 0.737135 (-0.593598) | 0.108607 / 0.296338 (-0.187731) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473896 / 0.215209 (0.258687) | 4.740386 / 2.077655 (2.662731) | 2.458046 / 1.504120 (0.953926) | 2.260895 / 1.541195 (0.719700) | 2.280218 / 1.468490 (0.811728) | 0.694843 / 4.584777 (-3.889934) | 3.349795 / 3.745712 (-0.395917) | 1.846970 / 5.269862 (-3.422892) | 1.151481 / 4.565676 (-3.414195) | 0.082054 / 0.424275 (-0.342221) | 0.012664 / 0.007607 (0.005057) | 0.573400 / 0.226044 (0.347355) | 5.750648 / 2.268929 (3.481720) | 2.904257 / 55.444624 (-52.540367) | 2.555181 / 6.876477 (-4.321295) | 2.595830 / 2.142072 (0.453758) | 0.799580 / 4.805227 (-4.005647) | 0.151088 / 6.500664 (-6.349576) | 0.066639 / 0.075469 (-0.008831) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251413 / 1.841788 (-0.590375) | 13.743368 / 8.074308 (5.669060) | 13.808729 / 10.191392 (3.617337) | 0.144765 / 0.680424 (-0.535659) | 0.016606 / 0.534201 (-0.517594) | 0.376503 / 0.579283 (-0.202780) | 0.381510 / 0.434364 (-0.052854) | 0.440295 / 0.540337 (-0.100043) | 0.524248 / 1.386936 (-0.862688) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eea1226779993687845da5ecd264cf047e46a128 \"CML watermark\")\n", "Thanks a lot, @albertvillanova - I validated that your fix solves the original problem!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011848 / 0.011353 (0.000495) | 0.006988 / 0.011008 (-0.004020) | 0.138078 / 0.038508 (0.099570) | 0.040310 / 0.023109 (0.017201) | 0.411857 / 0.275898 (0.135959) | 0.509496 / 0.323480 (0.186016) | 0.010695 / 0.007986 (0.002709) | 0.005275 / 0.004328 (0.000946) | 0.107157 / 0.004250 (0.102907) | 0.050987 / 0.037052 (0.013935) | 0.432387 / 0.258489 (0.173898) | 0.495136 / 0.293841 (0.201295) | 0.055273 / 0.128546 (-0.073273) | 0.019573 / 0.075646 (-0.056074) | 0.460356 / 0.419271 (0.041084) | 0.060916 / 0.043533 (0.017383) | 0.426140 / 0.255139 (0.171002) | 0.430461 / 0.283200 (0.147261) | 0.124569 / 0.141683 (-0.017114) | 1.989404 / 1.452155 (0.537250) | 1.942052 / 1.492716 (0.449335) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287233 / 0.018006 (0.269227) | 0.606056 / 0.000490 (0.605566) | 0.004435 / 0.000200 (0.004235) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032353 / 0.037411 (-0.005058) | 0.124237 / 0.014526 (0.109711) | 0.143280 / 0.176557 (-0.033276) | 0.182081 / 0.737135 (-0.555055) | 0.148085 / 0.296338 (-0.148253) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613550 / 0.215209 (0.398341) | 6.172421 / 2.077655 (4.094766) | 2.466018 / 1.504120 (0.961898) | 2.166433 / 1.541195 (0.625238) | 2.192511 / 1.468490 (0.724021) | 1.248777 / 4.584777 (-3.336000) | 5.746150 / 3.745712 (2.000438) | 3.097184 / 5.269862 (-2.172678) | 2.078176 / 4.565676 (-2.487501) | 0.144351 / 0.424275 (-0.279924) | 0.014830 / 0.007607 (0.007223) | 0.761699 / 0.226044 (0.535655) | 7.713201 / 2.268929 (5.444272) | 3.359647 / 55.444624 (-52.084977) | 2.652595 / 6.876477 (-4.223882) | 2.721952 / 2.142072 (0.579880) | 1.493036 / 4.805227 (-3.312192) | 0.252336 / 6.500664 (-6.248328) | 0.082906 / 0.075469 (0.007436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.643887 / 1.841788 (-0.197901) | 18.762775 / 8.074308 (10.688466) | 22.003583 / 10.191392 (11.812191) | 0.256361 / 0.680424 (-0.424062) | 0.048048 / 0.534201 (-0.486153) | 0.601971 / 0.579283 (0.022688) | 0.712801 / 0.434364 (0.278438) | 0.684473 / 0.540337 (0.144136) | 0.802566 / 1.386936 (-0.584370) |\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) | 0.010410 / 0.011353 (-0.000943) | 0.006719 / 0.011008 (-0.004289) | 0.132862 / 0.038508 (0.094354) | 0.036973 / 0.023109 (0.013863) | 0.470925 / 0.275898 (0.195027) | 0.502864 / 0.323480 (0.179384) | 0.007447 / 0.007986 (-0.000539) | 0.005629 / 0.004328 (0.001301) | 0.091985 / 0.004250 (0.087734) | 0.057537 / 0.037052 (0.020485) | 0.458362 / 0.258489 (0.199873) | 0.518324 / 0.293841 (0.224483) | 0.056540 / 0.128546 (-0.072007) | 0.021266 / 0.075646 (-0.054380) | 0.448289 / 0.419271 (0.029018) | 0.064211 / 0.043533 (0.020678) | 0.492596 / 0.255139 (0.237457) | 0.495030 / 0.283200 (0.211830) | 0.121858 / 0.141683 (-0.019825) | 1.823821 / 1.452155 (0.371667) | 2.012165 / 1.492716 (0.519449) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296252 / 0.018006 (0.278245) | 0.601688 / 0.000490 (0.601198) | 0.006369 / 0.000200 (0.006169) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035821 / 0.037411 (-0.001590) | 0.132722 / 0.014526 (0.118196) | 0.141819 / 0.176557 (-0.034738) | 0.205115 / 0.737135 (-0.532020) | 0.148917 / 0.296338 (-0.147422) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678207 / 0.215209 (0.462998) | 6.969918 / 2.077655 (4.892263) | 3.077831 / 1.504120 (1.573711) | 2.689296 / 1.541195 (1.148102) | 2.706462 / 1.468490 (1.237972) | 1.249125 / 4.584777 (-3.335652) | 5.793917 / 3.745712 (2.048205) | 3.137565 / 5.269862 (-2.132297) | 2.056880 / 4.565676 (-2.508796) | 0.151918 / 0.424275 (-0.272357) | 0.015029 / 0.007607 (0.007422) | 0.833975 / 0.226044 (0.607930) | 8.575649 / 2.268929 (6.306720) | 3.812115 / 55.444624 (-51.632509) | 3.124219 / 6.876477 (-3.752258) | 3.178645 / 2.142072 (1.036572) | 1.488260 / 4.805227 (-3.316967) | 0.268239 / 6.500664 (-6.232425) | 0.089463 / 0.075469 (0.013993) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.645461 / 1.841788 (-0.196327) | 19.074412 / 8.074308 (11.000104) | 21.626726 / 10.191392 (11.435334) | 0.210525 / 0.680424 (-0.469899) | 0.032166 / 0.534201 (-0.502035) | 0.555572 / 0.579283 (-0.023711) | 0.654667 / 0.434364 (0.220303) | 0.632471 / 0.540337 (0.092133) | 0.756510 / 1.386936 (-0.630426) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6681c36bbaae9b8b1daa3dbbd4a96b35aaae271b \"CML watermark\")\n" ]

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[ "Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640", "The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.", "For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. \r\n\r\nUpdating to `datasets==2.12.0` fixed the problem for me." ]

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[ "wtf", "Couldn't find what's causing this, this will need more investigation", "A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n\r\n", "If \"mp\" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR!", "Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent.", "After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.\r\n\r\nIn the meantime, I'm going to suggest switching from `return_tensors=\"tf\"` to `return_tensors=\"np\"` in most of our `transformers` code - this generally works better for pre-processing. Two relevant PRs are [here](https://github.com/huggingface/transformers/pull/21266) and [here](https://github.com/huggingface/notebooks/pull/308).", "Closing this issue as we've done what we can with this one! " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008227 / 0.011353 (-0.003126) | 0.004496 / 0.011008 (-0.006512) | 0.099319 / 0.038508 (0.060811) | 0.029929 / 0.023109 (0.006820) | 0.296686 / 0.275898 (0.020788) | 0.355372 / 0.323480 (0.031892) | 0.006864 / 0.007986 (-0.001122) | 0.003458 / 0.004328 (-0.000871) | 0.077234 / 0.004250 (0.072983) | 0.037072 / 0.037052 (0.000020) | 0.311675 / 0.258489 (0.053186) | 0.338965 / 0.293841 (0.045124) | 0.033562 / 0.128546 (-0.094985) | 0.011399 / 0.075646 (-0.064248) | 0.322406 / 0.419271 (-0.096865) | 0.043034 / 0.043533 (-0.000499) | 0.298083 / 0.255139 (0.042944) | 0.323661 / 0.283200 (0.040462) | 0.089380 / 0.141683 (-0.052303) | 1.479363 / 1.452155 (0.027208) | 1.518337 / 1.492716 (0.025620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.177822 / 0.018006 (0.159816) | 0.400806 / 0.000490 (0.400317) | 0.002121 / 0.000200 (0.001921) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021986 / 0.037411 (-0.015426) | 0.096749 / 0.014526 (0.082223) | 0.101443 / 0.176557 (-0.075113) | 0.137519 / 0.737135 (-0.599616) | 0.105558 / 0.296338 (-0.190780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418983 / 0.215209 (0.203774) | 4.189579 / 2.077655 (2.111924) | 1.877831 / 1.504120 (0.373711) | 1.666213 / 1.541195 (0.125019) | 1.680735 / 1.468490 (0.212245) | 0.693033 / 4.584777 (-3.891744) | 3.420553 / 3.745712 (-0.325160) | 1.819647 / 5.269862 (-3.450214) | 1.144934 / 4.565676 (-3.420743) | 0.082209 / 0.424275 (-0.342066) | 0.012433 / 0.007607 (0.004826) | 0.526781 / 0.226044 (0.300737) | 5.273689 / 2.268929 (3.004760) | 2.323468 / 55.444624 (-53.121156) | 1.960508 / 6.876477 (-4.915969) | 2.035338 / 2.142072 (-0.106735) | 0.812789 / 4.805227 (-3.992438) | 0.148429 / 6.500664 (-6.352235) | 0.064727 / 0.075469 (-0.010742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253218 / 1.841788 (-0.588569) | 13.303426 / 8.074308 (5.229118) | 13.651074 / 10.191392 (3.459682) | 0.135178 / 0.680424 (-0.545246) | 0.028483 / 0.534201 (-0.505717) | 0.393284 / 0.579283 (-0.185999) | 0.401957 / 0.434364 (-0.032407) | 0.457136 / 0.540337 (-0.083201) | 0.535835 / 1.386936 (-0.851101) |\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) | 0.006335 / 0.011353 (-0.005017) | 0.004454 / 0.011008 (-0.006554) | 0.097565 / 0.038508 (0.059057) | 0.026917 / 0.023109 (0.003808) | 0.350779 / 0.275898 (0.074881) | 0.391979 / 0.323480 (0.068499) | 0.004648 / 0.007986 (-0.003337) | 0.003204 / 0.004328 (-0.001124) | 0.076987 / 0.004250 (0.072737) | 0.035257 / 0.037052 (-0.001796) | 0.347193 / 0.258489 (0.088704) | 0.391462 / 0.293841 (0.097621) | 0.031244 / 0.128546 (-0.097302) | 0.011460 / 0.075646 (-0.064186) | 0.321606 / 0.419271 (-0.097665) | 0.041218 / 0.043533 (-0.002315) | 0.341884 / 0.255139 (0.086745) | 0.374920 / 0.283200 (0.091720) | 0.086383 / 0.141683 (-0.055300) | 1.501750 / 1.452155 (0.049595) | 1.565060 / 1.492716 (0.072344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.165447 / 0.018006 (0.147441) | 0.401885 / 0.000490 (0.401395) | 0.000975 / 0.000200 (0.000775) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024494 / 0.037411 (-0.012917) | 0.097334 / 0.014526 (0.082808) | 0.105324 / 0.176557 (-0.071232) | 0.142430 / 0.737135 (-0.594705) | 0.107249 / 0.296338 (-0.189089) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441632 / 0.215209 (0.226423) | 4.407729 / 2.077655 (2.330074) | 2.078167 / 1.504120 (0.574047) | 1.864210 / 1.541195 (0.323015) | 1.885948 / 1.468490 (0.417458) | 0.693974 / 4.584777 (-3.890803) | 3.386837 / 3.745712 (-0.358875) | 1.840291 / 5.269862 (-3.429571) | 1.150524 / 4.565676 (-3.415153) | 0.082240 / 0.424275 (-0.342035) | 0.012488 / 0.007607 (0.004881) | 0.537589 / 0.226044 (0.311545) | 5.404007 / 2.268929 (3.135078) | 2.537467 / 55.444624 (-52.907157) | 2.190775 / 6.876477 (-4.685702) | 2.224746 / 2.142072 (0.082674) | 0.799524 / 4.805227 (-4.005703) | 0.150639 / 6.500664 (-6.350025) | 0.066473 / 0.075469 (-0.008997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258559 / 1.841788 (-0.583228) | 13.773583 / 8.074308 (5.699275) | 13.964322 / 10.191392 (3.772930) | 0.156295 / 0.680424 (-0.524129) | 0.016824 / 0.534201 (-0.517377) | 0.377476 / 0.579283 (-0.201807) | 0.390163 / 0.434364 (-0.044201) | 0.442541 / 0.540337 (-0.097796) | 0.529404 / 1.386936 (-0.857532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011875 / 0.011353 (0.000522) | 0.008188 / 0.011008 (-0.002821) | 0.131137 / 0.038508 (0.092629) | 0.038127 / 0.023109 (0.015018) | 0.383864 / 0.275898 (0.107966) | 0.458617 / 0.323480 (0.135137) | 0.010989 / 0.007986 (0.003003) | 0.004892 / 0.004328 (0.000563) | 0.101955 / 0.004250 (0.097704) | 0.045081 / 0.037052 (0.008029) | 0.409768 / 0.258489 (0.151279) | 0.446597 / 0.293841 (0.152756) | 0.058588 / 0.128546 (-0.069958) | 0.020872 / 0.075646 (-0.054774) | 0.432982 / 0.419271 (0.013711) | 0.075875 / 0.043533 (0.032342) | 0.380923 / 0.255139 (0.125784) | 0.432994 / 0.283200 (0.149795) | 0.122678 / 0.141683 (-0.019005) | 1.857865 / 1.452155 (0.405710) | 1.927801 / 1.492716 (0.435085) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212941 / 0.018006 (0.194935) | 0.527977 / 0.000490 (0.527488) | 0.002996 / 0.000200 (0.002797) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030046 / 0.037411 (-0.007366) | 0.126384 / 0.014526 (0.111858) | 0.138307 / 0.176557 (-0.038250) | 0.185338 / 0.737135 (-0.551797) | 0.144733 / 0.296338 (-0.151606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627096 / 0.215209 (0.411887) | 6.418014 / 2.077655 (4.340360) | 2.547675 / 1.504120 (1.043555) | 2.195552 / 1.541195 (0.654357) | 2.200377 / 1.468490 (0.731887) | 1.289935 / 4.584777 (-3.294842) | 5.670839 / 3.745712 (1.925127) | 5.252597 / 5.269862 (-0.017265) | 2.878470 / 4.565676 (-1.687207) | 0.143754 / 0.424275 (-0.280521) | 0.014814 / 0.007607 (0.007207) | 0.810073 / 0.226044 (0.584028) | 8.183757 / 2.268929 (5.914829) | 3.375525 / 55.444624 (-52.069099) | 2.594048 / 6.876477 (-4.282428) | 2.598095 / 2.142072 (0.456023) | 1.554493 / 4.805227 (-3.250734) | 0.263159 / 6.500664 (-6.237505) | 0.089822 / 0.075469 (0.014353) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.660847 / 1.841788 (-0.180941) | 18.434283 / 8.074308 (10.359975) | 21.764887 / 10.191392 (11.573495) | 0.264524 / 0.680424 (-0.415900) | 0.048519 / 0.534201 (-0.485682) | 0.587468 / 0.579283 (0.008185) | 0.634142 / 0.434364 (0.199778) | 0.675374 / 0.540337 (0.135037) | 0.777510 / 1.386936 (-0.609426) |\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) | 0.010021 / 0.011353 (-0.001332) | 0.006207 / 0.011008 (-0.004801) | 0.130490 / 0.038508 (0.091982) | 0.037957 / 0.023109 (0.014848) | 0.489381 / 0.275898 (0.213483) | 0.536522 / 0.323480 (0.213042) | 0.008611 / 0.007986 (0.000626) | 0.004894 / 0.004328 (0.000565) | 0.101617 / 0.004250 (0.097367) | 0.052629 / 0.037052 (0.015577) | 0.509211 / 0.258489 (0.250721) | 0.545023 / 0.293841 (0.251182) | 0.057468 / 0.128546 (-0.071078) | 0.023393 / 0.075646 (-0.052253) | 0.431408 / 0.419271 (0.012137) | 0.064967 / 0.043533 (0.021434) | 0.495261 / 0.255139 (0.240122) | 0.527098 / 0.283200 (0.243898) | 0.113172 / 0.141683 (-0.028511) | 1.937072 / 1.452155 (0.484918) | 2.048413 / 1.492716 (0.555697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245406 / 0.018006 (0.227399) | 0.526772 / 0.000490 (0.526283) | 0.004379 / 0.000200 (0.004179) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031785 / 0.037411 (-0.005626) | 0.130949 / 0.014526 (0.116424) | 0.145660 / 0.176557 (-0.030896) | 0.186991 / 0.737135 (-0.550144) | 0.151000 / 0.296338 (-0.145338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.708643 / 0.215209 (0.493434) | 7.179252 / 2.077655 (5.101597) | 3.143375 / 1.504120 (1.639255) | 2.714298 / 1.541195 (1.173103) | 2.773441 / 1.468490 (1.304951) | 1.312821 / 4.584777 (-3.271956) | 5.798396 / 3.745712 (2.052684) | 3.253215 / 5.269862 (-2.016646) | 2.147260 / 4.565676 (-2.418416) | 0.154673 / 0.424275 (-0.269602) | 0.014918 / 0.007607 (0.007311) | 0.860618 / 0.226044 (0.634573) | 8.774455 / 2.268929 (6.505527) | 3.925020 / 55.444624 (-51.519604) | 3.139361 / 6.876477 (-3.737115) | 3.208883 / 2.142072 (1.066810) | 1.547305 / 4.805227 (-3.257922) | 0.268814 / 6.500664 (-6.231850) | 0.084578 / 0.075469 (0.009109) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.694990 / 1.841788 (-0.146798) | 18.619183 / 8.074308 (10.544875) | 21.929886 / 10.191392 (11.738494) | 0.265763 / 0.680424 (-0.414661) | 0.028325 / 0.534201 (-0.505876) | 0.552910 / 0.579283 (-0.026373) | 0.616864 / 0.434364 (0.182500) | 0.637858 / 0.540337 (0.097521) | 0.744508 / 1.386936 (-0.642428) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f819ba3d0306748aaf9fd8ea040b981dd08e5e5 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0", "Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging everyone.\r\nI'm closing this one.", "@Wauplin in your Slack message, you asked people from every major dependent library to check that our CI work. That is why I am checking it... :)\r\n\r\nAlso, I think for this purpose it is better to test it in a dedicated branch, rather than opening and closing a PR.", "Yes, yes I know. Completely my fault on this one" ]

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[ "Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948. \r\n\r\nI also think these should be logged as INFO messages, but let's see what @lhoestq thinks.", "It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentioning that the cache was used. I don't expect first time users (only seeing warnings) to guess that the cache works this way", "Oh, so it's intentional? Do all Hugging Face packages use `warning` when using cache?\r\nI guess feel free to close this issue then.", "Yes it's intentional for `map`. For `load_dataset` it's also intentional but for a different reason: it shows where in the cache the dataset is located, in case the user wants to clear the cache.", "OK I see. It's surprising to me that these are considered \"something unexpected happened\", the concept of cache is pretty common.\r\n\r\nHas a user every actually complained that they ran their code once, and it took a minute while the data downloaded, then ran their code again and it ran really fast (and completed successfully) but they were so baffled by the fact that it ran quickly, _and_ didn't set the log level to INFO, _and_ hadn't read the docs (or thought about it) to know that datasets are cached, that they logged an issue asking that this information be output as a warning every time they run their code?\r\n\r\nThat seems like a very niche scenario to cater for, given that the side effect is to flood the console with irrelevant warnings for every other user every other time they run a bit of `datasets` code. And the real world impact is that people TURN OFF warnings, which is a pretty bad habit to get into.\r\n\r\nAnyhoo, if there's no chance I'm going to change your mind, please close the issue :)", "I see your point and I'm not closed to switching to INFO, but I think those logs are important to make the library less opaque. I also just checked `transformers` scripts and they default to INFO which is nice. However for colab users the default is still WARNING iirc, and it counts as one of the main env where `datasets` is used.\r\n\r\nWe also use progress bars a lot in `datasets`, that are shown if the logger is at the WARNING level. But we offer a function to disable the progress bars if necessary.", "These kinds of messages are logged as INFO in Transformers, so we should probably be consistent with them" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009885 / 0.011353 (-0.001468) | 0.005338 / 0.011008 (-0.005670) | 0.099967 / 0.038508 (0.061459) | 0.036860 / 0.023109 (0.013751) | 0.295283 / 0.275898 (0.019385) | 0.369504 / 0.323480 (0.046024) | 0.008267 / 0.007986 (0.000281) | 0.004375 / 0.004328 (0.000046) | 0.076294 / 0.004250 (0.072043) | 0.047058 / 0.037052 (0.010006) | 0.314463 / 0.258489 (0.055974) | 0.348125 / 0.293841 (0.054284) | 0.038334 / 0.128546 (-0.090213) | 0.012102 / 0.075646 (-0.063544) | 0.333049 / 0.419271 (-0.086223) | 0.050727 / 0.043533 (0.007195) | 0.299244 / 0.255139 (0.044105) | 0.318210 / 0.283200 (0.035010) | 0.112609 / 0.141683 (-0.029074) | 1.450377 / 1.452155 (-0.001778) | 1.485177 / 1.492716 (-0.007539) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287083 / 0.018006 (0.269077) | 0.564268 / 0.000490 (0.563778) | 0.003578 / 0.000200 (0.003378) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026755 / 0.037411 (-0.010657) | 0.105857 / 0.014526 (0.091331) | 0.118291 / 0.176557 (-0.058266) | 0.155735 / 0.737135 (-0.581401) | 0.122527 / 0.296338 (-0.173812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396992 / 0.215209 (0.181783) | 3.958562 / 2.077655 (1.880908) | 1.781570 / 1.504120 (0.277451) | 1.617743 / 1.541195 (0.076549) | 1.753504 / 1.468490 (0.285013) | 0.681509 / 4.584777 (-3.903268) | 3.816910 / 3.745712 (0.071198) | 2.087359 / 5.269862 (-3.182503) | 1.328380 / 4.565676 (-3.237297) | 0.083542 / 0.424275 (-0.340733) | 0.012081 / 0.007607 (0.004473) | 0.505127 / 0.226044 (0.279082) | 5.075136 / 2.268929 (2.806208) | 2.259871 / 55.444624 (-53.184753) | 1.944302 / 6.876477 (-4.932175) | 2.102624 / 2.142072 (-0.039449) | 0.819779 / 4.805227 (-3.985448) | 0.165584 / 6.500664 (-6.335080) | 0.061774 / 0.075469 (-0.013695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208258 / 1.841788 (-0.633530) | 14.841635 / 8.074308 (6.767327) | 14.484515 / 10.191392 (4.293123) | 0.156464 / 0.680424 (-0.523959) | 0.028839 / 0.534201 (-0.505362) | 0.440860 / 0.579283 (-0.138423) | 0.433892 / 0.434364 (-0.000472) | 0.515339 / 0.540337 (-0.024998) | 0.608838 / 1.386936 (-0.778098) |\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) | 0.007548 / 0.011353 (-0.003804) | 0.005464 / 0.011008 (-0.005544) | 0.096987 / 0.038508 (0.058479) | 0.034472 / 0.023109 (0.011363) | 0.391249 / 0.275898 (0.115351) | 0.432779 / 0.323480 (0.109299) | 0.006170 / 0.007986 (-0.001816) | 0.004316 / 0.004328 (-0.000013) | 0.074184 / 0.004250 (0.069934) | 0.054254 / 0.037052 (0.017202) | 0.397947 / 0.258489 (0.139458) | 0.451253 / 0.293841 (0.157412) | 0.037098 / 0.128546 (-0.091449) | 0.012649 / 0.075646 (-0.062997) | 0.333533 / 0.419271 (-0.085739) | 0.050247 / 0.043533 (0.006714) | 0.390446 / 0.255139 (0.135307) | 0.410547 / 0.283200 (0.127347) | 0.110888 / 0.141683 (-0.030795) | 1.452160 / 1.452155 (0.000006) | 1.596331 / 1.492716 (0.103615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256061 / 0.018006 (0.238055) | 0.552674 / 0.000490 (0.552184) | 0.003362 / 0.000200 (0.003162) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030199 / 0.037411 (-0.007213) | 0.110288 / 0.014526 (0.095762) | 0.127412 / 0.176557 (-0.049145) | 0.165428 / 0.737135 (-0.571707) | 0.131658 / 0.296338 (-0.164680) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441946 / 0.215209 (0.226737) | 4.414209 / 2.077655 (2.336555) | 2.284530 / 1.504120 (0.780410) | 2.110752 / 1.541195 (0.569557) | 2.210751 / 1.468490 (0.742260) | 0.698829 / 4.584777 (-3.885948) | 3.819044 / 3.745712 (0.073332) | 3.274021 / 5.269862 (-1.995840) | 1.781284 / 4.565676 (-2.784393) | 0.085264 / 0.424275 (-0.339011) | 0.012360 / 0.007607 (0.004753) | 0.553519 / 0.226044 (0.327475) | 5.466395 / 2.268929 (3.197467) | 2.825839 / 55.444624 (-52.618786) | 2.439451 / 6.876477 (-4.437026) | 2.582534 / 2.142072 (0.440462) | 0.841644 / 4.805227 (-3.963583) | 0.172288 / 6.500664 (-6.328376) | 0.067215 / 0.075469 (-0.008254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283623 / 1.841788 (-0.558165) | 15.753163 / 8.074308 (7.678855) | 14.983263 / 10.191392 (4.791871) | 0.187584 / 0.680424 (-0.492840) | 0.017999 / 0.534201 (-0.516202) | 0.427157 / 0.579283 (-0.152126) | 0.435456 / 0.434364 (0.001092) | 0.496800 / 0.540337 (-0.043537) | 0.592557 / 1.386936 (-0.794379) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8a72676689a4a3fb466cc5077884446c7302e605 \"CML watermark\")\n" ]

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[ "Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?", "I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility." ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.011815 / 0.011353 (0.000463) | 0.006407 / 0.011008 (-0.004601) | 0.132937 / 0.038508 (0.094429) | 0.040634 / 0.023109 (0.017525) | 0.398049 / 0.275898 (0.122151) | 0.498207 / 0.323480 (0.174727) | 0.010111 / 0.007986 (0.002126) | 0.007282 / 0.004328 (0.002954) | 0.103661 / 0.004250 (0.099411) | 0.046223 / 0.037052 (0.009171) | 0.411490 / 0.258489 (0.153001) | 0.480973 / 0.293841 (0.187132) | 0.058397 / 0.128546 (-0.070149) | 0.019952 / 0.075646 (-0.055695) | 0.440734 / 0.419271 (0.021463) | 0.064585 / 0.043533 (0.021052) | 0.392556 / 0.255139 (0.137417) | 0.437842 / 0.283200 (0.154643) | 0.130684 / 0.141683 (-0.010999) | 1.910552 / 1.452155 (0.458397) | 1.984644 / 1.492716 (0.491927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264417 / 0.018006 (0.246411) | 0.676519 / 0.000490 (0.676030) | 0.003369 / 0.000200 (0.003169) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034558 / 0.037411 (-0.002854) | 0.126561 / 0.014526 (0.112035) | 0.134478 / 0.176557 (-0.042079) | 0.202125 / 0.737135 (-0.535010) | 0.143273 / 0.296338 (-0.153066) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618592 / 0.215209 (0.403383) | 6.224435 / 2.077655 (4.146780) | 2.636689 / 1.504120 (1.132569) | 2.243507 / 1.541195 (0.702313) | 2.312449 / 1.468490 (0.843959) | 1.188499 / 4.584777 (-3.396277) | 5.738347 / 3.745712 (1.992635) | 4.891933 / 5.269862 (-0.377929) | 2.697631 / 4.565676 (-1.868046) | 0.140200 / 0.424275 (-0.284076) | 0.015484 / 0.007607 (0.007877) | 0.781947 / 0.226044 (0.555903) | 7.946600 / 2.268929 (5.677671) | 3.365574 / 55.444624 (-52.079050) | 2.783443 / 6.876477 (-4.093034) | 2.738634 / 2.142072 (0.596561) | 1.487247 / 4.805227 (-3.317980) | 0.255681 / 6.500664 (-6.244983) | 0.084607 / 0.075469 (0.009138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.717846 / 1.841788 (-0.123941) | 18.405566 / 8.074308 (10.331258) | 20.508578 / 10.191392 (10.317186) | 0.262364 / 0.680424 (-0.418060) | 0.050881 / 0.534201 (-0.483319) | 0.587516 / 0.579283 (0.008232) | 0.650900 / 0.434364 (0.216536) | 0.656168 / 0.540337 (0.115830) | 0.778876 / 1.386936 (-0.608061) |\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) | 0.010817 / 0.011353 (-0.000536) | 0.007338 / 0.011008 (-0.003670) | 0.131949 / 0.038508 (0.093441) | 0.037244 / 0.023109 (0.014135) | 0.565994 / 0.275898 (0.290096) | 0.567434 / 0.323480 (0.243954) | 0.007733 / 0.007986 (-0.000252) | 0.005216 / 0.004328 (0.000887) | 0.096578 / 0.004250 (0.092328) | 0.056001 / 0.037052 (0.018949) | 0.538209 / 0.258489 (0.279720) | 0.580385 / 0.293841 (0.286544) | 0.053654 / 0.128546 (-0.074892) | 0.019471 / 0.075646 (-0.056176) | 0.448781 / 0.419271 (0.029509) | 0.064774 / 0.043533 (0.021241) | 0.540222 / 0.255139 (0.285083) | 0.563058 / 0.283200 (0.279858) | 0.122716 / 0.141683 (-0.018967) | 1.839402 / 1.452155 (0.387247) | 1.915523 / 1.492716 (0.422806) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310448 / 0.018006 (0.292442) | 0.603664 / 0.000490 (0.603175) | 0.004833 / 0.000200 (0.004633) | 0.000145 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032340 / 0.037411 (-0.005072) | 0.130115 / 0.014526 (0.115589) | 0.154192 / 0.176557 (-0.022364) | 0.200655 / 0.737135 (-0.536480) | 0.144961 / 0.296338 (-0.151377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671588 / 0.215209 (0.456379) | 6.691642 / 2.077655 (4.613988) | 2.915230 / 1.504120 (1.411110) | 2.573337 / 1.541195 (1.032143) | 2.578204 / 1.468490 (1.109714) | 1.249028 / 4.584777 (-3.335749) | 5.808539 / 3.745712 (2.062827) | 3.079317 / 5.269862 (-2.190545) | 2.033308 / 4.565676 (-2.532369) | 0.142411 / 0.424275 (-0.281864) | 0.015525 / 0.007607 (0.007918) | 0.800389 / 0.226044 (0.574345) | 8.228236 / 2.268929 (5.959308) | 3.660207 / 55.444624 (-51.784417) | 3.021033 / 6.876477 (-3.855444) | 3.088335 / 2.142072 (0.946263) | 1.380137 / 4.805227 (-3.425091) | 0.252065 / 6.500664 (-6.248599) | 0.084302 / 0.075469 (0.008833) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.709429 / 1.841788 (-0.132359) | 18.358770 / 8.074308 (10.284462) | 21.109844 / 10.191392 (10.918452) | 0.231549 / 0.680424 (-0.448875) | 0.029251 / 0.534201 (-0.504950) | 0.560719 / 0.579283 (-0.018564) | 0.610125 / 0.434364 (0.175761) | 0.630015 / 0.540337 (0.089678) | 0.751656 / 1.386936 (-0.635280) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18baf4eebf71c0db1d9980f7ee164f1272ff8f26 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5441). All of your documentation changes will be reflected on that endpoint.", "I think I managed to reproduce it:\r\n\r\n```\r\nrm -rf ~/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing\r\nmkdir -p /tmp/xxx/hf-data\r\nsudo ln -s /tmp/xxx /test\r\nmkdir -p /tmp/yyy\r\nln -sf /test/hf-data /tmp/yyy/data\r\ncd /tmp/yyy\r\npython -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/cm4-synthetic-testing\r\n```\r\n\r\nPlease note it includes a creation of a symlink from the `/` (so `sudo`) - may be there is a simpler way but I'm just trying to replicate the real setup. Of course please be careful - it's mostly under `/tmp` not to destroy anything if you try to run this.\r\n\r\nthis fails with:\r\n\r\n```\r\nNo config specified, defaulting to: cm4-synthetic-testing/100.unique\r\nDownloading and preparing dataset cm4-synthetic-testing/100.unique (download: 20.71 KiB, generated: 49.99 MiB, post-processed: Unknown size, total: 50.01 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing/100.unique/1.1.1/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b...\r\nExtraction of data is blocked (illegal path: /tmp/yyy)\r\n[...]\r\nExtraction of data/115/texts_03.txt is blocked (illegal path: /tmp/yyy)\r\nGenerating 100.unique split: 0%| | 0/100 [00:00<?, ? examples/s]Generating 100-long unique records split\r\n\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1571, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/stas/.cache/huggingface/modules/datasets_modules/datasets/HuggingFaceM4--cm4-synthetic-testing/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b/cm4-synthetic-testing.py\", line 190, in _generate_examples\r\n raise ValueError(f\"can't find any data - check {data_path}\")\r\nValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/load.py\", line 1757, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 860, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1612, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 953, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1450, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1607, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nnote that `illegal path: /tmp/yyy` is now with the mods of this PR.\r\n\r\n----------------------\r\n\r\nAlso I think the whole thing should have failed at the first `illegal path` and not continue running. But as it continued and gave:\r\n\r\n\r\n> ValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nwhat can a user do with that other than confirming that that dir is indeed empty, but no clue is given to why and it's far from obvious that one needs to scroll up and discover earlier issues. Most users won't do that.\r\n\r\n(my apologies for writing out so much - was trying to make the situation clear)", "Thank you, Albert, for the explanation.\r\n\r\nTo summarize I think what's needed is:\r\n\r\n1. add a comment in the code to why this is done for someone being puzzled over the odd code\r\n2. and to use an actionable by the user error message\r\n3. perform an untrapped assert on that tar extract error and not continue, so that the user will not get a later misleading error that the folder is empty and is completely not actionable and it's is far from obvious that one needs to scroll up to find earlier errors, which were trapped.\r\n\r\nAfter reading the advisory I'm still not sure why `cwd` is used and not a designated `~/.cache/huggingface/datasets/downloads/extracted`, I can't see what difference does it make since I could `chdir` to the designated directory and it would be `cwd`. The security solution is trying to ensure that `/etc/passwd` won't get overriden. So why is the check done in `.` and not the real target base directory, since the extraction isn't done in the current working dir. By not using `.` you lower the chances that the user will have all sorts of local symlinks that could trigger the issue since `datasets` typically is the only one managing it's `~/.cache/huggingface/datasets` domain and 99.9% of the time the user won't manually create files in it.\r\n\r\nthank you!\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008874 / 0.011353 (-0.002479) | 0.004685 / 0.011008 (-0.006323) | 0.101478 / 0.038508 (0.062970) | 0.031409 / 0.023109 (0.008300) | 0.305429 / 0.275898 (0.029531) | 0.371777 / 0.323480 (0.048297) | 0.007282 / 0.007986 (-0.000704) | 0.005545 / 0.004328 (0.001217) | 0.078583 / 0.004250 (0.074333) | 0.037171 / 0.037052 (0.000118) | 0.320186 / 0.258489 (0.061696) | 0.347881 / 0.293841 (0.054040) | 0.034005 / 0.128546 (-0.094541) | 0.011534 / 0.075646 (-0.064113) | 0.326079 / 0.419271 (-0.093193) | 0.040856 / 0.043533 (-0.002677) | 0.307327 / 0.255139 (0.052188) | 0.323521 / 0.283200 (0.040321) | 0.090407 / 0.141683 (-0.051276) | 1.481994 / 1.452155 (0.029840) | 1.490372 / 1.492716 (-0.002345) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.175161 / 0.018006 (0.157155) | 0.447009 / 0.000490 (0.446519) | 0.003570 / 0.000200 (0.003370) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023868 / 0.037411 (-0.013543) | 0.100791 / 0.014526 (0.086265) | 0.108131 / 0.176557 (-0.068425) | 0.147993 / 0.737135 (-0.589142) | 0.111205 / 0.296338 (-0.185133) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425369 / 0.215209 (0.210160) | 4.241694 / 2.077655 (2.164040) | 2.145403 / 1.504120 (0.641283) | 1.913517 / 1.541195 (0.372322) | 1.887307 / 1.468490 (0.418817) | 0.691615 / 4.584777 (-3.893162) | 3.402233 / 3.745712 (-0.343480) | 1.992532 / 5.269862 (-3.277330) | 1.322292 / 4.565676 (-3.243385) | 0.082862 / 0.424275 (-0.341413) | 0.012595 / 0.007607 (0.004988) | 0.528490 / 0.226044 (0.302445) | 5.313338 / 2.268929 (3.044409) | 2.645037 / 55.444624 (-52.799587) | 2.326279 / 6.876477 (-4.550198) | 2.396955 / 2.142072 (0.254883) | 0.819354 / 4.805227 (-3.985873) | 0.150889 / 6.500664 (-6.349775) | 0.066517 / 0.075469 (-0.008952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233673 / 1.841788 (-0.608114) | 14.563293 / 8.074308 (6.488985) | 14.317989 / 10.191392 (4.126597) | 0.150767 / 0.680424 (-0.529657) | 0.028972 / 0.534201 (-0.505229) | 0.400547 / 0.579283 (-0.178736) | 0.402267 / 0.434364 (-0.032097) | 0.459375 / 0.540337 (-0.080962) | 0.544419 / 1.386936 (-0.842517) |\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) | 0.006817 / 0.011353 (-0.004536) | 0.004588 / 0.011008 (-0.006421) | 0.099224 / 0.038508 (0.060716) | 0.027730 / 0.023109 (0.004621) | 0.412310 / 0.275898 (0.136412) | 0.445731 / 0.323480 (0.122252) | 0.005197 / 0.007986 (-0.002788) | 0.003601 / 0.004328 (-0.000728) | 0.076200 / 0.004250 (0.071950) | 0.041813 / 0.037052 (0.004761) | 0.415282 / 0.258489 (0.156793) | 0.457182 / 0.293841 (0.163341) | 0.031920 / 0.128546 (-0.096626) | 0.011712 / 0.075646 (-0.063934) | 0.320859 / 0.419271 (-0.098412) | 0.041466 / 0.043533 (-0.002067) | 0.418156 / 0.255139 (0.163017) | 0.435501 / 0.283200 (0.152302) | 0.090727 / 0.141683 (-0.050955) | 1.484014 / 1.452155 (0.031859) | 1.568072 / 1.492716 (0.075356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263356 / 0.018006 (0.245350) | 0.410768 / 0.000490 (0.410278) | 0.015983 / 0.000200 (0.015783) | 0.000301 / 0.000054 (0.000246) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024522 / 0.037411 (-0.012889) | 0.103986 / 0.014526 (0.089460) | 0.109253 / 0.176557 (-0.067303) | 0.142308 / 0.737135 (-0.594827) | 0.114037 / 0.296338 (-0.182302) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452617 / 0.215209 (0.237407) | 4.505215 / 2.077655 (2.427560) | 2.185546 / 1.504120 (0.681426) | 1.995540 / 1.541195 (0.454345) | 1.962875 / 1.468490 (0.494385) | 0.690237 / 4.584777 (-3.894540) | 3.448311 / 3.745712 (-0.297401) | 1.901572 / 5.269862 (-3.368289) | 1.170832 / 4.565676 (-3.394844) | 0.082333 / 0.424275 (-0.341942) | 0.012569 / 0.007607 (0.004962) | 0.547822 / 0.226044 (0.321778) | 5.504180 / 2.268929 (3.235251) | 2.693981 / 55.444624 (-52.750644) | 2.320710 / 6.876477 (-4.555767) | 2.270508 / 2.142072 (0.128435) | 0.803145 / 4.805227 (-4.002083) | 0.152168 / 6.500664 (-6.348496) | 0.067408 / 0.075469 (-0.008061) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260689 / 1.841788 (-0.581099) | 14.281112 / 8.074308 (6.206804) | 14.549742 / 10.191392 (4.358350) | 0.129337 / 0.680424 (-0.551087) | 0.017181 / 0.534201 (-0.517020) | 0.380473 / 0.579283 (-0.198810) | 0.387689 / 0.434364 (-0.046675) | 0.446734 / 0.540337 (-0.093603) | 0.532479 / 1.386936 (-0.854457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7972a0b5f1ad2c36023a79686f6ef026f4ffa64f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008953 / 0.011353 (-0.002400) | 0.004917 / 0.011008 (-0.006091) | 0.098699 / 0.038508 (0.060191) | 0.034460 / 0.023109 (0.011351) | 0.294604 / 0.275898 (0.018706) | 0.322709 / 0.323480 (-0.000770) | 0.007780 / 0.007986 (-0.000206) | 0.004061 / 0.004328 (-0.000267) | 0.076134 / 0.004250 (0.071883) | 0.043786 / 0.037052 (0.006734) | 0.302155 / 0.258489 (0.043666) | 0.339779 / 0.293841 (0.045938) | 0.038305 / 0.128546 (-0.090241) | 0.012131 / 0.075646 (-0.063515) | 0.332656 / 0.419271 (-0.086615) | 0.048029 / 0.043533 (0.004496) | 0.303859 / 0.255139 (0.048720) | 0.315861 / 0.283200 (0.032662) | 0.100758 / 0.141683 (-0.040925) | 1.468072 / 1.452155 (0.015918) | 1.521325 / 1.492716 (0.028609) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244975 / 0.018006 (0.226969) | 0.524392 / 0.000490 (0.523902) | 0.003720 / 0.000200 (0.003520) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027704 / 0.037411 (-0.009707) | 0.109048 / 0.014526 (0.094522) | 0.118298 / 0.176557 (-0.058259) | 0.158748 / 0.737135 (-0.578388) | 0.125654 / 0.296338 (-0.170684) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406973 / 0.215209 (0.191764) | 4.057502 / 2.077655 (1.979847) | 1.939847 / 1.504120 (0.435727) | 1.746457 / 1.541195 (0.205262) | 1.698866 / 1.468490 (0.230376) | 0.692884 / 4.584777 (-3.891893) | 3.736988 / 3.745712 (-0.008724) | 2.050122 / 5.269862 (-3.219740) | 1.299808 / 4.565676 (-3.265868) | 0.085285 / 0.424275 (-0.338990) | 0.012768 / 0.007607 (0.005161) | 0.510814 / 0.226044 (0.284770) | 5.105319 / 2.268929 (2.836391) | 2.304003 / 55.444624 (-53.140621) | 1.951123 / 6.876477 (-4.925354) | 1.998504 / 2.142072 (-0.143568) | 0.840235 / 4.805227 (-3.964993) | 0.164521 / 6.500664 (-6.336143) | 0.064215 / 0.075469 (-0.011254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272520 / 1.841788 (-0.569268) | 14.648110 / 8.074308 (6.573802) | 14.573754 / 10.191392 (4.382362) | 0.170053 / 0.680424 (-0.510371) | 0.029389 / 0.534201 (-0.504811) | 0.438924 / 0.579283 (-0.140359) | 0.433572 / 0.434364 (-0.000792) | 0.517702 / 0.540337 (-0.022635) | 0.600389 / 1.386936 (-0.786547) |\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) | 0.007362 / 0.011353 (-0.003991) | 0.005451 / 0.011008 (-0.005557) | 0.099336 / 0.038508 (0.060828) | 0.033284 / 0.023109 (0.010174) | 0.377143 / 0.275898 (0.101245) | 0.423724 / 0.323480 (0.100244) | 0.006194 / 0.007986 (-0.001792) | 0.004208 / 0.004328 (-0.000121) | 0.074473 / 0.004250 (0.070223) | 0.049874 / 0.037052 (0.012821) | 0.376012 / 0.258489 (0.117523) | 0.439942 / 0.293841 (0.146101) | 0.037860 / 0.128546 (-0.090686) | 0.012546 / 0.075646 (-0.063100) | 0.349123 / 0.419271 (-0.070148) | 0.048980 / 0.043533 (0.005447) | 0.391205 / 0.255139 (0.136066) | 0.396474 / 0.283200 (0.113274) | 0.105846 / 0.141683 (-0.035836) | 1.502475 / 1.452155 (0.050321) | 1.612303 / 1.492716 (0.119587) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300815 / 0.018006 (0.282809) | 0.542171 / 0.000490 (0.541681) | 0.005465 / 0.000200 (0.005265) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028904 / 0.037411 (-0.008508) | 0.110352 / 0.014526 (0.095827) | 0.123275 / 0.176557 (-0.053282) | 0.161958 / 0.737135 (-0.575178) | 0.133595 / 0.296338 (-0.162743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438724 / 0.215209 (0.223515) | 4.373633 / 2.077655 (2.295979) | 2.178981 / 1.504120 (0.674861) | 1.992442 / 1.541195 (0.451247) | 2.063149 / 1.468490 (0.594659) | 0.696688 / 4.584777 (-3.888089) | 3.849370 / 3.745712 (0.103658) | 3.509495 / 5.269862 (-1.760367) | 1.923320 / 4.565676 (-2.642356) | 0.085554 / 0.424275 (-0.338721) | 0.012510 / 0.007607 (0.004903) | 0.535953 / 0.226044 (0.309909) | 5.365684 / 2.268929 (3.096755) | 2.686902 / 55.444624 (-52.757723) | 2.330922 / 6.876477 (-4.545554) | 2.353445 / 2.142072 (0.211373) | 0.878336 / 4.805227 (-3.926891) | 0.167296 / 6.500664 (-6.333368) | 0.064564 / 0.075469 (-0.010905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244696 / 1.841788 (-0.597091) | 15.027981 / 8.074308 (6.953673) | 14.545797 / 10.191392 (4.354405) | 0.147229 / 0.680424 (-0.533194) | 0.018007 / 0.534201 (-0.516194) | 0.446196 / 0.579283 (-0.133087) | 0.437418 / 0.434364 (0.003054) | 0.510732 / 0.540337 (-0.029606) | 0.594814 / 1.386936 (-0.792122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80eb8db74f49b7ee9c0f73a819c22177fabd61db \"CML watermark\")\n" ]

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[ "@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?" ]

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[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008470 / 0.011353 (-0.002883) | 0.004721 / 0.011008 (-0.006287) | 0.099024 / 0.038508 (0.060516) | 0.029831 / 0.023109 (0.006722) | 0.325887 / 0.275898 (0.049989) | 0.380753 / 0.323480 (0.057273) | 0.007101 / 0.007986 (-0.000885) | 0.004734 / 0.004328 (0.000406) | 0.077576 / 0.004250 (0.073326) | 0.037207 / 0.037052 (0.000154) | 0.320463 / 0.258489 (0.061974) | 0.369284 / 0.293841 (0.075443) | 0.033411 / 0.128546 (-0.095135) | 0.011610 / 0.075646 (-0.064037) | 0.321460 / 0.419271 (-0.097811) | 0.041315 / 0.043533 (-0.002217) | 0.349186 / 0.255139 (0.094047) | 0.384546 / 0.283200 (0.101347) | 0.088045 / 0.141683 (-0.053637) | 1.536341 / 1.452155 (0.084186) | 1.527806 / 1.492716 (0.035089) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193435 / 0.018006 (0.175429) | 0.451732 / 0.000490 (0.451243) | 0.003165 / 0.000200 (0.002965) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023203 / 0.037411 (-0.014208) | 0.096211 / 0.014526 (0.081685) | 0.105665 / 0.176557 (-0.070891) | 0.141074 / 0.737135 (-0.596061) | 0.108584 / 0.296338 (-0.187755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419041 / 0.215209 (0.203832) | 4.187915 / 2.077655 (2.110261) | 1.855336 / 1.504120 (0.351216) | 1.660046 / 1.541195 (0.118851) | 1.674646 / 1.468490 (0.206156) | 0.692257 / 4.584777 (-3.892520) | 3.466853 / 3.745712 (-0.278860) | 1.900925 / 5.269862 (-3.368936) | 1.294696 / 4.565676 (-3.270980) | 0.082792 / 0.424275 (-0.341483) | 0.012808 / 0.007607 (0.005201) | 0.529622 / 0.226044 (0.303578) | 5.337025 / 2.268929 (3.068096) | 2.326558 / 55.444624 (-53.118066) | 1.956256 / 6.876477 (-4.920221) | 2.035911 / 2.142072 (-0.106161) | 0.815824 / 4.805227 (-3.989403) | 0.148720 / 6.500664 (-6.351944) | 0.064226 / 0.075469 (-0.011243) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231347 / 1.841788 (-0.610440) | 13.724596 / 8.074308 (5.650288) | 13.933878 / 10.191392 (3.742486) | 0.150913 / 0.680424 (-0.529511) | 0.028460 / 0.534201 (-0.505741) | 0.393564 / 0.579283 (-0.185719) | 0.407185 / 0.434364 (-0.027179) | 0.458250 / 0.540337 (-0.082087) | 0.547993 / 1.386936 (-0.838943) |\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) | 0.006653 / 0.011353 (-0.004699) | 0.004615 / 0.011008 (-0.006393) | 0.098062 / 0.038508 (0.059554) | 0.027849 / 0.023109 (0.004740) | 0.409116 / 0.275898 (0.133218) | 0.448770 / 0.323480 (0.125290) | 0.004856 / 0.007986 (-0.003130) | 0.003427 / 0.004328 (-0.000901) | 0.075748 / 0.004250 (0.071498) | 0.037942 / 0.037052 (0.000889) | 0.410232 / 0.258489 (0.151743) | 0.457394 / 0.293841 (0.163553) | 0.031927 / 0.128546 (-0.096620) | 0.011618 / 0.075646 (-0.064028) | 0.321231 / 0.419271 (-0.098040) | 0.041416 / 0.043533 (-0.002117) | 0.413535 / 0.255139 (0.158396) | 0.438196 / 0.283200 (0.154997) | 0.089551 / 0.141683 (-0.052132) | 1.459298 / 1.452155 (0.007143) | 1.552594 / 1.492716 (0.059878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228186 / 0.018006 (0.210180) | 0.404393 / 0.000490 (0.403904) | 0.006944 / 0.000200 (0.006744) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025167 / 0.037411 (-0.012244) | 0.101282 / 0.014526 (0.086756) | 0.107282 / 0.176557 (-0.069275) | 0.139797 / 0.737135 (-0.597339) | 0.110477 / 0.296338 (-0.185861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479121 / 0.215209 (0.263912) | 4.778210 / 2.077655 (2.700555) | 2.464687 / 1.504120 (0.960567) | 2.255312 / 1.541195 (0.714118) | 2.287348 / 1.468490 (0.818858) | 0.694769 / 4.584777 (-3.890008) | 3.460860 / 3.745712 (-0.284852) | 3.078881 / 5.269862 (-2.190980) | 1.297726 / 4.565676 (-3.267950) | 0.082699 / 0.424275 (-0.341576) | 0.012652 / 0.007607 (0.005045) | 0.583308 / 0.226044 (0.357263) | 5.839199 / 2.268929 (3.570271) | 2.893724 / 55.444624 (-52.550900) | 2.546503 / 6.876477 (-4.329974) | 2.559570 / 2.142072 (0.417498) | 0.802357 / 4.805227 (-4.002870) | 0.151890 / 6.500664 (-6.348774) | 0.068593 / 0.075469 (-0.006876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262421 / 1.841788 (-0.579367) | 13.771848 / 8.074308 (5.697540) | 14.046017 / 10.191392 (3.854625) | 0.140950 / 0.680424 (-0.539474) | 0.016839 / 0.534201 (-0.517362) | 0.378870 / 0.579283 (-0.200413) | 0.385908 / 0.434364 (-0.048456) | 0.438539 / 0.540337 (-0.101799) | 0.522761 / 1.386936 (-0.864175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8145ebfd4fc3508d0be0de9a0f9c58877f2b32f8 \"CML watermark\")\n" ]

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[ "Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n", "> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\n> \n> \n\nI have only 1 folder that I use in the load_dataset function with the name \"IMGDATA\" and all my 9000 images are located in this folder.\n`\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"IMGDATA\")\n`\nAt the same time, using another data set with images consisting of 3 RGB channels, everything works", "Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.013736 / 0.011353 (0.002383) | 0.006253 / 0.011008 (-0.004755) | 0.127076 / 0.038508 (0.088568) | 0.040997 / 0.023109 (0.017888) | 0.394744 / 0.275898 (0.118846) | 0.454285 / 0.323480 (0.130805) | 0.009864 / 0.007986 (0.001878) | 0.005093 / 0.004328 (0.000765) | 0.098714 / 0.004250 (0.094464) | 0.044308 / 0.037052 (0.007255) | 0.421951 / 0.258489 (0.163462) | 0.462280 / 0.293841 (0.168439) | 0.059979 / 0.128546 (-0.068567) | 0.020607 / 0.075646 (-0.055039) | 0.443593 / 0.419271 (0.024321) | 0.062332 / 0.043533 (0.018799) | 0.411335 / 0.255139 (0.156196) | 0.426524 / 0.283200 (0.143324) | 0.118233 / 0.141683 (-0.023450) | 1.877681 / 1.452155 (0.425527) | 1.865271 / 1.492716 (0.372555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234791 / 0.018006 (0.216784) | 0.557322 / 0.000490 (0.556833) | 0.000528 / 0.000200 (0.000328) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030260 / 0.037411 (-0.007151) | 0.122594 / 0.014526 (0.108068) | 0.142142 / 0.176557 (-0.034414) | 0.197098 / 0.737135 (-0.540037) | 0.150978 / 0.296338 (-0.145360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.622644 / 0.215209 (0.407435) | 6.320078 / 2.077655 (4.242423) | 2.552755 / 1.504120 (1.048635) | 2.188647 / 1.541195 (0.647453) | 2.226602 / 1.468490 (0.758112) | 1.288083 / 4.584777 (-3.296694) | 5.624143 / 3.745712 (1.878431) | 3.208382 / 5.269862 (-2.061480) | 2.115222 / 4.565676 (-2.450455) | 0.146420 / 0.424275 (-0.277856) | 0.014464 / 0.007607 (0.006857) | 0.816470 / 0.226044 (0.590425) | 7.984049 / 2.268929 (5.715120) | 3.364942 / 55.444624 (-52.079682) | 2.552306 / 6.876477 (-4.324171) | 2.664575 / 2.142072 (0.522503) | 1.556177 / 4.805227 (-3.249050) | 0.263389 / 6.500664 (-6.237275) | 0.076861 / 0.075469 (0.001391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553734 / 1.841788 (-0.288054) | 18.365029 / 8.074308 (10.290721) | 20.993993 / 10.191392 (10.802601) | 0.235642 / 0.680424 (-0.444782) | 0.047084 / 0.534201 (-0.487117) | 0.555610 / 0.579283 (-0.023673) | 0.659413 / 0.434364 (0.225049) | 0.639284 / 0.540337 (0.098947) | 0.756317 / 1.386936 (-0.630620) |\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) | 0.014709 / 0.011353 (0.003356) | 0.006673 / 0.011008 (-0.004335) | 0.133718 / 0.038508 (0.095210) | 0.035699 / 0.023109 (0.012590) | 0.459089 / 0.275898 (0.183191) | 0.538071 / 0.323480 (0.214591) | 0.007376 / 0.007986 (-0.000610) | 0.004688 / 0.004328 (0.000360) | 0.104909 / 0.004250 (0.100659) | 0.064942 / 0.037052 (0.027890) | 0.466158 / 0.258489 (0.207669) | 0.566100 / 0.293841 (0.272259) | 0.057368 / 0.128546 (-0.071178) | 0.021572 / 0.075646 (-0.054075) | 0.413826 / 0.419271 (-0.005446) | 0.079543 / 0.043533 (0.036010) | 0.493313 / 0.255139 (0.238174) | 0.517787 / 0.283200 (0.234587) | 0.119836 / 0.141683 (-0.021847) | 1.833956 / 1.452155 (0.381801) | 2.003288 / 1.492716 (0.510572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276013 / 0.018006 (0.258007) | 0.549194 / 0.000490 (0.548704) | 0.010939 / 0.000200 (0.010739) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034983 / 0.037411 (-0.002428) | 0.131576 / 0.014526 (0.117050) | 0.140651 / 0.176557 (-0.035906) | 0.186455 / 0.737135 (-0.550681) | 0.146309 / 0.296338 (-0.150029) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675973 / 0.215209 (0.460763) | 6.821862 / 2.077655 (4.744208) | 3.090307 / 1.504120 (1.586187) | 2.710679 / 1.541195 (1.169484) | 2.891577 / 1.468490 (1.423087) | 1.306160 / 4.584777 (-3.278617) | 5.629763 / 3.745712 (1.884051) | 4.662578 / 5.269862 (-0.607283) | 2.670195 / 4.565676 (-1.895482) | 0.153867 / 0.424275 (-0.270408) | 0.016028 / 0.007607 (0.008421) | 0.878702 / 0.226044 (0.652658) | 8.801612 / 2.268929 (6.532683) | 4.005520 / 55.444624 (-51.439104) | 3.124755 / 6.876477 (-3.751721) | 3.382132 / 2.142072 (1.240060) | 1.525951 / 4.805227 (-3.279277) | 0.263350 / 6.500664 (-6.237315) | 0.079285 / 0.075469 (0.003815) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.647591 / 1.841788 (-0.194197) | 18.281646 / 8.074308 (10.207338) | 21.072142 / 10.191392 (10.880750) | 0.232236 / 0.680424 (-0.448188) | 0.026126 / 0.534201 (-0.508075) | 0.546926 / 0.579283 (-0.032357) | 0.634496 / 0.434364 (0.200132) | 0.604345 / 0.540337 (0.064007) | 0.730159 / 1.386936 (-0.656777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfe8a6aa4cd2d3d0d7067f390152d1a4aeb4c710 \"CML watermark\")\n" ]

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[ "Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)", "Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.\r\n\r\nOur `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.\r\n\r\nI think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279", "Also note that you are referring to an outdated documentation page: datasets 1.10.2 version\r\n\r\nCurrent datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset", "Hi @albertvillanova thanks for your reply and your explaination here. \r\n\r\nSorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).\r\n\r\nGreat to know that and you made everything very clear now.\r\n\r\nThanks for your time and sorry for the consusion.\r\n\r\nWishing you a wonderful time. \r\n\r\nRegards" ]

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[ "Hi! Can you describe what the actual error is?", "working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from that, I also had to hack these loads to import thses modules:\r\n from datasets.load import load_dataset \r\n from datasets.arrow_dataset import Dataset\r\n from datasets.dataset_dict import DatasetDict", "Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?" ]

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[ "Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.", "Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.", "Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008519 / 0.011353 (-0.002834) | 0.004451 / 0.011008 (-0.006558) | 0.102401 / 0.038508 (0.063893) | 0.029779 / 0.023109 (0.006669) | 0.302654 / 0.275898 (0.026756) | 0.366002 / 0.323480 (0.042522) | 0.007044 / 0.007986 (-0.000942) | 0.003350 / 0.004328 (-0.000978) | 0.078213 / 0.004250 (0.073963) | 0.035208 / 0.037052 (-0.001844) | 0.312980 / 0.258489 (0.054491) | 0.344217 / 0.293841 (0.050376) | 0.033089 / 0.128546 (-0.095457) | 0.011443 / 0.075646 (-0.064203) | 0.353143 / 0.419271 (-0.066128) | 0.040851 / 0.043533 (-0.002682) | 0.304501 / 0.255139 (0.049362) | 0.329118 / 0.283200 (0.045918) | 0.087399 / 0.141683 (-0.054284) | 1.500200 / 1.452155 (0.048046) | 1.536176 / 1.492716 (0.043459) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209626 / 0.018006 (0.191619) | 0.425551 / 0.000490 (0.425061) | 0.001168 / 0.000200 (0.000968) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023664 / 0.037411 (-0.013748) | 0.096792 / 0.014526 (0.082266) | 0.105652 / 0.176557 (-0.070905) | 0.140796 / 0.737135 (-0.596340) | 0.109319 / 0.296338 (-0.187019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414802 / 0.215209 (0.199593) | 4.152619 / 2.077655 (2.074964) | 1.814403 / 1.504120 (0.310283) | 1.611392 / 1.541195 (0.070198) | 1.667350 / 1.468490 (0.198860) | 0.691855 / 4.584777 (-3.892922) | 3.406584 / 3.745712 (-0.339128) | 1.940332 / 5.269862 (-3.329530) | 1.279061 / 4.565676 (-3.286615) | 0.082938 / 0.424275 (-0.341337) | 0.012388 / 0.007607 (0.004781) | 0.521738 / 0.226044 (0.295693) | 5.233764 / 2.268929 (2.964835) | 2.306573 / 55.444624 (-53.138051) | 1.954631 / 6.876477 (-4.921845) | 2.048315 / 2.142072 (-0.093757) | 0.816921 / 4.805227 (-3.988306) | 0.150983 / 6.500664 (-6.349681) | 0.066628 / 0.075469 (-0.008842) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235939 / 1.841788 (-0.605849) | 14.047114 / 8.074308 (5.972806) | 14.149842 / 10.191392 (3.958450) | 0.152836 / 0.680424 (-0.527588) | 0.028837 / 0.534201 (-0.505364) | 0.396232 / 0.579283 (-0.183051) | 0.409950 / 0.434364 (-0.024414) | 0.460296 / 0.540337 (-0.080041) | 0.556787 / 1.386936 (-0.830149) |\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) | 0.006582 / 0.011353 (-0.004771) | 0.004491 / 0.011008 (-0.006518) | 0.100093 / 0.038508 (0.061585) | 0.026826 / 0.023109 (0.003717) | 0.413971 / 0.275898 (0.138073) | 0.445625 / 0.323480 (0.122145) | 0.004892 / 0.007986 (-0.003094) | 0.003295 / 0.004328 (-0.001034) | 0.077879 / 0.004250 (0.073628) | 0.039177 / 0.037052 (0.002125) | 0.353299 / 0.258489 (0.094810) | 0.406566 / 0.293841 (0.112725) | 0.031633 / 0.128546 (-0.096913) | 0.011517 / 0.075646 (-0.064130) | 0.320939 / 0.419271 (-0.098332) | 0.041487 / 0.043533 (-0.002046) | 0.353735 / 0.255139 (0.098596) | 0.434786 / 0.283200 (0.151586) | 0.087722 / 0.141683 (-0.053961) | 1.515134 / 1.452155 (0.062979) | 1.588908 / 1.492716 (0.096191) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225312 / 0.018006 (0.207305) | 0.398324 / 0.000490 (0.397834) | 0.000453 / 0.000200 (0.000253) | 0.000064 / 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) | 0.024645 / 0.037411 (-0.012766) | 0.099399 / 0.014526 (0.084873) | 0.107006 / 0.176557 (-0.069550) | 0.145090 / 0.737135 (-0.592045) | 0.110046 / 0.296338 (-0.186292) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450573 / 0.215209 (0.235364) | 4.498030 / 2.077655 (2.420375) | 2.193164 / 1.504120 (0.689044) | 1.940103 / 1.541195 (0.398908) | 1.957137 / 1.468490 (0.488647) | 0.697599 / 4.584777 (-3.887178) | 3.465146 / 3.745712 (-0.280566) | 1.918209 / 5.269862 (-3.351653) | 1.183921 / 4.565676 (-3.381756) | 0.082540 / 0.424275 (-0.341735) | 0.012495 / 0.007607 (0.004888) | 0.549702 / 0.226044 (0.323658) | 5.526841 / 2.268929 (3.257912) | 2.658611 / 55.444624 (-52.786014) | 2.259542 / 6.876477 (-4.616935) | 2.310139 / 2.142072 (0.168066) | 0.810550 / 4.805227 (-3.994677) | 0.152369 / 6.500664 (-6.348295) | 0.066295 / 0.075469 (-0.009174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289240 / 1.841788 (-0.552547) | 14.032143 / 8.074308 (5.957834) | 13.973492 / 10.191392 (3.782100) | 0.140082 / 0.680424 (-0.540342) | 0.017113 / 0.534201 (-0.517088) | 0.386534 / 0.579283 (-0.192749) | 0.393723 / 0.434364 (-0.040641) | 0.448891 / 0.540337 (-0.091446) | 0.533085 / 1.386936 (-0.853851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]

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[ "Some of the shard files now have 0 number of rows.\r\n\r\nWe have opened an issue in the Apache Beam repo:\r\n- https://github.com/apache/beam/issues/25041" ]

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

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[ "Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.", "That's a great idea! I'll do that instead. " ]

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[ "Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 " ]

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[ "Hi! \r\n\r\n`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the \"sort\" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multiple keys and currently loads the data into memory; however, there is a plan to eventually implement \"memory-map\" friendly kernels for the Arrow compute ops (using the Acero execution engine). \r\n\r\nSo to address this issue, you should replace `df.sort_values` with `pyarrow.compute.sort_indices` in `Dataset.sort` and adjust the signature of this function (deprecate the `kind` parameter, etc.).\r\n\r\nPS: Feel free to ping us if you need some additional help/pointers", "@mariosasko If I understand the code right, using `pyarrow.compute.sort_indices` would also require changes to the `select` method if it is meant to sort multiple keys. That's because `select` only accepts 1D input for `indices`, not an iterable or similar which would be required for multiple keys unless you want some looping over selects. Doesn't seem that straight-forward but I might be missing something here... ", "@MichlF No, it doesn't require modifying select because sorting on multiple keys also returns a 1D array.\r\n\r\nIt's easier to understand with an example:\r\n```python\r\n>>> import pyarrow as pa\r\n>>> import pyarrow.compute as pc\r\n>>> table = pa.table({\r\n... \"name\": [\"John\", \"Eve\", \"Peter\", \"John\"],\r\n... \"surname\": [\"Johnson\", \"Smith\", \"Smith\", \"Doe\"],\r\n... \"age\": [20, 40, 30, 50],\r\n... })\r\n>>> indices = pc.sort_indices(table, sort_keys=[(\"name\", \"ascending\"), (\"surname\", \"ascending\")])\r\n>>> print(indices)\r\n[\r\n 1,\r\n 3,\r\n 0,\r\n 2\r\n]\r\n```\r\n\r\n", "Thanks for clarifying.\r\nI can prepare a PR to address this issue. This would be my first PR here so I have a few maybe silly questions but:\r\n- What is the preferred input type of `sort_keys` for the sort method? A sequence with name, order tuples like pyarrow's `sort_indices` requires?\r\n- What about backwards compatability: is it supposed to also accept the old way of calling sort() or should both `column` and `kind` be deprecated?\r\n- If `sort_keys` is provided in the same format as for pyarrow's `sort_indices` - i.e. along with order for each column -, `reverse` doesn't make much sense either and should be deprecated as well I assume.", "I think we can have the following signature:\r\n```python\r\ndef sort(\r\n self,\r\n column_names: Union[str, Sequence[str]],\r\n reverse: Union[bool, Sequence[bool]] = False,\r\n kind=\"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n``` \r\n\r\nSo we should:\r\n* rename`column` to `column_names`. `column` is a positional argument, so it's OK to rename it (not marked as positional-only with \"/\", but still should be fine)\r\n* deprecate `kind`\r\n* keep `reverse` instead of introducing `sort_keys`, but we should allow passing a list of booleans that defines the sort order of each column from `column_names` to it (`reverse = False` would be equal to `[False] * len(column_names)` and `reverse = True` to `[True] * len(column_names)`)", "I am pretty much done with the PR. Just one clarification: `Sequence` in `arrow_dataset.py` is a custom dataclass from `features.py` instead of the `type.hinting` class `Sequence` from Python. Do you suggest using that custom `Sequence` class somehow ? Otherwise signature currently reads instead:\r\n```Python\r\n def sort(\r\n self,\r\n column_names: Union[str, List[str]],\r\n reverse: Union[bool, List[bool]] = False,\r\n kind = \"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n )\r\n```\r\n\r\nAlso, to maintain backwards compatibility, I added conditionals for `null_placement`, because pyarrow's `null_placement` only accepts `at_start` and `at_end`, and not `last` and `first`.\r\nIf that is all good, I think I can open the PR.", "I meant `typing.Sequence` (`datasets.Sequence` is a feature type). \r\n\r\nRegarding `null_placement`, I think we can support both `at_start` and `at_end`, and `last` and `first` (for backward compatibility; convert internally to `at_end` and `at_start` respectively).", "> I meant typing.Sequence (datasets.Sequence is a feature type).\r\n\r\nSorry, I actually meant `typing.Sequence` and not `type.hinting`. However, the issue is still that `dataset.Sequence` is imported in `arrow_dataset.py` so I cannot import and use `typing.Sequence` for the `sort`'s signature without overwriting the `dataset.Sequence` import. The latter is used in the `align_labels_with_mapping` method so it's a necessary import for `arrow_dataset.py`. \r\nTo import `typing.Sequence` as something else than `Sequence` to avoid overwriting may only be confusing and doesn't seem good practice!? The other solution is to keep `List` type hinting as in the signature I posted in my previous post but this excludes other Sequence types and may cause problems further down the line.\r\nPlease advise,\r\nThanks for all the clarifications!", "You can avoid the name collision by renaming `typing.Sequence` to `Sequence_` when importing:\r\n```python\r\nfrom typing import Sequence as Sequence_\r\n```", "Resolved via #5502 " ]

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[ "Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"test\", from_=0, to=5, unit='%', rounding='closest')\r\n]\r\n\r\ndataset = load_dataset('csv', data_dir=\"data/\", data_files={\"train\":\"train.tsv\", \"dev\":\"dev.tsv\", \"test\":\"test.tsv\"}, delimiter=\"\\t\", split={inst.split_name: inst for inst in instructions})\r\n```\r\n" ]

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[ "I can confirm that the error exists!\r\nI'm trying to read 3 parquet files locally:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n)\r\n```\r\n\r\nBut you can fix it, by specifying `features` for `load_dataset()` function like this:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nfeatures = Features(\r\n {\r\n \"label\": ClassLabel(\r\n num_classes=3,\r\n names=[\"negative\", \"neutral\", \"positive\"],\r\n ),\r\n \"text\": Value(dtype=\"string\"),\r\n }\r\n)\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n features=features,\r\n)\r\n\r\nprint(review_dataset)\r\n```", "@Extremesarova I think this is a different issue, but understand using features could be a work-around.\r\nIt seems the field `closed_at` is `null` in many cases.\r\n\r\nI've not found a way to specify only a single feature without (succesfully) specifiying the full and quite detailed set of expected features. Using this features set gives an error the column names don't match.\r\n`features = Features({'closed_at': Value(dtype='timestamp[s]', id=None)})`\r\n\r\n", "Found this when searching for the same error, looks like based on #3965 it's just an issue with the data. I found that changing `df = pd.DataFrame.from_records(all_issues)` to `df = pd.DataFrame.from_records(all_issues).dropna(axis=1, how='all').drop(['milestone'], axis=1)` from the fetch_issues function fixed the issue. \r\nThe \"milestone\" column seemed to be problematic (only ~50 non null rows) and dropped any columns that were all null as well just in case.", "I have this same issue. I saved a dataset to disk and now I can't load it.", "Ok the solution was to use load_from_disk instead of load_dataset." ]

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[ "Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008450 / 0.011353 (-0.002902) | 0.004478 / 0.011008 (-0.006530) | 0.100440 / 0.038508 (0.061931) | 0.029568 / 0.023109 (0.006459) | 0.296705 / 0.275898 (0.020807) | 0.354565 / 0.323480 (0.031085) | 0.006887 / 0.007986 (-0.001098) | 0.003415 / 0.004328 (-0.000914) | 0.078876 / 0.004250 (0.074626) | 0.034927 / 0.037052 (-0.002125) | 0.307695 / 0.258489 (0.049206) | 0.340917 / 0.293841 (0.047076) | 0.033630 / 0.128546 (-0.094916) | 0.011626 / 0.075646 (-0.064020) | 0.322644 / 0.419271 (-0.096627) | 0.040254 / 0.043533 (-0.003279) | 0.297419 / 0.255139 (0.042280) | 0.321584 / 0.283200 (0.038384) | 0.086202 / 0.141683 (-0.055481) | 1.465579 / 1.452155 (0.013425) | 1.521456 / 1.492716 (0.028740) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200890 / 0.018006 (0.182884) | 0.410300 / 0.000490 (0.409811) | 0.001647 / 0.000200 (0.001447) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022569 / 0.037411 (-0.014843) | 0.096062 / 0.014526 (0.081536) | 0.102474 / 0.176557 (-0.074082) | 0.138596 / 0.737135 (-0.598539) | 0.106262 / 0.296338 (-0.190077) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415976 / 0.215209 (0.200766) | 4.144322 / 2.077655 (2.066667) | 1.871783 / 1.504120 (0.367663) | 1.669478 / 1.541195 (0.128283) | 1.718214 / 1.468490 (0.249724) | 0.687870 / 4.584777 (-3.896907) | 3.362084 / 3.745712 (-0.383628) | 1.844127 / 5.269862 (-3.425735) | 1.149611 / 4.565676 (-3.416066) | 0.081410 / 0.424275 (-0.342865) | 0.012278 / 0.007607 (0.004671) | 0.518245 / 0.226044 (0.292200) | 5.185164 / 2.268929 (2.916236) | 2.299029 / 55.444624 (-53.145595) | 1.960021 / 6.876477 (-4.916456) | 2.009751 / 2.142072 (-0.132322) | 0.803759 / 4.805227 (-4.001468) | 0.147340 / 6.500664 (-6.353324) | 0.063896 / 0.075469 (-0.011573) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254142 / 1.841788 (-0.587646) | 13.799683 / 8.074308 (5.725375) | 13.940387 / 10.191392 (3.748995) | 0.151246 / 0.680424 (-0.529178) | 0.028709 / 0.534201 (-0.505491) | 0.391600 / 0.579283 (-0.187683) | 0.405750 / 0.434364 (-0.028614) | 0.455479 / 0.540337 (-0.084858) | 0.541022 / 1.386936 (-0.845914) |\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) | 0.006462 / 0.011353 (-0.004891) | 0.004462 / 0.011008 (-0.006547) | 0.096588 / 0.038508 (0.058080) | 0.026931 / 0.023109 (0.003822) | 0.344595 / 0.275898 (0.068697) | 0.378743 / 0.323480 (0.055264) | 0.005672 / 0.007986 (-0.002314) | 0.003345 / 0.004328 (-0.000984) | 0.074363 / 0.004250 (0.070112) | 0.037300 / 0.037052 (0.000248) | 0.346895 / 0.258489 (0.088406) | 0.388585 / 0.293841 (0.094744) | 0.031459 / 0.128546 (-0.097088) | 0.011522 / 0.075646 (-0.064124) | 0.318507 / 0.419271 (-0.100764) | 0.041145 / 0.043533 (-0.002388) | 0.343866 / 0.255139 (0.088727) | 0.366490 / 0.283200 (0.083291) | 0.086793 / 0.141683 (-0.054890) | 1.483859 / 1.452155 (0.031704) | 1.574006 / 1.492716 (0.081290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220436 / 0.018006 (0.202430) | 0.402988 / 0.000490 (0.402498) | 0.000435 / 0.000200 (0.000235) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024573 / 0.037411 (-0.012838) | 0.099190 / 0.014526 (0.084664) | 0.106796 / 0.176557 (-0.069761) | 0.142387 / 0.737135 (-0.594748) | 0.109991 / 0.296338 (-0.186347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473452 / 0.215209 (0.258243) | 4.749554 / 2.077655 (2.671899) | 2.433482 / 1.504120 (0.929362) | 2.224276 / 1.541195 (0.683082) | 2.261579 / 1.468490 (0.793088) | 0.699876 / 4.584777 (-3.884901) | 3.378366 / 3.745712 (-0.367346) | 1.835062 / 5.269862 (-3.434799) | 1.161249 / 4.565676 (-3.404427) | 0.082967 / 0.424275 (-0.341308) | 0.012745 / 0.007607 (0.005138) | 0.580006 / 0.226044 (0.353962) | 5.789868 / 2.268929 (3.520939) | 2.909496 / 55.444624 (-52.535128) | 2.539196 / 6.876477 (-4.337280) | 2.617737 / 2.142072 (0.475665) | 0.810320 / 4.805227 (-3.994907) | 0.152501 / 6.500664 (-6.348163) | 0.067201 / 0.075469 (-0.008268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257844 / 1.841788 (-0.583943) | 13.865295 / 8.074308 (5.790987) | 14.169073 / 10.191392 (3.977680) | 0.135655 / 0.680424 (-0.544769) | 0.016597 / 0.534201 (-0.517604) | 0.374915 / 0.579283 (-0.204368) | 0.382771 / 0.434364 (-0.051593) | 0.431934 / 0.540337 (-0.108403) | 0.524617 / 1.386936 (-0.862319) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008748 / 0.011353 (-0.002605) | 0.004489 / 0.011008 (-0.006519) | 0.100923 / 0.038508 (0.062415) | 0.031436 / 0.023109 (0.008326) | 0.306508 / 0.275898 (0.030610) | 0.365110 / 0.323480 (0.041630) | 0.007161 / 0.007986 (-0.000824) | 0.005489 / 0.004328 (0.001160) | 0.078909 / 0.004250 (0.074658) | 0.036097 / 0.037052 (-0.000955) | 0.307907 / 0.258489 (0.049418) | 0.370277 / 0.293841 (0.076436) | 0.034184 / 0.128546 (-0.094362) | 0.011613 / 0.075646 (-0.064033) | 0.322896 / 0.419271 (-0.096375) | 0.041829 / 0.043533 (-0.001704) | 0.299669 / 0.255139 (0.044530) | 0.322217 / 0.283200 (0.039017) | 0.087751 / 0.141683 (-0.053932) | 1.476277 / 1.452155 (0.024122) | 1.548196 / 1.492716 (0.055480) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183002 / 0.018006 (0.164995) | 0.415627 / 0.000490 (0.415138) | 0.003272 / 0.000200 (0.003072) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024881 / 0.037411 (-0.012531) | 0.103424 / 0.014526 (0.088898) | 0.106446 / 0.176557 (-0.070110) | 0.142806 / 0.737135 (-0.594330) | 0.110938 / 0.296338 (-0.185401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421669 / 0.215209 (0.206460) | 4.207457 / 2.077655 (2.129802) | 1.882176 / 1.504120 (0.378056) | 1.677609 / 1.541195 (0.136415) | 1.734065 / 1.468490 (0.265575) | 0.695915 / 4.584777 (-3.888862) | 3.416731 / 3.745712 (-0.328981) | 1.872575 / 5.269862 (-3.397286) | 1.163612 / 4.565676 (-3.402064) | 0.082710 / 0.424275 (-0.341565) | 0.012659 / 0.007607 (0.005052) | 0.528785 / 0.226044 (0.302741) | 5.305328 / 2.268929 (3.036399) | 2.299850 / 55.444624 (-53.144774) | 1.968137 / 6.876477 (-4.908339) | 2.028326 / 2.142072 (-0.113746) | 0.813157 / 4.805227 (-3.992070) | 0.149997 / 6.500664 (-6.350668) | 0.066739 / 0.075469 (-0.008730) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206332 / 1.841788 (-0.635456) | 13.795510 / 8.074308 (5.721202) | 14.367695 / 10.191392 (4.176303) | 0.138106 / 0.680424 (-0.542318) | 0.028760 / 0.534201 (-0.505441) | 0.394822 / 0.579283 (-0.184461) | 0.403291 / 0.434364 (-0.031073) | 0.463273 / 0.540337 (-0.077065) | 0.540881 / 1.386936 (-0.846055) |\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) | 0.006830 / 0.011353 (-0.004523) | 0.004606 / 0.011008 (-0.006402) | 0.097763 / 0.038508 (0.059255) | 0.027832 / 0.023109 (0.004723) | 0.422970 / 0.275898 (0.147072) | 0.460313 / 0.323480 (0.136833) | 0.005110 / 0.007986 (-0.002876) | 0.003428 / 0.004328 (-0.000901) | 0.075047 / 0.004250 (0.070797) | 0.038374 / 0.037052 (0.001322) | 0.422762 / 0.258489 (0.164273) | 0.469886 / 0.293841 (0.176045) | 0.032391 / 0.128546 (-0.096155) | 0.011804 / 0.075646 (-0.063843) | 0.320439 / 0.419271 (-0.098832) | 0.041939 / 0.043533 (-0.001594) | 0.422521 / 0.255139 (0.167382) | 0.446420 / 0.283200 (0.163220) | 0.090715 / 0.141683 (-0.050968) | 1.484578 / 1.452155 (0.032423) | 1.556154 / 1.492716 (0.063438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260735 / 0.018006 (0.242728) | 0.415586 / 0.000490 (0.415096) | 0.026960 / 0.000200 (0.026760) | 0.000296 / 0.000054 (0.000241) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024926 / 0.037411 (-0.012486) | 0.099651 / 0.014526 (0.085125) | 0.107810 / 0.176557 (-0.068747) | 0.148685 / 0.737135 (-0.588451) | 0.112725 / 0.296338 (-0.183614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472669 / 0.215209 (0.257460) | 4.718827 / 2.077655 (2.641172) | 2.475583 / 1.504120 (0.971463) | 2.260862 / 1.541195 (0.719667) | 2.307820 / 1.468490 (0.839330) | 0.699464 / 4.584777 (-3.885313) | 3.376282 / 3.745712 (-0.369431) | 1.872650 / 5.269862 (-3.397211) | 1.176399 / 4.565676 (-3.389277) | 0.082854 / 0.424275 (-0.341421) | 0.012845 / 0.007607 (0.005237) | 0.582088 / 0.226044 (0.356044) | 5.861609 / 2.268929 (3.592681) | 2.930728 / 55.444624 (-52.513896) | 2.624310 / 6.876477 (-4.252167) | 2.762130 / 2.142072 (0.620058) | 0.811902 / 4.805227 (-3.993325) | 0.152516 / 6.500664 (-6.348149) | 0.067670 / 0.075469 (-0.007799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289790 / 1.841788 (-0.551997) | 14.267607 / 8.074308 (6.193299) | 14.120655 / 10.191392 (3.929263) | 0.128442 / 0.680424 (-0.551982) | 0.017079 / 0.534201 (-0.517121) | 0.381807 / 0.579283 (-0.197476) | 0.400546 / 0.434364 (-0.033818) | 0.447629 / 0.540337 (-0.092709) | 0.532006 / 1.386936 (-0.854930) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]

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[ "Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards." ]

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[ "Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!", "@albertvillanova I’m happy to make a quick PR for the feature! let me know ", "That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review", "Closing as this has been merged @lhoestq " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009256 / 0.011353 (-0.002097) | 0.005033 / 0.011008 (-0.005975) | 0.099346 / 0.038508 (0.060838) | 0.035204 / 0.023109 (0.012095) | 0.303017 / 0.275898 (0.027119) | 0.335632 / 0.323480 (0.012152) | 0.007953 / 0.007986 (-0.000033) | 0.005806 / 0.004328 (0.001477) | 0.076121 / 0.004250 (0.071871) | 0.041164 / 0.037052 (0.004112) | 0.305536 / 0.258489 (0.047047) | 0.348452 / 0.293841 (0.054611) | 0.037704 / 0.128546 (-0.090842) | 0.011982 / 0.075646 (-0.063664) | 0.333264 / 0.419271 (-0.086008) | 0.047738 / 0.043533 (0.004205) | 0.310126 / 0.255139 (0.054987) | 0.318719 / 0.283200 (0.035519) | 0.098933 / 0.141683 (-0.042750) | 1.421058 / 1.452155 (-0.031096) | 1.554771 / 1.492716 (0.062054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258627 / 0.018006 (0.240620) | 0.450814 / 0.000490 (0.450324) | 0.011288 / 0.000200 (0.011088) | 0.000136 / 0.000054 (0.000081) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027004 / 0.037411 (-0.010407) | 0.109067 / 0.014526 (0.094541) | 0.120401 / 0.176557 (-0.056155) | 0.158336 / 0.737135 (-0.578799) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401847 / 0.215209 (0.186638) | 4.006003 / 2.077655 (1.928348) | 1.806342 / 1.504120 (0.302223) | 1.619751 / 1.541195 (0.078556) | 1.709660 / 1.468490 (0.241170) | 0.692444 / 4.584777 (-3.892333) | 3.853691 / 3.745712 (0.107979) | 2.143910 / 5.269862 (-3.125951) | 1.471600 / 4.565676 (-3.094076) | 0.084589 / 0.424275 (-0.339686) | 0.012276 / 0.007607 (0.004669) | 0.506529 / 0.226044 (0.280485) | 5.028361 / 2.268929 (2.759432) | 2.277660 / 55.444624 (-53.166964) | 1.930365 / 6.876477 (-4.946112) | 1.965494 / 2.142072 (-0.176579) | 0.826752 / 4.805227 (-3.978475) | 0.165050 / 6.500664 (-6.335614) | 0.062702 / 0.075469 (-0.012767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234539 / 1.841788 (-0.607249) | 15.067401 / 8.074308 (6.993093) | 14.041920 / 10.191392 (3.850528) | 0.162590 / 0.680424 (-0.517834) | 0.028941 / 0.534201 (-0.505260) | 0.438518 / 0.579283 (-0.140765) | 0.443787 / 0.434364 (0.009423) | 0.516671 / 0.540337 (-0.023666) | 0.609036 / 1.386936 (-0.777900) |\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) | 0.007535 / 0.011353 (-0.003818) | 0.005283 / 0.011008 (-0.005725) | 0.097116 / 0.038508 (0.058608) | 0.033357 / 0.023109 (0.010247) | 0.383398 / 0.275898 (0.107500) | 0.425516 / 0.323480 (0.102037) | 0.006039 / 0.007986 (-0.001947) | 0.004074 / 0.004328 (-0.000255) | 0.073207 / 0.004250 (0.068956) | 0.052153 / 0.037052 (0.015101) | 0.386385 / 0.258489 (0.127896) | 0.429900 / 0.293841 (0.136059) | 0.038341 / 0.128546 (-0.090205) | 0.012417 / 0.075646 (-0.063230) | 0.333859 / 0.419271 (-0.085413) | 0.051157 / 0.043533 (0.007625) | 0.395022 / 0.255139 (0.139883) | 0.402462 / 0.283200 (0.119262) | 0.105207 / 0.141683 (-0.036475) | 1.510679 / 1.452155 (0.058524) | 1.584205 / 1.492716 (0.091489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225805 / 0.018006 (0.207799) | 0.452109 / 0.000490 (0.451619) | 0.000429 / 0.000200 (0.000229) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029653 / 0.037411 (-0.007759) | 0.112609 / 0.014526 (0.098083) | 0.121828 / 0.176557 (-0.054728) | 0.159003 / 0.737135 (-0.578133) | 0.129306 / 0.296338 (-0.167033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453001 / 0.215209 (0.237792) | 4.514882 / 2.077655 (2.437228) | 2.277494 / 1.504120 (0.773374) | 2.073870 / 1.541195 (0.532675) | 2.153346 / 1.468490 (0.684856) | 0.698363 / 4.584777 (-3.886414) | 3.921763 / 3.745712 (0.176051) | 2.123133 / 5.269862 (-3.146729) | 1.347618 / 4.565676 (-3.218058) | 0.085654 / 0.424275 (-0.338621) | 0.012059 / 0.007607 (0.004452) | 0.568183 / 0.226044 (0.342139) | 5.720047 / 2.268929 (3.451119) | 2.777973 / 55.444624 (-52.666651) | 2.453426 / 6.876477 (-4.423051) | 2.523977 / 2.142072 (0.381905) | 0.841979 / 4.805227 (-3.963248) | 0.167958 / 6.500664 (-6.332706) | 0.064929 / 0.075469 (-0.010540) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235297 / 1.841788 (-0.606491) | 15.883598 / 8.074308 (7.809290) | 14.395328 / 10.191392 (4.203936) | 0.162401 / 0.680424 (-0.518022) | 0.017806 / 0.534201 (-0.516394) | 0.423853 / 0.579283 (-0.155430) | 0.423266 / 0.434364 (-0.011098) | 0.490351 / 0.540337 (-0.049986) | 0.588116 / 1.386936 (-0.798820) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010759 / 0.011353 (-0.000594) | 0.005748 / 0.011008 (-0.005260) | 0.119195 / 0.038508 (0.080687) | 0.033476 / 0.023109 (0.010367) | 0.364081 / 0.275898 (0.088183) | 0.422456 / 0.323480 (0.098976) | 0.009780 / 0.007986 (0.001795) | 0.006170 / 0.004328 (0.001841) | 0.093242 / 0.004250 (0.088991) | 0.041049 / 0.037052 (0.003997) | 0.372132 / 0.258489 (0.113643) | 0.442501 / 0.293841 (0.148660) | 0.054889 / 0.128546 (-0.073657) | 0.018302 / 0.075646 (-0.057345) | 0.378899 / 0.419271 (-0.040373) | 0.058455 / 0.043533 (0.014922) | 0.356141 / 0.255139 (0.101002) | 0.400866 / 0.283200 (0.117666) | 0.103384 / 0.141683 (-0.038299) | 1.629867 / 1.452155 (0.177713) | 1.693939 / 1.492716 (0.201222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240484 / 0.018006 (0.222478) | 0.509137 / 0.000490 (0.508648) | 0.000450 / 0.000200 (0.000250) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025856 / 0.037411 (-0.011555) | 0.113214 / 0.014526 (0.098689) | 0.119420 / 0.176557 (-0.057136) | 0.158663 / 0.737135 (-0.578473) | 0.123542 / 0.296338 (-0.172797) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.555900 / 0.215209 (0.340691) | 5.580295 / 2.077655 (3.502640) | 2.216640 / 1.504120 (0.712520) | 1.904944 / 1.541195 (0.363749) | 1.865839 / 1.468490 (0.397349) | 1.158325 / 4.584777 (-3.426452) | 5.097420 / 3.745712 (1.351708) | 2.881775 / 5.269862 (-2.388087) | 2.068896 / 4.565676 (-2.496780) | 0.129028 / 0.424275 (-0.295247) | 0.014075 / 0.007607 (0.006468) | 0.698874 / 0.226044 (0.472830) | 7.131225 / 2.268929 (4.862296) | 2.901686 / 55.444624 (-52.542939) | 2.186146 / 6.876477 (-4.690330) | 2.251172 / 2.142072 (0.109100) | 1.342264 / 4.805227 (-3.462963) | 0.232045 / 6.500664 (-6.268619) | 0.073520 / 0.075469 (-0.001949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.431314 / 1.841788 (-0.410474) | 16.313055 / 8.074308 (8.238747) | 18.451552 / 10.191392 (8.260160) | 0.232875 / 0.680424 (-0.447549) | 0.042170 / 0.534201 (-0.492031) | 0.495261 / 0.579283 (-0.084022) | 0.582901 / 0.434364 (0.148537) | 0.582049 / 0.540337 (0.041712) | 0.681122 / 1.386936 (-0.705814) |\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) | 0.008131 / 0.011353 (-0.003222) | 0.006162 / 0.011008 (-0.004847) | 0.113721 / 0.038508 (0.075213) | 0.030797 / 0.023109 (0.007688) | 0.413108 / 0.275898 (0.137210) | 0.449968 / 0.323480 (0.126488) | 0.006126 / 0.007986 (-0.001860) | 0.004848 / 0.004328 (0.000519) | 0.085465 / 0.004250 (0.081214) | 0.045817 / 0.037052 (0.008764) | 0.419360 / 0.258489 (0.160871) | 0.489077 / 0.293841 (0.195236) | 0.050841 / 0.128546 (-0.077705) | 0.020646 / 0.075646 (-0.055000) | 0.379838 / 0.419271 (-0.039434) | 0.068897 / 0.043533 (0.025365) | 0.422182 / 0.255139 (0.167043) | 0.435529 / 0.283200 (0.152330) | 0.115299 / 0.141683 (-0.026384) | 1.655134 / 1.452155 (0.202979) | 1.835198 / 1.492716 (0.342481) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207041 / 0.018006 (0.189034) | 0.491263 / 0.000490 (0.490773) | 0.003554 / 0.000200 (0.003354) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030830 / 0.037411 (-0.006582) | 0.127003 / 0.014526 (0.112477) | 0.142901 / 0.176557 (-0.033656) | 0.177570 / 0.737135 (-0.559565) | 0.137758 / 0.296338 (-0.158580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632820 / 0.215209 (0.417611) | 6.215535 / 2.077655 (4.137880) | 2.615310 / 1.504120 (1.111190) | 2.261431 / 1.541195 (0.720236) | 2.220570 / 1.468490 (0.752080) | 1.215820 / 4.584777 (-3.368957) | 5.247680 / 3.745712 (1.501968) | 3.120054 / 5.269862 (-2.149807) | 1.950947 / 4.565676 (-2.614730) | 0.149980 / 0.424275 (-0.274295) | 0.015241 / 0.007607 (0.007634) | 0.879714 / 0.226044 (0.653670) | 7.941913 / 2.268929 (5.672984) | 3.512456 / 55.444624 (-51.932168) | 2.693833 / 6.876477 (-4.182644) | 2.772780 / 2.142072 (0.630708) | 1.459581 / 4.805227 (-3.345646) | 0.264820 / 6.500664 (-6.235844) | 0.076698 / 0.075469 (0.001228) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.437719 / 1.841788 (-0.404068) | 16.750309 / 8.074308 (8.676001) | 18.646776 / 10.191392 (8.455384) | 0.227858 / 0.680424 (-0.452566) | 0.024239 / 0.534201 (-0.509962) | 0.486172 / 0.579283 (-0.093111) | 0.574731 / 0.434364 (0.140367) | 0.557776 / 0.540337 (0.017439) | 0.672921 / 1.386936 (-0.714015) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n" ]

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[ "Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co/datasets/ami/discussions/3", "Main issue:\r\n- #5415", "@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?", "Yes, @Nithin-Holla, in the meantime you can use this dataset in streaming mode." ]

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[ "Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same" ]

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