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[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5924). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```" ]

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[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list | grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list | grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n" ]

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[ "As already replied by @lhoestq (private channel):\r\n> `.train_test_split` (as well as `.shard`, `.select`) doesn't create a new arrow table to save time and disk space. Instead, it uses an indices mapping on top of the table that locate which examples are part of train or test.", "This is an optimization that we don't plan to \"fix\", so I'm closing this issue." ]

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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007088 / 0.011353 (-0.004265) | 0.005216 / 0.011008 (-0.005793) | 0.097572 / 0.038508 (0.059064) | 0.036510 / 0.023109 (0.013401) | 0.316885 / 0.275898 (0.040987) | 0.348541 / 0.323480 (0.025061) | 0.006513 / 0.007986 (-0.001473) | 0.004579 / 0.004328 (0.000251) | 0.073779 / 0.004250 (0.069529) | 0.057500 / 0.037052 (0.020448) | 0.329840 / 0.258489 (0.071351) | 0.357530 / 0.293841 (0.063690) | 0.028515 / 0.128546 (-0.100031) | 0.009156 / 0.075646 (-0.066491) | 0.328340 / 0.419271 (-0.090932) | 0.068400 / 0.043533 (0.024867) | 0.313692 / 0.255139 (0.058553) | 0.329170 / 0.283200 (0.045971) | 0.111969 / 0.141683 (-0.029714) | 1.422096 / 1.452155 (-0.030059) | 1.550042 / 1.492716 (0.057326) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285113 / 0.018006 (0.267107) | 0.546788 / 0.000490 (0.546298) | 0.006992 / 0.000200 (0.006792) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026841 / 0.037411 (-0.010570) | 0.108413 / 0.014526 (0.093887) | 0.118375 / 0.176557 (-0.058181) | 0.174889 / 0.737135 (-0.562246) | 0.122781 / 0.296338 (-0.173558) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404187 / 0.215209 (0.188978) | 4.039673 / 2.077655 (1.962019) | 1.894616 / 1.504120 (0.390496) | 1.729182 / 1.541195 (0.187987) | 1.772917 / 1.468490 (0.304427) | 0.524046 / 4.584777 (-4.060731) | 3.628111 / 3.745712 (-0.117601) | 1.866075 / 5.269862 (-3.403787) | 1.026435 / 4.565676 (-3.539242) | 0.065328 / 0.424275 (-0.358947) | 0.012717 / 0.007607 (0.005110) | 0.505821 / 0.226044 (0.279777) | 5.049518 / 2.268929 (2.780589) | 2.338486 / 55.444624 (-53.106139) | 2.002874 / 6.876477 (-4.873602) | 2.193049 / 2.142072 (0.050976) | 0.664638 / 4.805227 (-4.140589) | 0.151323 / 6.500664 (-6.349341) | 0.063774 / 0.075469 (-0.011695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168168 / 1.841788 (-0.673620) | 15.289200 / 8.074308 (7.214891) | 13.614249 / 10.191392 (3.422857) | 0.167950 / 0.680424 (-0.512474) | 0.017522 / 0.534201 (-0.516679) | 0.393480 / 0.579283 (-0.185803) | 0.420549 / 0.434364 (-0.013815) | 0.461425 / 0.540337 (-0.078912) | 0.563583 / 1.386936 (-0.823353) |\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 (-0.004493) | 0.004864 / 0.011008 (-0.006144) | 0.075084 / 0.038508 (0.036576) | 0.033989 / 0.023109 (0.010880) | 0.372512 / 0.275898 (0.096614) | 0.394725 / 0.323480 (0.071246) | 0.006382 / 0.007986 (-0.001604) | 0.004521 / 0.004328 (0.000193) | 0.076422 / 0.004250 (0.072172) | 0.055383 / 0.037052 (0.018331) | 0.400974 / 0.258489 (0.142485) | 0.411570 / 0.293841 (0.117729) | 0.028264 / 0.128546 (-0.100282) | 0.009123 / 0.075646 (-0.066523) | 0.081257 / 0.419271 (-0.338015) | 0.048147 / 0.043533 (0.004614) | 0.390735 / 0.255139 (0.135596) | 0.376426 / 0.283200 (0.093226) | 0.108164 / 0.141683 (-0.033518) | 1.429667 / 1.452155 (-0.022488) | 1.556291 / 1.492716 (0.063575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289514 / 0.018006 (0.271508) | 0.532860 / 0.000490 (0.532370) | 0.003810 / 0.000200 (0.003611) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031292 / 0.037411 (-0.006119) | 0.116530 / 0.014526 (0.102005) | 0.127624 / 0.176557 (-0.048932) | 0.178276 / 0.737135 (-0.558859) | 0.133742 / 0.296338 (-0.162597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431505 / 0.215209 (0.216296) | 4.309206 / 2.077655 (2.231551) | 2.174779 / 1.504120 (0.670659) | 1.998122 / 1.541195 (0.456927) | 2.126478 / 1.468490 (0.657988) | 0.528971 / 4.584777 (-4.055806) | 3.797608 / 3.745712 (0.051895) | 1.876275 / 5.269862 (-3.393586) | 1.087458 / 4.565676 (-3.478218) | 0.066940 / 0.424275 (-0.357335) | 0.012432 / 0.007607 (0.004825) | 0.538346 / 0.226044 (0.312301) | 5.370968 / 2.268929 (3.102039) | 2.613718 / 55.444624 (-52.830906) | 2.246585 / 6.876477 (-4.629892) | 2.375695 / 2.142072 (0.233622) | 0.652227 / 4.805227 (-4.153001) | 0.143246 / 6.500664 (-6.357418) | 0.066163 / 0.075469 (-0.009306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291263 / 1.841788 (-0.550524) | 16.532281 / 8.074308 (8.457973) | 15.038471 / 10.191392 (4.847079) | 0.168139 / 0.680424 (-0.512285) | 0.017724 / 0.534201 (-0.516477) | 0.391636 / 0.579283 (-0.187648) | 0.429690 / 0.434364 (-0.004674) | 0.474941 / 0.540337 (-0.065396) | 0.579461 / 1.386936 (-0.807475) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006083 / 0.011353 (-0.005269) | 0.004085 / 0.011008 (-0.006923) | 0.098337 / 0.038508 (0.059829) | 0.027573 / 0.023109 (0.004464) | 0.305688 / 0.275898 (0.029790) | 0.341767 / 0.323480 (0.018287) | 0.005143 / 0.007986 (-0.002842) | 0.003396 / 0.004328 (-0.000932) | 0.076925 / 0.004250 (0.072674) | 0.041027 / 0.037052 (0.003975) | 0.307877 / 0.258489 (0.049388) | 0.346559 / 0.293841 (0.052718) | 0.025183 / 0.128546 (-0.103363) | 0.008575 / 0.075646 (-0.067071) | 0.319449 / 0.419271 (-0.099823) | 0.043378 / 0.043533 (-0.000154) | 0.304563 / 0.255139 (0.049424) | 0.332019 / 0.283200 (0.048819) | 0.087725 / 0.141683 (-0.053958) | 1.484904 / 1.452155 (0.032749) | 1.582780 / 1.492716 (0.090064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197503 / 0.018006 (0.179497) | 0.410370 / 0.000490 (0.409880) | 0.003840 / 0.000200 (0.003640) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024179 / 0.037411 (-0.013232) | 0.098876 / 0.014526 (0.084350) | 0.106189 / 0.176557 (-0.070367) | 0.168964 / 0.737135 (-0.568171) | 0.109723 / 0.296338 (-0.186616) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429453 / 0.215209 (0.214244) | 4.295584 / 2.077655 (2.217929) | 2.014330 / 1.504120 (0.510210) | 1.841119 / 1.541195 (0.299924) | 1.928378 / 1.468490 (0.459888) | 0.554571 / 4.584777 (-4.030206) | 3.431769 / 3.745712 (-0.313943) | 1.716204 / 5.269862 (-3.553658) | 0.995054 / 4.565676 (-3.570622) | 0.067374 / 0.424275 (-0.356902) | 0.012557 / 0.007607 (0.004950) | 0.533785 / 0.226044 (0.307740) | 5.363360 / 2.268929 (3.094431) | 2.535190 / 55.444624 (-52.909434) | 2.191646 / 6.876477 (-4.684831) | 2.400799 / 2.142072 (0.258727) | 0.663961 / 4.805227 (-4.141266) | 0.135992 / 6.500664 (-6.364672) | 0.067378 / 0.075469 (-0.008092) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235110 / 1.841788 (-0.606678) | 13.820695 / 8.074308 (5.746387) | 13.667202 / 10.191392 (3.475810) | 0.143025 / 0.680424 (-0.537399) | 0.016757 / 0.534201 (-0.517444) | 0.356262 / 0.579283 (-0.223021) | 0.401871 / 0.434364 (-0.032493) | 0.423928 / 0.540337 (-0.116410) | 0.514598 / 1.386936 (-0.872338) |\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) | 0.006260 / 0.011353 (-0.005093) | 0.004159 / 0.011008 (-0.006850) | 0.076780 / 0.038508 (0.038272) | 0.027899 / 0.023109 (0.004789) | 0.412756 / 0.275898 (0.136858) | 0.455145 / 0.323480 (0.131665) | 0.005029 / 0.007986 (-0.002956) | 0.003482 / 0.004328 (-0.000847) | 0.076148 / 0.004250 (0.071898) | 0.038969 / 0.037052 (0.001917) | 0.429975 / 0.258489 (0.171486) | 0.465880 / 0.293841 (0.172039) | 0.025555 / 0.128546 (-0.102991) | 0.008612 / 0.075646 (-0.067034) | 0.082604 / 0.419271 (-0.336667) | 0.039690 / 0.043533 (-0.003842) | 0.403644 / 0.255139 (0.148505) | 0.440438 / 0.283200 (0.157238) | 0.090984 / 0.141683 (-0.050699) | 1.465915 / 1.452155 (0.013760) | 1.564227 / 1.492716 (0.071511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010502 / 0.018006 (-0.007504) | 0.410573 / 0.000490 (0.410083) | 0.000384 / 0.000200 (0.000184) | 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) | 0.025726 / 0.037411 (-0.011686) | 0.101760 / 0.014526 (0.087235) | 0.110102 / 0.176557 (-0.066454) | 0.161321 / 0.737135 (-0.575815) | 0.112507 / 0.296338 (-0.183832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469925 / 0.215209 (0.254716) | 4.718740 / 2.077655 (2.641085) | 2.466272 / 1.504120 (0.962152) | 2.267357 / 1.541195 (0.726162) | 2.331343 / 1.468490 (0.862853) | 0.553448 / 4.584777 (-4.031329) | 3.464228 / 3.745712 (-0.281484) | 3.060957 / 5.269862 (-2.208905) | 1.387261 / 4.565676 (-3.178415) | 0.067989 / 0.424275 (-0.356286) | 0.012349 / 0.007607 (0.004741) | 0.575046 / 0.226044 (0.349001) | 5.740322 / 2.268929 (3.471394) | 2.925666 / 55.444624 (-52.518958) | 2.606535 / 6.876477 (-4.269942) | 2.658144 / 2.142072 (0.516072) | 0.655157 / 4.805227 (-4.150071) | 0.138520 / 6.500664 (-6.362144) | 0.069442 / 0.075469 (-0.006027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306523 / 1.841788 (-0.535265) | 14.400380 / 8.074308 (6.326072) | 14.231519 / 10.191392 (4.040127) | 0.146194 / 0.680424 (-0.534230) | 0.016632 / 0.534201 (-0.517569) | 0.361151 / 0.579283 (-0.218132) | 0.388838 / 0.434364 (-0.045526) | 0.419337 / 0.540337 (-0.121001) | 0.500483 / 1.386936 (-0.886453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009430 / 0.011353 (-0.001923) | 0.006673 / 0.011008 (-0.004335) | 0.125151 / 0.038508 (0.086643) | 0.038258 / 0.023109 (0.015149) | 0.426383 / 0.275898 (0.150485) | 0.432327 / 0.323480 (0.108847) | 0.006964 / 0.007986 (-0.001022) | 0.005140 / 0.004328 (0.000811) | 0.100767 / 0.004250 (0.096517) | 0.058663 / 0.037052 (0.021610) | 0.424709 / 0.258489 (0.166220) | 0.453049 / 0.293841 (0.159208) | 0.051042 / 0.128546 (-0.077505) | 0.015291 / 0.075646 (-0.060355) | 0.456549 / 0.419271 (0.037278) | 0.067106 / 0.043533 (0.023573) | 0.408959 / 0.255139 (0.153820) | 0.445067 / 0.283200 (0.161867) | 0.115590 / 0.141683 (-0.026092) | 1.929439 / 1.452155 (0.477284) | 2.045709 / 1.492716 (0.552992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250726 / 0.018006 (0.232720) | 0.598976 / 0.000490 (0.598486) | 0.007542 / 0.000200 (0.007342) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030317 / 0.037411 (-0.007094) | 0.133177 / 0.014526 (0.118651) | 0.152761 / 0.176557 (-0.023795) | 0.233708 / 0.737135 (-0.503428) | 0.147303 / 0.296338 (-0.149036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633562 / 0.215209 (0.418353) | 6.235021 / 2.077655 (4.157366) | 2.652573 / 1.504120 (1.148454) | 2.223363 / 1.541195 (0.682168) | 2.231022 / 1.468490 (0.762531) | 0.942218 / 4.584777 (-3.642559) | 6.068661 / 3.745712 (2.322949) | 2.778604 / 5.269862 (-2.491257) | 1.787939 / 4.565676 (-2.777737) | 0.117749 / 0.424275 (-0.306526) | 0.015613 / 0.007607 (0.008006) | 0.810222 / 0.226044 (0.584177) | 7.931509 / 2.268929 (5.662581) | 3.260679 / 55.444624 (-52.183945) | 2.609085 / 6.876477 (-4.267391) | 2.867838 / 2.142072 (0.725766) | 1.144672 / 4.805227 (-3.660555) | 0.224379 / 6.500664 (-6.276285) | 0.084490 / 0.075469 (0.009021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.650608 / 1.841788 (-0.191179) | 18.919748 / 8.074308 (10.845440) | 20.163162 / 10.191392 (9.971770) | 0.229427 / 0.680424 (-0.450997) | 0.033090 / 0.534201 (-0.501111) | 0.535549 / 0.579283 (-0.043734) | 0.658629 / 0.434364 (0.224265) | 0.631526 / 0.540337 (0.091189) | 0.748701 / 1.386936 (-0.638235) |\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) | 0.009157 / 0.011353 (-0.002196) | 0.006153 / 0.011008 (-0.004856) | 0.106294 / 0.038508 (0.067786) | 0.040947 / 0.023109 (0.017837) | 0.493242 / 0.275898 (0.217344) | 0.563525 / 0.323480 (0.240045) | 0.007256 / 0.007986 (-0.000730) | 0.006757 / 0.004328 (0.002429) | 0.105151 / 0.004250 (0.100901) | 0.056262 / 0.037052 (0.019209) | 0.573341 / 0.258489 (0.314852) | 0.591125 / 0.293841 (0.297284) | 0.047935 / 0.128546 (-0.080611) | 0.015385 / 0.075646 (-0.060262) | 0.119457 / 0.419271 (-0.299814) | 0.066510 / 0.043533 (0.022977) | 0.485622 / 0.255139 (0.230483) | 0.540929 / 0.283200 (0.257730) | 0.132619 / 0.141683 (-0.009064) | 1.916905 / 1.452155 (0.464750) | 2.152722 / 1.492716 (0.660006) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294823 / 0.018006 (0.276817) | 0.569371 / 0.000490 (0.568882) | 0.000642 / 0.000200 (0.000442) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034321 / 0.037411 (-0.003090) | 0.134165 / 0.014526 (0.119639) | 0.157871 / 0.176557 (-0.018685) | 0.210753 / 0.737135 (-0.526382) | 0.152961 / 0.296338 (-0.143377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686810 / 0.215209 (0.471601) | 6.890432 / 2.077655 (4.812778) | 3.182875 / 1.504120 (1.678755) | 2.770836 / 1.541195 (1.229641) | 2.790785 / 1.468490 (1.322295) | 0.938145 / 4.584777 (-3.646632) | 5.861093 / 3.745712 (2.115381) | 2.719862 / 5.269862 (-2.550000) | 1.760834 / 4.565676 (-2.804842) | 0.111317 / 0.424275 (-0.312958) | 0.015722 / 0.007607 (0.008115) | 0.863032 / 0.226044 (0.636988) | 8.482433 / 2.268929 (6.213504) | 3.892621 / 55.444624 (-51.552003) | 3.207370 / 6.876477 (-3.669106) | 3.344412 / 2.142072 (1.202339) | 1.133903 / 4.805227 (-3.671324) | 0.223456 / 6.500664 (-6.277209) | 0.084335 / 0.075469 (0.008866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.794116 / 1.841788 (-0.047672) | 19.077447 / 8.074308 (11.003139) | 23.102309 / 10.191392 (12.910917) | 0.268806 / 0.680424 (-0.411617) | 0.027709 / 0.534201 (-0.506492) | 0.540488 / 0.579283 (-0.038796) | 0.658478 / 0.434364 (0.224114) | 0.604769 / 0.540337 (0.064431) | 0.722768 / 1.386936 (-0.664168) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007439 / 0.011353 (-0.003914) | 0.004884 / 0.011008 (-0.006124) | 0.098750 / 0.038508 (0.060242) | 0.040723 / 0.023109 (0.017613) | 0.347242 / 0.275898 (0.071344) | 0.381202 / 0.323480 (0.057722) | 0.006814 / 0.007986 (-0.001171) | 0.004543 / 0.004328 (0.000215) | 0.075338 / 0.004250 (0.071088) | 0.058976 / 0.037052 (0.021924) | 0.344746 / 0.258489 (0.086257) | 0.406761 / 0.293841 (0.112920) | 0.028961 / 0.128546 (-0.099585) | 0.009531 / 0.075646 (-0.066115) | 0.337324 / 0.419271 (-0.081947) | 0.051071 / 0.043533 (0.007538) | 0.341251 / 0.255139 (0.086112) | 0.362773 / 0.283200 (0.079573) | 0.109423 / 0.141683 (-0.032260) | 1.457420 / 1.452155 (0.005266) | 1.588824 / 1.492716 (0.096108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288620 / 0.018006 (0.270614) | 0.568975 / 0.000490 (0.568485) | 0.003350 / 0.000200 (0.003150) | 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) | 0.028732 / 0.037411 (-0.008680) | 0.117820 / 0.014526 (0.103294) | 0.120180 / 0.176557 (-0.056376) | 0.178736 / 0.737135 (-0.558399) | 0.126399 / 0.296338 (-0.169939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428357 / 0.215209 (0.213148) | 4.251989 / 2.077655 (2.174334) | 2.005239 / 1.504120 (0.501119) | 1.784009 / 1.541195 (0.242815) | 1.883763 / 1.468490 (0.415272) | 0.555429 / 4.584777 (-4.029348) | 3.868146 / 3.745712 (0.122434) | 2.081896 / 5.269862 (-3.187965) | 1.126047 / 4.565676 (-3.439629) | 0.069496 / 0.424275 (-0.354779) | 0.012926 / 0.007607 (0.005318) | 0.536989 / 0.226044 (0.310944) | 5.256052 / 2.268929 (2.987124) | 2.526802 / 55.444624 (-52.917822) | 2.233346 / 6.876477 (-4.643131) | 2.389063 / 2.142072 (0.246990) | 0.677107 / 4.805227 (-4.128120) | 0.147212 / 6.500664 (-6.353452) | 0.067061 / 0.075469 (-0.008408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210651 / 1.841788 (-0.631137) | 17.236898 / 8.074308 (9.162589) | 14.427301 / 10.191392 (4.235909) | 0.207194 / 0.680424 (-0.473229) | 0.018079 / 0.534201 (-0.516122) | 0.398355 / 0.579283 (-0.180929) | 0.462453 / 0.434364 (0.028089) | 0.484544 / 0.540337 (-0.055794) | 0.590119 / 1.386936 (-0.796817) |\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) | 0.007392 / 0.011353 (-0.003961) | 0.005614 / 0.011008 (-0.005394) | 0.075587 / 0.038508 (0.037079) | 0.040429 / 0.023109 (0.017320) | 0.389901 / 0.275898 (0.114003) | 0.429466 / 0.323480 (0.105986) | 0.006790 / 0.007986 (-0.001196) | 0.006627 / 0.004328 (0.002299) | 0.075227 / 0.004250 (0.070976) | 0.060298 / 0.037052 (0.023246) | 0.391905 / 0.258489 (0.133416) | 0.449385 / 0.293841 (0.155544) | 0.028794 / 0.128546 (-0.099753) | 0.009461 / 0.075646 (-0.066185) | 0.083386 / 0.419271 (-0.335886) | 0.057968 / 0.043533 (0.014435) | 0.377327 / 0.255139 (0.122188) | 0.402825 / 0.283200 (0.119626) | 0.125477 / 0.141683 (-0.016206) | 1.462986 / 1.452155 (0.010832) | 1.595959 / 1.492716 (0.103243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304179 / 0.018006 (0.286173) | 0.543113 / 0.000490 (0.542623) | 0.004136 / 0.000200 (0.003936) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032617 / 0.037411 (-0.004794) | 0.123596 / 0.014526 (0.109070) | 0.128714 / 0.176557 (-0.047842) | 0.176344 / 0.737135 (-0.560792) | 0.132525 / 0.296338 (-0.163813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446041 / 0.215209 (0.230832) | 4.438799 / 2.077655 (2.361144) | 2.210815 / 1.504120 (0.706695) | 2.052025 / 1.541195 (0.510830) | 2.204687 / 1.468490 (0.736197) | 0.535219 / 4.584777 (-4.049558) | 3.858407 / 3.745712 (0.112695) | 3.826043 / 5.269862 (-1.443819) | 1.334149 / 4.565676 (-3.231527) | 0.067454 / 0.424275 (-0.356821) | 0.012566 / 0.007607 (0.004958) | 0.551597 / 0.226044 (0.325553) | 5.520054 / 2.268929 (3.251126) | 2.817976 / 55.444624 (-52.626649) | 2.528074 / 6.876477 (-4.348403) | 2.622391 / 2.142072 (0.480319) | 0.657632 / 4.805227 (-4.147595) | 0.147039 / 6.500664 (-6.353625) | 0.069603 / 0.075469 (-0.005866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300140 / 1.841788 (-0.541648) | 17.303907 / 8.074308 (9.229599) | 15.657887 / 10.191392 (5.466495) | 0.168991 / 0.680424 (-0.511433) | 0.021332 / 0.534201 (-0.512869) | 0.487261 / 0.579283 (-0.092022) | 0.450073 / 0.434364 (0.015709) | 0.465865 / 0.540337 (-0.074473) | 0.565501 / 1.386936 (-0.821435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006536 / 0.011353 (-0.004817) | 0.004254 / 0.011008 (-0.006755) | 0.095387 / 0.038508 (0.056878) | 0.032885 / 0.023109 (0.009776) | 0.298580 / 0.275898 (0.022682) | 0.319771 / 0.323480 (-0.003709) | 0.005510 / 0.007986 (-0.002476) | 0.003891 / 0.004328 (-0.000437) | 0.073763 / 0.004250 (0.069513) | 0.041625 / 0.037052 (0.004573) | 0.294896 / 0.258489 (0.036407) | 0.341308 / 0.293841 (0.047467) | 0.027898 / 0.128546 (-0.100648) | 0.008837 / 0.075646 (-0.066809) | 0.325055 / 0.419271 (-0.094216) | 0.050652 / 0.043533 (0.007119) | 0.298756 / 0.255139 (0.043617) | 0.318261 / 0.283200 (0.035061) | 0.098927 / 0.141683 (-0.042756) | 1.450356 / 1.452155 (-0.001798) | 1.508034 / 1.492716 (0.015318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209009 / 0.018006 (0.191003) | 0.439154 / 0.000490 (0.438665) | 0.004299 / 0.000200 (0.004099) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025938 / 0.037411 (-0.011473) | 0.105954 / 0.014526 (0.091429) | 0.113858 / 0.176557 (-0.062698) | 0.168887 / 0.737135 (-0.568249) | 0.121292 / 0.296338 (-0.175046) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402050 / 0.215209 (0.186841) | 4.002310 / 2.077655 (1.924655) | 1.816190 / 1.504120 (0.312070) | 1.634404 / 1.541195 (0.093209) | 1.713632 / 1.468490 (0.245142) | 0.519633 / 4.584777 (-4.065144) | 3.740291 / 3.745712 (-0.005421) | 1.787602 / 5.269862 (-3.482260) | 1.038844 / 4.565676 (-3.526833) | 0.064973 / 0.424275 (-0.359302) | 0.012475 / 0.007607 (0.004868) | 0.498152 / 0.226044 (0.272108) | 4.970941 / 2.268929 (2.702013) | 2.287429 / 55.444624 (-53.157195) | 1.998050 / 6.876477 (-4.878427) | 2.091903 / 2.142072 (-0.050169) | 0.630363 / 4.805227 (-4.174864) | 0.138623 / 6.500664 (-6.362041) | 0.063293 / 0.075469 (-0.012176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201802 / 1.841788 (-0.639986) | 14.073836 / 8.074308 (5.999528) | 12.968665 / 10.191392 (2.777273) | 0.144653 / 0.680424 (-0.535771) | 0.017613 / 0.534201 (-0.516588) | 0.392067 / 0.579283 (-0.187216) | 0.416955 / 0.434364 (-0.017409) | 0.471492 / 0.540337 (-0.068845) | 0.554576 / 1.386936 (-0.832360) |\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) | 0.006408 / 0.011353 (-0.004945) | 0.004452 / 0.011008 (-0.006556) | 0.073648 / 0.038508 (0.035140) | 0.032536 / 0.023109 (0.009427) | 0.358546 / 0.275898 (0.082648) | 0.387330 / 0.323480 (0.063850) | 0.005542 / 0.007986 (-0.002444) | 0.003882 / 0.004328 (-0.000447) | 0.073867 / 0.004250 (0.069617) | 0.044798 / 0.037052 (0.007746) | 0.362303 / 0.258489 (0.103814) | 0.400496 / 0.293841 (0.106655) | 0.028244 / 0.128546 (-0.100302) | 0.008931 / 0.075646 (-0.066715) | 0.080617 / 0.419271 (-0.338654) | 0.046575 / 0.043533 (0.003043) | 0.364283 / 0.255139 (0.109145) | 0.373215 / 0.283200 (0.090015) | 0.100080 / 0.141683 (-0.041603) | 1.430047 / 1.452155 (-0.022108) | 1.530957 / 1.492716 (0.038240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221061 / 0.018006 (0.203055) | 0.441753 / 0.000490 (0.441263) | 0.003626 / 0.000200 (0.003426) | 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) | 0.029509 / 0.037411 (-0.007902) | 0.109578 / 0.014526 (0.095053) | 0.121009 / 0.176557 (-0.055548) | 0.168950 / 0.737135 (-0.568185) | 0.124475 / 0.296338 (-0.171864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431355 / 0.215209 (0.216146) | 4.295507 / 2.077655 (2.217852) | 2.167514 / 1.504120 (0.663394) | 2.013073 / 1.541195 (0.471879) | 1.973730 / 1.468490 (0.505240) | 0.529778 / 4.584777 (-4.054999) | 3.794702 / 3.745712 (0.048989) | 3.062940 / 5.269862 (-2.206922) | 1.503426 / 4.565676 (-3.062251) | 0.066692 / 0.424275 (-0.357583) | 0.011682 / 0.007607 (0.004075) | 0.539311 / 0.226044 (0.313266) | 5.406342 / 2.268929 (3.137414) | 2.652709 / 55.444624 (-52.791916) | 2.260066 / 6.876477 (-4.616410) | 2.295752 / 2.142072 (0.153680) | 0.647199 / 4.805227 (-4.158029) | 0.142981 / 6.500664 (-6.357683) | 0.065082 / 0.075469 (-0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279788 / 1.841788 (-0.562000) | 14.982845 / 8.074308 (6.908536) | 14.277166 / 10.191392 (4.085774) | 0.145082 / 0.680424 (-0.535342) | 0.017885 / 0.534201 (-0.516316) | 0.392071 / 0.579283 (-0.187212) | 0.420425 / 0.434364 (-0.013939) | 0.461244 / 0.540337 (-0.079093) | 0.559956 / 1.386936 (-0.826980) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.005673 / 0.011008 (-0.005335) | 0.124034 / 0.038508 (0.085526) | 0.037550 / 0.023109 (0.014441) | 0.331301 / 0.275898 (0.055403) | 0.383542 / 0.323480 (0.060062) | 0.006940 / 0.007986 (-0.001046) | 0.005959 / 0.004328 (0.001631) | 0.084670 / 0.004250 (0.080419) | 0.054214 / 0.037052 (0.017162) | 0.359897 / 0.258489 (0.101408) | 0.383260 / 0.293841 (0.089419) | 0.047642 / 0.128546 (-0.080904) | 0.013902 / 0.075646 (-0.061744) | 0.380232 / 0.419271 (-0.039040) | 0.077790 / 0.043533 (0.034257) | 0.376648 / 0.255139 (0.121509) | 0.387536 / 0.283200 (0.104336) | 0.104644 / 0.141683 (-0.037038) | 1.618560 / 1.452155 (0.166406) | 1.742569 / 1.492716 (0.249853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257218 / 0.018006 (0.239212) | 0.636801 / 0.000490 (0.636311) | 0.000634 / 0.000200 (0.000434) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037874 / 0.037411 (0.000462) | 0.107454 / 0.014526 (0.092928) | 0.117855 / 0.176557 (-0.058702) | 0.204067 / 0.737135 (-0.533068) | 0.134029 / 0.296338 (-0.162310) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583657 / 0.215209 (0.368447) | 5.761289 / 2.077655 (3.683635) | 2.280201 / 1.504120 (0.776081) | 2.033442 / 1.541195 (0.492247) | 2.035343 / 1.468490 (0.566853) | 0.868122 / 4.584777 (-3.716655) | 5.352591 / 3.745712 (1.606879) | 2.432814 / 5.269862 (-2.837047) | 1.560765 / 4.565676 (-3.004911) | 0.098793 / 0.424275 (-0.325482) | 0.017327 / 0.007607 (0.009720) | 0.734676 / 0.226044 (0.508631) | 7.070318 / 2.268929 (4.801390) | 2.972701 / 55.444624 (-52.471924) | 2.442189 / 6.876477 (-4.434288) | 2.604379 / 2.142072 (0.462307) | 1.028853 / 4.805227 (-3.776374) | 0.210390 / 6.500664 (-6.290274) | 0.069329 / 0.075469 (-0.006140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.469586 / 1.841788 (-0.372202) | 16.570305 / 8.074308 (8.495997) | 19.187845 / 10.191392 (8.996453) | 0.219162 / 0.680424 (-0.461262) | 0.026356 / 0.534201 (-0.507845) | 0.447370 / 0.579283 (-0.131913) | 0.555893 / 0.434364 (0.121529) | 0.574958 / 0.540337 (0.034621) | 0.639166 / 1.386936 (-0.747770) |\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) | 0.008166 / 0.011353 (-0.003187) | 0.005577 / 0.011008 (-0.005431) | 0.103578 / 0.038508 (0.065070) | 0.040563 / 0.023109 (0.017454) | 0.441996 / 0.275898 (0.166098) | 0.483594 / 0.323480 (0.160114) | 0.007329 / 0.007986 (-0.000657) | 0.004546 / 0.004328 (0.000218) | 0.090471 / 0.004250 (0.086220) | 0.052740 / 0.037052 (0.015688) | 0.442197 / 0.258489 (0.183708) | 0.524310 / 0.293841 (0.230469) | 0.042487 / 0.128546 (-0.086060) | 0.012917 / 0.075646 (-0.062730) | 0.103992 / 0.419271 (-0.315280) | 0.060570 / 0.043533 (0.017037) | 0.441956 / 0.255139 (0.186817) | 0.477084 / 0.283200 (0.193885) | 0.103815 / 0.141683 (-0.037868) | 1.696963 / 1.452155 (0.244809) | 1.747849 / 1.492716 (0.255132) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292465 / 0.018006 (0.274458) | 0.571518 / 0.000490 (0.571028) | 0.000476 / 0.000200 (0.000276) | 0.000077 / 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) | 0.028697 / 0.037411 (-0.008714) | 0.111671 / 0.014526 (0.097145) | 0.138826 / 0.176557 (-0.037731) | 0.189697 / 0.737135 (-0.547439) | 0.125454 / 0.296338 (-0.170884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.619273 / 0.215209 (0.404064) | 6.138669 / 2.077655 (4.061015) | 2.558622 / 1.504120 (1.054502) | 2.201550 / 1.541195 (0.660356) | 2.279034 / 1.468490 (0.810544) | 0.850752 / 4.584777 (-3.734025) | 5.438185 / 3.745712 (1.692473) | 2.529343 / 5.269862 (-2.740518) | 1.572178 / 4.565676 (-2.993499) | 0.100768 / 0.424275 (-0.323507) | 0.013902 / 0.007607 (0.006295) | 0.726660 / 0.226044 (0.500616) | 7.794918 / 2.268929 (5.525990) | 3.311695 / 55.444624 (-52.132930) | 2.729167 / 6.876477 (-4.147310) | 2.630984 / 2.142072 (0.488911) | 1.018534 / 4.805227 (-3.786693) | 0.194602 / 6.500664 (-6.306062) | 0.070876 / 0.075469 (-0.004593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573005 / 1.841788 (-0.268783) | 17.042710 / 8.074308 (8.968401) | 19.615320 / 10.191392 (9.423928) | 0.229405 / 0.680424 (-0.451019) | 0.027560 / 0.534201 (-0.506641) | 0.447984 / 0.579283 (-0.131299) | 0.598392 / 0.434364 (0.164028) | 0.571769 / 0.540337 (0.031431) | 0.653025 / 1.386936 (-0.733911) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006113 / 0.011353 (-0.005239) | 0.004195 / 0.011008 (-0.006813) | 0.098103 / 0.038508 (0.059595) | 0.027970 / 0.023109 (0.004860) | 0.300992 / 0.275898 (0.025094) | 0.335402 / 0.323480 (0.011922) | 0.005079 / 0.007986 (-0.002906) | 0.003516 / 0.004328 (-0.000813) | 0.077311 / 0.004250 (0.073061) | 0.037863 / 0.037052 (0.000810) | 0.302638 / 0.258489 (0.044149) | 0.346554 / 0.293841 (0.052713) | 0.025218 / 0.128546 (-0.103328) | 0.008630 / 0.075646 (-0.067017) | 0.319748 / 0.419271 (-0.099523) | 0.049182 / 0.043533 (0.005650) | 0.306233 / 0.255139 (0.051094) | 0.331040 / 0.283200 (0.047840) | 0.089203 / 0.141683 (-0.052480) | 1.496104 / 1.452155 (0.043949) | 1.567878 / 1.492716 (0.075162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215774 / 0.018006 (0.197768) | 0.436810 / 0.000490 (0.436320) | 0.000307 / 0.000200 (0.000107) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024102 / 0.037411 (-0.013310) | 0.095459 / 0.014526 (0.080933) | 0.106564 / 0.176557 (-0.069992) | 0.169894 / 0.737135 (-0.567241) | 0.109152 / 0.296338 (-0.187186) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429066 / 0.215209 (0.213857) | 4.297385 / 2.077655 (2.219730) | 2.054854 / 1.504120 (0.550734) | 1.846844 / 1.541195 (0.305649) | 1.840807 / 1.468490 (0.372317) | 0.553193 / 4.584777 (-4.031584) | 3.366788 / 3.745712 (-0.378924) | 1.727337 / 5.269862 (-3.542525) | 0.994357 / 4.565676 (-3.571319) | 0.067790 / 0.424275 (-0.356485) | 0.012002 / 0.007607 (0.004395) | 0.533335 / 0.226044 (0.307291) | 5.341341 / 2.268929 (3.072412) | 2.543581 / 55.444624 (-52.901043) | 2.220374 / 6.876477 (-4.656103) | 2.321656 / 2.142072 (0.179583) | 0.654408 / 4.805227 (-4.150819) | 0.134693 / 6.500664 (-6.365971) | 0.066926 / 0.075469 (-0.008544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209463 / 1.841788 (-0.632325) | 13.568221 / 8.074308 (5.493913) | 13.965418 / 10.191392 (3.774026) | 0.145049 / 0.680424 (-0.535375) | 0.016936 / 0.534201 (-0.517265) | 0.371587 / 0.579283 (-0.207696) | 0.386363 / 0.434364 (-0.048001) | 0.437137 / 0.540337 (-0.103201) | 0.514779 / 1.386936 (-0.872157) |\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) | 0.006245 / 0.011353 (-0.005108) | 0.004232 / 0.011008 (-0.006776) | 0.075682 / 0.038508 (0.037174) | 0.027858 / 0.023109 (0.004749) | 0.425325 / 0.275898 (0.149427) | 0.466732 / 0.323480 (0.143253) | 0.005240 / 0.007986 (-0.002745) | 0.003506 / 0.004328 (-0.000823) | 0.075294 / 0.004250 (0.071044) | 0.041677 / 0.037052 (0.004624) | 0.426552 / 0.258489 (0.168063) | 0.469452 / 0.293841 (0.175611) | 0.025443 / 0.128546 (-0.103104) | 0.008526 / 0.075646 (-0.067120) | 0.082190 / 0.419271 (-0.337081) | 0.040906 / 0.043533 (-0.002626) | 0.428406 / 0.255139 (0.173267) | 0.446795 / 0.283200 (0.163595) | 0.093837 / 0.141683 (-0.047846) | 1.518639 / 1.452155 (0.066484) | 1.620214 / 1.492716 (0.127498) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223259 / 0.018006 (0.205253) | 0.425077 / 0.000490 (0.424588) | 0.001980 / 0.000200 (0.001780) | 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) | 0.025813 / 0.037411 (-0.011599) | 0.103062 / 0.014526 (0.088536) | 0.108958 / 0.176557 (-0.067598) | 0.161591 / 0.737135 (-0.575544) | 0.112130 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472843 / 0.215209 (0.257634) | 4.713281 / 2.077655 (2.635626) | 2.458216 / 1.504120 (0.954096) | 2.272467 / 1.541195 (0.731273) | 2.324456 / 1.468490 (0.855965) | 0.554686 / 4.584777 (-4.030091) | 3.445079 / 3.745712 (-0.300634) | 3.451896 / 5.269862 (-1.817966) | 1.431065 / 4.565676 (-3.134612) | 0.067868 / 0.424275 (-0.356407) | 0.012093 / 0.007607 (0.004486) | 0.573571 / 0.226044 (0.347526) | 5.820452 / 2.268929 (3.551523) | 2.934858 / 55.444624 (-52.509767) | 2.602719 / 6.876477 (-4.273758) | 2.645999 / 2.142072 (0.503927) | 0.660688 / 4.805227 (-4.144540) | 0.137490 / 6.500664 (-6.363174) | 0.068311 / 0.075469 (-0.007158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.321709 / 1.841788 (-0.520079) | 14.592346 / 8.074308 (6.518038) | 14.520748 / 10.191392 (4.329356) | 0.132689 / 0.680424 (-0.547735) | 0.016422 / 0.534201 (-0.517779) | 0.370071 / 0.579283 (-0.209212) | 0.397091 / 0.434364 (-0.037273) | 0.431979 / 0.540337 (-0.108358) | 0.509965 / 1.386936 (-0.876971) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006182 / 0.011353 (-0.005171) | 0.004153 / 0.011008 (-0.006855) | 0.095715 / 0.038508 (0.057207) | 0.032457 / 0.023109 (0.009347) | 0.314961 / 0.275898 (0.039063) | 0.353696 / 0.323480 (0.030216) | 0.005256 / 0.007986 (-0.002729) | 0.004870 / 0.004328 (0.000541) | 0.072442 / 0.004250 (0.068192) | 0.046102 / 0.037052 (0.009050) | 0.324410 / 0.258489 (0.065921) | 0.366861 / 0.293841 (0.073020) | 0.027088 / 0.128546 (-0.101458) | 0.008572 / 0.075646 (-0.067075) | 0.325988 / 0.419271 (-0.093284) | 0.049494 / 0.043533 (0.005961) | 0.311221 / 0.255139 (0.056082) | 0.359720 / 0.283200 (0.076521) | 0.095101 / 0.141683 (-0.046581) | 1.472821 / 1.452155 (0.020667) | 1.516157 / 1.492716 (0.023441) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210456 / 0.018006 (0.192450) | 0.439440 / 0.000490 (0.438950) | 0.003764 / 0.000200 (0.003564) | 0.000087 / 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) | 0.024076 / 0.037411 (-0.013335) | 0.104886 / 0.014526 (0.090360) | 0.114164 / 0.176557 (-0.062393) | 0.167289 / 0.737135 (-0.569847) | 0.116457 / 0.296338 (-0.179882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400039 / 0.215209 (0.184830) | 3.973243 / 2.077655 (1.895588) | 1.801991 / 1.504120 (0.297871) | 1.592017 / 1.541195 (0.050822) | 1.612564 / 1.468490 (0.144074) | 0.527475 / 4.584777 (-4.057302) | 3.676246 / 3.745712 (-0.069466) | 1.806423 / 5.269862 (-3.463438) | 1.176921 / 4.565676 (-3.388756) | 0.065902 / 0.424275 (-0.358373) | 0.012245 / 0.007607 (0.004638) | 0.490883 / 0.226044 (0.264838) | 4.905270 / 2.268929 (2.636341) | 2.218694 / 55.444624 (-53.225930) | 1.903074 / 6.876477 (-4.973403) | 1.979505 / 2.142072 (-0.162567) | 0.644415 / 4.805227 (-4.160812) | 0.142433 / 6.500664 (-6.358231) | 0.063564 / 0.075469 (-0.011905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193756 / 1.841788 (-0.648032) | 14.673103 / 8.074308 (6.598795) | 13.410951 / 10.191392 (3.219559) | 0.159175 / 0.680424 (-0.521249) | 0.017076 / 0.534201 (-0.517125) | 0.388880 / 0.579283 (-0.190403) | 0.409974 / 0.434364 (-0.024390) | 0.454494 / 0.540337 (-0.085844) | 0.556873 / 1.386936 (-0.830063) |\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) | 0.006107 / 0.011353 (-0.005246) | 0.004433 / 0.011008 (-0.006575) | 0.073892 / 0.038508 (0.035384) | 0.032386 / 0.023109 (0.009277) | 0.370339 / 0.275898 (0.094441) | 0.388996 / 0.323480 (0.065516) | 0.005438 / 0.007986 (-0.002548) | 0.003875 / 0.004328 (-0.000454) | 0.073867 / 0.004250 (0.069617) | 0.048350 / 0.037052 (0.011298) | 0.380328 / 0.258489 (0.121839) | 0.411373 / 0.293841 (0.117532) | 0.028183 / 0.128546 (-0.100363) | 0.008924 / 0.075646 (-0.066723) | 0.082484 / 0.419271 (-0.336787) | 0.047321 / 0.043533 (0.003788) | 0.371702 / 0.255139 (0.116563) | 0.380535 / 0.283200 (0.097335) | 0.100772 / 0.141683 (-0.040911) | 1.475038 / 1.452155 (0.022883) | 1.564293 / 1.492716 (0.071577) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214589 / 0.018006 (0.196583) | 0.437193 / 0.000490 (0.436703) | 0.003676 / 0.000200 (0.003476) | 0.000094 / 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) | 0.027991 / 0.037411 (-0.009421) | 0.111154 / 0.014526 (0.096628) | 0.120365 / 0.176557 (-0.056191) | 0.173601 / 0.737135 (-0.563535) | 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) | 0.442848 / 0.215209 (0.227639) | 4.398336 / 2.077655 (2.320681) | 2.217058 / 1.504120 (0.712938) | 2.011155 / 1.541195 (0.469960) | 2.123086 / 1.468490 (0.654596) | 0.525857 / 4.584777 (-4.058920) | 3.730191 / 3.745712 (-0.015521) | 3.517680 / 5.269862 (-1.752181) | 1.557940 / 4.565676 (-3.007736) | 0.066309 / 0.424275 (-0.357967) | 0.011788 / 0.007607 (0.004181) | 0.548506 / 0.226044 (0.322462) | 5.483615 / 2.268929 (3.214687) | 2.663784 / 55.444624 (-52.780840) | 2.325744 / 6.876477 (-4.550732) | 2.344179 / 2.142072 (0.202106) | 0.644217 / 4.805227 (-4.161010) | 0.141546 / 6.500664 (-6.359118) | 0.063730 / 0.075469 (-0.011739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296032 / 1.841788 (-0.545756) | 14.903729 / 8.074308 (6.829421) | 14.505409 / 10.191392 (4.314017) | 0.170478 / 0.680424 (-0.509946) | 0.017876 / 0.534201 (-0.516325) | 0.401047 / 0.579283 (-0.178236) | 0.417855 / 0.434364 (-0.016509) | 0.472138 / 0.540337 (-0.068200) | 0.570859 / 1.386936 (-0.816077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008495 / 0.011353 (-0.002858) | 0.005322 / 0.011008 (-0.005686) | 0.125471 / 0.038508 (0.086962) | 0.034604 / 0.023109 (0.011495) | 0.419831 / 0.275898 (0.143933) | 0.415707 / 0.323480 (0.092227) | 0.007471 / 0.007986 (-0.000515) | 0.005441 / 0.004328 (0.001112) | 0.095412 / 0.004250 (0.091162) | 0.053865 / 0.037052 (0.016812) | 0.375257 / 0.258489 (0.116768) | 0.438114 / 0.293841 (0.144273) | 0.046183 / 0.128546 (-0.082363) | 0.013663 / 0.075646 (-0.061984) | 0.438317 / 0.419271 (0.019045) | 0.065665 / 0.043533 (0.022133) | 0.387640 / 0.255139 (0.132501) | 0.431350 / 0.283200 (0.148150) | 0.112841 / 0.141683 (-0.028842) | 1.778639 / 1.452155 (0.326484) | 1.891948 / 1.492716 (0.399232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284371 / 0.018006 (0.266365) | 0.598247 / 0.000490 (0.597758) | 0.013674 / 0.000200 (0.013474) | 0.000483 / 0.000054 (0.000428) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032437 / 0.037411 (-0.004974) | 0.120547 / 0.014526 (0.106021) | 0.129845 / 0.176557 (-0.046711) | 0.203455 / 0.737135 (-0.533680) | 0.140039 / 0.296338 (-0.156300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596549 / 0.215209 (0.381340) | 6.138766 / 2.077655 (4.061111) | 2.515506 / 1.504120 (1.011386) | 2.124472 / 1.541195 (0.583277) | 2.160812 / 1.468490 (0.692322) | 0.898965 / 4.584777 (-3.685812) | 5.588152 / 3.745712 (1.842440) | 2.717580 / 5.269862 (-2.552282) | 1.683641 / 4.565676 (-2.882036) | 0.108045 / 0.424275 (-0.316230) | 0.014089 / 0.007607 (0.006481) | 0.749567 / 0.226044 (0.523523) | 7.518051 / 2.268929 (5.249123) | 3.198238 / 55.444624 (-52.246386) | 2.575156 / 6.876477 (-4.301321) | 2.725818 / 2.142072 (0.583745) | 1.149338 / 4.805227 (-3.655889) | 0.220443 / 6.500664 (-6.280221) | 0.081452 / 0.075469 (0.005983) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624462 / 1.841788 (-0.217325) | 18.204963 / 8.074308 (10.130655) | 21.379169 / 10.191392 (11.187777) | 0.248520 / 0.680424 (-0.431903) | 0.030121 / 0.534201 (-0.504080) | 0.499542 / 0.579283 (-0.079741) | 0.599783 / 0.434364 (0.165419) | 0.597642 / 0.540337 (0.057305) | 0.681948 / 1.386936 (-0.704988) |\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) | 0.008431 / 0.011353 (-0.002921) | 0.006143 / 0.011008 (-0.004865) | 0.107531 / 0.038508 (0.069023) | 0.036308 / 0.023109 (0.013199) | 0.480555 / 0.275898 (0.204657) | 0.556407 / 0.323480 (0.232927) | 0.007614 / 0.007986 (-0.000372) | 0.004749 / 0.004328 (0.000421) | 0.105734 / 0.004250 (0.101484) | 0.051619 / 0.037052 (0.014567) | 0.514821 / 0.258489 (0.256332) | 0.562143 / 0.293841 (0.268302) | 0.042957 / 0.128546 (-0.085589) | 0.015142 / 0.075646 (-0.060505) | 0.143161 / 0.419271 (-0.276111) | 0.061910 / 0.043533 (0.018377) | 0.496923 / 0.255139 (0.241784) | 0.556302 / 0.283200 (0.273102) | 0.136700 / 0.141683 (-0.004983) | 1.886184 / 1.452155 (0.434029) | 2.004087 / 1.492716 (0.511371) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235530 / 0.018006 (0.217523) | 0.600796 / 0.000490 (0.600306) | 0.009074 / 0.000200 (0.008874) | 0.000203 / 0.000054 (0.000149) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036345 / 0.037411 (-0.001066) | 0.126112 / 0.014526 (0.111586) | 0.143369 / 0.176557 (-0.033188) | 0.211381 / 0.737135 (-0.525755) | 0.151095 / 0.296338 (-0.145243) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.695022 / 0.215209 (0.479813) | 6.685981 / 2.077655 (4.608326) | 3.104521 / 1.504120 (1.600401) | 2.758323 / 1.541195 (1.217128) | 2.706286 / 1.468490 (1.237796) | 0.941182 / 4.584777 (-3.643595) | 5.715839 / 3.745712 (1.970127) | 5.089636 / 5.269862 (-0.180226) | 2.594739 / 4.565676 (-1.970937) | 0.112621 / 0.424275 (-0.311655) | 0.014001 / 0.007607 (0.006394) | 0.812990 / 0.226044 (0.586945) | 8.060890 / 2.268929 (5.791961) | 3.832506 / 55.444624 (-51.612119) | 3.148051 / 6.876477 (-3.728425) | 3.110096 / 2.142072 (0.968023) | 1.105050 / 4.805227 (-3.700178) | 0.219835 / 6.500664 (-6.280829) | 0.078600 / 0.075469 (0.003131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.707551 / 1.841788 (-0.134237) | 19.238194 / 8.074308 (11.163885) | 22.167076 / 10.191392 (11.975684) | 0.233458 / 0.680424 (-0.446966) | 0.025131 / 0.534201 (-0.509070) | 0.525241 / 0.579283 (-0.054042) | 0.649666 / 0.434364 (0.215303) | 0.602941 / 0.540337 (0.062603) | 0.718472 / 1.386936 (-0.668464) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006416 / 0.011353 (-0.004937) | 0.004278 / 0.011008 (-0.006731) | 0.097562 / 0.038508 (0.059054) | 0.029488 / 0.023109 (0.006379) | 0.308648 / 0.275898 (0.032750) | 0.339879 / 0.323480 (0.016399) | 0.005288 / 0.007986 (-0.002697) | 0.005033 / 0.004328 (0.000704) | 0.074666 / 0.004250 (0.070416) | 0.034888 / 0.037052 (-0.002164) | 0.309960 / 0.258489 (0.051471) | 0.344276 / 0.293841 (0.050435) | 0.025564 / 0.128546 (-0.102982) | 0.008579 / 0.075646 (-0.067067) | 0.319796 / 0.419271 (-0.099476) | 0.044786 / 0.043533 (0.001253) | 0.308888 / 0.255139 (0.053749) | 0.334001 / 0.283200 (0.050802) | 0.089917 / 0.141683 (-0.051766) | 1.456696 / 1.452155 (0.004541) | 1.542273 / 1.492716 (0.049557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213236 / 0.018006 (0.195230) | 0.425139 / 0.000490 (0.424650) | 0.008831 / 0.000200 (0.008631) | 0.000209 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023990 / 0.037411 (-0.013421) | 0.096787 / 0.014526 (0.082261) | 0.105783 / 0.176557 (-0.070774) | 0.167182 / 0.737135 (-0.569954) | 0.108896 / 0.296338 (-0.187442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419844 / 0.215209 (0.204635) | 4.201909 / 2.077655 (2.124254) | 1.910784 / 1.504120 (0.406664) | 1.685183 / 1.541195 (0.143988) | 1.716927 / 1.468490 (0.248437) | 0.548261 / 4.584777 (-4.036516) | 3.414168 / 3.745712 (-0.331544) | 1.695446 / 5.269862 (-3.574415) | 0.989668 / 4.565676 (-3.576008) | 0.067328 / 0.424275 (-0.356948) | 0.012084 / 0.007607 (0.004477) | 0.523799 / 0.226044 (0.297754) | 5.240589 / 2.268929 (2.971661) | 2.331618 / 55.444624 (-53.113007) | 1.996094 / 6.876477 (-4.880383) | 2.105450 / 2.142072 (-0.036623) | 0.654614 / 4.805227 (-4.150613) | 0.134721 / 6.500664 (-6.365943) | 0.066227 / 0.075469 (-0.009242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196266 / 1.841788 (-0.645521) | 13.990045 / 8.074308 (5.915737) | 13.928126 / 10.191392 (3.736734) | 0.142600 / 0.680424 (-0.537824) | 0.016462 / 0.534201 (-0.517739) | 0.363113 / 0.579283 (-0.216170) | 0.428590 / 0.434364 (-0.005773) | 0.452594 / 0.540337 (-0.087743) | 0.551678 / 1.386936 (-0.835258) |\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) | 0.005992 / 0.011353 (-0.005361) | 0.004161 / 0.011008 (-0.006847) | 0.076098 / 0.038508 (0.037589) | 0.028559 / 0.023109 (0.005450) | 0.411696 / 0.275898 (0.135798) | 0.444519 / 0.323480 (0.121040) | 0.004965 / 0.007986 (-0.003021) | 0.003452 / 0.004328 (-0.000876) | 0.075107 / 0.004250 (0.070857) | 0.037305 / 0.037052 (0.000252) | 0.429728 / 0.258489 (0.171239) | 0.444313 / 0.293841 (0.150472) | 0.025278 / 0.128546 (-0.103268) | 0.008527 / 0.075646 (-0.067120) | 0.081502 / 0.419271 (-0.337770) | 0.041237 / 0.043533 (-0.002296) | 0.417848 / 0.255139 (0.162709) | 0.426615 / 0.283200 (0.143415) | 0.094641 / 0.141683 (-0.047041) | 1.525141 / 1.452155 (0.072987) | 1.615608 / 1.492716 (0.122892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192867 / 0.018006 (0.174861) | 0.414979 / 0.000490 (0.414490) | 0.000815 / 0.000200 (0.000615) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025354 / 0.037411 (-0.012058) | 0.102085 / 0.014526 (0.087559) | 0.107930 / 0.176557 (-0.068626) | 0.160483 / 0.737135 (-0.576652) | 0.112341 / 0.296338 (-0.183997) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446938 / 0.215209 (0.231728) | 4.480057 / 2.077655 (2.402402) | 2.154825 / 1.504120 (0.650705) | 1.942774 / 1.541195 (0.401580) | 1.996418 / 1.468490 (0.527928) | 0.556728 / 4.584777 (-4.028049) | 3.441228 / 3.745712 (-0.304484) | 3.004179 / 5.269862 (-2.265683) | 1.314104 / 4.565676 (-3.251573) | 0.068670 / 0.424275 (-0.355606) | 0.011972 / 0.007607 (0.004365) | 0.556604 / 0.226044 (0.330560) | 5.561783 / 2.268929 (3.292855) | 2.631262 / 55.444624 (-52.813363) | 2.262143 / 6.876477 (-4.614333) | 2.364243 / 2.142072 (0.222170) | 0.660621 / 4.805227 (-4.144607) | 0.137371 / 6.500664 (-6.363293) | 0.069104 / 0.075469 (-0.006365) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305706 / 1.841788 (-0.536081) | 14.015932 / 8.074308 (5.941624) | 14.353580 / 10.191392 (4.162187) | 0.146172 / 0.680424 (-0.534251) | 0.016699 / 0.534201 (-0.517502) | 0.357970 / 0.579283 (-0.221313) | 0.389067 / 0.434364 (-0.045297) | 0.415470 / 0.540337 (-0.124867) | 0.501359 / 1.386936 (-0.885577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006800 / 0.011353 (-0.004552) | 0.004721 / 0.011008 (-0.006287) | 0.097760 / 0.038508 (0.059252) | 0.034192 / 0.023109 (0.011083) | 0.298240 / 0.275898 (0.022342) | 0.331119 / 0.323480 (0.007639) | 0.005826 / 0.007986 (-0.002160) | 0.003968 / 0.004328 (-0.000360) | 0.073833 / 0.004250 (0.069582) | 0.046288 / 0.037052 (0.009236) | 0.303018 / 0.258489 (0.044529) | 0.342163 / 0.293841 (0.048322) | 0.028504 / 0.128546 (-0.100042) | 0.009031 / 0.075646 (-0.066615) | 0.331617 / 0.419271 (-0.087655) | 0.060911 / 0.043533 (0.017379) | 0.304044 / 0.255139 (0.048905) | 0.328959 / 0.283200 (0.045759) | 0.113174 / 0.141683 (-0.028509) | 1.424652 / 1.452155 (-0.027502) | 1.531392 / 1.492716 (0.038676) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206175 / 0.018006 (0.188169) | 0.435916 / 0.000490 (0.435426) | 0.002587 / 0.000200 (0.002387) | 0.000083 / 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) | 0.026996 / 0.037411 (-0.010415) | 0.106722 / 0.014526 (0.092196) | 0.117655 / 0.176557 (-0.058902) | 0.176969 / 0.737135 (-0.560166) | 0.122577 / 0.296338 (-0.173762) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396086 / 0.215209 (0.180877) | 3.972465 / 2.077655 (1.894811) | 1.800798 / 1.504120 (0.296678) | 1.616747 / 1.541195 (0.075552) | 1.680711 / 1.468490 (0.212221) | 0.526479 / 4.584777 (-4.058298) | 3.791528 / 3.745712 (0.045816) | 2.989518 / 5.269862 (-2.280344) | 1.463221 / 4.565676 (-3.102455) | 0.065649 / 0.424275 (-0.358626) | 0.012155 / 0.007607 (0.004548) | 0.500241 / 0.226044 (0.274197) | 5.008895 / 2.268929 (2.739966) | 2.315288 / 55.444624 (-53.129336) | 1.959409 / 6.876477 (-4.917067) | 2.102371 / 2.142072 (-0.039701) | 0.639611 / 4.805227 (-4.165617) | 0.140101 / 6.500664 (-6.360563) | 0.063599 / 0.075469 (-0.011870) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206729 / 1.841788 (-0.635059) | 15.127250 / 8.074308 (7.052942) | 14.397228 / 10.191392 (4.205836) | 0.148802 / 0.680424 (-0.531622) | 0.017628 / 0.534201 (-0.516573) | 0.396150 / 0.579283 (-0.183133) | 0.435826 / 0.434364 (0.001462) | 0.471215 / 0.540337 (-0.069122) | 0.559413 / 1.386936 (-0.827523) |\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) | 0.004520 / 0.011008 (-0.006488) | 0.074395 / 0.038508 (0.035887) | 0.033400 / 0.023109 (0.010291) | 0.388411 / 0.275898 (0.112513) | 0.396714 / 0.323480 (0.073234) | 0.005736 / 0.007986 (-0.002250) | 0.004038 / 0.004328 (-0.000291) | 0.073595 / 0.004250 (0.069345) | 0.045207 / 0.037052 (0.008155) | 0.378096 / 0.258489 (0.119607) | 0.417830 / 0.293841 (0.123989) | 0.028365 / 0.128546 (-0.100181) | 0.008887 / 0.075646 (-0.066760) | 0.080766 / 0.419271 (-0.338505) | 0.046923 / 0.043533 (0.003390) | 0.376190 / 0.255139 (0.121051) | 0.385875 / 0.283200 (0.102675) | 0.107542 / 0.141683 (-0.034141) | 1.409257 / 1.452155 (-0.042898) | 1.518475 / 1.492716 (0.025759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223299 / 0.018006 (0.205292) | 0.440640 / 0.000490 (0.440150) | 0.000397 / 0.000200 (0.000197) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031388 / 0.037411 (-0.006024) | 0.113078 / 0.014526 (0.098552) | 0.124398 / 0.176557 (-0.052159) | 0.173802 / 0.737135 (-0.563333) | 0.129555 / 0.296338 (-0.166783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440220 / 0.215209 (0.225011) | 4.398052 / 2.077655 (2.320398) | 2.188396 / 1.504120 (0.684276) | 1.997811 / 1.541195 (0.456616) | 2.093338 / 1.468490 (0.624847) | 0.519597 / 4.584777 (-4.065180) | 3.885795 / 3.745712 (0.140083) | 2.896327 / 5.269862 (-2.373534) | 1.245785 / 4.565676 (-3.319891) | 0.065675 / 0.424275 (-0.358600) | 0.011729 / 0.007607 (0.004121) | 0.541526 / 0.226044 (0.315482) | 5.406763 / 2.268929 (3.137834) | 2.722914 / 55.444624 (-52.721711) | 2.471111 / 6.876477 (-4.405366) | 2.541488 / 2.142072 (0.399415) | 0.633566 / 4.805227 (-4.171661) | 0.139622 / 6.500664 (-6.361042) | 0.064220 / 0.075469 (-0.011249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296097 / 1.841788 (-0.545690) | 15.095320 / 8.074308 (7.021012) | 14.300821 / 10.191392 (4.109429) | 0.145470 / 0.680424 (-0.534954) | 0.017496 / 0.534201 (-0.516705) | 0.400589 / 0.579283 (-0.178694) | 0.423091 / 0.434364 (-0.011273) | 0.468258 / 0.540337 (-0.072079) | 0.570873 / 1.386936 (-0.816063) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005918 / 0.011353 (-0.005435) | 0.004393 / 0.011008 (-0.006615) | 0.091677 / 0.038508 (0.053169) | 0.033546 / 0.023109 (0.010437) | 0.344682 / 0.275898 (0.068784) | 0.388906 / 0.323480 (0.065426) | 0.005412 / 0.007986 (-0.002574) | 0.004909 / 0.004328 (0.000580) | 0.082589 / 0.004250 (0.078339) | 0.045242 / 0.037052 (0.008190) | 0.339191 / 0.258489 (0.080702) | 0.349673 / 0.293841 (0.055832) | 0.026805 / 0.128546 (-0.101742) | 0.007529 / 0.075646 (-0.068117) | 0.319108 / 0.419271 (-0.100164) | 0.049482 / 0.043533 (0.005949) | 0.320013 / 0.255139 (0.064874) | 0.342059 / 0.283200 (0.058859) | 0.096623 / 0.141683 (-0.045060) | 1.458204 / 1.452155 (0.006049) | 1.571172 / 1.492716 (0.078455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235171 / 0.018006 (0.217165) | 0.479678 / 0.000490 (0.479188) | 0.006627 / 0.000200 (0.006427) | 0.000257 / 0.000054 (0.000202) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025716 / 0.037411 (-0.011696) | 0.107730 / 0.014526 (0.093204) | 0.111595 / 0.176557 (-0.064962) | 0.171316 / 0.737135 (-0.565819) | 0.118962 / 0.296338 (-0.177377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376318 / 0.215209 (0.161109) | 4.039484 / 2.077655 (1.961829) | 1.811548 / 1.504120 (0.307428) | 1.646728 / 1.541195 (0.105533) | 1.688071 / 1.468490 (0.219581) | 0.551256 / 4.584777 (-4.033520) | 4.153931 / 3.745712 (0.408218) | 3.424154 / 5.269862 (-1.845707) | 1.734860 / 4.565676 (-2.830816) | 0.067753 / 0.424275 (-0.356522) | 0.012699 / 0.007607 (0.005092) | 0.505722 / 0.226044 (0.279677) | 4.997321 / 2.268929 (2.728392) | 2.258755 / 55.444624 (-53.185869) | 1.954382 / 6.876477 (-4.922095) | 1.967545 / 2.142072 (-0.174527) | 0.630489 / 4.805227 (-4.174738) | 0.138738 / 6.500664 (-6.361926) | 0.064907 / 0.075469 (-0.010562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209634 / 1.841788 (-0.632154) | 15.055062 / 8.074308 (6.980754) | 12.721606 / 10.191392 (2.530214) | 0.164908 / 0.680424 (-0.515516) | 0.019528 / 0.534201 (-0.514673) | 0.400136 / 0.579283 (-0.179147) | 0.451640 / 0.434364 (0.017276) | 0.466272 / 0.540337 (-0.074065) | 0.553258 / 1.386936 (-0.833679) |\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) | 0.006341 / 0.011353 (-0.005011) | 0.004617 / 0.011008 (-0.006391) | 0.077953 / 0.038508 (0.039445) | 0.031104 / 0.023109 (0.007995) | 0.360328 / 0.275898 (0.084430) | 0.408403 / 0.323480 (0.084923) | 0.005704 / 0.007986 (-0.002282) | 0.003588 / 0.004328 (-0.000741) | 0.071441 / 0.004250 (0.067190) | 0.043520 / 0.037052 (0.006468) | 0.375798 / 0.258489 (0.117309) | 0.400955 / 0.293841 (0.107114) | 0.028166 / 0.128546 (-0.100381) | 0.008578 / 0.075646 (-0.067068) | 0.086673 / 0.419271 (-0.332598) | 0.046424 / 0.043533 (0.002891) | 0.367276 / 0.255139 (0.112137) | 0.414550 / 0.283200 (0.131351) | 0.097355 / 0.141683 (-0.044328) | 1.465191 / 1.452155 (0.013036) | 1.555028 / 1.492716 (0.062312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196642 / 0.018006 (0.178636) | 0.464221 / 0.000490 (0.463731) | 0.002726 / 0.000200 (0.002526) | 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) | 0.028078 / 0.037411 (-0.009333) | 0.110762 / 0.014526 (0.096236) | 0.122212 / 0.176557 (-0.054344) | 0.164758 / 0.737135 (-0.572377) | 0.133969 / 0.296338 (-0.162370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448134 / 0.215209 (0.232925) | 4.339335 / 2.077655 (2.261680) | 2.129209 / 1.504120 (0.625089) | 1.957805 / 1.541195 (0.416611) | 1.994038 / 1.468490 (0.525548) | 0.497101 / 4.584777 (-4.087676) | 4.114432 / 3.745712 (0.368720) | 3.437305 / 5.269862 (-1.832556) | 1.692810 / 4.565676 (-2.872866) | 0.071077 / 0.424275 (-0.353198) | 0.012735 / 0.007607 (0.005128) | 0.534393 / 0.226044 (0.308348) | 5.217445 / 2.268929 (2.948517) | 2.594858 / 55.444624 (-52.849766) | 2.317464 / 6.876477 (-4.559012) | 2.337974 / 2.142072 (0.195902) | 0.622291 / 4.805227 (-4.182936) | 0.144934 / 6.500664 (-6.355730) | 0.068524 / 0.075469 (-0.006945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310601 / 1.841788 (-0.531187) | 15.771527 / 8.074308 (7.697219) | 13.952032 / 10.191392 (3.760640) | 0.212473 / 0.680424 (-0.467951) | 0.017963 / 0.534201 (-0.516238) | 0.400755 / 0.579283 (-0.178528) | 0.439817 / 0.434364 (0.005453) | 0.472614 / 0.540337 (-0.067724) | 0.558410 / 1.386936 (-0.828526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. " ]

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[ "Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008156 / 0.011353 (-0.003197) | 0.005563 / 0.011008 (-0.005445) | 0.118319 / 0.038508 (0.079810) | 0.044305 / 0.023109 (0.021195) | 0.366221 / 0.275898 (0.090323) | 0.407585 / 0.323480 (0.084105) | 0.006961 / 0.007986 (-0.001024) | 0.004841 / 0.004328 (0.000513) | 0.089949 / 0.004250 (0.085698) | 0.062197 / 0.037052 (0.025144) | 0.360721 / 0.258489 (0.102232) | 0.415332 / 0.293841 (0.121491) | 0.035709 / 0.128546 (-0.092837) | 0.010617 / 0.075646 (-0.065030) | 0.397454 / 0.419271 (-0.021817) | 0.063490 / 0.043533 (0.019958) | 0.374289 / 0.255139 (0.119150) | 0.382827 / 0.283200 (0.099628) | 0.121014 / 0.141683 (-0.020669) | 1.729933 / 1.452155 (0.277779) | 1.896222 / 1.492716 (0.403506) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254030 / 0.018006 (0.236023) | 0.491225 / 0.000490 (0.490736) | 0.018933 / 0.000200 (0.018734) | 0.000413 / 0.000054 (0.000358) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033085 / 0.037411 (-0.004327) | 0.132837 / 0.014526 (0.118311) | 0.143275 / 0.176557 (-0.033282) | 0.215800 / 0.737135 (-0.521335) | 0.149802 / 0.296338 (-0.146536) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474688 / 0.215209 (0.259479) | 4.743223 / 2.077655 (2.665569) | 2.163107 / 1.504120 (0.658988) | 1.946396 / 1.541195 (0.405201) | 2.057538 / 1.468490 (0.589047) | 0.618836 / 4.584777 (-3.965941) | 4.605934 / 3.745712 (0.860222) | 2.201537 / 5.269862 (-3.068324) | 1.275758 / 4.565676 (-3.289919) | 0.077782 / 0.424275 (-0.346493) | 0.014830 / 0.007607 (0.007223) | 0.593372 / 0.226044 (0.367328) | 5.927000 / 2.268929 (3.658072) | 2.687293 / 55.444624 (-52.757331) | 2.301797 / 6.876477 (-4.574679) | 2.489928 / 2.142072 (0.347856) | 0.756779 / 4.805227 (-4.048449) | 0.168065 / 6.500664 (-6.332600) | 0.077276 / 0.075469 (0.001807) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.608169 / 1.841788 (-0.233619) | 19.048790 / 8.074308 (10.974482) | 16.100228 / 10.191392 (5.908836) | 0.215346 / 0.680424 (-0.465077) | 0.022293 / 0.534201 (-0.511907) | 0.535899 / 0.579283 (-0.043384) | 0.533729 / 0.434364 (0.099365) | 0.562697 / 0.540337 (0.022360) | 0.764082 / 1.386936 (-0.622854) |\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) | 0.010087 / 0.011353 (-0.001266) | 0.005357 / 0.011008 (-0.005651) | 0.092678 / 0.038508 (0.054170) | 0.041207 / 0.023109 (0.018098) | 0.437464 / 0.275898 (0.161566) | 0.527867 / 0.323480 (0.204387) | 0.006861 / 0.007986 (-0.001125) | 0.006131 / 0.004328 (0.001802) | 0.093741 / 0.004250 (0.089490) | 0.064142 / 0.037052 (0.027090) | 0.433577 / 0.258489 (0.175088) | 0.537148 / 0.293841 (0.243307) | 0.035339 / 0.128546 (-0.093207) | 0.010432 / 0.075646 (-0.065214) | 0.102838 / 0.419271 (-0.316434) | 0.057905 / 0.043533 (0.014372) | 0.437956 / 0.255139 (0.182817) | 0.509562 / 0.283200 (0.226362) | 0.120620 / 0.141683 (-0.021063) | 1.798686 / 1.452155 (0.346531) | 2.013290 / 1.492716 (0.520574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249067 / 0.018006 (0.231061) | 0.462219 / 0.000490 (0.461729) | 0.000476 / 0.000200 (0.000276) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033988 / 0.037411 (-0.003424) | 0.135863 / 0.014526 (0.121337) | 0.144082 / 0.176557 (-0.032474) | 0.201715 / 0.737135 (-0.535421) | 0.152079 / 0.296338 (-0.144259) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522820 / 0.215209 (0.307611) | 5.216723 / 2.077655 (3.139068) | 2.582355 / 1.504120 (1.078235) | 2.352799 / 1.541195 (0.811604) | 2.451943 / 1.468490 (0.983453) | 0.620381 / 4.584777 (-3.964396) | 4.537841 / 3.745712 (0.792129) | 2.206431 / 5.269862 (-3.063431) | 1.269865 / 4.565676 (-3.295811) | 0.078744 / 0.424275 (-0.345531) | 0.014375 / 0.007607 (0.006768) | 0.648215 / 0.226044 (0.422171) | 6.482809 / 2.268929 (4.213881) | 3.210670 / 55.444624 (-52.233954) | 2.847485 / 6.876477 (-4.028992) | 2.820946 / 2.142072 (0.678873) | 0.762711 / 4.805227 (-4.042516) | 0.171235 / 6.500664 (-6.329429) | 0.080230 / 0.075469 (0.004761) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646840 / 1.841788 (-0.194948) | 19.400451 / 8.074308 (11.326142) | 16.758845 / 10.191392 (6.567453) | 0.171377 / 0.680424 (-0.509046) | 0.020400 / 0.534201 (-0.513801) | 0.467675 / 0.579283 (-0.111608) | 0.529745 / 0.434364 (0.095381) | 0.605989 / 0.540337 (0.065652) | 0.694659 / 1.386936 (-0.692277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]

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[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well" ]

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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006192 / 0.011353 (-0.005161) | 0.004410 / 0.011008 (-0.006598) | 0.095990 / 0.038508 (0.057482) | 0.032662 / 0.023109 (0.009553) | 0.322827 / 0.275898 (0.046929) | 0.352542 / 0.323480 (0.029062) | 0.005398 / 0.007986 (-0.002588) | 0.003926 / 0.004328 (-0.000403) | 0.075131 / 0.004250 (0.070880) | 0.046205 / 0.037052 (0.009153) | 0.330957 / 0.258489 (0.072468) | 0.360166 / 0.293841 (0.066325) | 0.027880 / 0.128546 (-0.100666) | 0.008813 / 0.075646 (-0.066833) | 0.327316 / 0.419271 (-0.091955) | 0.050071 / 0.043533 (0.006539) | 0.319939 / 0.255139 (0.064800) | 0.331593 / 0.283200 (0.048393) | 0.096745 / 0.141683 (-0.044938) | 1.445165 / 1.452155 (-0.006990) | 1.515538 / 1.492716 (0.022821) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209365 / 0.018006 (0.191358) | 0.437007 / 0.000490 (0.436518) | 0.003207 / 0.000200 (0.003007) | 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) | 0.027261 / 0.037411 (-0.010151) | 0.105101 / 0.014526 (0.090575) | 0.117163 / 0.176557 (-0.059394) | 0.176237 / 0.737135 (-0.560898) | 0.122559 / 0.296338 (-0.173779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406792 / 0.215209 (0.191583) | 4.060831 / 2.077655 (1.983176) | 1.829691 / 1.504120 (0.325571) | 1.633155 / 1.541195 (0.091960) | 1.704817 / 1.468490 (0.236327) | 0.525325 / 4.584777 (-4.059452) | 3.752907 / 3.745712 (0.007194) | 1.857513 / 5.269862 (-3.412349) | 1.222237 / 4.565676 (-3.343439) | 0.065941 / 0.424275 (-0.358334) | 0.012498 / 0.007607 (0.004891) | 0.495009 / 0.226044 (0.268965) | 4.968074 / 2.268929 (2.699145) | 2.277898 / 55.444624 (-53.166727) | 1.936656 / 6.876477 (-4.939821) | 1.970698 / 2.142072 (-0.171374) | 0.635221 / 4.805227 (-4.170006) | 0.140539 / 6.500664 (-6.360125) | 0.064111 / 0.075469 (-0.011358) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238151 / 1.841788 (-0.603637) | 14.681262 / 8.074308 (6.606954) | 13.405525 / 10.191392 (3.214133) | 0.163225 / 0.680424 (-0.517199) | 0.017282 / 0.534201 (-0.516918) | 0.395526 / 0.579283 (-0.183757) | 0.429156 / 0.434364 (-0.005208) | 0.470806 / 0.540337 (-0.069531) | 0.571290 / 1.386936 (-0.815646) |\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) | 0.006444 / 0.011353 (-0.004909) | 0.004388 / 0.011008 (-0.006621) | 0.075004 / 0.038508 (0.036496) | 0.032904 / 0.023109 (0.009795) | 0.375360 / 0.275898 (0.099462) | 0.413684 / 0.323480 (0.090204) | 0.005854 / 0.007986 (-0.002132) | 0.005504 / 0.004328 (0.001175) | 0.075049 / 0.004250 (0.070799) | 0.047973 / 0.037052 (0.010920) | 0.377943 / 0.258489 (0.119454) | 0.427039 / 0.293841 (0.133198) | 0.028248 / 0.128546 (-0.100298) | 0.008972 / 0.075646 (-0.066674) | 0.081848 / 0.419271 (-0.337424) | 0.047935 / 0.043533 (0.004402) | 0.377980 / 0.255139 (0.122841) | 0.407856 / 0.283200 (0.124656) | 0.103454 / 0.141683 (-0.038229) | 1.469051 / 1.452155 (0.016896) | 1.590657 / 1.492716 (0.097941) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192380 / 0.018006 (0.174374) | 0.440995 / 0.000490 (0.440505) | 0.004082 / 0.000200 (0.003882) | 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) | 0.029584 / 0.037411 (-0.007828) | 0.110051 / 0.014526 (0.095525) | 0.121196 / 0.176557 (-0.055361) | 0.172249 / 0.737135 (-0.564886) | 0.125380 / 0.296338 (-0.170958) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435218 / 0.215209 (0.220009) | 4.354811 / 2.077655 (2.277156) | 2.102050 / 1.504120 (0.597930) | 1.913454 / 1.541195 (0.372260) | 1.974624 / 1.468490 (0.506134) | 0.529975 / 4.584777 (-4.054802) | 3.801605 / 3.745712 (0.055893) | 3.162408 / 5.269862 (-2.107454) | 1.599576 / 4.565676 (-2.966101) | 0.066710 / 0.424275 (-0.357565) | 0.012158 / 0.007607 (0.004551) | 0.549187 / 0.226044 (0.323142) | 5.489930 / 2.268929 (3.221002) | 2.646787 / 55.444624 (-52.797837) | 2.311915 / 6.876477 (-4.564562) | 2.335645 / 2.142072 (0.193572) | 0.641067 / 4.805227 (-4.164160) | 0.142227 / 6.500664 (-6.358437) | 0.065303 / 0.075469 (-0.010166) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283209 / 1.841788 (-0.558579) | 15.241809 / 8.074308 (7.167501) | 14.131471 / 10.191392 (3.940079) | 0.143921 / 0.680424 (-0.536503) | 0.017497 / 0.534201 (-0.516704) | 0.402236 / 0.579283 (-0.177047) | 0.418917 / 0.434364 (-0.015447) | 0.461745 / 0.540337 (-0.078593) | 0.560212 / 1.386936 (-0.826724) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007401 / 0.011353 (-0.003952) | 0.005198 / 0.011008 (-0.005810) | 0.112317 / 0.038508 (0.073809) | 0.038406 / 0.023109 (0.015297) | 0.358008 / 0.275898 (0.082110) | 0.395350 / 0.323480 (0.071870) | 0.006201 / 0.007986 (-0.001785) | 0.004368 / 0.004328 (0.000039) | 0.087718 / 0.004250 (0.083467) | 0.055299 / 0.037052 (0.018247) | 0.350481 / 0.258489 (0.091992) | 0.419876 / 0.293841 (0.126035) | 0.032459 / 0.128546 (-0.096087) | 0.010635 / 0.075646 (-0.065011) | 0.383282 / 0.419271 (-0.035989) | 0.059241 / 0.043533 (0.015708) | 0.365101 / 0.255139 (0.109962) | 0.378144 / 0.283200 (0.094944) | 0.114287 / 0.141683 (-0.027396) | 1.680870 / 1.452155 (0.228715) | 1.788183 / 1.492716 (0.295467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242919 / 0.018006 (0.224913) | 0.489850 / 0.000490 (0.489360) | 0.011408 / 0.000200 (0.011208) | 0.000444 / 0.000054 (0.000389) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030742 / 0.037411 (-0.006669) | 0.123092 / 0.014526 (0.108566) | 0.138246 / 0.176557 (-0.038311) | 0.207299 / 0.737135 (-0.529836) | 0.142647 / 0.296338 (-0.153691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472553 / 0.215209 (0.257344) | 4.671763 / 2.077655 (2.594108) | 2.119986 / 1.504120 (0.615866) | 1.891851 / 1.541195 (0.350656) | 1.979094 / 1.468490 (0.510604) | 0.617956 / 4.584777 (-3.966821) | 4.969418 / 3.745712 (1.223706) | 4.672083 / 5.269862 (-0.597779) | 2.119049 / 4.565676 (-2.446627) | 0.077466 / 0.424275 (-0.346809) | 0.014434 / 0.007607 (0.006827) | 0.580746 / 0.226044 (0.354701) | 5.805458 / 2.268929 (3.536530) | 2.622498 / 55.444624 (-52.822126) | 2.259499 / 6.876477 (-4.616978) | 2.362078 / 2.142072 (0.220006) | 0.719911 / 4.805227 (-4.085317) | 0.164939 / 6.500664 (-6.335725) | 0.074762 / 0.075469 (-0.000707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.496709 / 1.841788 (-0.345079) | 18.247499 / 8.074308 (10.173191) | 15.397075 / 10.191392 (5.205683) | 0.181163 / 0.680424 (-0.499261) | 0.022604 / 0.534201 (-0.511597) | 0.462791 / 0.579283 (-0.116492) | 0.504473 / 0.434364 (0.070109) | 0.582254 / 0.540337 (0.041917) | 0.673849 / 1.386936 (-0.713087) |\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) | 0.007633 / 0.011353 (-0.003720) | 0.004859 / 0.011008 (-0.006149) | 0.091194 / 0.038508 (0.052686) | 0.038255 / 0.023109 (0.015146) | 0.460972 / 0.275898 (0.185074) | 0.470441 / 0.323480 (0.146961) | 0.006482 / 0.007986 (-0.001504) | 0.004500 / 0.004328 (0.000172) | 0.089998 / 0.004250 (0.085748) | 0.055470 / 0.037052 (0.018418) | 0.459188 / 0.258489 (0.200699) | 0.491255 / 0.293841 (0.197414) | 0.032200 / 0.128546 (-0.096346) | 0.010372 / 0.075646 (-0.065274) | 0.097429 / 0.419271 (-0.321843) | 0.052469 / 0.043533 (0.008936) | 0.452492 / 0.255139 (0.197353) | 0.475210 / 0.283200 (0.192010) | 0.116976 / 0.141683 (-0.024707) | 1.752742 / 1.452155 (0.300587) | 1.849535 / 1.492716 (0.356819) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.472259 / 0.000490 (0.471770) | 0.000455 / 0.000200 (0.000255) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033796 / 0.037411 (-0.003615) | 0.136151 / 0.014526 (0.121625) | 0.144015 / 0.176557 (-0.032542) | 0.199337 / 0.737135 (-0.537798) | 0.150024 / 0.296338 (-0.146315) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522737 / 0.215209 (0.307528) | 5.165223 / 2.077655 (3.087568) | 2.630334 / 1.504120 (1.126214) | 2.392383 / 1.541195 (0.851188) | 2.488966 / 1.468490 (1.020476) | 0.608981 / 4.584777 (-3.975796) | 4.711545 / 3.745712 (0.965833) | 2.121537 / 5.269862 (-3.148325) | 1.205477 / 4.565676 (-3.360199) | 0.078277 / 0.424275 (-0.345998) | 0.014175 / 0.007607 (0.006568) | 0.640720 / 0.226044 (0.414675) | 6.391173 / 2.268929 (4.122245) | 3.265131 / 55.444624 (-52.179493) | 2.939188 / 6.876477 (-3.937289) | 2.919217 / 2.142072 (0.777145) | 0.745095 / 4.805227 (-4.060132) | 0.164065 / 6.500664 (-6.336599) | 0.076993 / 0.075469 (0.001524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.539971 / 1.841788 (-0.301817) | 18.597296 / 8.074308 (10.522988) | 16.899330 / 10.191392 (6.707938) | 0.169005 / 0.680424 (-0.511419) | 0.020447 / 0.534201 (-0.513754) | 0.465862 / 0.579283 (-0.113421) | 0.522819 / 0.434364 (0.088455) | 0.547111 / 0.540337 (0.006773) | 0.657777 / 1.386936 (-0.729159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]

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[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint." ]

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[ "Random fact: previous run was showing that the Hub was hosting 13336 datasets, while the most recent run shows 36662 👀🎉", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5902). All of your documentation changes will be reflected on that endpoint." ]

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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008809 / 0.011353 (-0.002544) | 0.005641 / 0.011008 (-0.005367) | 0.124986 / 0.038508 (0.086477) | 0.037311 / 0.023109 (0.014202) | 0.388915 / 0.275898 (0.113017) | 0.430123 / 0.323480 (0.106643) | 0.007447 / 0.007986 (-0.000538) | 0.009593 / 0.004328 (0.005264) | 0.099148 / 0.004250 (0.094898) | 0.052393 / 0.037052 (0.015341) | 0.399779 / 0.258489 (0.141290) | 0.439109 / 0.293841 (0.145268) | 0.043409 / 0.128546 (-0.085137) | 0.016286 / 0.075646 (-0.059360) | 0.431198 / 0.419271 (0.011927) | 0.064932 / 0.043533 (0.021400) | 0.390650 / 0.255139 (0.135511) | 0.432883 / 0.283200 (0.149684) | 0.110978 / 0.141683 (-0.030705) | 1.796121 / 1.452155 (0.343967) | 1.960097 / 1.492716 (0.467381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286292 / 0.018006 (0.268286) | 0.659495 / 0.000490 (0.659005) | 0.008294 / 0.000200 (0.008094) | 0.000485 / 0.000054 (0.000431) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029325 / 0.037411 (-0.008086) | 0.125454 / 0.014526 (0.110928) | 0.136459 / 0.176557 (-0.040097) | 0.221075 / 0.737135 (-0.516060) | 0.140281 / 0.296338 (-0.156058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602401 / 0.215209 (0.387192) | 6.124553 / 2.077655 (4.046898) | 2.453141 / 1.504120 (0.949021) | 2.038611 / 1.541195 (0.497416) | 2.073611 / 1.468490 (0.605121) | 0.938040 / 4.584777 (-3.646737) | 5.755972 / 3.745712 (2.010260) | 4.450935 / 5.269862 (-0.818926) | 2.337219 / 4.565676 (-2.228457) | 0.107118 / 0.424275 (-0.317157) | 0.015201 / 0.007607 (0.007594) | 0.785833 / 0.226044 (0.559788) | 7.732984 / 2.268929 (5.464055) | 3.236892 / 55.444624 (-52.207733) | 2.696402 / 6.876477 (-4.180074) | 2.805036 / 2.142072 (0.662964) | 1.108612 / 4.805227 (-3.696616) | 0.221067 / 6.500664 (-6.279597) | 0.085538 / 0.075469 (0.010068) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.600311 / 1.841788 (-0.241476) | 18.528118 / 8.074308 (10.453810) | 21.107199 / 10.191392 (10.915807) | 0.219489 / 0.680424 (-0.460934) | 0.028927 / 0.534201 (-0.505274) | 0.503446 / 0.579283 (-0.075837) | 0.619833 / 0.434364 (0.185469) | 0.582454 / 0.540337 (0.042117) | 0.709154 / 1.386936 (-0.677782) |\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) | 0.008516 / 0.011353 (-0.002837) | 0.006090 / 0.011008 (-0.004918) | 0.104574 / 0.038508 (0.066066) | 0.042676 / 0.023109 (0.019566) | 0.458623 / 0.275898 (0.182725) | 0.568479 / 0.323480 (0.244999) | 0.008374 / 0.007986 (0.000389) | 0.004677 / 0.004328 (0.000349) | 0.105946 / 0.004250 (0.101695) | 0.055256 / 0.037052 (0.018204) | 0.511036 / 0.258489 (0.252547) | 0.598383 / 0.293841 (0.304542) | 0.043612 / 0.128546 (-0.084934) | 0.014707 / 0.075646 (-0.060940) | 0.116350 / 0.419271 (-0.302921) | 0.061413 / 0.043533 (0.017880) | 0.477785 / 0.255139 (0.222646) | 0.542643 / 0.283200 (0.259443) | 0.120431 / 0.141683 (-0.021252) | 1.994083 / 1.452155 (0.541928) | 2.100600 / 1.492716 (0.607883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298480 / 0.018006 (0.280474) | 0.601921 / 0.000490 (0.601432) | 0.000445 / 0.000200 (0.000245) | 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) | 0.034784 / 0.037411 (-0.002627) | 0.133555 / 0.014526 (0.119029) | 0.138541 / 0.176557 (-0.038015) | 0.203114 / 0.737135 (-0.534021) | 0.153477 / 0.296338 (-0.142861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.780484 / 0.215209 (0.565275) | 7.150876 / 2.077655 (5.073222) | 3.168590 / 1.504120 (1.664470) | 2.698746 / 1.541195 (1.157552) | 2.695678 / 1.468490 (1.227188) | 1.037706 / 4.584777 (-3.547071) | 5.672631 / 3.745712 (1.926918) | 2.798137 / 5.269862 (-2.471725) | 1.738588 / 4.565676 (-2.827088) | 0.111160 / 0.424275 (-0.313115) | 0.013878 / 0.007607 (0.006271) | 0.800191 / 0.226044 (0.574146) | 8.546676 / 2.268929 (6.277748) | 4.116852 / 55.444624 (-51.327773) | 3.331271 / 6.876477 (-3.545206) | 3.307410 / 2.142072 (1.165337) | 1.191019 / 4.805227 (-3.614208) | 0.248953 / 6.500664 (-6.251711) | 0.086632 / 0.075469 (0.011162) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.795057 / 1.841788 (-0.046730) | 18.038785 / 8.074308 (9.964476) | 21.865566 / 10.191392 (11.674174) | 0.211058 / 0.680424 (-0.469366) | 0.026956 / 0.534201 (-0.507245) | 0.518855 / 0.579283 (-0.060428) | 0.618105 / 0.434364 (0.183741) | 0.569227 / 0.540337 (0.028889) | 0.705431 / 1.386936 (-0.681505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008900 / 0.011353 (-0.002453) | 0.005726 / 0.011008 (-0.005283) | 0.131747 / 0.038508 (0.093239) | 0.040585 / 0.023109 (0.017476) | 0.420531 / 0.275898 (0.144633) | 0.459430 / 0.323480 (0.135950) | 0.007642 / 0.007986 (-0.000344) | 0.006750 / 0.004328 (0.002421) | 0.099147 / 0.004250 (0.094897) | 0.055852 / 0.037052 (0.018799) | 0.423653 / 0.258489 (0.165164) | 0.453304 / 0.293841 (0.159463) | 0.045247 / 0.128546 (-0.083300) | 0.016034 / 0.075646 (-0.059612) | 0.443115 / 0.419271 (0.023843) | 0.078853 / 0.043533 (0.035320) | 0.417508 / 0.255139 (0.162369) | 0.440936 / 0.283200 (0.157736) | 0.115603 / 0.141683 (-0.026080) | 1.844610 / 1.452155 (0.392456) | 1.998497 / 1.492716 (0.505781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272622 / 0.018006 (0.254616) | 0.598045 / 0.000490 (0.597556) | 0.007088 / 0.000200 (0.006888) | 0.000159 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032976 / 0.037411 (-0.004436) | 0.143970 / 0.014526 (0.129444) | 0.142172 / 0.176557 (-0.034384) | 0.216747 / 0.737135 (-0.520389) | 0.146004 / 0.296338 (-0.150334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.687507 / 0.215209 (0.472298) | 6.549524 / 2.077655 (4.471870) | 2.924142 / 1.504120 (1.420022) | 2.504471 / 1.541195 (0.963277) | 2.496280 / 1.468490 (1.027790) | 0.959054 / 4.584777 (-3.625723) | 5.851742 / 3.745712 (2.106030) | 4.983357 / 5.269862 (-0.286504) | 2.627403 / 4.565676 (-1.938274) | 0.112955 / 0.424275 (-0.311320) | 0.016206 / 0.007607 (0.008599) | 0.819158 / 0.226044 (0.593114) | 8.416949 / 2.268929 (6.148020) | 3.776765 / 55.444624 (-51.667859) | 3.002397 / 6.876477 (-3.874080) | 3.158852 / 2.142072 (1.016779) | 1.197099 / 4.805227 (-3.608129) | 0.280654 / 6.500664 (-6.220010) | 0.099471 / 0.075469 (0.024002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687007 / 1.841788 (-0.154781) | 19.411976 / 8.074308 (11.337668) | 22.053482 / 10.191392 (11.862090) | 0.228038 / 0.680424 (-0.452386) | 0.028226 / 0.534201 (-0.505975) | 0.527695 / 0.579283 (-0.051588) | 0.635911 / 0.434364 (0.201547) | 0.618205 / 0.540337 (0.077868) | 0.735164 / 1.386936 (-0.651772) |\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) | 0.009450 / 0.011353 (-0.001903) | 0.006566 / 0.011008 (-0.004442) | 0.108919 / 0.038508 (0.070411) | 0.050010 / 0.023109 (0.026900) | 0.505168 / 0.275898 (0.229270) | 0.552190 / 0.323480 (0.228710) | 0.007569 / 0.007986 (-0.000417) | 0.006807 / 0.004328 (0.002478) | 0.116621 / 0.004250 (0.112371) | 0.060374 / 0.037052 (0.023321) | 0.515165 / 0.258489 (0.256676) | 0.572125 / 0.293841 (0.278284) | 0.046561 / 0.128546 (-0.081986) | 0.016159 / 0.075646 (-0.059487) | 0.114568 / 0.419271 (-0.304704) | 0.064689 / 0.043533 (0.021157) | 0.497870 / 0.255139 (0.242731) | 0.567332 / 0.283200 (0.284132) | 0.126254 / 0.141683 (-0.015429) | 1.954074 / 1.452155 (0.501919) | 2.057682 / 1.492716 (0.564966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.013857 / 0.018006 (-0.004149) | 0.601561 / 0.000490 (0.601071) | 0.002897 / 0.000200 (0.002697) | 0.000108 / 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) | 0.038480 / 0.037411 (0.001069) | 0.142480 / 0.014526 (0.127954) | 0.160479 / 0.176557 (-0.016077) | 0.217942 / 0.737135 (-0.519194) | 0.159908 / 0.296338 (-0.136431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.697926 / 0.215209 (0.482717) | 6.869754 / 2.077655 (4.792100) | 3.125463 / 1.504120 (1.621343) | 2.729123 / 1.541195 (1.187928) | 2.855747 / 1.468490 (1.387257) | 1.015345 / 4.584777 (-3.569432) | 5.839176 / 3.745712 (2.093463) | 5.019678 / 5.269862 (-0.250184) | 2.080489 / 4.565676 (-2.485187) | 0.118884 / 0.424275 (-0.305391) | 0.021381 / 0.007607 (0.013774) | 0.877847 / 0.226044 (0.651803) | 8.714561 / 2.268929 (6.445633) | 3.933399 / 55.444624 (-51.511226) | 3.281809 / 6.876477 (-3.594668) | 3.330342 / 2.142072 (1.188269) | 1.235005 / 4.805227 (-3.570222) | 0.239686 / 6.500664 (-6.260978) | 0.093546 / 0.075469 (0.018077) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.787916 / 1.841788 (-0.053872) | 20.094828 / 8.074308 (12.020520) | 22.902101 / 10.191392 (12.710709) | 0.249315 / 0.680424 (-0.431109) | 0.028058 / 0.534201 (-0.506143) | 0.524960 / 0.579283 (-0.054323) | 0.643881 / 0.434364 (0.209517) | 0.621203 / 0.540337 (0.080866) | 0.723337 / 1.386936 (-0.663599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006763 / 0.011353 (-0.004589) | 0.004548 / 0.011008 (-0.006460) | 0.095631 / 0.038508 (0.057123) | 0.034046 / 0.023109 (0.010936) | 0.298064 / 0.275898 (0.022166) | 0.330391 / 0.323480 (0.006911) | 0.006058 / 0.007986 (-0.001928) | 0.004163 / 0.004328 (-0.000165) | 0.073260 / 0.004250 (0.069010) | 0.048885 / 0.037052 (0.011832) | 0.304651 / 0.258489 (0.046162) | 0.345882 / 0.293841 (0.052042) | 0.028061 / 0.128546 (-0.100485) | 0.008823 / 0.075646 (-0.066823) | 0.325620 / 0.419271 (-0.093651) | 0.064480 / 0.043533 (0.020948) | 0.303373 / 0.255139 (0.048234) | 0.321672 / 0.283200 (0.038472) | 0.116353 / 0.141683 (-0.025330) | 1.442327 / 1.452155 (-0.009827) | 1.567553 / 1.492716 (0.074837) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213042 / 0.018006 (0.195035) | 0.457646 / 0.000490 (0.457156) | 0.003989 / 0.000200 (0.003789) | 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) | 0.028068 / 0.037411 (-0.009344) | 0.114791 / 0.014526 (0.100265) | 0.120870 / 0.176557 (-0.055686) | 0.183006 / 0.737135 (-0.554130) | 0.126772 / 0.296338 (-0.169567) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406438 / 0.215209 (0.191229) | 4.041890 / 2.077655 (1.964235) | 1.839967 / 1.504120 (0.335847) | 1.646857 / 1.541195 (0.105662) | 1.729372 / 1.468490 (0.260882) | 0.525540 / 4.584777 (-4.059237) | 3.809996 / 3.745712 (0.064284) | 1.842598 / 5.269862 (-3.427263) | 1.062815 / 4.565676 (-3.502862) | 0.065301 / 0.424275 (-0.358974) | 0.012027 / 0.007607 (0.004420) | 0.505459 / 0.226044 (0.279415) | 5.051177 / 2.268929 (2.782248) | 2.354368 / 55.444624 (-53.090256) | 2.035482 / 6.876477 (-4.840995) | 2.120493 / 2.142072 (-0.021579) | 0.642233 / 4.805227 (-4.162994) | 0.141690 / 6.500664 (-6.358974) | 0.063933 / 0.075469 (-0.011536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186261 / 1.841788 (-0.655527) | 14.919653 / 8.074308 (6.845345) | 14.534003 / 10.191392 (4.342611) | 0.183165 / 0.680424 (-0.497259) | 0.017581 / 0.534201 (-0.516620) | 0.397284 / 0.579283 (-0.181999) | 0.431363 / 0.434364 (-0.003001) | 0.510774 / 0.540337 (-0.029564) | 0.614421 / 1.386936 (-0.772516) |\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) | 0.006682 / 0.011353 (-0.004671) | 0.004558 / 0.011008 (-0.006450) | 0.076272 / 0.038508 (0.037764) | 0.034285 / 0.023109 (0.011176) | 0.395594 / 0.275898 (0.119696) | 0.402702 / 0.323480 (0.079222) | 0.006093 / 0.007986 (-0.001893) | 0.005538 / 0.004328 (0.001209) | 0.075797 / 0.004250 (0.071547) | 0.051638 / 0.037052 (0.014585) | 0.396071 / 0.258489 (0.137582) | 0.409282 / 0.293841 (0.115441) | 0.028193 / 0.128546 (-0.100354) | 0.008827 / 0.075646 (-0.066819) | 0.083182 / 0.419271 (-0.336089) | 0.047605 / 0.043533 (0.004072) | 0.391148 / 0.255139 (0.136009) | 0.386784 / 0.283200 (0.103584) | 0.115303 / 0.141683 (-0.026380) | 1.463666 / 1.452155 (0.011512) | 1.566147 / 1.492716 (0.073431) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213846 / 0.018006 (0.195839) | 0.454769 / 0.000490 (0.454279) | 0.004767 / 0.000200 (0.004567) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030369 / 0.037411 (-0.007042) | 0.115585 / 0.014526 (0.101059) | 0.125181 / 0.176557 (-0.051376) | 0.179247 / 0.737135 (-0.557888) | 0.129336 / 0.296338 (-0.167003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446040 / 0.215209 (0.230831) | 4.462644 / 2.077655 (2.384989) | 2.254511 / 1.504120 (0.750392) | 2.062679 / 1.541195 (0.521484) | 2.180766 / 1.468490 (0.712276) | 0.530928 / 4.584777 (-4.053849) | 3.781392 / 3.745712 (0.035680) | 3.522539 / 5.269862 (-1.747322) | 1.506960 / 4.565676 (-3.058717) | 0.067101 / 0.424275 (-0.357174) | 0.012011 / 0.007607 (0.004404) | 0.546407 / 0.226044 (0.320362) | 5.429894 / 2.268929 (3.160965) | 2.702244 / 55.444624 (-52.742381) | 2.367559 / 6.876477 (-4.508917) | 2.556032 / 2.142072 (0.413960) | 0.639690 / 4.805227 (-4.165538) | 0.144538 / 6.500664 (-6.356126) | 0.067822 / 0.075469 (-0.007647) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284977 / 1.841788 (-0.556811) | 15.546489 / 8.074308 (7.472181) | 14.747519 / 10.191392 (4.556127) | 0.160044 / 0.680424 (-0.520380) | 0.017746 / 0.534201 (-0.516454) | 0.390140 / 0.579283 (-0.189143) | 0.420342 / 0.434364 (-0.014021) | 0.459788 / 0.540337 (-0.080549) | 0.556360 / 1.386936 (-0.830576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d646afbac7ea3dc0996fa2cb6ffd8a98e158e742 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006493 / 0.011353 (-0.004860) | 0.004532 / 0.011008 (-0.006476) | 0.096509 / 0.038508 (0.058001) | 0.033084 / 0.023109 (0.009974) | 0.297802 / 0.275898 (0.021904) | 0.345880 / 0.323480 (0.022400) | 0.005461 / 0.007986 (-0.002525) | 0.005282 / 0.004328 (0.000954) | 0.073719 / 0.004250 (0.069469) | 0.045035 / 0.037052 (0.007983) | 0.295504 / 0.258489 (0.037015) | 0.345400 / 0.293841 (0.051559) | 0.027880 / 0.128546 (-0.100666) | 0.008804 / 0.075646 (-0.066842) | 0.328017 / 0.419271 (-0.091255) | 0.050169 / 0.043533 (0.006637) | 0.299642 / 0.255139 (0.044503) | 0.313573 / 0.283200 (0.030374) | 0.103359 / 0.141683 (-0.038323) | 1.482145 / 1.452155 (0.029990) | 1.554584 / 1.492716 (0.061867) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212860 / 0.018006 (0.194853) | 0.444823 / 0.000490 (0.444334) | 0.003014 / 0.000200 (0.002815) | 0.000108 / 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) | 0.026906 / 0.037411 (-0.010506) | 0.108056 / 0.014526 (0.093530) | 0.118721 / 0.176557 (-0.057835) | 0.176646 / 0.737135 (-0.560489) | 0.123285 / 0.296338 (-0.173053) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430157 / 0.215209 (0.214948) | 4.279362 / 2.077655 (2.201707) | 1.999732 / 1.504120 (0.495612) | 1.803787 / 1.541195 (0.262592) | 1.868322 / 1.468490 (0.399832) | 0.529314 / 4.584777 (-4.055463) | 3.785101 / 3.745712 (0.039389) | 2.812608 / 5.269862 (-2.457254) | 1.373460 / 4.565676 (-3.192216) | 0.066208 / 0.424275 (-0.358067) | 0.012173 / 0.007607 (0.004566) | 0.528716 / 0.226044 (0.302672) | 5.295003 / 2.268929 (3.026074) | 2.450188 / 55.444624 (-52.994437) | 2.114560 / 6.876477 (-4.761917) | 2.268468 / 2.142072 (0.126395) | 0.651706 / 4.805227 (-4.153521) | 0.142185 / 6.500664 (-6.358479) | 0.064862 / 0.075469 (-0.010607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.184933 / 1.841788 (-0.656854) | 14.503903 / 8.074308 (6.429595) | 13.928965 / 10.191392 (3.737573) | 0.156788 / 0.680424 (-0.523636) | 0.017320 / 0.534201 (-0.516881) | 0.391366 / 0.579283 (-0.187918) | 0.416261 / 0.434364 (-0.018103) | 0.461951 / 0.540337 (-0.078387) | 0.553496 / 1.386936 (-0.833440) |\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) | 0.006623 / 0.011353 (-0.004730) | 0.004617 / 0.011008 (-0.006392) | 0.075579 / 0.038508 (0.037071) | 0.033863 / 0.023109 (0.010754) | 0.357097 / 0.275898 (0.081199) | 0.396177 / 0.323480 (0.072697) | 0.005712 / 0.007986 (-0.002274) | 0.004232 / 0.004328 (-0.000097) | 0.074669 / 0.004250 (0.070418) | 0.048253 / 0.037052 (0.011201) | 0.362453 / 0.258489 (0.103964) | 0.405423 / 0.293841 (0.111582) | 0.028709 / 0.128546 (-0.099837) | 0.008884 / 0.075646 (-0.066763) | 0.083042 / 0.419271 (-0.336230) | 0.048074 / 0.043533 (0.004541) | 0.355314 / 0.255139 (0.100175) | 0.372536 / 0.283200 (0.089336) | 0.111548 / 0.141683 (-0.030135) | 1.466353 / 1.452155 (0.014198) | 1.555077 / 1.492716 (0.062361) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217016 / 0.018006 (0.199010) | 0.450145 / 0.000490 (0.449655) | 0.001910 / 0.000200 (0.001711) | 0.000098 / 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) | 0.029787 / 0.037411 (-0.007624) | 0.115282 / 0.014526 (0.100756) | 0.121962 / 0.176557 (-0.054595) | 0.173424 / 0.737135 (-0.563711) | 0.127519 / 0.296338 (-0.168819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438211 / 0.215209 (0.223002) | 4.346352 / 2.077655 (2.268697) | 2.140197 / 1.504120 (0.636077) | 1.957890 / 1.541195 (0.416696) | 2.044300 / 1.468490 (0.575810) | 0.527958 / 4.584777 (-4.056819) | 3.805079 / 3.745712 (0.059367) | 2.601763 / 5.269862 (-2.668098) | 1.359469 / 4.565676 (-3.206208) | 0.065358 / 0.424275 (-0.358917) | 0.011571 / 0.007607 (0.003964) | 0.538513 / 0.226044 (0.312469) | 5.363508 / 2.268929 (3.094580) | 2.640495 / 55.444624 (-52.804129) | 2.335930 / 6.876477 (-4.540547) | 2.407782 / 2.142072 (0.265710) | 0.641637 / 4.805227 (-4.163590) | 0.142196 / 6.500664 (-6.358468) | 0.065041 / 0.075469 (-0.010428) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296031 / 1.841788 (-0.545757) | 14.950424 / 8.074308 (6.876115) | 14.371304 / 10.191392 (4.179912) | 0.148157 / 0.680424 (-0.532267) | 0.017506 / 0.534201 (-0.516695) | 0.392037 / 0.579283 (-0.187246) | 0.423238 / 0.434364 (-0.011126) | 0.464608 / 0.540337 (-0.075730) | 0.563876 / 1.386936 (-0.823060) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04b1d0371408beb0c7bc587a69c382bd8d0bec36 \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009137 / 0.011353 (-0.002216) | 0.006119 / 0.011008 (-0.004889) | 0.136530 / 0.038508 (0.098022) | 0.038434 / 0.023109 (0.015325) | 0.427900 / 0.275898 (0.152002) | 0.449757 / 0.323480 (0.126277) | 0.007673 / 0.007986 (-0.000313) | 0.007147 / 0.004328 (0.002818) | 0.108029 / 0.004250 (0.103778) | 0.055072 / 0.037052 (0.018020) | 0.439245 / 0.258489 (0.180756) | 0.477285 / 0.293841 (0.183444) | 0.044838 / 0.128546 (-0.083708) | 0.020814 / 0.075646 (-0.054832) | 0.436098 / 0.419271 (0.016826) | 0.067459 / 0.043533 (0.023926) | 0.427470 / 0.255139 (0.172331) | 0.443260 / 0.283200 (0.160060) | 0.125466 / 0.141683 (-0.016216) | 1.996756 / 1.452155 (0.544601) | 2.100679 / 1.492716 (0.607962) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278407 / 0.018006 (0.260401) | 0.625855 / 0.000490 (0.625365) | 0.005544 / 0.000200 (0.005344) | 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) | 0.033495 / 0.037411 (-0.003916) | 0.134718 / 0.014526 (0.120192) | 0.150151 / 0.176557 (-0.026406) | 0.221385 / 0.737135 (-0.515751) | 0.150932 / 0.296338 (-0.145406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668845 / 0.215209 (0.453636) | 6.678436 / 2.077655 (4.600781) | 2.714074 / 1.504120 (1.209954) | 2.275784 / 1.541195 (0.734589) | 2.332852 / 1.468490 (0.864361) | 1.014877 / 4.584777 (-3.569900) | 6.086455 / 3.745712 (2.340743) | 2.990029 / 5.269862 (-2.279832) | 1.862236 / 4.565676 (-2.703441) | 0.122179 / 0.424275 (-0.302096) | 0.015706 / 0.007607 (0.008099) | 0.873473 / 0.226044 (0.647429) | 8.580109 / 2.268929 (6.311180) | 3.458360 / 55.444624 (-51.986264) | 2.738801 / 6.876477 (-4.137676) | 2.918428 / 2.142072 (0.776356) | 1.224910 / 4.805227 (-3.580317) | 0.243006 / 6.500664 (-6.257658) | 0.087121 / 0.075469 (0.011652) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.757802 / 1.841788 (-0.083986) | 19.447999 / 8.074308 (11.373691) | 24.518157 / 10.191392 (14.326765) | 0.245013 / 0.680424 (-0.435411) | 0.032290 / 0.534201 (-0.501911) | 0.542043 / 0.579283 (-0.037240) | 0.708154 / 0.434364 (0.273790) | 0.660584 / 0.540337 (0.120247) | 0.794868 / 1.386936 (-0.592068) |\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) | 0.009496 / 0.011353 (-0.001857) | 0.005842 / 0.011008 (-0.005166) | 0.112813 / 0.038508 (0.074305) | 0.039120 / 0.023109 (0.016011) | 0.489717 / 0.275898 (0.213819) | 0.532586 / 0.323480 (0.209107) | 0.007681 / 0.007986 (-0.000304) | 0.005337 / 0.004328 (0.001009) | 0.107244 / 0.004250 (0.102994) | 0.056847 / 0.037052 (0.019794) | 0.499447 / 0.258489 (0.240958) | 0.548995 / 0.293841 (0.255154) | 0.058047 / 0.128546 (-0.070499) | 0.015468 / 0.075646 (-0.060179) | 0.124600 / 0.419271 (-0.294671) | 0.060940 / 0.043533 (0.017407) | 0.488370 / 0.255139 (0.233231) | 0.518540 / 0.283200 (0.235341) | 0.124147 / 0.141683 (-0.017536) | 1.902922 / 1.452155 (0.450767) | 2.033519 / 1.492716 (0.540803) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319527 / 0.018006 (0.301521) | 0.629641 / 0.000490 (0.629152) | 0.000721 / 0.000200 (0.000521) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033150 / 0.037411 (-0.004262) | 0.134250 / 0.014526 (0.119724) | 0.161273 / 0.176557 (-0.015283) | 0.211471 / 0.737135 (-0.525664) | 0.155326 / 0.296338 (-0.141012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.705244 / 0.215209 (0.490035) | 7.043040 / 2.077655 (4.965386) | 3.308948 / 1.504120 (1.804828) | 2.885050 / 1.541195 (1.343855) | 2.810260 / 1.468490 (1.341770) | 1.027095 / 4.584777 (-3.557682) | 6.111398 / 3.745712 (2.365686) | 5.385545 / 5.269862 (0.115684) | 2.521668 / 4.565676 (-2.044009) | 0.122419 / 0.424275 (-0.301856) | 0.016376 / 0.007607 (0.008768) | 0.830856 / 0.226044 (0.604811) | 8.952199 / 2.268929 (6.683271) | 4.207875 / 55.444624 (-51.236749) | 3.346624 / 6.876477 (-3.529853) | 3.395316 / 2.142072 (1.253244) | 1.351816 / 4.805227 (-3.453411) | 0.303056 / 6.500664 (-6.197608) | 0.098713 / 0.075469 (0.023244) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.841903 / 1.841788 (0.000116) | 20.472125 / 8.074308 (12.397817) | 23.433200 / 10.191392 (13.241808) | 0.242599 / 0.680424 (-0.437825) | 0.030701 / 0.534201 (-0.503500) | 0.541614 / 0.579283 (-0.037669) | 0.657827 / 0.434364 (0.223463) | 0.652448 / 0.540337 (0.112111) | 0.773743 / 1.386936 (-0.613193) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ee418831aba68d0be93227bce8b3f42ef8980f \"CML watermark\")\n" ]

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[ "got that the syntax is like this\r\n\r\ndataset = load_dataset(\"facebook/flores\", \"ace_Arab\")" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006213 / 0.011353 (-0.005140) | 0.004230 / 0.011008 (-0.006778) | 0.098014 / 0.038508 (0.059506) | 0.028659 / 0.023109 (0.005550) | 0.303272 / 0.275898 (0.027374) | 0.337186 / 0.323480 (0.013706) | 0.005126 / 0.007986 (-0.002860) | 0.003563 / 0.004328 (-0.000765) | 0.075295 / 0.004250 (0.071045) | 0.036836 / 0.037052 (-0.000216) | 0.309612 / 0.258489 (0.051123) | 0.346484 / 0.293841 (0.052643) | 0.025714 / 0.128546 (-0.102832) | 0.008562 / 0.075646 (-0.067085) | 0.323475 / 0.419271 (-0.095796) | 0.044072 / 0.043533 (0.000539) | 0.308261 / 0.255139 (0.053122) | 0.330903 / 0.283200 (0.047703) | 0.091805 / 0.141683 (-0.049878) | 1.517011 / 1.452155 (0.064856) | 1.570815 / 1.492716 (0.078099) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211265 / 0.018006 (0.193259) | 0.438860 / 0.000490 (0.438370) | 0.001127 / 0.000200 (0.000927) | 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) | 0.023337 / 0.037411 (-0.014074) | 0.096243 / 0.014526 (0.081717) | 0.103529 / 0.176557 (-0.073028) | 0.161171 / 0.737135 (-0.575964) | 0.105904 / 0.296338 (-0.190435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417042 / 0.215209 (0.201833) | 4.155067 / 2.077655 (2.077412) | 1.879657 / 1.504120 (0.375537) | 1.669341 / 1.541195 (0.128146) | 1.717623 / 1.468490 (0.249133) | 0.556246 / 4.584777 (-4.028531) | 3.484535 / 3.745712 (-0.261177) | 1.728845 / 5.269862 (-3.541017) | 0.997477 / 4.565676 (-3.568199) | 0.068355 / 0.424275 (-0.355920) | 0.012445 / 0.007607 (0.004837) | 0.519023 / 0.226044 (0.292978) | 5.173506 / 2.268929 (2.904577) | 2.332435 / 55.444624 (-53.112190) | 1.986348 / 6.876477 (-4.890129) | 2.076885 / 2.142072 (-0.065187) | 0.656738 / 4.805227 (-4.148489) | 0.135308 / 6.500664 (-6.365356) | 0.065486 / 0.075469 (-0.009984) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208874 / 1.841788 (-0.632914) | 13.994200 / 8.074308 (5.919892) | 14.160978 / 10.191392 (3.969586) | 0.146009 / 0.680424 (-0.534415) | 0.016573 / 0.534201 (-0.517628) | 0.356082 / 0.579283 (-0.223202) | 0.387766 / 0.434364 (-0.046598) | 0.419130 / 0.540337 (-0.121208) | 0.508634 / 1.386936 (-0.878302) |\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) | 0.006238 / 0.011353 (-0.005115) | 0.004221 / 0.011008 (-0.006788) | 0.075155 / 0.038508 (0.036646) | 0.028491 / 0.023109 (0.005382) | 0.355606 / 0.275898 (0.079708) | 0.388986 / 0.323480 (0.065506) | 0.005941 / 0.007986 (-0.002044) | 0.003510 / 0.004328 (-0.000819) | 0.074905 / 0.004250 (0.070655) | 0.039111 / 0.037052 (0.002059) | 0.358492 / 0.258489 (0.100003) | 0.398763 / 0.293841 (0.104922) | 0.025535 / 0.128546 (-0.103012) | 0.008580 / 0.075646 (-0.067067) | 0.080461 / 0.419271 (-0.338811) | 0.041381 / 0.043533 (-0.002152) | 0.355498 / 0.255139 (0.100359) | 0.379163 / 0.283200 (0.095963) | 0.096450 / 0.141683 (-0.045233) | 1.503248 / 1.452155 (0.051093) | 1.595616 / 1.492716 (0.102900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238065 / 0.018006 (0.220058) | 0.422800 / 0.000490 (0.422311) | 0.002274 / 0.000200 (0.002074) | 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) | 0.025746 / 0.037411 (-0.011665) | 0.103319 / 0.014526 (0.088793) | 0.112155 / 0.176557 (-0.064401) | 0.163034 / 0.737135 (-0.574101) | 0.113377 / 0.296338 (-0.182962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440522 / 0.215209 (0.225313) | 4.398123 / 2.077655 (2.320468) | 2.143538 / 1.504120 (0.639418) | 1.946084 / 1.541195 (0.404890) | 1.996556 / 1.468490 (0.528066) | 0.550108 / 4.584777 (-4.034669) | 3.455774 / 3.745712 (-0.289938) | 2.862474 / 5.269862 (-2.407387) | 1.213446 / 4.565676 (-3.352230) | 0.067987 / 0.424275 (-0.356288) | 0.012413 / 0.007607 (0.004806) | 0.543990 / 0.226044 (0.317945) | 5.454807 / 2.268929 (3.185879) | 2.669195 / 55.444624 (-52.775429) | 2.332948 / 6.876477 (-4.543528) | 2.383870 / 2.142072 (0.241797) | 0.652017 / 4.805227 (-4.153210) | 0.135508 / 6.500664 (-6.365156) | 0.068238 / 0.075469 (-0.007231) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322669 / 1.841788 (-0.519118) | 14.368136 / 8.074308 (6.293828) | 14.167431 / 10.191392 (3.976039) | 0.159371 / 0.680424 (-0.521052) | 0.016638 / 0.534201 (-0.517563) | 0.357106 / 0.579283 (-0.222177) | 0.392491 / 0.434364 (-0.041873) | 0.419458 / 0.540337 (-0.120880) | 0.504662 / 1.386936 (-0.882274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf764819ba6754cb7edf15899db517be0548676f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006296 / 0.011353 (-0.005057) | 0.004185 / 0.011008 (-0.006823) | 0.096170 / 0.038508 (0.057662) | 0.029212 / 0.023109 (0.006102) | 0.315356 / 0.275898 (0.039458) | 0.335214 / 0.323480 (0.011734) | 0.005108 / 0.007986 (-0.002877) | 0.003634 / 0.004328 (-0.000694) | 0.074186 / 0.004250 (0.069936) | 0.038716 / 0.037052 (0.001663) | 0.311041 / 0.258489 (0.052551) | 0.341202 / 0.293841 (0.047361) | 0.025584 / 0.128546 (-0.102962) | 0.008499 / 0.075646 (-0.067148) | 0.318660 / 0.419271 (-0.100611) | 0.043745 / 0.043533 (0.000212) | 0.314824 / 0.255139 (0.059685) | 0.328117 / 0.283200 (0.044917) | 0.093425 / 0.141683 (-0.048258) | 1.478732 / 1.452155 (0.026578) | 1.531743 / 1.492716 (0.039027) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203484 / 0.018006 (0.185478) | 0.416131 / 0.000490 (0.415641) | 0.007352 / 0.000200 (0.007152) | 0.000211 / 0.000054 (0.000156) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022908 / 0.037411 (-0.014503) | 0.098641 / 0.014526 (0.084115) | 0.103426 / 0.176557 (-0.073131) | 0.161658 / 0.737135 (-0.575477) | 0.106506 / 0.296338 (-0.189832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430781 / 0.215209 (0.215572) | 4.315677 / 2.077655 (2.238022) | 2.022302 / 1.504120 (0.518182) | 1.832043 / 1.541195 (0.290849) | 1.789302 / 1.468490 (0.320812) | 0.560484 / 4.584777 (-4.024293) | 3.448204 / 3.745712 (-0.297508) | 1.725016 / 5.269862 (-3.544846) | 1.002649 / 4.565676 (-3.563027) | 0.068480 / 0.424275 (-0.355795) | 0.012617 / 0.007607 (0.005010) | 0.532291 / 0.226044 (0.306246) | 5.319352 / 2.268929 (3.050423) | 2.520730 / 55.444624 (-52.923894) | 2.213881 / 6.876477 (-4.662596) | 2.352477 / 2.142072 (0.210404) | 0.662516 / 4.805227 (-4.142711) | 0.136481 / 6.500664 (-6.364183) | 0.066597 / 0.075469 (-0.008872) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224537 / 1.841788 (-0.617251) | 13.849920 / 8.074308 (5.775612) | 14.026358 / 10.191392 (3.834966) | 0.131018 / 0.680424 (-0.549405) | 0.016756 / 0.534201 (-0.517445) | 0.358091 / 0.579283 (-0.221192) | 0.397709 / 0.434364 (-0.036655) | 0.450024 / 0.540337 (-0.090314) | 0.542609 / 1.386936 (-0.844327) |\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) | 0.006179 / 0.011353 (-0.005174) | 0.004145 / 0.011008 (-0.006863) | 0.077482 / 0.038508 (0.038974) | 0.028005 / 0.023109 (0.004896) | 0.400010 / 0.275898 (0.124112) | 0.408206 / 0.323480 (0.084726) | 0.005049 / 0.007986 (-0.002937) | 0.003608 / 0.004328 (-0.000721) | 0.076841 / 0.004250 (0.072590) | 0.036714 / 0.037052 (-0.000338) | 0.406020 / 0.258489 (0.147531) | 0.412392 / 0.293841 (0.118551) | 0.025626 / 0.128546 (-0.102920) | 0.008560 / 0.075646 (-0.067087) | 0.084088 / 0.419271 (-0.335183) | 0.039707 / 0.043533 (-0.003826) | 0.396909 / 0.255139 (0.141770) | 0.403623 / 0.283200 (0.120424) | 0.095137 / 0.141683 (-0.046546) | 1.515670 / 1.452155 (0.063515) | 1.568379 / 1.492716 (0.075662) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.181802 / 0.018006 (0.163795) | 0.408778 / 0.000490 (0.408289) | 0.000393 / 0.000200 (0.000193) | 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) | 0.025940 / 0.037411 (-0.011471) | 0.099992 / 0.014526 (0.085466) | 0.106280 / 0.176557 (-0.070276) | 0.161729 / 0.737135 (-0.575406) | 0.108625 / 0.296338 (-0.187713) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459802 / 0.215209 (0.244593) | 4.603002 / 2.077655 (2.525347) | 2.406851 / 1.504120 (0.902732) | 2.265422 / 1.541195 (0.724227) | 2.306305 / 1.468490 (0.837815) | 0.553903 / 4.584777 (-4.030874) | 3.482052 / 3.745712 (-0.263660) | 2.969855 / 5.269862 (-2.300007) | 1.309285 / 4.565676 (-3.256391) | 0.068130 / 0.424275 (-0.356145) | 0.012189 / 0.007607 (0.004582) | 0.571299 / 0.226044 (0.345254) | 5.711420 / 2.268929 (3.442492) | 2.716748 / 55.444624 (-52.727876) | 2.369869 / 6.876477 (-4.506608) | 2.544240 / 2.142072 (0.402167) | 0.659955 / 4.805227 (-4.145272) | 0.136684 / 6.500664 (-6.363980) | 0.068962 / 0.075469 (-0.006507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.297659 / 1.841788 (-0.544129) | 14.012758 / 8.074308 (5.938449) | 14.324644 / 10.191392 (4.133252) | 0.144894 / 0.680424 (-0.535530) | 0.016751 / 0.534201 (-0.517450) | 0.361547 / 0.579283 (-0.217736) | 0.396595 / 0.434364 (-0.037769) | 0.422375 / 0.540337 (-0.117962) | 0.508209 / 1.386936 (-0.878727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba5f81357b53099b1bedfbb277211dba3952257b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006303 / 0.011353 (-0.005050) | 0.004043 / 0.011008 (-0.006965) | 0.096239 / 0.038508 (0.057731) | 0.029608 / 0.023109 (0.006498) | 0.321058 / 0.275898 (0.045160) | 0.367066 / 0.323480 (0.043587) | 0.005236 / 0.007986 (-0.002749) | 0.003342 / 0.004328 (-0.000987) | 0.074407 / 0.004250 (0.070157) | 0.038810 / 0.037052 (0.001757) | 0.332597 / 0.258489 (0.074108) | 0.363562 / 0.293841 (0.069721) | 0.025460 / 0.128546 (-0.103086) | 0.008426 / 0.075646 (-0.067221) | 0.316998 / 0.419271 (-0.102273) | 0.043621 / 0.043533 (0.000088) | 0.338043 / 0.255139 (0.082904) | 0.366441 / 0.283200 (0.083241) | 0.092061 / 0.141683 (-0.049622) | 1.461531 / 1.452155 (0.009376) | 1.538047 / 1.492716 (0.045331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206796 / 0.018006 (0.188790) | 0.517959 / 0.000490 (0.517469) | 0.002745 / 0.000200 (0.002545) | 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) | 0.022902 / 0.037411 (-0.014510) | 0.097901 / 0.014526 (0.083375) | 0.103664 / 0.176557 (-0.072893) | 0.163516 / 0.737135 (-0.573619) | 0.108561 / 0.296338 (-0.187778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418964 / 0.215209 (0.203755) | 4.159113 / 2.077655 (2.081458) | 1.843946 / 1.504120 (0.339827) | 1.641083 / 1.541195 (0.099888) | 1.686848 / 1.468490 (0.218358) | 0.554583 / 4.584777 (-4.030194) | 3.409862 / 3.745712 (-0.335850) | 2.647904 / 5.269862 (-2.621958) | 1.355424 / 4.565676 (-3.210253) | 0.068229 / 0.424275 (-0.356046) | 0.012217 / 0.007607 (0.004610) | 0.515895 / 0.226044 (0.289851) | 5.144920 / 2.268929 (2.875991) | 2.298046 / 55.444624 (-53.146579) | 1.964735 / 6.876477 (-4.911741) | 2.075580 / 2.142072 (-0.066492) | 0.657104 / 4.805227 (-4.148123) | 0.134759 / 6.500664 (-6.365905) | 0.067545 / 0.075469 (-0.007924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233075 / 1.841788 (-0.608713) | 13.896762 / 8.074308 (5.822454) | 14.055143 / 10.191392 (3.863751) | 0.145507 / 0.680424 (-0.534917) | 0.016702 / 0.534201 (-0.517499) | 0.365157 / 0.579283 (-0.214126) | 0.385842 / 0.434364 (-0.048522) | 0.459993 / 0.540337 (-0.080344) | 0.547115 / 1.386936 (-0.839821) |\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) | 0.006174 / 0.011353 (-0.005179) | 0.004191 / 0.011008 (-0.006817) | 0.078311 / 0.038508 (0.039803) | 0.028038 / 0.023109 (0.004928) | 0.360056 / 0.275898 (0.084158) | 0.398081 / 0.323480 (0.074602) | 0.005069 / 0.007986 (-0.002916) | 0.003464 / 0.004328 (-0.000864) | 0.077858 / 0.004250 (0.073608) | 0.039420 / 0.037052 (0.002367) | 0.361743 / 0.258489 (0.103254) | 0.404829 / 0.293841 (0.110988) | 0.025604 / 0.128546 (-0.102943) | 0.008573 / 0.075646 (-0.067074) | 0.084944 / 0.419271 (-0.334328) | 0.042652 / 0.043533 (-0.000881) | 0.368549 / 0.255139 (0.113410) | 0.385682 / 0.283200 (0.102482) | 0.099085 / 0.141683 (-0.042598) | 1.495815 / 1.452155 (0.043661) | 1.548168 / 1.492716 (0.055452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193737 / 0.018006 (0.175730) | 0.421871 / 0.000490 (0.421381) | 0.002306 / 0.000200 (0.002106) | 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) | 0.025928 / 0.037411 (-0.011483) | 0.103410 / 0.014526 (0.088885) | 0.107931 / 0.176557 (-0.068626) | 0.157127 / 0.737135 (-0.580008) | 0.111892 / 0.296338 (-0.184446) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477562 / 0.215209 (0.262353) | 4.772711 / 2.077655 (2.695056) | 2.458725 / 1.504120 (0.954605) | 2.269871 / 1.541195 (0.728676) | 2.365502 / 1.468490 (0.897012) | 0.556182 / 4.584777 (-4.028595) | 3.408016 / 3.745712 (-0.337697) | 1.730639 / 5.269862 (-3.539222) | 1.000973 / 4.565676 (-3.564704) | 0.068293 / 0.424275 (-0.355982) | 0.012119 / 0.007607 (0.004512) | 0.581281 / 0.226044 (0.355236) | 5.811930 / 2.268929 (3.543001) | 2.890337 / 55.444624 (-52.554288) | 2.592156 / 6.876477 (-4.284321) | 2.687764 / 2.142072 (0.545691) | 0.664282 / 4.805227 (-4.140946) | 0.136029 / 6.500664 (-6.364635) | 0.067493 / 0.075469 (-0.007976) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330723 / 1.841788 (-0.511064) | 14.379172 / 8.074308 (6.304864) | 14.153286 / 10.191392 (3.961894) | 0.142942 / 0.680424 (-0.537482) | 0.016698 / 0.534201 (-0.517503) | 0.361044 / 0.579283 (-0.218239) | 0.393174 / 0.434364 (-0.041190) | 0.423107 / 0.540337 (-0.117231) | 0.514299 / 1.386936 (-0.872637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb02285358ab4be6386e0a2aae40d267ff561fc \"CML watermark\")\n" ]

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[ "Thanks for reporting, @DongHande.\r\n\r\nI think the issue is caused by the metadata in the dataset card: in the header of the `README.md`, they state that the dataset has 4 splits (\"finetune\", \"reward\", \"rl\", \"evaluation\"). \r\n```yaml\r\n splits:\r\n - name: finetune\r\n num_bytes: 6674567576\r\n num_examples: 3000000\r\n - name: reward\r\n num_bytes: 6674341521\r\n num_examples: 3000000\r\n - name: rl\r\n num_bytes: 6679279968\r\n num_examples: 3000000\r\n - name: evaluation\r\n num_bytes: 4022714493\r\n num_examples: 1807695\r\n```\r\n\r\n\r\nI guess the user wanted to define these as configs, instead of splits. This is not yet supported for no-script datasets, but will be soon supported. See:\r\n- #5331\r\n\r\nI think we should contact the dataset author to inform about the issue with the split names, as you already did: https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/discussions/1\r\nLet's continue the discussion there!", "Thank you! It has been fixed. " ]

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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009139 / 0.011353 (-0.002214) | 0.005634 / 0.011008 (-0.005374) | 0.129587 / 0.038508 (0.091079) | 0.038298 / 0.023109 (0.015189) | 0.428149 / 0.275898 (0.152251) | 0.443744 / 0.323480 (0.120264) | 0.007501 / 0.007986 (-0.000485) | 0.005999 / 0.004328 (0.001671) | 0.100796 / 0.004250 (0.096546) | 0.053236 / 0.037052 (0.016184) | 0.423868 / 0.258489 (0.165379) | 0.460110 / 0.293841 (0.166269) | 0.041255 / 0.128546 (-0.087291) | 0.013790 / 0.075646 (-0.061856) | 0.438398 / 0.419271 (0.019127) | 0.063086 / 0.043533 (0.019553) | 0.414826 / 0.255139 (0.159687) | 0.460652 / 0.283200 (0.177453) | 0.121223 / 0.141683 (-0.020460) | 1.754430 / 1.452155 (0.302275) | 1.900037 / 1.492716 (0.407320) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.027222 / 0.018006 (0.009216) | 0.617666 / 0.000490 (0.617176) | 0.022443 / 0.000200 (0.022243) | 0.000820 / 0.000054 (0.000766) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030397 / 0.037411 (-0.007014) | 0.125732 / 0.014526 (0.111206) | 0.149805 / 0.176557 (-0.026752) | 0.234048 / 0.737135 (-0.503087) | 0.143108 / 0.296338 (-0.153231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.631189 / 0.215209 (0.415980) | 6.182871 / 2.077655 (4.105216) | 2.635730 / 1.504120 (1.131610) | 2.231429 / 1.541195 (0.690235) | 2.438360 / 1.468490 (0.969870) | 0.861170 / 4.584777 (-3.723607) | 5.785984 / 3.745712 (2.040272) | 2.758358 / 5.269862 (-2.511504) | 1.678095 / 4.565676 (-2.887582) | 0.105961 / 0.424275 (-0.318314) | 0.013659 / 0.007607 (0.006052) | 0.762943 / 0.226044 (0.536898) | 7.774399 / 2.268929 (5.505471) | 3.319027 / 55.444624 (-52.125598) | 2.700248 / 6.876477 (-4.176229) | 3.008581 / 2.142072 (0.866509) | 1.122522 / 4.805227 (-3.682705) | 0.214832 / 6.500664 (-6.285832) | 0.085281 / 0.075469 (0.009811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.647610 / 1.841788 (-0.194177) | 18.178316 / 8.074308 (10.104008) | 21.199177 / 10.191392 (11.007785) | 0.247063 / 0.680424 (-0.433361) | 0.030443 / 0.534201 (-0.503758) | 0.512527 / 0.579283 (-0.066757) | 0.640758 / 0.434364 (0.206394) | 0.639986 / 0.540337 (0.099649) | 0.760113 / 1.386936 (-0.626823) |\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) | 0.008293 / 0.011353 (-0.003060) | 0.005360 / 0.011008 (-0.005648) | 0.102932 / 0.038508 (0.064424) | 0.037457 / 0.023109 (0.014347) | 0.444114 / 0.275898 (0.168216) | 0.512855 / 0.323480 (0.189375) | 0.007030 / 0.007986 (-0.000956) | 0.004954 / 0.004328 (0.000625) | 0.095757 / 0.004250 (0.091507) | 0.051239 / 0.037052 (0.014187) | 0.471118 / 0.258489 (0.212629) | 0.517764 / 0.293841 (0.223923) | 0.041953 / 0.128546 (-0.086593) | 0.013748 / 0.075646 (-0.061898) | 0.118089 / 0.419271 (-0.301182) | 0.060159 / 0.043533 (0.016626) | 0.466011 / 0.255139 (0.210872) | 0.489180 / 0.283200 (0.205980) | 0.123250 / 0.141683 (-0.018433) | 1.714738 / 1.452155 (0.262584) | 1.838571 / 1.492716 (0.345855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267792 / 0.018006 (0.249785) | 0.624313 / 0.000490 (0.623824) | 0.007315 / 0.000200 (0.007115) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033751 / 0.037411 (-0.003661) | 0.122819 / 0.014526 (0.108293) | 0.148270 / 0.176557 (-0.028286) | 0.198581 / 0.737135 (-0.538554) | 0.144845 / 0.296338 (-0.151494) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620631 / 0.215209 (0.405422) | 6.224665 / 2.077655 (4.147010) | 2.856592 / 1.504120 (1.352473) | 2.525089 / 1.541195 (0.983894) | 2.600198 / 1.468490 (1.131708) | 0.872038 / 4.584777 (-3.712739) | 5.571650 / 3.745712 (1.825937) | 5.907643 / 5.269862 (0.637782) | 2.348770 / 4.565676 (-2.216906) | 0.111665 / 0.424275 (-0.312610) | 0.013886 / 0.007607 (0.006278) | 0.762154 / 0.226044 (0.536109) | 7.792686 / 2.268929 (5.523758) | 3.601122 / 55.444624 (-51.843503) | 2.939412 / 6.876477 (-3.937064) | 2.973430 / 2.142072 (0.831358) | 1.065016 / 4.805227 (-3.740211) | 0.221701 / 6.500664 (-6.278963) | 0.088157 / 0.075469 (0.012688) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.771061 / 1.841788 (-0.070727) | 18.826926 / 8.074308 (10.752618) | 21.283830 / 10.191392 (11.092438) | 0.239233 / 0.680424 (-0.441191) | 0.026159 / 0.534201 (-0.508042) | 0.487074 / 0.579283 (-0.092209) | 0.623241 / 0.434364 (0.188877) | 0.600506 / 0.540337 (0.060169) | 0.691271 / 1.386936 (-0.695665) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bbe2c3496498a6415765b517ac4bc600a02ad06 \"CML watermark\")\n" ]

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[ "@lhoestq Could you please look into that and review?", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq I refactored the code. Could you please check is it what you requested?", "@lhoestq Thanks for a review. Excellent tips. All tips applied. ", "I think there is just PythonFormatter that needs to be imported in the test file and we should be good to merge", "@lhoestq that is weird. I have linter error when I do it.", "@lhoestq Now it should work properly.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006152 / 0.011353 (-0.005201) | 0.004169 / 0.011008 (-0.006839) | 0.097968 / 0.038508 (0.059460) | 0.028325 / 0.023109 (0.005216) | 0.308958 / 0.275898 (0.033060) | 0.341832 / 0.323480 (0.018352) | 0.005098 / 0.007986 (-0.002887) | 0.004721 / 0.004328 (0.000393) | 0.075067 / 0.004250 (0.070817) | 0.040514 / 0.037052 (0.003462) | 0.308355 / 0.258489 (0.049866) | 0.351063 / 0.293841 (0.057222) | 0.025261 / 0.128546 (-0.103285) | 0.008483 / 0.075646 (-0.067163) | 0.321219 / 0.419271 (-0.098052) | 0.058258 / 0.043533 (0.014725) | 0.312572 / 0.255139 (0.057433) | 0.330667 / 0.283200 (0.047467) | 0.091047 / 0.141683 (-0.050635) | 1.536541 / 1.452155 (0.084387) | 1.606566 / 1.492716 (0.113850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213234 / 0.018006 (0.195228) | 0.494801 / 0.000490 (0.494311) | 0.003764 / 0.000200 (0.003564) | 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) | 0.023653 / 0.037411 (-0.013758) | 0.097176 / 0.014526 (0.082650) | 0.102961 / 0.176557 (-0.073595) | 0.164285 / 0.737135 (-0.572851) | 0.107586 / 0.296338 (-0.188753) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421402 / 0.215209 (0.206193) | 4.195828 / 2.077655 (2.118174) | 1.884664 / 1.504120 (0.380544) | 1.679750 / 1.541195 (0.138556) | 1.719725 / 1.468490 (0.251235) | 0.552290 / 4.584777 (-4.032486) | 3.386337 / 3.745712 (-0.359375) | 1.771527 / 5.269862 (-3.498334) | 1.133327 / 4.565676 (-3.432349) | 0.067911 / 0.424275 (-0.356364) | 0.012572 / 0.007607 (0.004965) | 0.518004 / 0.226044 (0.291960) | 5.192381 / 2.268929 (2.923453) | 2.316032 / 55.444624 (-53.128592) | 1.993264 / 6.876477 (-4.883212) | 2.071009 / 2.142072 (-0.071063) | 0.655062 / 4.805227 (-4.150165) | 0.135488 / 6.500664 (-6.365177) | 0.067273 / 0.075469 (-0.008196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217731 / 1.841788 (-0.624056) | 13.812927 / 8.074308 (5.738619) | 13.137886 / 10.191392 (2.946494) | 0.143102 / 0.680424 (-0.537322) | 0.016884 / 0.534201 (-0.517317) | 0.370106 / 0.579283 (-0.209178) | 0.392349 / 0.434364 (-0.042015) | 0.424501 / 0.540337 (-0.115837) | 0.509830 / 1.386936 (-0.877106) |\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) | 0.006210 / 0.011353 (-0.005142) | 0.004215 / 0.011008 (-0.006793) | 0.076129 / 0.038508 (0.037621) | 0.027825 / 0.023109 (0.004716) | 0.403973 / 0.275898 (0.128075) | 0.441089 / 0.323480 (0.117609) | 0.005420 / 0.007986 (-0.002566) | 0.004870 / 0.004328 (0.000542) | 0.075558 / 0.004250 (0.071308) | 0.039464 / 0.037052 (0.002411) | 0.404329 / 0.258489 (0.145840) | 0.447213 / 0.293841 (0.153372) | 0.025877 / 0.128546 (-0.102669) | 0.008660 / 0.075646 (-0.066987) | 0.081849 / 0.419271 (-0.337422) | 0.044551 / 0.043533 (0.001018) | 0.379102 / 0.255139 (0.123963) | 0.403104 / 0.283200 (0.119905) | 0.094754 / 0.141683 (-0.046929) | 1.460772 / 1.452155 (0.008617) | 1.569531 / 1.492716 (0.076815) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183923 / 0.018006 (0.165917) | 0.420708 / 0.000490 (0.420219) | 0.002091 / 0.000200 (0.001891) | 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) | 0.026180 / 0.037411 (-0.011231) | 0.101529 / 0.014526 (0.087003) | 0.108739 / 0.176557 (-0.067818) | 0.160702 / 0.737135 (-0.576433) | 0.111739 / 0.296338 (-0.184600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448671 / 0.215209 (0.233462) | 4.469287 / 2.077655 (2.391632) | 2.244335 / 1.504120 (0.740215) | 2.107495 / 1.541195 (0.566301) | 2.224763 / 1.468490 (0.756272) | 0.554006 / 4.584777 (-4.030771) | 3.390109 / 3.745712 (-0.355603) | 1.744189 / 5.269862 (-3.525673) | 1.008515 / 4.565676 (-3.557161) | 0.067904 / 0.424275 (-0.356371) | 0.012243 / 0.007607 (0.004636) | 0.557635 / 0.226044 (0.331590) | 5.610383 / 2.268929 (3.341454) | 2.687326 / 55.444624 (-52.757298) | 2.405262 / 6.876477 (-4.471214) | 2.527300 / 2.142072 (0.385227) | 0.662282 / 4.805227 (-4.142945) | 0.136225 / 6.500664 (-6.364439) | 0.068136 / 0.075469 (-0.007334) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310791 / 1.841788 (-0.530997) | 14.370381 / 8.074308 (6.296072) | 14.122675 / 10.191392 (3.931283) | 0.152302 / 0.680424 (-0.528122) | 0.016624 / 0.534201 (-0.517577) | 0.359395 / 0.579283 (-0.219888) | 0.392131 / 0.434364 (-0.042233) | 0.423796 / 0.540337 (-0.116542) | 0.511387 / 1.386936 (-0.875549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6a61a1af1502677a6f2333896a6ffeede9ca21b \"CML watermark\")\n" ]

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[ "cc @SBrandeis" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006916 / 0.011353 (-0.004437) | 0.004749 / 0.011008 (-0.006259) | 0.096086 / 0.038508 (0.057578) | 0.035448 / 0.023109 (0.012338) | 0.299645 / 0.275898 (0.023747) | 0.331279 / 0.323480 (0.007799) | 0.006018 / 0.007986 (-0.001968) | 0.004210 / 0.004328 (-0.000118) | 0.072998 / 0.004250 (0.068747) | 0.050082 / 0.037052 (0.013030) | 0.297714 / 0.258489 (0.039225) | 0.365523 / 0.293841 (0.071682) | 0.028081 / 0.128546 (-0.100465) | 0.009072 / 0.075646 (-0.066574) | 0.327628 / 0.419271 (-0.091643) | 0.051165 / 0.043533 (0.007633) | 0.295091 / 0.255139 (0.039952) | 0.320052 / 0.283200 (0.036852) | 0.109841 / 0.141683 (-0.031842) | 1.467867 / 1.452155 (0.015712) | 1.572600 / 1.492716 (0.079884) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281490 / 0.018006 (0.263484) | 0.499259 / 0.000490 (0.498770) | 0.000691 / 0.000200 (0.000491) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027548 / 0.037411 (-0.009863) | 0.106592 / 0.014526 (0.092066) | 0.118654 / 0.176557 (-0.057902) | 0.174313 / 0.737135 (-0.562822) | 0.124491 / 0.296338 (-0.171848) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399674 / 0.215209 (0.184465) | 3.984092 / 2.077655 (1.906437) | 1.790935 / 1.504120 (0.286815) | 1.593612 / 1.541195 (0.052417) | 1.694595 / 1.468490 (0.226105) | 0.517588 / 4.584777 (-4.067189) | 3.724353 / 3.745712 (-0.021359) | 3.244807 / 5.269862 (-2.025054) | 1.602929 / 4.565676 (-2.962748) | 0.065334 / 0.424275 (-0.358941) | 0.012259 / 0.007607 (0.004652) | 0.501355 / 0.226044 (0.275311) | 4.996546 / 2.268929 (2.727618) | 2.279333 / 55.444624 (-53.165291) | 1.940126 / 6.876477 (-4.936351) | 2.122945 / 2.142072 (-0.019128) | 0.626104 / 4.805227 (-4.179123) | 0.141278 / 6.500664 (-6.359386) | 0.064522 / 0.075469 (-0.010947) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195351 / 1.841788 (-0.646436) | 15.258932 / 8.074308 (7.184624) | 14.627623 / 10.191392 (4.436231) | 0.266897 / 0.680424 (-0.413527) | 0.017557 / 0.534201 (-0.516644) | 0.392932 / 0.579283 (-0.186351) | 0.416409 / 0.434364 (-0.017955) | 0.469100 / 0.540337 (-0.071237) | 0.556247 / 1.386936 (-0.830689) |\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) | 0.006880 / 0.011353 (-0.004473) | 0.004837 / 0.011008 (-0.006171) | 0.074518 / 0.038508 (0.036010) | 0.034204 / 0.023109 (0.011095) | 0.365100 / 0.275898 (0.089202) | 0.394976 / 0.323480 (0.071496) | 0.006364 / 0.007986 (-0.001621) | 0.004269 / 0.004328 (-0.000060) | 0.073531 / 0.004250 (0.069281) | 0.051334 / 0.037052 (0.014281) | 0.373904 / 0.258489 (0.115415) | 0.413662 / 0.293841 (0.119821) | 0.028779 / 0.128546 (-0.099767) | 0.009292 / 0.075646 (-0.066354) | 0.081574 / 0.419271 (-0.337698) | 0.046531 / 0.043533 (0.002998) | 0.368995 / 0.255139 (0.113856) | 0.376938 / 0.283200 (0.093739) | 0.112576 / 0.141683 (-0.029107) | 1.458880 / 1.452155 (0.006725) | 1.550918 / 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) | 0.319521 / 0.018006 (0.301515) | 0.510146 / 0.000490 (0.509656) | 0.000438 / 0.000200 (0.000238) | 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) | 0.033082 / 0.037411 (-0.004329) | 0.118009 / 0.014526 (0.103483) | 0.127108 / 0.176557 (-0.049448) | 0.176600 / 0.737135 (-0.560535) | 0.133790 / 0.296338 (-0.162549) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437360 / 0.215209 (0.222151) | 4.367426 / 2.077655 (2.289771) | 2.193646 / 1.504120 (0.689526) | 2.025002 / 1.541195 (0.483808) | 2.142347 / 1.468490 (0.673856) | 0.525497 / 4.584777 (-4.059280) | 3.751275 / 3.745712 (0.005563) | 1.912271 / 5.269862 (-3.357590) | 1.087286 / 4.565676 (-3.478390) | 0.066328 / 0.424275 (-0.357947) | 0.011904 / 0.007607 (0.004297) | 0.545870 / 0.226044 (0.319825) | 5.434481 / 2.268929 (3.165552) | 2.719745 / 55.444624 (-52.724880) | 2.445001 / 6.876477 (-4.431476) | 2.500205 / 2.142072 (0.358133) | 0.645735 / 4.805227 (-4.159492) | 0.144210 / 6.500664 (-6.356455) | 0.065688 / 0.075469 (-0.009781) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273522 / 1.841788 (-0.568265) | 15.771778 / 8.074308 (7.697470) | 14.685261 / 10.191392 (4.493869) | 0.176523 / 0.680424 (-0.503900) | 0.017877 / 0.534201 (-0.516324) | 0.392687 / 0.579283 (-0.186596) | 0.449992 / 0.434364 (0.015628) | 0.462851 / 0.540337 (-0.077487) | 0.560178 / 1.386936 (-0.826758) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fa3ef6eba906ee1214e0596d15a78fc358909f4 \"CML watermark\")\n" ]

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[ "Nice catch @donhuvy, that's because some models don't need the `token_type_ids`, as in this case, as the example is using `distilbert-base-cased`, and according to the DistilBert documentation at https://huggingface.co/transformers/v3.0.2/model_doc/distilbert.html, `DistilBert doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP])`. `token_type_ids` are neither required in some other well known models such as RoBERTa. \r\n\r\nHere the issue comes due to a mismatch between the tokenizer and the model, as the Colab is using a BERT tokenizer (`bert-base-cased`), while the model is a DistilBERT (`distilbert-base-cased`), so aligning the tokenizer and the model solves it!", "#self-assign", "@donhuvy I've created https://github.com/huggingface/datasets/pull/5902 to solve it! 🤗" ]

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[ "Alternatively we could set the number of shards to be a factor than the number of processes (current they're equal) - this way it will be less likely to end up with a shard that is significantly slower than all the other ones." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5885). All of your documentation changes will be reflected on that endpoint.", "@lhoestq would you or another maintainer be able to review please? :)", "Why you do need to support FUSE mounted paths ?\r\n\r\n`datasets` uses data that live on disk for fast lookups - FUSE mounted disks would lead to poor performance and I wouldn't recomment using it.", "Fuse is commonly used to mount remote file systems (e.g. S3, DBFS) as a local directory. Since it's slower than using an actual local device, it's better to treat it as remote to reduce latency.", "I think people would be confused if they don't have the same dataset behavior depending on the disk type.\r\n\r\nIf they want to use a remote bucket they should use the remote URI instead, e.g. `s3://...`. Advancements on this are tracked at #5281 " ]

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[ "Hi! \r\n\r\nYou want to use `push_to_hub` (creates Parquet files) instead of `save_to_disk` (creates Arrow files) when creating a Hub dataset. Parquet is designed for long-term storage and takes less space than the Arrow format, and, most importantly, `load_dataset` can parse it, which should fix the viewer. \r\n\r\nRegarding the dataset generation, `Dataset.from_generator` with the video data represented as `datasets.Value(\"binary\")` followed by `push_to_hub` should work (if the `push_to_hub` step times out, restart it to resume uploading)\r\n\r\nPS: Once the dataset is uploaded, to make working with the dataset easier, it's a good idea to add a [transform](https://huggingface.co/docs/datasets/main/en/process#format-transform) to the README that shows how to decode the binary video data into something a model can understand. Also, if you get an `ArrowInvalid` error (can happen when working with large binary data) in `Dataset.from_generator`, reduce the value of `writer_batch_size` (the default is 1000) to fix it.", "One issue here is that Dataset.from_generator can work well for the non 'infinite sampling' version of the dataset. The training set for example is often sampled dynamically given the video files that I have uploaded. I worry that storing the video data as binary means that I'll end up duplicating a lot of the data. Furthermore, storing video data as anything but .mp4 would quickly make the dataset size from 1.9TB to 1PB. ", "> storing video data as anything but .mp4\r\n\r\nWhat I mean by storing as `datasets.Value(\"binary\")` is embedding raw MP4 bytes in the Arrow table, but, indeed, this would waste a lot of space if there are duplicates.\r\n\r\nSo I see two options:\r\n* if one video is not mapped to too many samples, you can embed the video bytes and do \"group by\" on the rest of the columns (this would turn them into lists) to avoid duplicating them (then, it should be easy to define a `map` in the README that samples the video data to \"unpack\" the samples)\r\n* you can create a dataset script that downloads the video files and embeds their file paths into the Arrow file\r\n\r\nAlso, I misread MP4 as MP3. We need to add a `Video` feature to the `datasets` lib to support MP4 files in the viewer (a bit trickier to implement than the `Image` feature due to the Arrow limitations).", "I'm transferring this issue to the `datasets` repo, as it's not related to `huggingface_hub`", "@mariosasko Right. If I want my dataset to be streamable, what are the necessary requirements to achieve that within the context of .mp4 binaries like we have here? I guess your second point here would not support that right?", "The streaming would work, but the video paths would require using `fsspec.open` to get the content.", "Are there any plans to make video playable on the hub?" ]

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[ "May eventually be solved in #5883 ", "#self-assign" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5883). All of your documentation changes will be reflected on that endpoint.", "To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r\nColab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nAlso, here's a quick sample of what's happening:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"imdb\", split=\"train\")\r\ntfds = ds.to_tf_dataset(batch_size=16)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nA more detailed version of it:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"a\": [1],\r\n \"b\": [\"é\"],\r\n }\r\n)\r\ntfds = ds.to_tf_dataset(batch_size=1)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nThe original issue comes from https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#LL234C4-L234C4, which could easily be solved by replacing that line with `return result.astype(np.unicode_)` but they are mentioning that it may lead to issues.\r\n\r\nEven the following fails in `numpy`:\r\n\r\n```python\r\nimport numpy as np\r\n\r\nx = np.array([\"é\"]).astype(np.bytes_)\r\n```", "cc. @lhoestq :hugs:", "cc @Rocketknight1 ", "> Nice ! Could you add some tests to make sure that batch_size=None works as expected ?\r\n\r\nSure, I'll add the tests for everything, including the string-encoding issue to make sure it's solved!", "Thanks for the review @lhoestq and @Rocketknight1! I do understand that processing it in batches is always more efficient than processing it one-by-one, it was just to make `batch_size` optional. What we can do is default it to a certain batch size e.g. 16 as before, and that's it, but I think it can still remain optional.", "@Rocketknight1 then I'll add the integration tests for the optional `batch_size` as well as for the encoding of non-ASCII compatible characters 😄 Do we set the default `batch_size` to 16 instead of `None`?", "@alvarobartt I think 16 is a reasonable default, yep!", "I think default should be None, not 16.\r\nUsers won't expect to have it batched by default.", "Then I'll leave it as is, and add the unit/integration tests, thanks @Rocketknight1 and @lhoestq ", "Hi @Rocketknight1 @lhoestq! So the string-encoding issue is already solved, but I've got one doubt about the `batch_size` being optional in the multiprocessing approach, since in that case I assume the `batch_size` should be mandatory, for the moment I'm assuming it is/should be mandatory, but let me know if you want me to add a check to disallow `batch_size=None` when `num_workers>1`. Thanks!", "> To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n> \r\n> Colab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nI've used the Colab shared above for testing purposes, and it works fine, plus the unit/integration tests are passing. I've also trained a `KerasNLP` model with incoming data from 🤗`datasets` with no issue at all!", "> in the multiprocessing approach, since in that case I assume the batch_size should be mandatory,\r\n\r\nNo I think they're quite orthogonal, no need to have it mandatory", "> No I think they're quite orthogonal, no need to have it mandatory\r\n\r\nBut it will break if `batch_size=None` as the multiprocessing approach will aim to prepare batches and distribute those to every worker, and assuming `batch_size=1` when `batch_size=None` I guess is not a good assumption, right?", "Ah I see. Multiprocessing should support batch_size=None indeed. If you have ideas you can do it in this PR, or raise a NotImplementedError and we can see later", "Sure @lhoestq, I can add a `NotImplementedError` for the moment, and prepare the next PR straight-away to tackle the multiprocessing approach with `batch_size=None`, but not sure if that may eventually collide with @Rocketknight1 PR at https://github.com/huggingface/datasets/pull/5863", "Yes, let me merge the PR at #5863 after this one, and then we can open another to improve the behaviour with multiprocessing and `batch_size=None`!", "Sure @Rocketknight1 makes complete sense to me! Do you want me to add the `raise NotImplementedError` and then we merge this PR? Or you prefer to directly merge the current?", "`raise NotImplementedError` for now with an error telling the user that multiprocessing needs them to specify a batch size, I think!", "Since you recently approved @Rocketknight1, are we ready to merge? Thanks 🤗", "Ah actually it looks like `minimal_tf_collate_fn` doesn't support batch_size=None", "Hi @lhoestq so I didn't include the call to `collate_fn`, as we won't need to collate the incoming data e.g. \"str\" should remain a \"str\" not a [\"str\"], and the `minimal_collate_fn` was indeed putting everything into a list, so the output was not un-batched, but batched with size 1", "What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n\r\nDoes my last change look of to you ? If so I think we can merge", "> What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n> \r\n> Does my last change look of to you ? If so I think we can merge\r\n\r\nI think we're good, since it won't batch it under the scenario of `str` being provided instead of `List[str]`, and the unit/integration tests are passing, so I'm OK to merge. Maybe we can double check with Matt? cc @Rocketknight1 " ]

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[ "cc @lhoestq in case you have any ideas here! Might need a multi-host set-up to debug (can give you access to a JAX one if you need)" ]

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[ "This sounds related to #5281.\r\n\r\nCan you try passing `storage_options=s3_client.storage_options` instead passing it to `use_auth_token=` ?", "I tried `storage_options` before, but it doesn't work, I checked our source code and I found that we even didn't pass this parameter to the following process. if I use `storage_options` instead of `use_auth_token`, then I also need to change another place of the code. the last line of `streaming_download_manager.py`. our code only passes the `use_auth_token` to the following handler, but does nothing to the `storage_options`\r\n<img width=\"1050\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/59083384/5be90933-3331-4ecf-9e11-34f9852d8f92\">\r\n", "Cloud storage support is still experimental indeed and you can expect some bugs.\r\n\r\nI think we need to pass the storage options anywhere use_auth_token is passed in indeed. Let me know if you'd be interested in contributing a fix !", "Oh, that's great, I really like to fix it. because datasets is really useful and most of our projects need to use it, but we can store our data on the internet due to security reasons. fix it not only make our own work more efficient but also can benefit others who use it." ]

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[ "Very cool! Do you have a link to the code that you're using to eagerly fetch the data? Would also be interested in hacking around something here for pre-fetching iterable datasets", "I ended up just switching back to the pytorch dataloader and using it's multiprocessing functionality to handle this :(. I'm just not that familiar with python multiprocessing to get something to work in jupyter (kept having weird behaviors happening with zombies living after the cell finished).", "Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: https://github.com/huggingface/datasets/issues/5337 was resolved.", "Hi! You can combine `datasets` with `torchdata` to prefetch `IterableDataset`'s samples:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchdata.datapipes.iter import IterableWrapper, HuggingFaceHubReader\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(\"sst\", split=\"train\", streaming=True)\r\n# processing...\r\ndp = IterableWrapper(ds)\r\ndp = dp.prefetch(100)\r\ndl = DataLoader(dp, batch_size=8)\r\n\r\ni = iter(dl)\r\nnext(i)\r\n```", "Hey @mariosasko! Thanks for the tip here - introducing prefetch with `torchdata` didn't really give me any performance difference vs not prefetching, but the concept is definitely one that could be really beneficial. Are there any benchmarks that show the speed-up you can get with `torchdata`'s prefetch just for comparison?" ]

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[ "The \"exact match\" deduplication will be possible when we resolve https://github.com/huggingface/datasets/issues/2514 (first, https://github.com/apache/arrow/issues/30950 needs to be addressed on the Arrow side). In the meantime, you can use Polars or DuckDB (e.g., via [datasets-sql](https://github.com/mariosasko/datasets_sql)).\r\n\r\nFuzzy deduplication is out-of-scope for now ([splink](https://github.com/moj-analytical-services/splink) is probably the best tool for it).", "This library can be an intermediate solution : https://github.com/ChenghaoMou/text-dedup/tree/main" ]

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[ "Indeed, `clobber=True` (with a warning if the existing protocol will be overwritten) should fix the issue, but maybe a better solution is to register our compression filesystem before the script is executed and unregister them afterward. WDYT @lhoestq @albertvillanova?", "I think we should use clobber and show a warning if it overwrote a registered filesystem indeed ! This way the user can re-register the filesystems if needed. Though they should probably be compatible (and maybe do the exact same thing) so I wouldn't de-register the `datasets` filesystems" ]

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[ "A duplicate of https://github.com/huggingface/datasets/issues/1774" ]

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[ "Hi! You can refer to [this doc](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) to see the intended usage (basically, it skips the Arrow -> Parquet conversion step in `ds = load_dataset(...); ds.to_parquet(\"path/to/parquet\")`) and allows writing Parquet to remote storage unlike `to_parquet`).\r\n\r\n> I guess I'd expect as_dataset to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with load_dataset to no avail, probably due to misunderstandings on my part).\r\n\r\n`as_dataset` does not work with `file_format=\"parquet\"` files as Parquet files cannot be memory-mapped, so I think we should just raise an error in that case.\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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007049 / 0.011353 (-0.004304) | 0.005034 / 0.011008 (-0.005974) | 0.097737 / 0.038508 (0.059229) | 0.033280 / 0.023109 (0.010170) | 0.301017 / 0.275898 (0.025119) | 0.336593 / 0.323480 (0.013113) | 0.005567 / 0.007986 (-0.002419) | 0.005384 / 0.004328 (0.001056) | 0.072980 / 0.004250 (0.068730) | 0.045030 / 0.037052 (0.007978) | 0.303280 / 0.258489 (0.044791) | 0.367528 / 0.293841 (0.073687) | 0.034131 / 0.128546 (-0.094415) | 0.012118 / 0.075646 (-0.063528) | 0.331677 / 0.419271 (-0.087594) | 0.049211 / 0.043533 (0.005678) | 0.297535 / 0.255139 (0.042396) | 0.318136 / 0.283200 (0.034936) | 0.101574 / 0.141683 (-0.040109) | 1.472769 / 1.452155 (0.020615) | 1.541724 / 1.492716 (0.049007) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014646 / 0.018006 (-0.003360) | 0.439050 / 0.000490 (0.438560) | 0.008575 / 0.000200 (0.008375) | 0.000297 / 0.000054 (0.000242) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027591 / 0.037411 (-0.009820) | 0.111639 / 0.014526 (0.097113) | 0.117098 / 0.176557 (-0.059458) | 0.173281 / 0.737135 (-0.563855) | 0.123197 / 0.296338 (-0.173141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397507 / 0.215209 (0.182298) | 3.971457 / 2.077655 (1.893803) | 1.781158 / 1.504120 (0.277038) | 1.590419 / 1.541195 (0.049224) | 1.716374 / 1.468490 (0.247884) | 0.687150 / 4.584777 (-3.897627) | 3.691009 / 3.745712 (-0.054703) | 2.050900 / 5.269862 (-3.218961) | 1.304893 / 4.565676 (-3.260784) | 0.084507 / 0.424275 (-0.339768) | 0.012231 / 0.007607 (0.004624) | 0.493033 / 0.226044 (0.266988) | 4.929957 / 2.268929 (2.661028) | 2.209069 / 55.444624 (-53.235555) | 1.885992 / 6.876477 (-4.990485) | 2.007004 / 2.142072 (-0.135069) | 0.827265 / 4.805227 (-3.977963) | 0.168225 / 6.500664 (-6.332439) | 0.064988 / 0.075469 (-0.010481) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.182341 / 1.841788 (-0.659447) | 14.691983 / 8.074308 (6.617674) | 14.350720 / 10.191392 (4.159328) | 0.164307 / 0.680424 (-0.516117) | 0.017480 / 0.534201 (-0.516720) | 0.421843 / 0.579283 (-0.157441) | 0.417481 / 0.434364 (-0.016883) | 0.496587 / 0.540337 (-0.043751) | 0.581208 / 1.386936 (-0.805728) |\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) | 0.007070 / 0.011353 (-0.004283) | 0.005083 / 0.011008 (-0.005926) | 0.075009 / 0.038508 (0.036500) | 0.032343 / 0.023109 (0.009234) | 0.366788 / 0.275898 (0.090890) | 0.392273 / 0.323480 (0.068794) | 0.005512 / 0.007986 (-0.002474) | 0.003999 / 0.004328 (-0.000329) | 0.073743 / 0.004250 (0.069492) | 0.046203 / 0.037052 (0.009151) | 0.367874 / 0.258489 (0.109385) | 0.409154 / 0.293841 (0.115313) | 0.035227 / 0.128546 (-0.093319) | 0.012223 / 0.075646 (-0.063424) | 0.087149 / 0.419271 (-0.332122) | 0.045648 / 0.043533 (0.002115) | 0.362414 / 0.255139 (0.107275) | 0.379970 / 0.283200 (0.096770) | 0.100631 / 0.141683 (-0.041052) | 1.439733 / 1.452155 (-0.012422) | 1.506266 / 1.492716 (0.013550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227071 / 0.018006 (0.209065) | 0.451243 / 0.000490 (0.450753) | 0.000406 / 0.000200 (0.000206) | 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) | 0.028952 / 0.037411 (-0.008459) | 0.111934 / 0.014526 (0.097408) | 0.124080 / 0.176557 (-0.052477) | 0.174022 / 0.737135 (-0.563113) | 0.126811 / 0.296338 (-0.169527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436423 / 0.215209 (0.221214) | 4.331959 / 2.077655 (2.254304) | 2.111914 / 1.504120 (0.607794) | 1.921338 / 1.541195 (0.380143) | 1.994425 / 1.468490 (0.525935) | 0.699164 / 4.584777 (-3.885613) | 3.722143 / 3.745712 (-0.023569) | 3.516538 / 5.269862 (-1.753323) | 1.867245 / 4.565676 (-2.698431) | 0.085923 / 0.424275 (-0.338352) | 0.012059 / 0.007607 (0.004452) | 0.586147 / 0.226044 (0.360102) | 5.395823 / 2.268929 (3.126894) | 2.594430 / 55.444624 (-52.850194) | 2.275021 / 6.876477 (-4.601456) | 2.347810 / 2.142072 (0.205737) | 0.835118 / 4.805227 (-3.970109) | 0.167089 / 6.500664 (-6.333575) | 0.064893 / 0.075469 (-0.010576) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291423 / 1.841788 (-0.550365) | 14.992696 / 8.074308 (6.918388) | 13.307842 / 10.191392 (3.116450) | 0.163799 / 0.680424 (-0.516625) | 0.017315 / 0.534201 (-0.516886) | 0.461319 / 0.579283 (-0.117965) | 0.430474 / 0.434364 (-0.003889) | 0.568115 / 0.540337 (0.027777) | 0.647909 / 1.386936 (-0.739027) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5161c9ecdcdde9cc99c7f212da13523d5ba6bdb \"CML watermark\")\n" ]

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[ "It could even use `os.path.realpath` to resolve symlinks.", "Indeed, it makes sense to normalize `data_dir`. Feel free to submit a PR (this can be \"fixed\" [here](https://github.com/huggingface/datasets/blob/89f775226321ba94e5bf4670a323c0fb44f5f65c/src/datasets/builder.py#L173))", "#self-assign" ]

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[ "PS - some work is definitely needed for 'special cases' docs, not explanations, just usages of 'functions' under mixture of special cases, like a combination of custom databuilder + iterable dataset for large size + dynamic .map() application." ]

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[ "Hi @PhilippeMoussalli thanks for opening a detailed issue. It seems the issue is more related to the `datasets` library so I'll ping @lhoestq @mariosasko on this one :) \n\n(edit: also can one of you move the issue to the datasets repo? Thanks in advance 🙏)", "Hi ! The `Image()` info is stored in the **schema metadata**. More precisely there should be a \"huggingface\" field in the schema metadata that contains the `datasets` feature type of each column.\r\n\r\nTo fix your issue, you can use the same schema as the original Parquet files to write the new ones. You can also get the schema with metadata from a `Features` object, e.g.\r\n\r\n```python\r\nfrom datasets import Features, Image, Value\r\n\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\nprint(schema.metadata)\r\n# {b'huggingface': b'{\"info\": {\"features\": {\"image\": {\"_type\": \"Image\"}, \"text\": {\"dtype\": \"string\", \"_type\": \"Value\"}}}}'}\r\n```", "It appears that the parquet files at `hf://datasets/lambdalabs/pokemon-blip-captions` don't have this metadata, and it is defined in the dataset_infos.json instead (legacy).\r\n\r\nYou can get the right schema with the HF metadata this way:\r\n\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nfeatures = load_dataset_builder(\"lambdalabs/pokemon-blip-captions\").info.features\r\nschema = features.arrow_schema\r\n```", "Btw in the future we might add support for an dedicated Image extension type in Arrow so that you won't need to add the schema metadata anymore ;)", "Thanks @Wauplin @lhoestq for the quick reply :)! \r\n\r\nI tried your approach by passing the huggingface schema to the dask writer \r\n\r\n```\r\nfrom datasets import Features, Image, Value\r\ndf = dd.read_parquet(f\"hf://datasets/lambdalabs/pokemon-blip-captions\",index=False)\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\ndd.to_parquet(df, path = \"hf://datasets/philippemo/dummy_dataset/data\", schema=schema)\r\n```\r\nAt first it didn't work as I was not able to visualize the images, so then I manually added the `dataset_infos.json` from the example dataset and it worked :)\r\n\r\nHowever, It's not very ideal since there are some metadata in that file that need to be computed in order to load the data properly such as `num_of_bytes` and `num_examples` which might be unknown in my use case. \r\n\r\n\r\n\r\nDo you have any pointers there? you mentioned that `datasets_info.json` will be deprecated/legacy. Could you point me to some example image datasets on the hub that are stored as parquet and don't have the `datasets_info.json`?\r\n\r\n", "You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;)\r\nI could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n\r\nWhat made you think it didn't work ?", "> You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;) I could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n> \r\n> What made you think it didn't work ?\r\n\r\nThose are two identical dataset repos where both were pushed with dask with the specified schema you mentioned above. I then uploaded the `dataset_infos.json` manually taken from the original example dataset into one of them. \r\n\r\n* **With schema**: https://huggingface.co/datasets/philippemo/dummy_dataset_with_schema\r\n* **Without schema**: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nYou can see that in the examples without schema the images fail to render properly. When loaded with `datasets` they return an dict and not a Pillow Image ", "I see ! I think it's a bug on our side - it should work without the metadata - let me investigate", "Alright, it's fixed: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nIt shows the image correctly now - even without the extra metadata :)", "Thanks @lhoestq! \r\nI tested pushing a dataset again without the metadata and it works perfectly! \r\nI appreciate the help" ]

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[ "Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.", "> \r\n\r\nGot it, thanks for your reply" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006598 / 0.011353 (-0.004755) | 0.004565 / 0.011008 (-0.006443) | 0.099063 / 0.038508 (0.060555) | 0.028334 / 0.023109 (0.005225) | 0.323539 / 0.275898 (0.047641) | 0.372462 / 0.323480 (0.048982) | 0.005120 / 0.007986 (-0.002865) | 0.004797 / 0.004328 (0.000468) | 0.076862 / 0.004250 (0.072611) | 0.038021 / 0.037052 (0.000968) | 0.337801 / 0.258489 (0.079312) | 0.374601 / 0.293841 (0.080760) | 0.031158 / 0.128546 (-0.097389) | 0.011672 / 0.075646 (-0.063974) | 0.324913 / 0.419271 (-0.094359) | 0.051702 / 0.043533 (0.008169) | 0.339440 / 0.255139 (0.084301) | 0.372502 / 0.283200 (0.089303) | 0.097590 / 0.141683 (-0.044093) | 1.534238 / 1.452155 (0.082083) | 1.599701 / 1.492716 (0.106985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204101 / 0.018006 (0.186095) | 0.416981 / 0.000490 (0.416491) | 0.003436 / 0.000200 (0.003236) | 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) | 0.023527 / 0.037411 (-0.013885) | 0.095748 / 0.014526 (0.081222) | 0.104498 / 0.176557 (-0.072059) | 0.164000 / 0.737135 (-0.573135) | 0.109170 / 0.296338 (-0.187168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418239 / 0.215209 (0.203030) | 4.153959 / 2.077655 (2.076305) | 1.856687 / 1.504120 (0.352567) | 1.657818 / 1.541195 (0.116623) | 1.715146 / 1.468490 (0.246656) | 0.700673 / 4.584777 (-3.884103) | 3.401060 / 3.745712 (-0.344652) | 2.891045 / 5.269862 (-2.378816) | 1.519433 / 4.565676 (-3.046243) | 0.083151 / 0.424275 (-0.341124) | 0.012352 / 0.007607 (0.004745) | 0.523901 / 0.226044 (0.297856) | 5.288871 / 2.268929 (3.019943) | 2.322806 / 55.444624 (-53.121818) | 1.982223 / 6.876477 (-4.894253) | 2.074883 / 2.142072 (-0.067189) | 0.812400 / 4.805227 (-3.992827) | 0.152183 / 6.500664 (-6.348481) | 0.066538 / 0.075469 (-0.008931) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223220 / 1.841788 (-0.618567) | 14.024391 / 8.074308 (5.950083) | 14.166657 / 10.191392 (3.975265) | 0.146017 / 0.680424 (-0.534407) | 0.016698 / 0.534201 (-0.517503) | 0.380779 / 0.579283 (-0.198504) | 0.387113 / 0.434364 (-0.047251) | 0.446329 / 0.540337 (-0.094009) | 0.523819 / 1.386936 (-0.863118) |\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) | 0.006803 / 0.011353 (-0.004549) | 0.004554 / 0.011008 (-0.006454) | 0.077406 / 0.038508 (0.038897) | 0.028495 / 0.023109 (0.005386) | 0.358847 / 0.275898 (0.082949) | 0.393256 / 0.323480 (0.069776) | 0.005317 / 0.007986 (-0.002669) | 0.004690 / 0.004328 (0.000362) | 0.075842 / 0.004250 (0.071592) | 0.041985 / 0.037052 (0.004933) | 0.367546 / 0.258489 (0.109057) | 0.408019 / 0.293841 (0.114178) | 0.030712 / 0.128546 (-0.097834) | 0.011756 / 0.075646 (-0.063891) | 0.086002 / 0.419271 (-0.333269) | 0.038949 / 0.043533 (-0.004583) | 0.361045 / 0.255139 (0.105906) | 0.381728 / 0.283200 (0.098528) | 0.090692 / 0.141683 (-0.050991) | 1.493251 / 1.452155 (0.041097) | 1.584566 / 1.492716 (0.091850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217470 / 0.018006 (0.199463) | 0.429955 / 0.000490 (0.429465) | 0.000394 / 0.000200 (0.000194) | 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) | 0.026223 / 0.037411 (-0.011189) | 0.102570 / 0.014526 (0.088045) | 0.110848 / 0.176557 (-0.065709) | 0.162413 / 0.737135 (-0.574722) | 0.114579 / 0.296338 (-0.181760) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464957 / 0.215209 (0.249748) | 4.656597 / 2.077655 (2.578942) | 2.279755 / 1.504120 (0.775636) | 2.230263 / 1.541195 (0.689068) | 2.341540 / 1.468490 (0.873050) | 0.699505 / 4.584777 (-3.885272) | 3.389003 / 3.745712 (-0.356709) | 1.867526 / 5.269862 (-3.402336) | 1.167171 / 4.565676 (-3.398506) | 0.083451 / 0.424275 (-0.340824) | 0.012348 / 0.007607 (0.004741) | 0.584205 / 0.226044 (0.358161) | 5.853623 / 2.268929 (3.584694) | 2.646650 / 55.444624 (-52.797974) | 2.286504 / 6.876477 (-4.589973) | 2.327536 / 2.142072 (0.185464) | 0.811209 / 4.805227 (-3.994018) | 0.151842 / 6.500664 (-6.348822) | 0.067783 / 0.075469 (-0.007686) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330427 / 1.841788 (-0.511360) | 14.668981 / 8.074308 (6.594673) | 13.321154 / 10.191392 (3.129762) | 0.164383 / 0.680424 (-0.516040) | 0.016667 / 0.534201 (-0.517534) | 0.383439 / 0.579283 (-0.195844) | 0.392988 / 0.434364 (-0.041376) | 0.443318 / 0.540337 (-0.097020) | 0.537849 / 1.386936 (-0.849087) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e99bd4583bd636074b1826e2d0581161807480f1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006379 / 0.011353 (-0.004974) | 0.004691 / 0.011008 (-0.006317) | 0.098047 / 0.038508 (0.059539) | 0.028126 / 0.023109 (0.005017) | 0.327143 / 0.275898 (0.051245) | 0.362482 / 0.323480 (0.039002) | 0.004953 / 0.007986 (-0.003033) | 0.003386 / 0.004328 (-0.000943) | 0.076222 / 0.004250 (0.071971) | 0.037583 / 0.037052 (0.000531) | 0.329661 / 0.258489 (0.071172) | 0.365945 / 0.293841 (0.072104) | 0.030455 / 0.128546 (-0.098091) | 0.011397 / 0.075646 (-0.064249) | 0.323889 / 0.419271 (-0.095383) | 0.043719 / 0.043533 (0.000186) | 0.331499 / 0.255139 (0.076360) | 0.359357 / 0.283200 (0.076158) | 0.088904 / 0.141683 (-0.052779) | 1.458584 / 1.452155 (0.006429) | 1.549375 / 1.492716 (0.056658) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195808 / 0.018006 (0.177802) | 0.411148 / 0.000490 (0.410659) | 0.003602 / 0.000200 (0.003402) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023278 / 0.037411 (-0.014133) | 0.097317 / 0.014526 (0.082791) | 0.102669 / 0.176557 (-0.073888) | 0.168203 / 0.737135 (-0.568933) | 0.105205 / 0.296338 (-0.191133) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424800 / 0.215209 (0.209591) | 4.228444 / 2.077655 (2.150790) | 1.895544 / 1.504120 (0.391424) | 1.698793 / 1.541195 (0.157598) | 1.717931 / 1.468490 (0.249441) | 0.702251 / 4.584777 (-3.882526) | 3.407013 / 3.745712 (-0.338699) | 2.784634 / 5.269862 (-2.485228) | 1.491317 / 4.565676 (-3.074359) | 0.082926 / 0.424275 (-0.341350) | 0.012320 / 0.007607 (0.004713) | 0.524188 / 0.226044 (0.298143) | 5.249798 / 2.268929 (2.980870) | 2.358953 / 55.444624 (-53.085672) | 1.985922 / 6.876477 (-4.890555) | 2.034293 / 2.142072 (-0.107779) | 0.815671 / 4.805227 (-3.989556) | 0.152583 / 6.500664 (-6.348081) | 0.066687 / 0.075469 (-0.008782) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210901 / 1.841788 (-0.630886) | 13.621765 / 8.074308 (5.547457) | 14.213215 / 10.191392 (4.021823) | 0.143346 / 0.680424 (-0.537078) | 0.016904 / 0.534201 (-0.517297) | 0.379795 / 0.579283 (-0.199489) | 0.381287 / 0.434364 (-0.053077) | 0.449086 / 0.540337 (-0.091251) | 0.538792 / 1.386936 (-0.848144) |\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) | 0.006207 / 0.011353 (-0.005146) | 0.004404 / 0.011008 (-0.006604) | 0.076363 / 0.038508 (0.037854) | 0.027335 / 0.023109 (0.004226) | 0.370967 / 0.275898 (0.095069) | 0.401936 / 0.323480 (0.078456) | 0.004835 / 0.007986 (-0.003151) | 0.004559 / 0.004328 (0.000231) | 0.074964 / 0.004250 (0.070713) | 0.038254 / 0.037052 (0.001202) | 0.374799 / 0.258489 (0.116310) | 0.425191 / 0.293841 (0.131350) | 0.035290 / 0.128546 (-0.093256) | 0.011379 / 0.075646 (-0.064267) | 0.085911 / 0.419271 (-0.333360) | 0.043073 / 0.043533 (-0.000460) | 0.373557 / 0.255139 (0.118418) | 0.395179 / 0.283200 (0.111979) | 0.098602 / 0.141683 (-0.043081) | 1.467234 / 1.452155 (0.015079) | 1.571868 / 1.492716 (0.079152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221848 / 0.018006 (0.203842) | 0.394943 / 0.000490 (0.394454) | 0.002983 / 0.000200 (0.002783) | 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) | 0.024385 / 0.037411 (-0.013027) | 0.100087 / 0.014526 (0.085561) | 0.104897 / 0.176557 (-0.071660) | 0.156150 / 0.737135 (-0.580985) | 0.109113 / 0.296338 (-0.187226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441995 / 0.215209 (0.226786) | 4.415423 / 2.077655 (2.337769) | 2.148791 / 1.504120 (0.644671) | 1.947061 / 1.541195 (0.405866) | 1.954807 / 1.468490 (0.486317) | 0.690245 / 4.584777 (-3.894532) | 3.372766 / 3.745712 (-0.372946) | 1.851073 / 5.269862 (-3.418789) | 1.155558 / 4.565676 (-3.410118) | 0.082796 / 0.424275 (-0.341479) | 0.012845 / 0.007607 (0.005238) | 0.548173 / 0.226044 (0.322129) | 5.530984 / 2.268929 (3.262056) | 2.665360 / 55.444624 (-52.779264) | 2.324266 / 6.876477 (-4.552211) | 2.329397 / 2.142072 (0.187324) | 0.801481 / 4.805227 (-4.003746) | 0.152145 / 6.500664 (-6.348519) | 0.067915 / 0.075469 (-0.007554) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291488 / 1.841788 (-0.550299) | 13.912143 / 8.074308 (5.837835) | 12.975493 / 10.191392 (2.784101) | 0.129915 / 0.680424 (-0.550509) | 0.016516 / 0.534201 (-0.517685) | 0.386979 / 0.579283 (-0.192304) | 0.389163 / 0.434364 (-0.045201) | 0.443324 / 0.540337 (-0.097014) | 0.533744 / 1.386936 (-0.853192) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb48834fc2aa45cad73fe70a7ecaa0dd6015b8d0 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5867). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008635 / 0.011353 (-0.002717) | 0.006014 / 0.011008 (-0.004995) | 0.116314 / 0.038508 (0.077806) | 0.041113 / 0.023109 (0.018004) | 0.358564 / 0.275898 (0.082666) | 0.397547 / 0.323480 (0.074067) | 0.007012 / 0.007986 (-0.000974) | 0.004638 / 0.004328 (0.000310) | 0.086509 / 0.004250 (0.082259) | 0.056731 / 0.037052 (0.019678) | 0.358859 / 0.258489 (0.100370) | 0.425339 / 0.293841 (0.131498) | 0.041780 / 0.128546 (-0.086767) | 0.014203 / 0.075646 (-0.061443) | 0.398240 / 0.419271 (-0.021031) | 0.060180 / 0.043533 (0.016647) | 0.352887 / 0.255139 (0.097748) | 0.381793 / 0.283200 (0.098594) | 0.148578 / 0.141683 (0.006895) | 1.749483 / 1.452155 (0.297328) | 1.869765 / 1.492716 (0.377049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244435 / 0.018006 (0.226428) | 0.499545 / 0.000490 (0.499055) | 0.004576 / 0.000200 (0.004376) | 0.000147 / 0.000054 (0.000093) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031163 / 0.037411 (-0.006249) | 0.131082 / 0.014526 (0.116556) | 0.137442 / 0.176557 (-0.039114) | 0.203783 / 0.737135 (-0.533352) | 0.144068 / 0.296338 (-0.152270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503587 / 0.215209 (0.288378) | 5.011953 / 2.077655 (2.934299) | 2.366968 / 1.504120 (0.862848) | 2.130914 / 1.541195 (0.589719) | 2.243560 / 1.468490 (0.775070) | 0.856719 / 4.584777 (-3.728058) | 4.707445 / 3.745712 (0.961733) | 2.506166 / 5.269862 (-2.763696) | 1.590400 / 4.565676 (-2.975277) | 0.102075 / 0.424275 (-0.322200) | 0.014499 / 0.007607 (0.006892) | 0.624966 / 0.226044 (0.398922) | 6.197671 / 2.268929 (3.928742) | 2.898481 / 55.444624 (-52.546143) | 2.499590 / 6.876477 (-4.376886) | 2.649690 / 2.142072 (0.507617) | 1.012542 / 4.805227 (-3.792685) | 0.202833 / 6.500664 (-6.297831) | 0.078033 / 0.075469 (0.002564) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.448321 / 1.841788 (-0.393467) | 18.084909 / 8.074308 (10.010601) | 17.383027 / 10.191392 (7.191635) | 0.212167 / 0.680424 (-0.468256) | 0.020754 / 0.534201 (-0.513447) | 0.514653 / 0.579283 (-0.064630) | 0.543307 / 0.434364 (0.108944) | 0.653066 / 0.540337 (0.112728) | 0.745773 / 1.386936 (-0.641164) |\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) | 0.008576 / 0.011353 (-0.002777) | 0.005834 / 0.011008 (-0.005174) | 0.089842 / 0.038508 (0.051334) | 0.040035 / 0.023109 (0.016926) | 0.449329 / 0.275898 (0.173431) | 0.471572 / 0.323480 (0.148092) | 0.006771 / 0.007986 (-0.001215) | 0.006129 / 0.004328 (0.001800) | 0.090370 / 0.004250 (0.086119) | 0.056924 / 0.037052 (0.019872) | 0.455134 / 0.258489 (0.196645) | 0.502670 / 0.293841 (0.208829) | 0.041689 / 0.128546 (-0.086857) | 0.014447 / 0.075646 (-0.061200) | 0.104528 / 0.419271 (-0.314744) | 0.055535 / 0.043533 (0.012003) | 0.450667 / 0.255139 (0.195528) | 0.453108 / 0.283200 (0.169908) | 0.119296 / 0.141683 (-0.022387) | 1.747359 / 1.452155 (0.295204) | 1.839421 / 1.492716 (0.346705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314910 / 0.018006 (0.296904) | 0.495575 / 0.000490 (0.495085) | 0.054702 / 0.000200 (0.054503) | 0.000505 / 0.000054 (0.000450) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033991 / 0.037411 (-0.003420) | 0.133268 / 0.014526 (0.118742) | 0.142286 / 0.176557 (-0.034271) | 0.200562 / 0.737135 (-0.536573) | 0.147161 / 0.296338 (-0.149178) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520288 / 0.215209 (0.305079) | 5.227684 / 2.077655 (3.150029) | 2.553330 / 1.504120 (1.049210) | 2.324338 / 1.541195 (0.783143) | 2.406790 / 1.468490 (0.938300) | 0.850404 / 4.584777 (-3.734373) | 4.612156 / 3.745712 (0.866444) | 2.592546 / 5.269862 (-2.677316) | 1.708984 / 4.565676 (-2.856692) | 0.103751 / 0.424275 (-0.320524) | 0.014379 / 0.007607 (0.006772) | 0.634661 / 0.226044 (0.408616) | 6.344939 / 2.268929 (4.076010) | 3.179807 / 55.444624 (-52.264817) | 2.831856 / 6.876477 (-4.044621) | 2.866729 / 2.142072 (0.724656) | 0.994519 / 4.805227 (-3.810708) | 0.201566 / 6.500664 (-6.299098) | 0.078902 / 0.075469 (0.003433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538738 / 1.841788 (-0.303049) | 18.746367 / 8.074308 (10.672059) | 16.504763 / 10.191392 (6.313371) | 0.197898 / 0.680424 (-0.482526) | 0.020469 / 0.534201 (-0.513732) | 0.529106 / 0.579283 (-0.050177) | 0.536891 / 0.434364 (0.102527) | 0.600947 / 0.540337 (0.060610) | 0.701713 / 1.386936 (-0.685223) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3054f66b4765a520e6fe165c44a4307d40775229 \"CML watermark\")\n" ]

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

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5865). All of your documentation changes will be reflected on that endpoint.", "If it's easy to keep supporting it we can keep it no ? There are many datasets on the hub that implement the tasks templates in dataset scripts and it's maybe easier to keep task templates than opening PRs to those datasets.", "do we know if people use the tasks api?\r\n\r\nedit: i mean, i'm fine with removing it if it's not used much, especially considering that it's not documented well." ]

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[ "I am highly interested performance of dataset so I ran your example as a curious user.\r\n```python\r\ntrain_dataset.cast_column(\"x\", Array3D(shape=img_shape, dtype=\"float32\"))\r\n```\r\nhave return values and \"x\" is a new column, it shoulde be\r\n```python\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\n```\r\nI rewrite your example as\r\n```python\r\ntrain_dataset = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\ntransform_func = torchvision.transforms.Compose([\r\n ToTensor(), \r\n Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] \r\n)\r\n \r\ntrain_dataset = train_dataset.map(\r\n desc=f\"Preprocessing samples\",\r\n function=lambda x: {\"img\": transform_func(x[\"img\"])},\r\n)\r\nds=train_dataset.cast_column(\"img\", Array3D(shape=(3,32,32), dtype=\"float32\"))\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nthat require ~11s in my environment. While\r\n```python\r\nds = load_dataset(\r\n 'cifar100',\r\n split='train',\r\n use_auth_token=True,\r\n)\r\n\r\nfor i in tqdm(ds):\r\n pass\r\n```\r\nonly need ~6s. (So I guess it's still undesirable)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5863). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007764 / 0.011353 (-0.003588) | 0.005397 / 0.011008 (-0.005611) | 0.097995 / 0.038508 (0.059487) | 0.036360 / 0.023109 (0.013251) | 0.312148 / 0.275898 (0.036250) | 0.349427 / 0.323480 (0.025947) | 0.006635 / 0.007986 (-0.001350) | 0.004373 / 0.004328 (0.000044) | 0.074350 / 0.004250 (0.070099) | 0.054667 / 0.037052 (0.017614) | 0.301621 / 0.258489 (0.043132) | 0.364233 / 0.293841 (0.070392) | 0.035356 / 0.128546 (-0.093191) | 0.012512 / 0.075646 (-0.063134) | 0.333399 / 0.419271 (-0.085873) | 0.051363 / 0.043533 (0.007830) | 0.302372 / 0.255139 (0.047233) | 0.326542 / 0.283200 (0.043343) | 0.118610 / 0.141683 (-0.023073) | 1.438485 / 1.452155 (-0.013669) | 1.539131 / 1.492716 (0.046415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010920 / 0.018006 (-0.007086) | 0.561263 / 0.000490 (0.560773) | 0.003972 / 0.000200 (0.003772) | 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) | 0.030333 / 0.037411 (-0.007078) | 0.113608 / 0.014526 (0.099083) | 0.125802 / 0.176557 (-0.050755) | 0.183885 / 0.737135 (-0.553250) | 0.130242 / 0.296338 (-0.166097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404147 / 0.215209 (0.188938) | 4.021990 / 2.077655 (1.944335) | 1.821450 / 1.504120 (0.317330) | 1.619032 / 1.541195 (0.077837) | 1.791267 / 1.468490 (0.322777) | 0.706683 / 4.584777 (-3.878094) | 3.819056 / 3.745712 (0.073344) | 3.485714 / 5.269862 (-1.784147) | 1.938968 / 4.565676 (-2.626709) | 0.086501 / 0.424275 (-0.337774) | 0.012300 / 0.007607 (0.004693) | 0.503600 / 0.226044 (0.277555) | 5.042123 / 2.268929 (2.773195) | 2.269712 / 55.444624 (-53.174912) | 1.944912 / 6.876477 (-4.931565) | 2.155196 / 2.142072 (0.013123) | 0.853434 / 4.805227 (-3.951793) | 0.175554 / 6.500664 (-6.325110) | 0.072005 / 0.075469 (-0.003464) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203765 / 1.841788 (-0.638022) | 15.836634 / 8.074308 (7.762326) | 15.707348 / 10.191392 (5.515956) | 0.164828 / 0.680424 (-0.515596) | 0.018115 / 0.534201 (-0.516086) | 0.434591 / 0.579283 (-0.144692) | 0.437858 / 0.434364 (0.003495) | 0.524672 / 0.540337 (-0.015665) | 0.610535 / 1.386936 (-0.776401) |\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) | 0.007558 / 0.011353 (-0.003795) | 0.005258 / 0.011008 (-0.005750) | 0.075263 / 0.038508 (0.036755) | 0.033915 / 0.023109 (0.010805) | 0.371368 / 0.275898 (0.095470) | 0.399239 / 0.323480 (0.075760) | 0.006547 / 0.007986 (-0.001439) | 0.004675 / 0.004328 (0.000347) | 0.074230 / 0.004250 (0.069980) | 0.054653 / 0.037052 (0.017601) | 0.376655 / 0.258489 (0.118166) | 0.438437 / 0.293841 (0.144596) | 0.035838 / 0.128546 (-0.092709) | 0.012641 / 0.075646 (-0.063005) | 0.087279 / 0.419271 (-0.331993) | 0.046311 / 0.043533 (0.002778) | 0.356649 / 0.255139 (0.101510) | 0.377876 / 0.283200 (0.094677) | 0.108097 / 0.141683 (-0.033586) | 1.478461 / 1.452155 (0.026306) | 1.560375 / 1.492716 (0.067658) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316384 / 0.018006 (0.298378) | 0.539382 / 0.000490 (0.538892) | 0.002029 / 0.000200 (0.001829) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029950 / 0.037411 (-0.007462) | 0.111371 / 0.014526 (0.096846) | 0.125254 / 0.176557 (-0.051303) | 0.173064 / 0.737135 (-0.564071) | 0.130446 / 0.296338 (-0.165893) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424882 / 0.215209 (0.209673) | 4.241575 / 2.077655 (2.163920) | 2.096216 / 1.504120 (0.592096) | 1.916017 / 1.541195 (0.374823) | 2.016318 / 1.468490 (0.547828) | 0.701197 / 4.584777 (-3.883580) | 3.762365 / 3.745712 (0.016652) | 3.307805 / 5.269862 (-1.962057) | 1.841752 / 4.565676 (-2.723925) | 0.086003 / 0.424275 (-0.338272) | 0.012247 / 0.007607 (0.004640) | 0.532926 / 0.226044 (0.306882) | 5.370509 / 2.268929 (3.101580) | 2.587853 / 55.444624 (-52.856772) | 2.264541 / 6.876477 (-4.611936) | 2.374833 / 2.142072 (0.232760) | 0.827751 / 4.805227 (-3.977476) | 0.169454 / 6.500664 (-6.331210) | 0.066340 / 0.075469 (-0.009129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.319128 / 1.841788 (-0.522660) | 16.702085 / 8.074308 (8.627777) | 13.559957 / 10.191392 (3.368565) | 0.146659 / 0.680424 (-0.533765) | 0.017384 / 0.534201 (-0.516817) | 0.421126 / 0.579283 (-0.158157) | 0.422067 / 0.434364 (-0.012297) | 0.490615 / 0.540337 (-0.049723) | 0.587151 / 1.386936 (-0.799785) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79f4b6de25128999f5fc0a7bde9aa71c461f518f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006604 / 0.011353 (-0.004749) | 0.004508 / 0.011008 (-0.006500) | 0.098652 / 0.038508 (0.060144) | 0.028172 / 0.023109 (0.005063) | 0.366997 / 0.275898 (0.091099) | 0.403691 / 0.323480 (0.080211) | 0.005127 / 0.007986 (-0.002859) | 0.003340 / 0.004328 (-0.000989) | 0.075408 / 0.004250 (0.071157) | 0.038049 / 0.037052 (0.000996) | 0.367914 / 0.258489 (0.109425) | 0.410958 / 0.293841 (0.117118) | 0.030454 / 0.128546 (-0.098093) | 0.011422 / 0.075646 (-0.064224) | 0.325048 / 0.419271 (-0.094223) | 0.042959 / 0.043533 (-0.000574) | 0.374536 / 0.255139 (0.119397) | 0.394738 / 0.283200 (0.111538) | 0.090481 / 0.141683 (-0.051201) | 1.504858 / 1.452155 (0.052703) | 1.569072 / 1.492716 (0.076356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010062 / 0.018006 (-0.007945) | 0.408619 / 0.000490 (0.408130) | 0.002307 / 0.000200 (0.002107) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022898 / 0.037411 (-0.014514) | 0.096975 / 0.014526 (0.082449) | 0.103032 / 0.176557 (-0.073524) | 0.164877 / 0.737135 (-0.572259) | 0.107324 / 0.296338 (-0.189014) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446652 / 0.215209 (0.231442) | 4.466939 / 2.077655 (2.389285) | 2.204590 / 1.504120 (0.700471) | 2.004048 / 1.541195 (0.462853) | 2.053035 / 1.468490 (0.584545) | 0.696617 / 4.584777 (-3.888160) | 3.391173 / 3.745712 (-0.354539) | 1.863306 / 5.269862 (-3.406556) | 1.160637 / 4.565676 (-3.405039) | 0.083115 / 0.424275 (-0.341160) | 0.012470 / 0.007607 (0.004862) | 0.547207 / 0.226044 (0.321163) | 5.500667 / 2.268929 (3.231739) | 2.656615 / 55.444624 (-52.788009) | 2.313281 / 6.876477 (-4.563195) | 2.395632 / 2.142072 (0.253559) | 0.815361 / 4.805227 (-3.989867) | 0.152112 / 6.500664 (-6.348552) | 0.067485 / 0.075469 (-0.007984) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206975 / 1.841788 (-0.634813) | 13.684136 / 8.074308 (5.609828) | 13.919129 / 10.191392 (3.727737) | 0.140767 / 0.680424 (-0.539657) | 0.016445 / 0.534201 (-0.517756) | 0.379136 / 0.579283 (-0.200147) | 0.385395 / 0.434364 (-0.048969) | 0.445781 / 0.540337 (-0.094556) | 0.522056 / 1.386936 (-0.864880) |\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) | 0.006370 / 0.011353 (-0.004983) | 0.004514 / 0.011008 (-0.006495) | 0.075671 / 0.038508 (0.037163) | 0.026723 / 0.023109 (0.003614) | 0.359819 / 0.275898 (0.083921) | 0.387935 / 0.323480 (0.064456) | 0.004888 / 0.007986 (-0.003098) | 0.004619 / 0.004328 (0.000290) | 0.075546 / 0.004250 (0.071295) | 0.039024 / 0.037052 (0.001971) | 0.361173 / 0.258489 (0.102684) | 0.411425 / 0.293841 (0.117584) | 0.030842 / 0.128546 (-0.097705) | 0.011555 / 0.075646 (-0.064091) | 0.084697 / 0.419271 (-0.334574) | 0.039281 / 0.043533 (-0.004252) | 0.370082 / 0.255139 (0.114943) | 0.382113 / 0.283200 (0.098913) | 0.091237 / 0.141683 (-0.050445) | 1.534185 / 1.452155 (0.082030) | 1.576488 / 1.492716 (0.083772) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226568 / 0.018006 (0.208562) | 0.401566 / 0.000490 (0.401076) | 0.002915 / 0.000200 (0.002715) | 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) | 0.025357 / 0.037411 (-0.012054) | 0.099747 / 0.014526 (0.085221) | 0.106443 / 0.176557 (-0.070113) | 0.157147 / 0.737135 (-0.579989) | 0.110759 / 0.296338 (-0.185580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444648 / 0.215209 (0.229439) | 4.437930 / 2.077655 (2.360275) | 2.154033 / 1.504120 (0.649913) | 1.958351 / 1.541195 (0.417157) | 1.991031 / 1.468490 (0.522541) | 0.691440 / 4.584777 (-3.893337) | 3.369087 / 3.745712 (-0.376625) | 1.847103 / 5.269862 (-3.422758) | 1.152509 / 4.565676 (-3.413168) | 0.082519 / 0.424275 (-0.341756) | 0.012609 / 0.007607 (0.005001) | 0.547267 / 0.226044 (0.321222) | 5.501335 / 2.268929 (3.232407) | 2.621079 / 55.444624 (-52.823545) | 2.281332 / 6.876477 (-4.595145) | 2.300427 / 2.142072 (0.158354) | 0.803611 / 4.805227 (-4.001616) | 0.151784 / 6.500664 (-6.348880) | 0.067801 / 0.075469 (-0.007669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.343201 / 1.841788 (-0.498587) | 13.901033 / 8.074308 (5.826725) | 13.114738 / 10.191392 (2.923346) | 0.149358 / 0.680424 (-0.531066) | 0.016596 / 0.534201 (-0.517605) | 0.377310 / 0.579283 (-0.201973) | 0.387045 / 0.434364 (-0.047319) | 0.441272 / 0.540337 (-0.099065) | 0.525783 / 1.386936 (-0.861153) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c127e5575ab4e22648976ad268d76264ef5d04f8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008147 / 0.011353 (-0.003205) | 0.005531 / 0.011008 (-0.005477) | 0.099796 / 0.038508 (0.061288) | 0.041574 / 0.023109 (0.018465) | 0.315752 / 0.275898 (0.039854) | 0.369846 / 0.323480 (0.046366) | 0.006489 / 0.007986 (-0.001497) | 0.004339 / 0.004328 (0.000010) | 0.074769 / 0.004250 (0.070519) | 0.051313 / 0.037052 (0.014261) | 0.313463 / 0.258489 (0.054974) | 0.369918 / 0.293841 (0.076077) | 0.035893 / 0.128546 (-0.092653) | 0.012487 / 0.075646 (-0.063159) | 0.336464 / 0.419271 (-0.082807) | 0.052870 / 0.043533 (0.009337) | 0.310795 / 0.255139 (0.055656) | 0.333146 / 0.283200 (0.049946) | 0.112813 / 0.141683 (-0.028870) | 1.488192 / 1.452155 (0.036038) | 1.563438 / 1.492716 (0.070721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.015015 / 0.018006 (-0.002991) | 0.531783 / 0.000490 (0.531294) | 0.005039 / 0.000200 (0.004839) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030205 / 0.037411 (-0.007207) | 0.115997 / 0.014526 (0.101471) | 0.122958 / 0.176557 (-0.053599) | 0.186956 / 0.737135 (-0.550180) | 0.130268 / 0.296338 (-0.166071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402648 / 0.215209 (0.187439) | 3.996121 / 2.077655 (1.918466) | 1.811715 / 1.504120 (0.307595) | 1.640805 / 1.541195 (0.099610) | 1.810478 / 1.468490 (0.341988) | 0.699996 / 4.584777 (-3.884781) | 3.834020 / 3.745712 (0.088308) | 3.688364 / 5.269862 (-1.581498) | 1.973828 / 4.565676 (-2.591849) | 0.087085 / 0.424275 (-0.337190) | 0.012501 / 0.007607 (0.004894) | 0.498934 / 0.226044 (0.272889) | 4.977608 / 2.268929 (2.708680) | 2.258678 / 55.444624 (-53.185947) | 1.934251 / 6.876477 (-4.942226) | 2.177409 / 2.142072 (0.035337) | 0.873470 / 4.805227 (-3.931757) | 0.173132 / 6.500664 (-6.327532) | 0.069144 / 0.075469 (-0.006325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.181554 / 1.841788 (-0.660234) | 15.694468 / 8.074308 (7.620160) | 15.026954 / 10.191392 (4.835562) | 0.167092 / 0.680424 (-0.513332) | 0.017921 / 0.534201 (-0.516280) | 0.425649 / 0.579283 (-0.153634) | 0.423225 / 0.434364 (-0.011139) | 0.522132 / 0.540337 (-0.018205) | 0.612806 / 1.386936 (-0.774130) |\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) | 0.007896 / 0.011353 (-0.003457) | 0.005581 / 0.011008 (-0.005427) | 0.076338 / 0.038508 (0.037830) | 0.037064 / 0.023109 (0.013954) | 0.399706 / 0.275898 (0.123808) | 0.431698 / 0.323480 (0.108218) | 0.006846 / 0.007986 (-0.001140) | 0.006010 / 0.004328 (0.001682) | 0.075771 / 0.004250 (0.071520) | 0.058214 / 0.037052 (0.021161) | 0.395753 / 0.258489 (0.137264) | 0.459925 / 0.293841 (0.166084) | 0.036349 / 0.128546 (-0.092197) | 0.012720 / 0.075646 (-0.062926) | 0.087248 / 0.419271 (-0.332024) | 0.049405 / 0.043533 (0.005872) | 0.387576 / 0.255139 (0.132437) | 0.409861 / 0.283200 (0.126661) | 0.111639 / 0.141683 (-0.030043) | 1.482840 / 1.452155 (0.030685) | 1.574465 / 1.492716 (0.081749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.320628 / 0.018006 (0.302622) | 0.556338 / 0.000490 (0.555848) | 0.000445 / 0.000200 (0.000245) | 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) | 0.032905 / 0.037411 (-0.004507) | 0.121253 / 0.014526 (0.106727) | 0.127241 / 0.176557 (-0.049316) | 0.178090 / 0.737135 (-0.559045) | 0.143285 / 0.296338 (-0.153054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437852 / 0.215209 (0.222643) | 4.369770 / 2.077655 (2.292115) | 2.219932 / 1.504120 (0.715812) | 2.032520 / 1.541195 (0.491325) | 2.154300 / 1.468490 (0.685810) | 0.678942 / 4.584777 (-3.905835) | 3.768148 / 3.745712 (0.022436) | 2.152738 / 5.269862 (-3.117124) | 1.341480 / 4.565676 (-3.224197) | 0.084326 / 0.424275 (-0.339949) | 0.012288 / 0.007607 (0.004681) | 0.547677 / 0.226044 (0.321633) | 5.496777 / 2.268929 (3.227848) | 2.702267 / 55.444624 (-52.742357) | 2.388580 / 6.876477 (-4.487897) | 2.471673 / 2.142072 (0.329601) | 0.833645 / 4.805227 (-3.971582) | 0.167113 / 6.500664 (-6.333551) | 0.067658 / 0.075469 (-0.007811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282050 / 1.841788 (-0.559737) | 16.413677 / 8.074308 (8.339369) | 14.080910 / 10.191392 (3.889518) | 0.171782 / 0.680424 (-0.508642) | 0.018186 / 0.534201 (-0.516015) | 0.425244 / 0.579283 (-0.154039) | 0.430260 / 0.434364 (-0.004104) | 0.500838 / 0.540337 (-0.039499) | 0.591900 / 1.386936 (-0.795036) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fc5c538de84da400118e3712077acc580ce85c4 \"CML watermark\")\n", "The approach we take here is to no longer materialize the entire index array or shuffle buffer. Instead, we do the following:\r\n\r\n1) Generate a dataset with `tf.data.Dataset.range`. This dataset is not materialized - it's basically a range iterator.\r\n2) When we begin iterating over a dataset, generate a random seed. This value is constant for each pass over the dataset, and is regenerated if we start a new iteration or epoch over the dataset.\r\n3) Map the range dataset and the random seed with `tf.random.index_shuffle`. This converts indices into the equivalent values in a permuted array. In other words `tf.random.index_shuffle(indices, maxval=50_000_000)` is equivalent to `np.random.permutation(50_000_000)[indices]`, but without ever materializing the `np.random.permutation(50_000_000)` array.\r\n\r\nUsing this approach gives us a complete iteration over the dataset that does not skip any samples, compiles in TF and also never materializes the complete index array, which should avoid the memory usage issues. I'm testing that now!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008395 / 0.011353 (-0.002958) | 0.005893 / 0.011008 (-0.005115) | 0.117081 / 0.038508 (0.078573) | 0.040987 / 0.023109 (0.017878) | 0.394234 / 0.275898 (0.118336) | 0.447036 / 0.323480 (0.123556) | 0.006703 / 0.007986 (-0.001283) | 0.006085 / 0.004328 (0.001757) | 0.086479 / 0.004250 (0.082228) | 0.050192 / 0.037052 (0.013140) | 0.400958 / 0.258489 (0.142469) | 0.455551 / 0.293841 (0.161710) | 0.041481 / 0.128546 (-0.087065) | 0.014135 / 0.075646 (-0.061511) | 0.399929 / 0.419271 (-0.019343) | 0.060824 / 0.043533 (0.017291) | 0.395946 / 0.255139 (0.140807) | 0.428811 / 0.283200 (0.145611) | 0.120057 / 0.141683 (-0.021626) | 1.703244 / 1.452155 (0.251090) | 1.841153 / 1.492716 (0.348436) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.021826 / 0.018006 (0.003820) | 0.494279 / 0.000490 (0.493789) | 0.011258 / 0.000200 (0.011058) | 0.000382 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031651 / 0.037411 (-0.005760) | 0.132871 / 0.014526 (0.118345) | 0.137388 / 0.176557 (-0.039169) | 0.205808 / 0.737135 (-0.531327) | 0.147585 / 0.296338 (-0.148753) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474483 / 0.215209 (0.259274) | 4.726568 / 2.077655 (2.648914) | 2.136172 / 1.504120 (0.632052) | 1.918364 / 1.541195 (0.377169) | 2.068794 / 1.468490 (0.600304) | 0.836481 / 4.584777 (-3.748296) | 4.550583 / 3.745712 (0.804871) | 2.456287 / 5.269862 (-2.813574) | 1.563127 / 4.565676 (-3.002550) | 0.102541 / 0.424275 (-0.321734) | 0.014492 / 0.007607 (0.006885) | 0.598572 / 0.226044 (0.372528) | 5.953321 / 2.268929 (3.684392) | 2.695210 / 55.444624 (-52.749414) | 2.294317 / 6.876477 (-4.582160) | 2.456585 / 2.142072 (0.314513) | 1.019907 / 4.805227 (-3.785320) | 0.201225 / 6.500664 (-6.299439) | 0.077113 / 0.075469 (0.001644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.497662 / 1.841788 (-0.344126) | 18.216941 / 8.074308 (10.142633) | 17.016638 / 10.191392 (6.825246) | 0.193271 / 0.680424 (-0.487153) | 0.020440 / 0.534201 (-0.513761) | 0.509361 / 0.579283 (-0.069922) | 0.513389 / 0.434364 (0.079025) | 0.622266 / 0.540337 (0.081928) | 0.741733 / 1.386936 (-0.645203) |\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) | 0.008641 / 0.011353 (-0.002712) | 0.005792 / 0.011008 (-0.005216) | 0.086020 / 0.038508 (0.047512) | 0.040005 / 0.023109 (0.016896) | 0.435120 / 0.275898 (0.159222) | 0.480269 / 0.323480 (0.156789) | 0.006669 / 0.007986 (-0.001317) | 0.006039 / 0.004328 (0.001711) | 0.083468 / 0.004250 (0.079218) | 0.057700 / 0.037052 (0.020648) | 0.416418 / 0.258489 (0.157929) | 0.508286 / 0.293841 (0.214445) | 0.041198 / 0.128546 (-0.087349) | 0.014346 / 0.075646 (-0.061301) | 0.100553 / 0.419271 (-0.318718) | 0.054201 / 0.043533 (0.010668) | 0.438232 / 0.255139 (0.183093) | 0.454707 / 0.283200 (0.171508) | 0.118332 / 0.141683 (-0.023351) | 1.657607 / 1.452155 (0.205452) | 1.825510 / 1.492716 (0.332794) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236156 / 0.018006 (0.218150) | 0.487612 / 0.000490 (0.487123) | 0.005747 / 0.000200 (0.005547) | 0.000111 / 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) | 0.035127 / 0.037411 (-0.002284) | 0.132013 / 0.014526 (0.117487) | 0.142316 / 0.176557 (-0.034241) | 0.198627 / 0.737135 (-0.538508) | 0.145454 / 0.296338 (-0.150885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513041 / 0.215209 (0.297832) | 5.066197 / 2.077655 (2.988542) | 2.508779 / 1.504120 (1.004659) | 2.273901 / 1.541195 (0.732706) | 2.364958 / 1.468490 (0.896468) | 0.811367 / 4.584777 (-3.773410) | 4.504744 / 3.745712 (0.759032) | 2.499811 / 5.269862 (-2.770050) | 1.583349 / 4.565676 (-2.982328) | 0.101701 / 0.424275 (-0.322574) | 0.014379 / 0.007607 (0.006772) | 0.669506 / 0.226044 (0.443462) | 6.556702 / 2.268929 (4.287774) | 3.123457 / 55.444624 (-52.321167) | 2.731997 / 6.876477 (-4.144480) | 2.862866 / 2.142072 (0.720794) | 0.992956 / 4.805227 (-3.812271) | 0.200473 / 6.500664 (-6.300191) | 0.078780 / 0.075469 (0.003311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540718 / 1.841788 (-0.301070) | 18.749344 / 8.074308 (10.675036) | 15.648983 / 10.191392 (5.457591) | 0.174089 / 0.680424 (-0.506335) | 0.020441 / 0.534201 (-0.513760) | 0.503742 / 0.579283 (-0.075541) | 0.500648 / 0.434364 (0.066284) | 0.598558 / 0.540337 (0.058221) | 0.712093 / 1.386936 (-0.674843) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#621554280f964b5fe87ece1a46b794406d943b1e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009940 / 0.011353 (-0.001412) | 0.006193 / 0.011008 (-0.004815) | 0.125874 / 0.038508 (0.087366) | 0.038664 / 0.023109 (0.015555) | 0.380013 / 0.275898 (0.104115) | 0.430152 / 0.323480 (0.106672) | 0.006961 / 0.007986 (-0.001025) | 0.004749 / 0.004328 (0.000420) | 0.099743 / 0.004250 (0.095492) | 0.052349 / 0.037052 (0.015297) | 0.433354 / 0.258489 (0.174865) | 0.436273 / 0.293841 (0.142433) | 0.053929 / 0.128546 (-0.074617) | 0.019369 / 0.075646 (-0.056278) | 0.421783 / 0.419271 (0.002511) | 0.062746 / 0.043533 (0.019213) | 0.377225 / 0.255139 (0.122086) | 0.413708 / 0.283200 (0.130508) | 0.111371 / 0.141683 (-0.030312) | 1.819166 / 1.452155 (0.367011) | 1.974527 / 1.492716 (0.481810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090664 / 0.018006 (0.072658) | 0.566166 / 0.000490 (0.565676) | 0.079305 / 0.000200 (0.079105) | 0.000755 / 0.000054 (0.000700) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029720 / 0.037411 (-0.007691) | 0.126030 / 0.014526 (0.111504) | 0.146020 / 0.176557 (-0.030537) | 0.210354 / 0.737135 (-0.526781) | 0.149428 / 0.296338 (-0.146910) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624371 / 0.215209 (0.409162) | 6.332839 / 2.077655 (4.255184) | 2.547784 / 1.504120 (1.043664) | 2.150508 / 1.541195 (0.609313) | 2.240816 / 1.468490 (0.772326) | 1.271131 / 4.584777 (-3.313646) | 5.642726 / 3.745712 (1.897014) | 3.212988 / 5.269862 (-2.056874) | 2.258123 / 4.565676 (-2.307553) | 0.149477 / 0.424275 (-0.274798) | 0.014603 / 0.007607 (0.006996) | 0.782155 / 0.226044 (0.556111) | 7.855191 / 2.268929 (5.586262) | 3.308638 / 55.444624 (-52.135986) | 2.548142 / 6.876477 (-4.328335) | 2.627374 / 2.142072 (0.485301) | 1.515170 / 4.805227 (-3.290058) | 0.262479 / 6.500664 (-6.238185) | 0.082181 / 0.075469 (0.006712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573169 / 1.841788 (-0.268618) | 18.105719 / 8.074308 (10.031411) | 22.015179 / 10.191392 (11.823787) | 0.254678 / 0.680424 (-0.425746) | 0.027098 / 0.534201 (-0.507103) | 0.578045 / 0.579283 (-0.001238) | 0.647130 / 0.434364 (0.212766) | 0.650522 / 0.540337 (0.110185) | 0.797713 / 1.386936 (-0.589223) |\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) | 0.010376 / 0.011353 (-0.000977) | 0.005990 / 0.011008 (-0.005018) | 0.097144 / 0.038508 (0.058635) | 0.038205 / 0.023109 (0.015096) | 0.468347 / 0.275898 (0.192449) | 0.497646 / 0.323480 (0.174166) | 0.006916 / 0.007986 (-0.001069) | 0.004760 / 0.004328 (0.000431) | 0.109838 / 0.004250 (0.105587) | 0.048321 / 0.037052 (0.011269) | 0.437458 / 0.258489 (0.178969) | 0.534864 / 0.293841 (0.241023) | 0.053655 / 0.128546 (-0.074892) | 0.021915 / 0.075646 (-0.053732) | 0.121047 / 0.419271 (-0.298224) | 0.059694 / 0.043533 (0.016162) | 0.466937 / 0.255139 (0.211798) | 0.482030 / 0.283200 (0.198831) | 0.117458 / 0.141683 (-0.024225) | 1.835551 / 1.452155 (0.383396) | 1.965748 / 1.492716 (0.473031) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234885 / 0.018006 (0.216879) | 0.529925 / 0.000490 (0.529436) | 0.000484 / 0.000200 (0.000284) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030959 / 0.037411 (-0.006453) | 0.128905 / 0.014526 (0.114379) | 0.136913 / 0.176557 (-0.039643) | 0.195133 / 0.737135 (-0.542002) | 0.147929 / 0.296338 (-0.148410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.715661 / 0.215209 (0.500451) | 6.994125 / 2.077655 (4.916470) | 3.033178 / 1.504120 (1.529058) | 2.663709 / 1.541195 (1.122515) | 2.707558 / 1.468490 (1.239068) | 1.316195 / 4.584777 (-3.268582) | 5.688264 / 3.745712 (1.942552) | 3.260897 / 5.269862 (-2.008964) | 2.134985 / 4.565676 (-2.430691) | 0.153945 / 0.424275 (-0.270330) | 0.014727 / 0.007607 (0.007119) | 0.911339 / 0.226044 (0.685294) | 8.902640 / 2.268929 (6.633711) | 3.806606 / 55.444624 (-51.638018) | 3.052238 / 6.876477 (-3.824238) | 3.046945 / 2.142072 (0.904873) | 1.559837 / 4.805227 (-3.245390) | 0.272276 / 6.500664 (-6.228388) | 0.087728 / 0.075469 (0.012259) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712691 / 1.841788 (-0.129097) | 18.127575 / 8.074308 (10.053267) | 19.734063 / 10.191392 (9.542671) | 0.235006 / 0.680424 (-0.445418) | 0.027581 / 0.534201 (-0.506620) | 0.551080 / 0.579283 (-0.028203) | 0.608564 / 0.434364 (0.174200) | 0.636578 / 0.540337 (0.096241) | 0.732374 / 1.386936 (-0.654562) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#36911ca06d9c4e37ce36da6228cb3af8b40c2add \"CML watermark\")\n", "Looks good in testing - this should be ready for review! cc @lhoestq @massquantity", "Looks good to me, though i doubt that very few people will upgrade to TF >= 2.9 unless their memory is full:)", "Is it more efficient than using numpy to shuffle as in multiprocessing ? Why not use the same strategy ?", "Good question, honestly! The NumPy strategy works fine, but requires us to handle multiple processes instead of doing everything in `tf.data`. We could just scrap this entire code path and always use the multiprocessing NumPy approach, but I think single-threaded throughput would be lower if we did that. If you prefer it for code simplicity, though, I can do that.\r\n\r\nIn the longer term, I'm hoping that `tf.data` gets native support for our data structures and we can transition the whole pipeline to pure `tf.data`, but that still hasn't happened 🫠", "And @massquantity TF 2.13 is going to release in a couple of days, so I hope most users are at least on TF 2.9 by now!", "Unless there is a big gap in performance I think code simplicity would be appreciated ^^", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008638 / 0.011353 (-0.002715) | 0.006013 / 0.011008 (-0.004995) | 0.116456 / 0.038508 (0.077948) | 0.040419 / 0.023109 (0.017310) | 0.418374 / 0.275898 (0.142476) | 0.447693 / 0.323480 (0.124213) | 0.007002 / 0.007986 (-0.000984) | 0.006175 / 0.004328 (0.001847) | 0.087801 / 0.004250 (0.083550) | 0.051980 / 0.037052 (0.014928) | 0.393275 / 0.258489 (0.134786) | 0.449601 / 0.293841 (0.155760) | 0.041670 / 0.128546 (-0.086876) | 0.014396 / 0.075646 (-0.061251) | 0.399175 / 0.419271 (-0.020096) | 0.060635 / 0.043533 (0.017102) | 0.391449 / 0.255139 (0.136310) | 0.420713 / 0.283200 (0.137513) | 0.121369 / 0.141683 (-0.020314) | 1.692630 / 1.452155 (0.240475) | 1.815526 / 1.492716 (0.322810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244321 / 0.018006 (0.226315) | 0.487947 / 0.000490 (0.487458) | 0.004563 / 0.000200 (0.004363) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033425 / 0.037411 (-0.003987) | 0.134458 / 0.014526 (0.119932) | 0.138810 / 0.176557 (-0.037746) | 0.208871 / 0.737135 (-0.528264) | 0.147964 / 0.296338 (-0.148374) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483347 / 0.215209 (0.268138) | 4.799550 / 2.077655 (2.721895) | 2.174149 / 1.504120 (0.670029) | 1.943276 / 1.541195 (0.402081) | 2.010884 / 1.468490 (0.542394) | 0.832030 / 4.584777 (-3.752747) | 4.716713 / 3.745712 (0.971001) | 4.615810 / 5.269862 (-0.654052) | 2.379600 / 4.565676 (-2.186077) | 0.103560 / 0.424275 (-0.320715) | 0.014683 / 0.007607 (0.007076) | 0.598558 / 0.226044 (0.372514) | 5.999126 / 2.268929 (3.730197) | 2.677819 / 55.444624 (-52.766805) | 2.320838 / 6.876477 (-4.555639) | 2.503684 / 2.142072 (0.361611) | 1.016459 / 4.805227 (-3.788769) | 0.201672 / 6.500664 (-6.298992) | 0.079310 / 0.075469 (0.003841) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.446374 / 1.841788 (-0.395413) | 19.219310 / 8.074308 (11.145002) | 17.294665 / 10.191392 (7.103273) | 0.246115 / 0.680424 (-0.434309) | 0.021406 / 0.534201 (-0.512795) | 0.524084 / 0.579283 (-0.055200) | 0.511254 / 0.434364 (0.076890) | 0.621304 / 0.540337 (0.080966) | 0.727088 / 1.386936 (-0.659848) |\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) | 0.008907 / 0.011353 (-0.002446) | 0.006165 / 0.011008 (-0.004843) | 0.090786 / 0.038508 (0.052278) | 0.040893 / 0.023109 (0.017784) | 0.451252 / 0.275898 (0.175354) | 0.477811 / 0.323480 (0.154331) | 0.007418 / 0.007986 (-0.000568) | 0.005789 / 0.004328 (0.001461) | 0.087422 / 0.004250 (0.083171) | 0.061800 / 0.037052 (0.024748) | 0.459085 / 0.258489 (0.200596) | 0.488897 / 0.293841 (0.195056) | 0.048157 / 0.128546 (-0.080389) | 0.014676 / 0.075646 (-0.060970) | 0.104372 / 0.419271 (-0.314900) | 0.058066 / 0.043533 (0.014534) | 0.446131 / 0.255139 (0.190992) | 0.460428 / 0.283200 (0.177228) | 0.128492 / 0.141683 (-0.013191) | 1.811419 / 1.452155 (0.359265) | 1.894781 / 1.492716 (0.402064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220527 / 0.018006 (0.202520) | 0.487663 / 0.000490 (0.487173) | 0.003864 / 0.000200 (0.003664) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036354 / 0.037411 (-0.001057) | 0.140469 / 0.014526 (0.125944) | 0.149990 / 0.176557 (-0.026566) | 0.212369 / 0.737135 (-0.524766) | 0.154000 / 0.296338 (-0.142338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.514172 / 0.215209 (0.298963) | 5.129247 / 2.077655 (3.051593) | 2.536773 / 1.504120 (1.032653) | 2.317253 / 1.541195 (0.776058) | 2.424066 / 1.468490 (0.955576) | 0.836160 / 4.584777 (-3.748617) | 4.906235 / 3.745712 (1.160523) | 4.431395 / 5.269862 (-0.838467) | 2.332845 / 4.565676 (-2.232831) | 0.102867 / 0.424275 (-0.321409) | 0.014851 / 0.007607 (0.007244) | 0.644104 / 0.226044 (0.418060) | 6.415847 / 2.268929 (4.146918) | 3.186984 / 55.444624 (-52.257641) | 2.774125 / 6.876477 (-4.102352) | 2.848045 / 2.142072 (0.705972) | 1.018757 / 4.805227 (-3.786470) | 0.212333 / 6.500664 (-6.288331) | 0.079405 / 0.075469 (0.003936) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748375 / 1.841788 (-0.093412) | 19.733829 / 8.074308 (11.659521) | 15.766665 / 10.191392 (5.575273) | 0.192087 / 0.680424 (-0.488337) | 0.027641 / 0.534201 (-0.506560) | 0.504101 / 0.579283 (-0.075182) | 0.493815 / 0.434364 (0.059451) | 0.583247 / 0.540337 (0.042910) | 0.697432 / 1.386936 (-0.689504) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#95c177e02ca20bf7bb3ed8f185d2d6f05a5e5f30 \"CML watermark\")\n", "Hi @lhoestq, I tried moving everything to the NumPy path but ran into issues - the `SharedMemory` constructs it depends on were only added in Python 3.8. As a result, if we move everything to that path then `to_tf_dataset` does not work on older Python versions.\r\n\r\nFor now, how do you feel about reverting and using my original solution, which has fallbacks for all versions of Python and TensorFlow? Once our minimum versions pass Python 3.8 or TF 2.9 we can remove the older code paths.", "Gentle ping on this question @lhoestq!", "Ah yes indeed. Feel free to revert and add comments to explain why you needed to have a different approach for single process", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008395 / 0.011353 (-0.002958) | 0.005773 / 0.011008 (-0.005235) | 0.115702 / 0.038508 (0.077194) | 0.039897 / 0.023109 (0.016788) | 0.483140 / 0.275898 (0.207242) | 0.531288 / 0.323480 (0.207808) | 0.006739 / 0.007986 (-0.001246) | 0.004419 / 0.004328 (0.000090) | 0.086374 / 0.004250 (0.082124) | 0.056498 / 0.037052 (0.019446) | 0.491589 / 0.258489 (0.233100) | 0.556366 / 0.293841 (0.262525) | 0.041366 / 0.128546 (-0.087181) | 0.014373 / 0.075646 (-0.061274) | 0.395504 / 0.419271 (-0.023767) | 0.094382 / 0.043533 (0.050849) | 0.483000 / 0.255139 (0.227861) | 0.522693 / 0.283200 (0.239494) | 0.138804 / 0.141683 (-0.002879) | 1.719563 / 1.452155 (0.267409) | 1.853470 / 1.492716 (0.360753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235616 / 0.018006 (0.217610) | 0.483267 / 0.000490 (0.482777) | 0.008663 / 0.000200 (0.008463) | 0.000401 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033124 / 0.037411 (-0.004287) | 0.128821 / 0.014526 (0.114295) | 0.138910 / 0.176557 (-0.037647) | 0.213570 / 0.737135 (-0.523566) | 0.146646 / 0.296338 (-0.149693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479998 / 0.215209 (0.264789) | 4.772325 / 2.077655 (2.694670) | 2.228424 / 1.504120 (0.724304) | 2.000915 / 1.541195 (0.459721) | 2.105799 / 1.468490 (0.637309) | 0.824235 / 4.584777 (-3.760542) | 4.511902 / 3.745712 (0.766189) | 4.723073 / 5.269862 (-0.546789) | 2.333442 / 4.565676 (-2.232235) | 0.101161 / 0.424275 (-0.323114) | 0.014403 / 0.007607 (0.006796) | 0.596395 / 0.226044 (0.370351) | 5.961046 / 2.268929 (3.692117) | 2.746679 / 55.444624 (-52.697946) | 2.352085 / 6.876477 (-4.524392) | 2.609812 / 2.142072 (0.467740) | 0.996950 / 4.805227 (-3.808277) | 0.197923 / 6.500664 (-6.302741) | 0.075546 / 0.075469 (0.000077) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.529896 / 1.841788 (-0.311892) | 18.183887 / 8.074308 (10.109578) | 16.352332 / 10.191392 (6.160940) | 0.213504 / 0.680424 (-0.466920) | 0.020388 / 0.534201 (-0.513813) | 0.497832 / 0.579283 (-0.081451) | 0.495477 / 0.434364 (0.061113) | 0.585984 / 0.540337 (0.045647) | 0.688726 / 1.386936 (-0.698210) |\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) | 0.008422 / 0.011353 (-0.002931) | 0.005876 / 0.011008 (-0.005132) | 0.089310 / 0.038508 (0.050802) | 0.039769 / 0.023109 (0.016660) | 0.425279 / 0.275898 (0.149381) | 0.470818 / 0.323480 (0.147338) | 0.006519 / 0.007986 (-0.001467) | 0.006276 / 0.004328 (0.001948) | 0.085753 / 0.004250 (0.081503) | 0.053867 / 0.037052 (0.016815) | 0.429193 / 0.258489 (0.170704) | 0.480278 / 0.293841 (0.186437) | 0.040657 / 0.128546 (-0.087889) | 0.014055 / 0.075646 (-0.061591) | 0.101422 / 0.419271 (-0.317849) | 0.053803 / 0.043533 (0.010271) | 0.428348 / 0.255139 (0.173209) | 0.452193 / 0.283200 (0.168994) | 0.124914 / 0.141683 (-0.016769) | 1.750122 / 1.452155 (0.297968) | 1.850875 / 1.492716 (0.358159) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249958 / 0.018006 (0.231952) | 0.485183 / 0.000490 (0.484694) | 0.000472 / 0.000200 (0.000272) | 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) | 0.034563 / 0.037411 (-0.002848) | 0.135565 / 0.014526 (0.121039) | 0.143271 / 0.176557 (-0.033285) | 0.199080 / 0.737135 (-0.538056) | 0.149336 / 0.296338 (-0.147003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.526170 / 0.215209 (0.310961) | 5.270960 / 2.077655 (3.193305) | 2.664585 / 1.504120 (1.160465) | 2.440027 / 1.541195 (0.898832) | 2.612764 / 1.468490 (1.144274) | 0.828965 / 4.584777 (-3.755812) | 4.769983 / 3.745712 (1.024271) | 2.441962 / 5.269862 (-2.827900) | 1.549032 / 4.565676 (-3.016644) | 0.100851 / 0.424275 (-0.323424) | 0.014425 / 0.007607 (0.006818) | 0.640908 / 0.226044 (0.414864) | 6.399041 / 2.268929 (4.130113) | 3.242424 / 55.444624 (-52.202200) | 2.836317 / 6.876477 (-4.040160) | 2.933010 / 2.142072 (0.790938) | 1.002277 / 4.805227 (-3.802950) | 0.201247 / 6.500664 (-6.299417) | 0.078777 / 0.075469 (0.003308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620415 / 1.841788 (-0.221373) | 19.153631 / 8.074308 (11.079323) | 16.744068 / 10.191392 (6.552676) | 0.167327 / 0.680424 (-0.513097) | 0.020186 / 0.534201 (-0.514015) | 0.503683 / 0.579283 (-0.075600) | 0.500051 / 0.434364 (0.065687) | 0.587188 / 0.540337 (0.046850) | 0.699975 / 1.386936 (-0.686961) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#291d7ffa695edb4b4e818c783b16d3466246cd56 \"CML watermark\")\n", "This is probably ready, but likely conflicts with #5883. I'll wait for that PR to be merged and then rebase and merge this one.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008387 / 0.011353 (-0.002965) | 0.005824 / 0.011008 (-0.005184) | 0.117721 / 0.038508 (0.079213) | 0.040420 / 0.023109 (0.017311) | 0.404961 / 0.275898 (0.129063) | 0.426695 / 0.323480 (0.103215) | 0.006634 / 0.007986 (-0.001352) | 0.006033 / 0.004328 (0.001705) | 0.088652 / 0.004250 (0.084402) | 0.048075 / 0.037052 (0.011022) | 0.400683 / 0.258489 (0.142194) | 0.432489 / 0.293841 (0.138648) | 0.042065 / 0.128546 (-0.086482) | 0.014071 / 0.075646 (-0.061575) | 0.399398 / 0.419271 (-0.019873) | 0.066034 / 0.043533 (0.022501) | 0.400056 / 0.255139 (0.144918) | 0.421130 / 0.283200 (0.137930) | 0.119721 / 0.141683 (-0.021962) | 1.752166 / 1.452155 (0.300011) | 1.820161 / 1.492716 (0.327444) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244264 / 0.018006 (0.226258) | 0.480882 / 0.000490 (0.480392) | 0.005604 / 0.000200 (0.005404) | 0.000175 / 0.000054 (0.000121) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032397 / 0.037411 (-0.005015) | 0.131632 / 0.014526 (0.117106) | 0.139765 / 0.176557 (-0.036792) | 0.213135 / 0.737135 (-0.524000) | 0.147891 / 0.296338 (-0.148447) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474534 / 0.215209 (0.259325) | 4.730424 / 2.077655 (2.652770) | 2.163706 / 1.504120 (0.659586) | 1.936051 / 1.541195 (0.394857) | 2.012185 / 1.468490 (0.543695) | 0.826583 / 4.584777 (-3.758194) | 4.921494 / 3.745712 (1.175782) | 2.431401 / 5.269862 (-2.838460) | 1.566020 / 4.565676 (-2.999656) | 0.101255 / 0.424275 (-0.323020) | 0.014553 / 0.007607 (0.006946) | 0.608301 / 0.226044 (0.382256) | 6.089801 / 2.268929 (3.820873) | 2.691986 / 55.444624 (-52.752638) | 2.296498 / 6.876477 (-4.579979) | 2.455388 / 2.142072 (0.313315) | 0.984342 / 4.805227 (-3.820885) | 0.200447 / 6.500664 (-6.300217) | 0.077602 / 0.075469 (0.002133) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445067 / 1.841788 (-0.396721) | 18.588670 / 8.074308 (10.514362) | 16.950216 / 10.191392 (6.758824) | 0.169688 / 0.680424 (-0.510736) | 0.020544 / 0.534201 (-0.513657) | 0.508506 / 0.579283 (-0.070777) | 0.516218 / 0.434364 (0.081854) | 0.646072 / 0.540337 (0.105734) | 0.763227 / 1.386936 (-0.623709) |\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) | 0.008816 / 0.011353 (-0.002537) | 0.006016 / 0.011008 (-0.004992) | 0.090946 / 0.038508 (0.052438) | 0.040189 / 0.023109 (0.017080) | 0.446723 / 0.275898 (0.170825) | 0.494633 / 0.323480 (0.171153) | 0.007206 / 0.007986 (-0.000779) | 0.004508 / 0.004328 (0.000180) | 0.088477 / 0.004250 (0.084226) | 0.055587 / 0.037052 (0.018535) | 0.445349 / 0.258489 (0.186860) | 0.504940 / 0.293841 (0.211099) | 0.041976 / 0.128546 (-0.086570) | 0.014296 / 0.075646 (-0.061351) | 0.102835 / 0.419271 (-0.316436) | 0.054786 / 0.043533 (0.011253) | 0.444789 / 0.255139 (0.189651) | 0.472306 / 0.283200 (0.189106) | 0.123365 / 0.141683 (-0.018318) | 1.725803 / 1.452155 (0.273648) | 1.832216 / 1.492716 (0.339500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252680 / 0.018006 (0.234674) | 0.476719 / 0.000490 (0.476229) | 0.000461 / 0.000200 (0.000261) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035961 / 0.037411 (-0.001450) | 0.135399 / 0.014526 (0.120873) | 0.147549 / 0.176557 (-0.029007) | 0.207468 / 0.737135 (-0.529667) | 0.151591 / 0.296338 (-0.144747) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528143 / 0.215209 (0.312934) | 5.270766 / 2.077655 (3.193111) | 2.675644 / 1.504120 (1.171524) | 2.472855 / 1.541195 (0.931660) | 2.636020 / 1.468490 (1.167530) | 0.841325 / 4.584777 (-3.743452) | 4.702290 / 3.745712 (0.956578) | 2.523537 / 5.269862 (-2.746325) | 1.595617 / 4.565676 (-2.970059) | 0.102095 / 0.424275 (-0.322180) | 0.014568 / 0.007607 (0.006961) | 0.652090 / 0.226044 (0.426046) | 6.503086 / 2.268929 (4.234158) | 3.277025 / 55.444624 (-52.167599) | 2.931264 / 6.876477 (-3.945213) | 3.021667 / 2.142072 (0.879594) | 1.002560 / 4.805227 (-3.802668) | 0.202621 / 6.500664 (-6.298043) | 0.080583 / 0.075469 (0.005114) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.639281 / 1.841788 (-0.202507) | 18.911529 / 8.074308 (10.837220) | 17.082795 / 10.191392 (6.891403) | 0.179456 / 0.680424 (-0.500968) | 0.021740 / 0.534201 (-0.512460) | 0.526426 / 0.579283 (-0.052857) | 0.535083 / 0.434364 (0.100719) | 0.583304 / 0.540337 (0.042967) | 0.696733 / 1.386936 (-0.690203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#757f19283f22eeb3e9aedefd82abc0aa2235f797 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006823 / 0.011353 (-0.004530) | 0.004847 / 0.011008 (-0.006161) | 0.096038 / 0.038508 (0.057530) | 0.033037 / 0.023109 (0.009928) | 0.298379 / 0.275898 (0.022481) | 0.333319 / 0.323480 (0.009839) | 0.005343 / 0.007986 (-0.002643) | 0.003863 / 0.004328 (-0.000465) | 0.072928 / 0.004250 (0.068678) | 0.040898 / 0.037052 (0.003846) | 0.303116 / 0.258489 (0.044627) | 0.334021 / 0.293841 (0.040181) | 0.034780 / 0.128546 (-0.093767) | 0.011978 / 0.075646 (-0.063668) | 0.331642 / 0.419271 (-0.087629) | 0.052729 / 0.043533 (0.009196) | 0.298586 / 0.255139 (0.043447) | 0.319296 / 0.283200 (0.036097) | 0.097711 / 0.141683 (-0.043972) | 1.416899 / 1.452155 (-0.035256) | 1.546008 / 1.492716 (0.053292) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234303 / 0.018006 (0.216296) | 0.492767 / 0.000490 (0.492278) | 0.004935 / 0.000200 (0.004736) | 0.000106 / 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) | 0.030617 / 0.037411 (-0.006795) | 0.121203 / 0.014526 (0.106677) | 0.126677 / 0.176557 (-0.049879) | 0.186379 / 0.737135 (-0.550756) | 0.129849 / 0.296338 (-0.166490) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416324 / 0.215209 (0.201115) | 4.135563 / 2.077655 (2.057908) | 1.976182 / 1.504120 (0.472062) | 1.807611 / 1.541195 (0.266416) | 1.886282 / 1.468490 (0.417792) | 0.713006 / 4.584777 (-3.871771) | 3.899205 / 3.745712 (0.153493) | 2.283427 / 5.269862 (-2.986435) | 1.543088 / 4.565676 (-3.022589) | 0.086189 / 0.424275 (-0.338087) | 0.012908 / 0.007607 (0.005301) | 0.516156 / 0.226044 (0.290112) | 5.144199 / 2.268929 (2.875271) | 2.460142 / 55.444624 (-52.984482) | 2.209054 / 6.876477 (-4.667423) | 2.325277 / 2.142072 (0.183204) | 0.849890 / 4.805227 (-3.955337) | 0.173687 / 6.500664 (-6.326977) | 0.070178 / 0.075469 (-0.005291) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241790 / 1.841788 (-0.599997) | 16.047257 / 8.074308 (7.972949) | 15.774146 / 10.191392 (5.582754) | 0.145871 / 0.680424 (-0.534553) | 0.018106 / 0.534201 (-0.516095) | 0.433642 / 0.579283 (-0.145641) | 0.425311 / 0.434364 (-0.009053) | 0.533963 / 0.540337 (-0.006375) | 0.638786 / 1.386936 (-0.748151) |\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) | 0.007242 / 0.011353 (-0.004111) | 0.005599 / 0.011008 (-0.005410) | 0.073443 / 0.038508 (0.034935) | 0.033764 / 0.023109 (0.010655) | 0.365990 / 0.275898 (0.090092) | 0.392943 / 0.323480 (0.069463) | 0.005987 / 0.007986 (-0.001999) | 0.004312 / 0.004328 (-0.000016) | 0.072831 / 0.004250 (0.068580) | 0.048854 / 0.037052 (0.011802) | 0.362477 / 0.258489 (0.103988) | 0.399993 / 0.293841 (0.106152) | 0.035602 / 0.128546 (-0.092944) | 0.012445 / 0.075646 (-0.063202) | 0.085768 / 0.419271 (-0.333504) | 0.048544 / 0.043533 (0.005011) | 0.362246 / 0.255139 (0.107107) | 0.388753 / 0.283200 (0.105554) | 0.109829 / 0.141683 (-0.031854) | 1.546881 / 1.452155 (0.094726) | 1.619454 / 1.492716 (0.126737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189926 / 0.018006 (0.171920) | 0.447936 / 0.000490 (0.447446) | 0.002354 / 0.000200 (0.002155) | 0.000090 / 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) | 0.031740 / 0.037411 (-0.005671) | 0.122595 / 0.014526 (0.108069) | 0.128389 / 0.176557 (-0.048168) | 0.180570 / 0.737135 (-0.556566) | 0.132939 / 0.296338 (-0.163399) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425073 / 0.215209 (0.209863) | 4.238964 / 2.077655 (2.161309) | 2.095116 / 1.504120 (0.590996) | 1.913925 / 1.541195 (0.372730) | 2.024669 / 1.468490 (0.556179) | 0.699172 / 4.584777 (-3.885605) | 3.845807 / 3.745712 (0.100094) | 2.167502 / 5.269862 (-3.102360) | 1.375267 / 4.565676 (-3.190410) | 0.086739 / 0.424275 (-0.337536) | 0.012198 / 0.007607 (0.004591) | 0.525975 / 0.226044 (0.299931) | 5.249449 / 2.268929 (2.980521) | 2.550565 / 55.444624 (-52.894060) | 2.257557 / 6.876477 (-4.618920) | 2.298936 / 2.142072 (0.156863) | 0.850295 / 4.805227 (-3.954932) | 0.170506 / 6.500664 (-6.330158) | 0.065659 / 0.075469 (-0.009810) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330556 / 1.841788 (-0.511231) | 16.920203 / 8.074308 (8.845894) | 15.966739 / 10.191392 (5.775347) | 0.164000 / 0.680424 (-0.516424) | 0.018211 / 0.534201 (-0.515990) | 0.436253 / 0.579283 (-0.143030) | 0.449666 / 0.434364 (0.015302) | 0.522287 / 0.540337 (-0.018050) | 0.615944 / 1.386936 (-0.770992) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824f96c11a02b3817d6b1bf4dfed0abab27777f0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007273 / 0.011353 (-0.004080) | 0.005198 / 0.011008 (-0.005810) | 0.114362 / 0.038508 (0.075854) | 0.031113 / 0.023109 (0.008003) | 0.378568 / 0.275898 (0.102670) | 0.441695 / 0.323480 (0.118215) | 0.006037 / 0.007986 (-0.001949) | 0.005102 / 0.004328 (0.000774) | 0.098682 / 0.004250 (0.094432) | 0.042797 / 0.037052 (0.005745) | 0.360028 / 0.258489 (0.101539) | 0.435757 / 0.293841 (0.141916) | 0.041438 / 0.128546 (-0.087109) | 0.013728 / 0.075646 (-0.061918) | 0.376154 / 0.419271 (-0.043117) | 0.075324 / 0.043533 (0.031791) | 0.357221 / 0.255139 (0.102082) | 0.416378 / 0.283200 (0.133178) | 0.110707 / 0.141683 (-0.030975) | 1.603215 / 1.452155 (0.151061) | 1.736843 / 1.492716 (0.244127) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249479 / 0.018006 (0.231473) | 0.513205 / 0.000490 (0.512715) | 0.003856 / 0.000200 (0.003656) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027750 / 0.037411 (-0.009661) | 0.105437 / 0.014526 (0.090911) | 0.115903 / 0.176557 (-0.060653) | 0.179662 / 0.737135 (-0.557474) | 0.116305 / 0.296338 (-0.180033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.551681 / 0.215209 (0.336472) | 5.544590 / 2.077655 (3.466935) | 2.193933 / 1.504120 (0.689813) | 1.898395 / 1.541195 (0.357201) | 1.877288 / 1.468490 (0.408798) | 0.858097 / 4.584777 (-3.726680) | 4.920982 / 3.745712 (1.175270) | 2.478220 / 5.269862 (-2.791641) | 1.779608 / 4.565676 (-2.786069) | 0.101321 / 0.424275 (-0.322954) | 0.012627 / 0.007607 (0.005020) | 0.674865 / 0.226044 (0.448820) | 6.808224 / 2.268929 (4.539295) | 2.822466 / 55.444624 (-52.622159) | 2.170379 / 6.876477 (-4.706098) | 2.224278 / 2.142072 (0.082205) | 1.032763 / 4.805227 (-3.772464) | 0.198851 / 6.500664 (-6.301813) | 0.069249 / 0.075469 (-0.006220) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.425987 / 1.841788 (-0.415801) | 16.212942 / 8.074308 (8.138634) | 18.945770 / 10.191392 (8.754378) | 0.192901 / 0.680424 (-0.487522) | 0.025343 / 0.534201 (-0.508858) | 0.465441 / 0.579283 (-0.113842) | 0.540966 / 0.434364 (0.106602) | 0.576736 / 0.540337 (0.036399) | 0.675717 / 1.386936 (-0.711219) |\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) | 0.007426 / 0.011353 (-0.003927) | 0.005023 / 0.011008 (-0.005985) | 0.085083 / 0.038508 (0.046575) | 0.030559 / 0.023109 (0.007449) | 0.398461 / 0.275898 (0.122563) | 0.418998 / 0.323480 (0.095518) | 0.006697 / 0.007986 (-0.001288) | 0.004665 / 0.004328 (0.000337) | 0.087724 / 0.004250 (0.083473) | 0.045799 / 0.037052 (0.008747) | 0.395165 / 0.258489 (0.136676) | 0.430172 / 0.293841 (0.136331) | 0.040486 / 0.128546 (-0.088060) | 0.014237 / 0.075646 (-0.061409) | 0.099429 / 0.419271 (-0.319843) | 0.056006 / 0.043533 (0.012473) | 0.389046 / 0.255139 (0.133907) | 0.419559 / 0.283200 (0.136359) | 0.108550 / 0.141683 (-0.033132) | 1.614052 / 1.452155 (0.161897) | 1.677785 / 1.492716 (0.185069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202178 / 0.018006 (0.184172) | 0.486365 / 0.000490 (0.485875) | 0.003844 / 0.000200 (0.003644) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027963 / 0.037411 (-0.009449) | 0.110399 / 0.014526 (0.095873) | 0.122266 / 0.176557 (-0.054291) | 0.178551 / 0.737135 (-0.558585) | 0.129259 / 0.296338 (-0.167080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604178 / 0.215209 (0.388969) | 6.135943 / 2.077655 (4.058288) | 2.547576 / 1.504120 (1.043456) | 2.262470 / 1.541195 (0.721276) | 2.275402 / 1.468490 (0.806912) | 0.878804 / 4.584777 (-3.705972) | 5.152200 / 3.745712 (1.406488) | 2.553715 / 5.269862 (-2.716147) | 1.580959 / 4.565676 (-2.984717) | 0.107895 / 0.424275 (-0.316380) | 0.012751 / 0.007607 (0.005143) | 0.770678 / 0.226044 (0.544633) | 7.744303 / 2.268929 (5.475374) | 3.342037 / 55.444624 (-52.102588) | 2.756848 / 6.876477 (-4.119629) | 2.739357 / 2.142072 (0.597285) | 1.086330 / 4.805227 (-3.718897) | 0.230983 / 6.500664 (-6.269681) | 0.073771 / 0.075469 (-0.001698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.493441 / 1.841788 (-0.348347) | 16.621611 / 8.074308 (8.547303) | 19.081000 / 10.191392 (8.889608) | 0.215623 / 0.680424 (-0.464801) | 0.025660 / 0.534201 (-0.508541) | 0.446490 / 0.579283 (-0.132793) | 0.560078 / 0.434364 (0.125714) | 0.527231 / 0.540337 (-0.013106) | 0.636551 / 1.386936 (-0.750385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b899ea45c0a7e724ceb5f43c3a8b9fdb081fa67a \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007167 / 0.011353 (-0.004185) | 0.004914 / 0.011008 (-0.006094) | 0.096858 / 0.038508 (0.058350) | 0.033468 / 0.023109 (0.010359) | 0.297276 / 0.275898 (0.021378) | 0.344289 / 0.323480 (0.020809) | 0.005703 / 0.007986 (-0.002282) | 0.003972 / 0.004328 (-0.000357) | 0.075191 / 0.004250 (0.070940) | 0.046247 / 0.037052 (0.009194) | 0.317857 / 0.258489 (0.059368) | 0.347263 / 0.293841 (0.053422) | 0.035017 / 0.128546 (-0.093529) | 0.012036 / 0.075646 (-0.063611) | 0.332522 / 0.419271 (-0.086750) | 0.050188 / 0.043533 (0.006655) | 0.296627 / 0.255139 (0.041488) | 0.319196 / 0.283200 (0.035997) | 0.101100 / 0.141683 (-0.040583) | 1.484536 / 1.452155 (0.032382) | 1.606364 / 1.492716 (0.113648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203954 / 0.018006 (0.185948) | 0.436505 / 0.000490 (0.436015) | 0.003853 / 0.000200 (0.003654) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025834 / 0.037411 (-0.011578) | 0.105759 / 0.014526 (0.091233) | 0.114289 / 0.176557 (-0.062268) | 0.174388 / 0.737135 (-0.562748) | 0.122248 / 0.296338 (-0.174090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404218 / 0.215209 (0.189009) | 4.027900 / 2.077655 (1.950245) | 1.854757 / 1.504120 (0.350637) | 1.668882 / 1.541195 (0.127687) | 1.731451 / 1.468490 (0.262961) | 0.707843 / 4.584777 (-3.876934) | 3.756386 / 3.745712 (0.010674) | 2.067751 / 5.269862 (-3.202110) | 1.313039 / 4.565676 (-3.252638) | 0.086442 / 0.424275 (-0.337833) | 0.012329 / 0.007607 (0.004722) | 0.505964 / 0.226044 (0.279919) | 5.050788 / 2.268929 (2.781860) | 2.353936 / 55.444624 (-53.090688) | 2.055560 / 6.876477 (-4.820917) | 2.162948 / 2.142072 (0.020876) | 0.850532 / 4.805227 (-3.954696) | 0.168560 / 6.500664 (-6.332104) | 0.063143 / 0.075469 (-0.012326) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.182723 / 1.841788 (-0.659065) | 14.779342 / 8.074308 (6.705034) | 14.461572 / 10.191392 (4.270180) | 0.163120 / 0.680424 (-0.517303) | 0.017978 / 0.534201 (-0.516223) | 0.419168 / 0.579283 (-0.160115) | 0.420955 / 0.434364 (-0.013409) | 0.509710 / 0.540337 (-0.030628) | 0.619586 / 1.386936 (-0.767350) |\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) | 0.006804 / 0.011353 (-0.004549) | 0.005136 / 0.011008 (-0.005872) | 0.074910 / 0.038508 (0.036402) | 0.032552 / 0.023109 (0.009443) | 0.374998 / 0.275898 (0.099100) | 0.399219 / 0.323480 (0.075739) | 0.005615 / 0.007986 (-0.002371) | 0.004118 / 0.004328 (-0.000210) | 0.074219 / 0.004250 (0.069969) | 0.045924 / 0.037052 (0.008871) | 0.383228 / 0.258489 (0.124739) | 0.407195 / 0.293841 (0.113354) | 0.035460 / 0.128546 (-0.093086) | 0.012460 / 0.075646 (-0.063187) | 0.087077 / 0.419271 (-0.332195) | 0.050507 / 0.043533 (0.006974) | 0.369001 / 0.255139 (0.113862) | 0.385761 / 0.283200 (0.102561) | 0.106999 / 0.141683 (-0.034684) | 1.465456 / 1.452155 (0.013302) | 1.556962 / 1.492716 (0.064246) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214926 / 0.018006 (0.196920) | 0.436893 / 0.000490 (0.436403) | 0.003388 / 0.000200 (0.003188) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029919 / 0.037411 (-0.007492) | 0.110859 / 0.014526 (0.096333) | 0.120617 / 0.176557 (-0.055939) | 0.171781 / 0.737135 (-0.565355) | 0.125627 / 0.296338 (-0.170712) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436024 / 0.215209 (0.220815) | 4.359167 / 2.077655 (2.281512) | 2.188399 / 1.504120 (0.684279) | 2.001196 / 1.541195 (0.460001) | 2.023710 / 1.468490 (0.555220) | 0.713799 / 4.584777 (-3.870978) | 3.832217 / 3.745712 (0.086504) | 3.269351 / 5.269862 (-2.000510) | 1.534608 / 4.565676 (-3.031068) | 0.088505 / 0.424275 (-0.335770) | 0.012345 / 0.007607 (0.004738) | 0.542446 / 0.226044 (0.316401) | 5.377757 / 2.268929 (3.108828) | 2.659837 / 55.444624 (-52.784787) | 2.272356 / 6.876477 (-4.604120) | 2.297289 / 2.142072 (0.155217) | 0.855276 / 4.805227 (-3.949952) | 0.170666 / 6.500664 (-6.329998) | 0.064549 / 0.075469 (-0.010920) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255938 / 1.841788 (-0.585850) | 15.151471 / 8.074308 (7.077163) | 12.905762 / 10.191392 (2.714370) | 0.162425 / 0.680424 (-0.517999) | 0.017504 / 0.534201 (-0.516697) | 0.448671 / 0.579283 (-0.130612) | 0.422424 / 0.434364 (-0.011940) | 0.551772 / 0.540337 (0.011434) | 0.649115 / 1.386936 (-0.737821) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be73d9f192149727c5542ff257df81b03024fa39 \"CML watermark\")\n", "Having those different checks helps providing an appropriate error message.\r\n\r\nIf the input is a dict, we suggest to select a split. If the input lists is a mix of iterable and non-iterable, we mention that it must be one or the other.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006559 / 0.011353 (-0.004794) | 0.004569 / 0.011008 (-0.006439) | 0.104503 / 0.038508 (0.065995) | 0.028220 / 0.023109 (0.005111) | 0.365507 / 0.275898 (0.089609) | 0.400238 / 0.323480 (0.076758) | 0.004968 / 0.007986 (-0.003017) | 0.003271 / 0.004328 (-0.001057) | 0.082804 / 0.004250 (0.078554) | 0.036299 / 0.037052 (-0.000754) | 0.361201 / 0.258489 (0.102712) | 0.410962 / 0.293841 (0.117121) | 0.030423 / 0.128546 (-0.098123) | 0.011612 / 0.075646 (-0.064034) | 0.331820 / 0.419271 (-0.087452) | 0.043822 / 0.043533 (0.000289) | 0.356242 / 0.255139 (0.101103) | 0.393035 / 0.283200 (0.109836) | 0.088426 / 0.141683 (-0.053257) | 1.484139 / 1.452155 (0.031984) | 1.566712 / 1.492716 (0.073995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195887 / 0.018006 (0.177880) | 0.402720 / 0.000490 (0.402231) | 0.003516 / 0.000200 (0.003316) | 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) | 0.023270 / 0.037411 (-0.014141) | 0.095834 / 0.014526 (0.081308) | 0.102924 / 0.176557 (-0.073632) | 0.161397 / 0.737135 (-0.575738) | 0.105225 / 0.296338 (-0.191114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451701 / 0.215209 (0.236491) | 4.495171 / 2.077655 (2.417517) | 2.223203 / 1.504120 (0.719083) | 2.035533 / 1.541195 (0.494338) | 2.076182 / 1.468490 (0.607692) | 0.697317 / 4.584777 (-3.887460) | 3.406309 / 3.745712 (-0.339403) | 1.847179 / 5.269862 (-3.422683) | 1.158762 / 4.565676 (-3.406914) | 0.083067 / 0.424275 (-0.341208) | 0.012453 / 0.007607 (0.004846) | 0.546502 / 0.226044 (0.320458) | 5.455712 / 2.268929 (3.186784) | 2.654142 / 55.444624 (-52.790483) | 2.298722 / 6.876477 (-4.577755) | 2.383467 / 2.142072 (0.241395) | 0.805950 / 4.805227 (-3.999278) | 0.152479 / 6.500664 (-6.348185) | 0.066784 / 0.075469 (-0.008685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239129 / 1.841788 (-0.602659) | 13.603707 / 8.074308 (5.529398) | 14.062004 / 10.191392 (3.870612) | 0.130928 / 0.680424 (-0.549495) | 0.016907 / 0.534201 (-0.517294) | 0.381614 / 0.579283 (-0.197670) | 0.386770 / 0.434364 (-0.047594) | 0.455792 / 0.540337 (-0.084545) | 0.526092 / 1.386936 (-0.860844) |\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) | 0.006202 / 0.011353 (-0.005151) | 0.004478 / 0.011008 (-0.006531) | 0.076492 / 0.038508 (0.037984) | 0.026703 / 0.023109 (0.003594) | 0.355134 / 0.275898 (0.079236) | 0.391207 / 0.323480 (0.067727) | 0.004852 / 0.007986 (-0.003133) | 0.003271 / 0.004328 (-0.001057) | 0.075080 / 0.004250 (0.070830) | 0.038803 / 0.037052 (0.001750) | 0.359530 / 0.258489 (0.101041) | 0.409044 / 0.293841 (0.115203) | 0.030366 / 0.128546 (-0.098180) | 0.011544 / 0.075646 (-0.064102) | 0.084849 / 0.419271 (-0.334423) | 0.040076 / 0.043533 (-0.003457) | 0.357359 / 0.255139 (0.102220) | 0.384075 / 0.283200 (0.100875) | 0.089130 / 0.141683 (-0.052552) | 1.520400 / 1.452155 (0.068246) | 1.604403 / 1.492716 (0.111687) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257127 / 0.018006 (0.239121) | 0.403691 / 0.000490 (0.403202) | 0.006894 / 0.000200 (0.006694) | 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) | 0.024653 / 0.037411 (-0.012758) | 0.098834 / 0.014526 (0.084309) | 0.107276 / 0.176557 (-0.069281) | 0.158256 / 0.737135 (-0.578879) | 0.111339 / 0.296338 (-0.184999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445006 / 0.215209 (0.229797) | 4.452953 / 2.077655 (2.375299) | 2.168291 / 1.504120 (0.664171) | 1.969457 / 1.541195 (0.428262) | 2.003505 / 1.468490 (0.535015) | 0.695857 / 4.584777 (-3.888920) | 3.433424 / 3.745712 (-0.312288) | 2.466977 / 5.269862 (-2.802885) | 1.528167 / 4.565676 (-3.037509) | 0.082425 / 0.424275 (-0.341850) | 0.012470 / 0.007607 (0.004863) | 0.559039 / 0.226044 (0.332995) | 5.609496 / 2.268929 (3.340568) | 2.602898 / 55.444624 (-52.841726) | 2.273971 / 6.876477 (-4.602506) | 2.303370 / 2.142072 (0.161298) | 0.803875 / 4.805227 (-4.001352) | 0.151069 / 6.500664 (-6.349595) | 0.067956 / 0.075469 (-0.007513) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334443 / 1.841788 (-0.507345) | 13.773252 / 8.074308 (5.698944) | 13.007042 / 10.191392 (2.815650) | 0.127939 / 0.680424 (-0.552485) | 0.016412 / 0.534201 (-0.517789) | 0.374744 / 0.579283 (-0.204539) | 0.396912 / 0.434364 (-0.037452) | 0.443197 / 0.540337 (-0.097140) | 0.528338 / 1.386936 (-0.858598) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51d9f2a3064aa89a780e3d02c6cc34000c51c4fb \"CML watermark\")\n", "Just modified it to use only one loop. I think I managed to keep it readable as well", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007382 / 0.011353 (-0.003971) | 0.005143 / 0.011008 (-0.005865) | 0.097635 / 0.038508 (0.059127) | 0.034726 / 0.023109 (0.011616) | 0.315556 / 0.275898 (0.039658) | 0.355951 / 0.323480 (0.032472) | 0.006055 / 0.007986 (-0.001931) | 0.004264 / 0.004328 (-0.000065) | 0.073636 / 0.004250 (0.069386) | 0.050480 / 0.037052 (0.013428) | 0.316031 / 0.258489 (0.057542) | 0.363933 / 0.293841 (0.070092) | 0.035138 / 0.128546 (-0.093408) | 0.012407 / 0.075646 (-0.063239) | 0.333677 / 0.419271 (-0.085595) | 0.050586 / 0.043533 (0.007053) | 0.309507 / 0.255139 (0.054369) | 0.327043 / 0.283200 (0.043844) | 0.108975 / 0.141683 (-0.032708) | 1.447778 / 1.452155 (-0.004377) | 1.519971 / 1.492716 (0.027255) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248770 / 0.018006 (0.230764) | 0.603036 / 0.000490 (0.602546) | 0.000383 / 0.000200 (0.000183) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027094 / 0.037411 (-0.010317) | 0.104427 / 0.014526 (0.089901) | 0.120627 / 0.176557 (-0.055929) | 0.178790 / 0.737135 (-0.558346) | 0.124877 / 0.296338 (-0.171461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414442 / 0.215209 (0.199233) | 4.138009 / 2.077655 (2.060355) | 1.964642 / 1.504120 (0.460523) | 1.775940 / 1.541195 (0.234745) | 1.899719 / 1.468490 (0.431228) | 0.695406 / 4.584777 (-3.889371) | 3.760470 / 3.745712 (0.014758) | 3.906958 / 5.269862 (-1.362904) | 2.028164 / 4.565676 (-2.537513) | 0.086704 / 0.424275 (-0.337571) | 0.012465 / 0.007607 (0.004857) | 0.512336 / 0.226044 (0.286292) | 5.108587 / 2.268929 (2.839659) | 2.435273 / 55.444624 (-53.009352) | 2.142387 / 6.876477 (-4.734090) | 2.258234 / 2.142072 (0.116162) | 0.854035 / 4.805227 (-3.951193) | 0.170443 / 6.500664 (-6.330222) | 0.065762 / 0.075469 (-0.009707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187529 / 1.841788 (-0.654259) | 15.151164 / 8.074308 (7.076856) | 14.577545 / 10.191392 (4.386153) | 0.166973 / 0.680424 (-0.513450) | 0.017883 / 0.534201 (-0.516318) | 0.427607 / 0.579283 (-0.151676) | 0.417050 / 0.434364 (-0.017314) | 0.508116 / 0.540337 (-0.032221) | 0.590173 / 1.386936 (-0.796763) |\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) | 0.007499 / 0.011353 (-0.003854) | 0.005195 / 0.011008 (-0.005813) | 0.073600 / 0.038508 (0.035091) | 0.033574 / 0.023109 (0.010464) | 0.377506 / 0.275898 (0.101608) | 0.432752 / 0.323480 (0.109272) | 0.006042 / 0.007986 (-0.001944) | 0.006427 / 0.004328 (0.002098) | 0.071666 / 0.004250 (0.067416) | 0.053243 / 0.037052 (0.016190) | 0.363972 / 0.258489 (0.105483) | 0.454988 / 0.293841 (0.161147) | 0.035118 / 0.128546 (-0.093428) | 0.012395 / 0.075646 (-0.063251) | 0.084308 / 0.419271 (-0.334963) | 0.048589 / 0.043533 (0.005057) | 0.368036 / 0.255139 (0.112897) | 0.399414 / 0.283200 (0.116215) | 0.109043 / 0.141683 (-0.032640) | 1.462972 / 1.452155 (0.010817) | 1.574443 / 1.492716 (0.081726) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215107 / 0.018006 (0.197101) | 0.550255 / 0.000490 (0.549765) | 0.004630 / 0.000200 (0.004430) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.111866 / 0.014526 (0.097340) | 0.126559 / 0.176557 (-0.049997) | 0.181443 / 0.737135 (-0.555693) | 0.130559 / 0.296338 (-0.165779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441410 / 0.215209 (0.226201) | 4.403406 / 2.077655 (2.325752) | 2.180276 / 1.504120 (0.676156) | 2.003729 / 1.541195 (0.462534) | 2.079394 / 1.468490 (0.610904) | 0.706061 / 4.584777 (-3.878716) | 3.805668 / 3.745712 (0.059956) | 3.864941 / 5.269862 (-1.404921) | 1.970468 / 4.565676 (-2.595208) | 0.086033 / 0.424275 (-0.338242) | 0.012261 / 0.007607 (0.004654) | 0.550427 / 0.226044 (0.324383) | 5.542270 / 2.268929 (3.273342) | 2.717047 / 55.444624 (-52.727577) | 2.449022 / 6.876477 (-4.427455) | 2.549567 / 2.142072 (0.407495) | 0.854981 / 4.805227 (-3.950247) | 0.169756 / 6.500664 (-6.330908) | 0.067082 / 0.075469 (-0.008387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281369 / 1.841788 (-0.560419) | 15.445090 / 8.074308 (7.370781) | 13.205652 / 10.191392 (3.014260) | 0.170070 / 0.680424 (-0.510354) | 0.017815 / 0.534201 (-0.516385) | 0.425193 / 0.579283 (-0.154090) | 0.425205 / 0.434364 (-0.009159) | 0.493561 / 0.540337 (-0.046776) | 0.588994 / 1.386936 (-0.797942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e427105fc68fce04d0f3c74efb942cbf3a65d166 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006345 / 0.011353 (-0.005008) | 0.004330 / 0.011008 (-0.006678) | 0.096327 / 0.038508 (0.057819) | 0.032964 / 0.023109 (0.009855) | 0.335600 / 0.275898 (0.059702) | 0.365635 / 0.323480 (0.042155) | 0.005435 / 0.007986 (-0.002551) | 0.005005 / 0.004328 (0.000677) | 0.071107 / 0.004250 (0.066856) | 0.044363 / 0.037052 (0.007311) | 0.339988 / 0.258489 (0.081498) | 0.375575 / 0.293841 (0.081734) | 0.028343 / 0.128546 (-0.100203) | 0.008587 / 0.075646 (-0.067059) | 0.324349 / 0.419271 (-0.094922) | 0.050105 / 0.043533 (0.006573) | 0.327398 / 0.255139 (0.072259) | 0.348479 / 0.283200 (0.065279) | 0.102357 / 0.141683 (-0.039326) | 1.419905 / 1.452155 (-0.032250) | 1.534887 / 1.492716 (0.042171) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212418 / 0.018006 (0.194412) | 0.433183 / 0.000490 (0.432693) | 0.000595 / 0.000200 (0.000395) | 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) | 0.027520 / 0.037411 (-0.009891) | 0.109503 / 0.014526 (0.094977) | 0.118202 / 0.176557 (-0.058355) | 0.177236 / 0.737135 (-0.559899) | 0.123736 / 0.296338 (-0.172602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405734 / 0.215209 (0.190525) | 4.039566 / 2.077655 (1.961911) | 1.838211 / 1.504120 (0.334091) | 1.652650 / 1.541195 (0.111456) | 1.753488 / 1.468490 (0.284998) | 0.525258 / 4.584777 (-4.059519) | 3.704509 / 3.745712 (-0.041203) | 1.826794 / 5.269862 (-3.443067) | 1.236361 / 4.565676 (-3.329315) | 0.065619 / 0.424275 (-0.358656) | 0.011606 / 0.007607 (0.003999) | 0.505954 / 0.226044 (0.279910) | 5.054140 / 2.268929 (2.785211) | 2.352587 / 55.444624 (-53.092037) | 2.050601 / 6.876477 (-4.825875) | 2.097222 / 2.142072 (-0.044850) | 0.641044 / 4.805227 (-4.164183) | 0.140676 / 6.500664 (-6.359988) | 0.063217 / 0.075469 (-0.012253) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.177750 / 1.841788 (-0.664038) | 14.819346 / 8.074308 (6.745038) | 14.085937 / 10.191392 (3.894545) | 0.168618 / 0.680424 (-0.511806) | 0.017189 / 0.534201 (-0.517011) | 0.393415 / 0.579283 (-0.185868) | 0.422879 / 0.434364 (-0.011485) | 0.477289 / 0.540337 (-0.063048) | 0.569078 / 1.386936 (-0.817858) |\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) | 0.006502 / 0.011353 (-0.004850) | 0.004640 / 0.011008 (-0.006368) | 0.073272 / 0.038508 (0.034764) | 0.033225 / 0.023109 (0.010116) | 0.359165 / 0.275898 (0.083267) | 0.391659 / 0.323480 (0.068179) | 0.005684 / 0.007986 (-0.002302) | 0.004045 / 0.004328 (-0.000284) | 0.072880 / 0.004250 (0.068629) | 0.046260 / 0.037052 (0.009208) | 0.361772 / 0.258489 (0.103283) | 0.402905 / 0.293841 (0.109064) | 0.027732 / 0.128546 (-0.100814) | 0.008864 / 0.075646 (-0.066783) | 0.081961 / 0.419271 (-0.337310) | 0.046170 / 0.043533 (0.002637) | 0.364198 / 0.255139 (0.109059) | 0.387468 / 0.283200 (0.104269) | 0.105456 / 0.141683 (-0.036227) | 1.457176 / 1.452155 (0.005021) | 1.564899 / 1.492716 (0.072183) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179129 / 0.018006 (0.161123) | 0.439699 / 0.000490 (0.439209) | 0.002882 / 0.000200 (0.002682) | 0.000090 / 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) | 0.029123 / 0.037411 (-0.008288) | 0.112046 / 0.014526 (0.097520) | 0.122773 / 0.176557 (-0.053784) | 0.178404 / 0.737135 (-0.558732) | 0.127904 / 0.296338 (-0.168434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440413 / 0.215209 (0.225204) | 4.407334 / 2.077655 (2.329680) | 2.112932 / 1.504120 (0.608812) | 1.911034 / 1.541195 (0.369840) | 2.057168 / 1.468490 (0.588677) | 0.525472 / 4.584777 (-4.059305) | 3.738894 / 3.745712 (-0.006818) | 1.807592 / 5.269862 (-3.462270) | 1.053837 / 4.565676 (-3.511839) | 0.066203 / 0.424275 (-0.358072) | 0.011965 / 0.007607 (0.004358) | 0.541137 / 0.226044 (0.315093) | 5.415040 / 2.268929 (3.146112) | 2.580476 / 55.444624 (-52.864148) | 2.234144 / 6.876477 (-4.642333) | 2.306014 / 2.142072 (0.163942) | 0.644221 / 4.805227 (-4.161006) | 0.142870 / 6.500664 (-6.357794) | 0.065015 / 0.075469 (-0.010454) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303465 / 1.841788 (-0.538323) | 14.949683 / 8.074308 (6.875375) | 14.370871 / 10.191392 (4.179478) | 0.142714 / 0.680424 (-0.537710) | 0.017372 / 0.534201 (-0.516829) | 0.403898 / 0.579283 (-0.175385) | 0.424781 / 0.434364 (-0.009583) | 0.465984 / 0.540337 (-0.074353) | 0.570863 / 1.386936 (-0.816074) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22d1d533e8ab831b1aa1aab3e7d3c72ba42a83e8 \"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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006917 / 0.011353 (-0.004436) | 0.004803 / 0.011008 (-0.006205) | 0.097082 / 0.038508 (0.058574) | 0.035105 / 0.023109 (0.011996) | 0.325911 / 0.275898 (0.050013) | 0.371858 / 0.323480 (0.048378) | 0.006451 / 0.007986 (-0.001534) | 0.004421 / 0.004328 (0.000093) | 0.075738 / 0.004250 (0.071487) | 0.053624 / 0.037052 (0.016572) | 0.332661 / 0.258489 (0.074172) | 0.372729 / 0.293841 (0.078888) | 0.028279 / 0.128546 (-0.100267) | 0.009318 / 0.075646 (-0.066328) | 0.328505 / 0.419271 (-0.090766) | 0.066962 / 0.043533 (0.023429) | 0.316863 / 0.255139 (0.061724) | 0.344296 / 0.283200 (0.061096) | 0.120575 / 0.141683 (-0.021108) | 1.457867 / 1.452155 (0.005712) | 1.597361 / 1.492716 (0.104644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296399 / 0.018006 (0.278392) | 0.507196 / 0.000490 (0.506706) | 0.003036 / 0.000200 (0.002836) | 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) | 0.028535 / 0.037411 (-0.008876) | 0.110566 / 0.014526 (0.096040) | 0.122078 / 0.176557 (-0.054479) | 0.182926 / 0.737135 (-0.554210) | 0.125546 / 0.296338 (-0.170792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426952 / 0.215209 (0.211742) | 4.255608 / 2.077655 (2.177953) | 2.063865 / 1.504120 (0.559745) | 1.867198 / 1.541195 (0.326004) | 2.058236 / 1.468490 (0.589746) | 0.525885 / 4.584777 (-4.058892) | 3.723607 / 3.745712 (-0.022105) | 1.919144 / 5.269862 (-3.350718) | 1.235308 / 4.565676 (-3.330368) | 0.066423 / 0.424275 (-0.357852) | 0.012045 / 0.007607 (0.004438) | 0.528432 / 0.226044 (0.302388) | 5.268723 / 2.268929 (2.999794) | 2.504071 / 55.444624 (-52.940553) | 2.137999 / 6.876477 (-4.738477) | 2.229987 / 2.142072 (0.087914) | 0.641739 / 4.805227 (-4.163488) | 0.142635 / 6.500664 (-6.358029) | 0.065649 / 0.075469 (-0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.182710 / 1.841788 (-0.659078) | 15.339777 / 8.074308 (7.265469) | 14.722308 / 10.191392 (4.530916) | 0.145914 / 0.680424 (-0.534510) | 0.017861 / 0.534201 (-0.516340) | 0.393092 / 0.579283 (-0.186191) | 0.431179 / 0.434364 (-0.003185) | 0.485712 / 0.540337 (-0.054625) | 0.602634 / 1.386936 (-0.784302) |\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) | 0.006792 / 0.011353 (-0.004561) | 0.005118 / 0.011008 (-0.005890) | 0.073440 / 0.038508 (0.034932) | 0.033751 / 0.023109 (0.010642) | 0.389243 / 0.275898 (0.113345) | 0.397083 / 0.323480 (0.073603) | 0.005989 / 0.007986 (-0.001997) | 0.004289 / 0.004328 (-0.000040) | 0.073228 / 0.004250 (0.068977) | 0.053490 / 0.037052 (0.016438) | 0.396070 / 0.258489 (0.137581) | 0.415134 / 0.293841 (0.121293) | 0.028649 / 0.128546 (-0.099897) | 0.009159 / 0.075646 (-0.066487) | 0.080813 / 0.419271 (-0.338458) | 0.048200 / 0.043533 (0.004667) | 0.388009 / 0.255139 (0.132870) | 0.382174 / 0.283200 (0.098975) | 0.107807 / 0.141683 (-0.033876) | 1.467276 / 1.452155 (0.015121) | 1.568091 / 1.492716 (0.075375) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.328030 / 0.018006 (0.310024) | 0.498058 / 0.000490 (0.497568) | 0.002513 / 0.000200 (0.002313) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029835 / 0.037411 (-0.007576) | 0.113859 / 0.014526 (0.099333) | 0.130813 / 0.176557 (-0.045743) | 0.183646 / 0.737135 (-0.553490) | 0.136561 / 0.296338 (-0.159777) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438901 / 0.215209 (0.223692) | 4.376426 / 2.077655 (2.298771) | 2.220932 / 1.504120 (0.716812) | 2.043585 / 1.541195 (0.502390) | 2.161383 / 1.468490 (0.692893) | 0.523224 / 4.584777 (-4.061553) | 3.730589 / 3.745712 (-0.015123) | 1.859602 / 5.269862 (-3.410260) | 1.073415 / 4.565676 (-3.492261) | 0.066363 / 0.424275 (-0.357912) | 0.012491 / 0.007607 (0.004884) | 0.542052 / 0.226044 (0.316008) | 5.426246 / 2.268929 (3.157318) | 2.673884 / 55.444624 (-52.770740) | 2.372611 / 6.876477 (-4.503865) | 2.482216 / 2.142072 (0.340143) | 0.705669 / 4.805227 (-4.099558) | 0.141075 / 6.500664 (-6.359589) | 0.065339 / 0.075469 (-0.010130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316403 / 1.841788 (-0.525385) | 15.832870 / 8.074308 (7.758562) | 13.307045 / 10.191392 (3.115653) | 0.147258 / 0.680424 (-0.533166) | 0.017966 / 0.534201 (-0.516235) | 0.414396 / 0.579283 (-0.164887) | 0.431801 / 0.434364 (-0.002563) | 0.465483 / 0.540337 (-0.074855) | 0.577850 / 1.386936 (-0.809086) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c795c7e332a7c850c3e725f2034d4894b5e314f7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006368 / 0.011353 (-0.004985) | 0.004274 / 0.011008 (-0.006734) | 0.098799 / 0.038508 (0.060291) | 0.029096 / 0.023109 (0.005986) | 0.308009 / 0.275898 (0.032111) | 0.345701 / 0.323480 (0.022221) | 0.005312 / 0.007986 (-0.002674) | 0.003435 / 0.004328 (-0.000894) | 0.075912 / 0.004250 (0.071662) | 0.041993 / 0.037052 (0.004941) | 0.320075 / 0.258489 (0.061586) | 0.347506 / 0.293841 (0.053665) | 0.025456 / 0.128546 (-0.103091) | 0.008461 / 0.075646 (-0.067185) | 0.322823 / 0.419271 (-0.096448) | 0.044650 / 0.043533 (0.001117) | 0.314118 / 0.255139 (0.058979) | 0.333436 / 0.283200 (0.050237) | 0.093811 / 0.141683 (-0.047871) | 1.464464 / 1.452155 (0.012310) | 1.548098 / 1.492716 (0.055382) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.015905 / 0.018006 (-0.002101) | 0.427847 / 0.000490 (0.427357) | 0.007600 / 0.000200 (0.007400) | 0.000421 / 0.000054 (0.000366) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024530 / 0.037411 (-0.012882) | 0.099907 / 0.014526 (0.085381) | 0.107282 / 0.176557 (-0.069275) | 0.168332 / 0.737135 (-0.568804) | 0.109875 / 0.296338 (-0.186464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451064 / 0.215209 (0.235855) | 4.491434 / 2.077655 (2.413779) | 2.253251 / 1.504120 (0.749131) | 2.086740 / 1.541195 (0.545545) | 2.133288 / 1.468490 (0.664798) | 0.558801 / 4.584777 (-4.025976) | 3.463525 / 3.745712 (-0.282187) | 1.747657 / 5.269862 (-3.522205) | 1.005465 / 4.565676 (-3.560211) | 0.068341 / 0.424275 (-0.355934) | 0.012521 / 0.007607 (0.004914) | 0.567002 / 0.226044 (0.340957) | 5.689529 / 2.268929 (3.420601) | 2.700562 / 55.444624 (-52.744062) | 2.384888 / 6.876477 (-4.491589) | 2.503160 / 2.142072 (0.361088) | 0.667107 / 4.805227 (-4.138120) | 0.137253 / 6.500664 (-6.363412) | 0.068300 / 0.075469 (-0.007170) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202916 / 1.841788 (-0.638872) | 14.163393 / 8.074308 (6.089085) | 14.402463 / 10.191392 (4.211071) | 0.145273 / 0.680424 (-0.535151) | 0.016996 / 0.534201 (-0.517205) | 0.363520 / 0.579283 (-0.215763) | 0.421595 / 0.434364 (-0.012769) | 0.438413 / 0.540337 (-0.101925) | 0.508615 / 1.386936 (-0.878321) |\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) | 0.006419 / 0.011353 (-0.004934) | 0.004346 / 0.011008 (-0.006662) | 0.076356 / 0.038508 (0.037848) | 0.029370 / 0.023109 (0.006260) | 0.371046 / 0.275898 (0.095148) | 0.398279 / 0.323480 (0.074799) | 0.005258 / 0.007986 (-0.002728) | 0.003528 / 0.004328 (-0.000800) | 0.076787 / 0.004250 (0.072537) | 0.041575 / 0.037052 (0.004522) | 0.362319 / 0.258489 (0.103830) | 0.402134 / 0.293841 (0.108293) | 0.025633 / 0.128546 (-0.102913) | 0.008826 / 0.075646 (-0.066820) | 0.082380 / 0.419271 (-0.336892) | 0.041655 / 0.043533 (-0.001878) | 0.357583 / 0.255139 (0.102444) | 0.383486 / 0.283200 (0.100287) | 0.093682 / 0.141683 (-0.048001) | 1.488522 / 1.452155 (0.036367) | 1.576090 / 1.492716 (0.083373) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185556 / 0.018006 (0.167550) | 0.431345 / 0.000490 (0.430855) | 0.002290 / 0.000200 (0.002090) | 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) | 0.026030 / 0.037411 (-0.011382) | 0.102889 / 0.014526 (0.088364) | 0.109541 / 0.176557 (-0.067015) | 0.161050 / 0.737135 (-0.576085) | 0.113525 / 0.296338 (-0.182814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445301 / 0.215209 (0.230092) | 4.437320 / 2.077655 (2.359666) | 2.174181 / 1.504120 (0.670061) | 1.977440 / 1.541195 (0.436245) | 2.036323 / 1.468490 (0.567832) | 0.554227 / 4.584777 (-4.030550) | 3.462746 / 3.745712 (-0.282966) | 1.765257 / 5.269862 (-3.504604) | 1.014515 / 4.565676 (-3.551161) | 0.068391 / 0.424275 (-0.355884) | 0.013154 / 0.007607 (0.005546) | 0.546696 / 0.226044 (0.320652) | 5.490628 / 2.268929 (3.221699) | 2.611947 / 55.444624 (-52.832677) | 2.282659 / 6.876477 (-4.593818) | 2.333972 / 2.142072 (0.191899) | 0.663140 / 4.805227 (-4.142087) | 0.137996 / 6.500664 (-6.362668) | 0.069063 / 0.075469 (-0.006407) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.332147 / 1.841788 (-0.509641) | 14.781592 / 8.074308 (6.707284) | 13.399190 / 10.191392 (3.207798) | 0.139370 / 0.680424 (-0.541054) | 0.016742 / 0.534201 (-0.517459) | 0.364138 / 0.579283 (-0.215146) | 0.402479 / 0.434364 (-0.031885) | 0.427591 / 0.540337 (-0.112746) | 0.520864 / 1.386936 (-0.866072) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8279677b58b93f77995c7da67aea2a04b6a7395 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq any idea why this only fails (CI integration fails are unrelated) in \"Build PR Documentation / build / build_pr_documentation\" (which uses Python 3.8), with message:\r\n```\r\nTypeError: Type subscription requires python >= 3.9\r\n```\r\nwhereas the CI is green for unit tests, which use Python 3.7?", "Hmm I don't know sorry :/", "@lhoestq I am afraid I have to remove the generics I created for numpy and pandas (no subscriptable until Python 3.9) and just leave:\r\n```python\r\nListLike = Union[List[T], Tuple[T, ...]]\r\n```", "Ok sounds good - no need to spend more time on this", "I will merge once the CI is finished. The integration errors are unrelated: `502 Server Error: Bad Gateway`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006637 / 0.011353 (-0.004716) | 0.004578 / 0.011008 (-0.006430) | 0.097346 / 0.038508 (0.058838) | 0.034171 / 0.023109 (0.011062) | 0.315060 / 0.275898 (0.039162) | 0.354386 / 0.323480 (0.030907) | 0.005778 / 0.007986 (-0.002207) | 0.004123 / 0.004328 (-0.000206) | 0.073839 / 0.004250 (0.069589) | 0.046418 / 0.037052 (0.009366) | 0.325910 / 0.258489 (0.067421) | 0.368909 / 0.293841 (0.075068) | 0.027975 / 0.128546 (-0.100571) | 0.008885 / 0.075646 (-0.066761) | 0.327956 / 0.419271 (-0.091316) | 0.049911 / 0.043533 (0.006378) | 0.309424 / 0.255139 (0.054285) | 0.346543 / 0.283200 (0.063343) | 0.103429 / 0.141683 (-0.038253) | 1.517606 / 1.452155 (0.065451) | 1.536685 / 1.492716 (0.043969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211552 / 0.018006 (0.193546) | 0.449583 / 0.000490 (0.449094) | 0.002949 / 0.000200 (0.002750) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027603 / 0.037411 (-0.009808) | 0.108873 / 0.014526 (0.094347) | 0.117990 / 0.176557 (-0.058567) | 0.174202 / 0.737135 (-0.562933) | 0.123793 / 0.296338 (-0.172545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418449 / 0.215209 (0.203240) | 4.177753 / 2.077655 (2.100099) | 1.923446 / 1.504120 (0.419326) | 1.720576 / 1.541195 (0.179381) | 1.783723 / 1.468490 (0.315232) | 0.530068 / 4.584777 (-4.054709) | 3.709410 / 3.745712 (-0.036302) | 1.863924 / 5.269862 (-3.405938) | 1.149906 / 4.565676 (-3.415770) | 0.066595 / 0.424275 (-0.357680) | 0.011733 / 0.007607 (0.004126) | 0.519249 / 0.226044 (0.293205) | 5.179676 / 2.268929 (2.910748) | 2.389488 / 55.444624 (-53.055137) | 2.060006 / 6.876477 (-4.816471) | 2.160668 / 2.142072 (0.018596) | 0.641081 / 4.805227 (-4.164146) | 0.141962 / 6.500664 (-6.358702) | 0.063146 / 0.075469 (-0.012323) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197424 / 1.841788 (-0.644364) | 14.915321 / 8.074308 (6.841013) | 14.792302 / 10.191392 (4.600910) | 0.145436 / 0.680424 (-0.534988) | 0.017669 / 0.534201 (-0.516532) | 0.399060 / 0.579283 (-0.180223) | 0.416282 / 0.434364 (-0.018082) | 0.498392 / 0.540337 (-0.041946) | 0.600242 / 1.386936 (-0.786694) |\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) | 0.007246 / 0.011353 (-0.004106) | 0.005353 / 0.011008 (-0.005656) | 0.076357 / 0.038508 (0.037849) | 0.037662 / 0.023109 (0.014553) | 0.387862 / 0.275898 (0.111964) | 0.421610 / 0.323480 (0.098130) | 0.006424 / 0.007986 (-0.001561) | 0.004397 / 0.004328 (0.000069) | 0.074212 / 0.004250 (0.069961) | 0.054147 / 0.037052 (0.017095) | 0.393171 / 0.258489 (0.134682) | 0.424082 / 0.293841 (0.130241) | 0.029001 / 0.128546 (-0.099546) | 0.009381 / 0.075646 (-0.066265) | 0.082562 / 0.419271 (-0.336710) | 0.048004 / 0.043533 (0.004472) | 0.386895 / 0.255139 (0.131756) | 0.386104 / 0.283200 (0.102904) | 0.113714 / 0.141683 (-0.027969) | 1.435601 / 1.452155 (-0.016553) | 1.554940 / 1.492716 (0.062224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179288 / 0.018006 (0.161282) | 0.455301 / 0.000490 (0.454811) | 0.001469 / 0.000200 (0.001269) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030928 / 0.037411 (-0.006484) | 0.117833 / 0.014526 (0.103307) | 0.125088 / 0.176557 (-0.051468) | 0.178906 / 0.737135 (-0.558230) | 0.131264 / 0.296338 (-0.165075) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436900 / 0.215209 (0.221691) | 4.366094 / 2.077655 (2.288439) | 2.184398 / 1.504120 (0.680278) | 1.992779 / 1.541195 (0.451584) | 2.055260 / 1.468490 (0.586770) | 0.524136 / 4.584777 (-4.060641) | 3.750535 / 3.745712 (0.004823) | 2.985095 / 5.269862 (-2.284767) | 1.400291 / 4.565676 (-3.165385) | 0.065921 / 0.424275 (-0.358354) | 0.012110 / 0.007607 (0.004502) | 0.538239 / 0.226044 (0.312195) | 5.380613 / 2.268929 (3.111685) | 2.637509 / 55.444624 (-52.807116) | 2.352265 / 6.876477 (-4.524212) | 2.409829 / 2.142072 (0.267756) | 0.640428 / 4.805227 (-4.164799) | 0.142070 / 6.500664 (-6.358594) | 0.068171 / 0.075469 (-0.007298) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280080 / 1.841788 (-0.561707) | 15.588799 / 8.074308 (7.514491) | 14.648596 / 10.191392 (4.457204) | 0.147027 / 0.680424 (-0.533397) | 0.018981 / 0.534201 (-0.515220) | 0.394796 / 0.579283 (-0.184487) | 0.423686 / 0.434364 (-0.010678) | 0.467376 / 0.540337 (-0.072961) | 0.562247 / 1.386936 (-0.824689) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#680162303f4c5dae6ad2edef6b3efadded7d37bd \"CML watermark\")\n" ]

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[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note that in `datasets` we do not have vectorized operation like `pandas`. Therefore, your equality comparisons above are `False`:\r\n- For example: `squad['question']` returns a `list`, and this list is not equal to `\"Who was the Norse leader?\"`\r\n\r\nThe `False` value is equivalent to `0` when indexing a dataset, thus the reason why you get the first element (with index 0): \r\n- For example: `squad[False]` is equivalent to `squad[0]`\r\n\r\nMaybe we should an exception instead of assuming that `False` is equivalent to `0` (and `True` is equivalent to `1`) in the context of indexing." ]

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[ "Hi! \r\n\r\nThis would be a nice addition to the Hub! You can find the existing chemistry datasets/models on the Hub (using the `chemistry` tag) [here](https://huggingface.co/search/full-text?q=chemistry&type=model&type=dataset).\r\n\r\nFeel free to ping us here on the Hub if you need help adding the datasets.\r\n" ]

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[ "Hi! You can avoid this error by using the preprocessed version:\r\n```python\r\nimport datasets\r\nds = datasets.load_dataset('natural_questions')\r\n```\r\n\r\nPS: Once we finish https://github.com/huggingface/datasets/pull/5364, this error will no longer be a problem.", "> Hi! You can avoid this error by using the preprocessed version:\r\n> \r\n> ```python\r\n> import datasets\r\n> ds = datasets.load_dataset('natural_questions')\r\n> ```\r\n> \r\n> PS: Once we finish #5364, this error will no longer be a problem.\r\n\r\nThanks, wish #5364 finish early" ]

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[ "Cc @amyeroberts @Rocketknight1 \r\n\r\nIndded I think it's because it does something like this under the hood when there's no multiprocessing:\r\n\r\n```python\r\ntf_dataset = tf_dataset.shuffle(len(dataset))\r\n```\r\n\r\nPS: with multiprocessing it appears to be different:\r\n\r\n```python\r\nindices = np.arange(len(dataset))\r\nif shuffle:\r\n np.random.shuffle(indices)\r\n```", "Hi @massquantity, the dataset being shuffled there is not the full dataset. If you look at [the line above](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L182), the dataset is actually just a single indices array at that point, and that array is the only thing that gets fully loaded into memory and shuffled. We then load samples from the dataset by applying a transform function to the shuffled dataset, which fetches samples based on the indices it receives.\r\n\r\nIf your dataset is **really** gigantic, then this index tensor might be a memory issue, but since it's just an int64 tensor it will only use 1GB of memory per 125 million samples.\r\n\r\nStill, if you're encountering memory issues, there might be another cause here - can you share some code to reproduce the error, or does it depend on some internal/proprietary dataset?", "Hi @Rocketknight1, you're right and I also noticed that only indices are used in shuffling. My data has shape (50000000, 10), but really the problem doesn't relate to a specific dataset. Simply running the following code costs me 10GB of memory.\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for i in range(50000000):\r\n yield {\"data\": i}\r\n\r\nds = Dataset.from_generator(gen, cache_dir=\"./huggingface\")\r\n\r\ntf_ds = ds.to_tf_dataset(\r\n batch_size=1,\r\n shuffle=True,\r\n drop_remainder=False,\r\n prefetch=True,\r\n)\r\ntf_ds = iter(tf_ds)\r\nnext(tf_ds)\r\n# {'data': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>}\r\n```\r\n\r\nI just realized maybe it was an issue from tensorflow (I'm using tf 2.12). So I tried the following code, and it used 10GB of memory too.\r\n```python\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\ndata_size = 50000000\r\ntf_dataset = tf.data.Dataset.from_tensor_slices(np.arange(data_size))\r\ntf_dataset = iter(tf_dataset.shuffle(data_size))\r\nnext(tf_dataset)\r\n# <tf.Tensor: shape=(), dtype=int64, numpy=24774043>\r\n```\r\n\r\nBy the way, as @lhoestq mentioned, multiprocessing uses numpy shuffling, and it uses less than 1 GB of memory:\r\n```python\r\ntf_ds_mp = ds.to_tf_dataset(\r\n batch_size=1,\r\n shuffle=True,\r\n drop_remainder=False,\r\n prefetch=True,\r\n num_workers=2,\r\n)\r\n```", "Thanks for that reproduction script - I've confirmed the same issue is occurring for me. Investigating it now!", "Update: The memory usage is occurring in creation of the index and shuffle buffer. You can reproduce it very simply with:\r\n\r\n```python\r\nimport tensorflow as tf\r\nindices = tf.range(50_000_000, dtype=tf.int64)\r\ndataset = tf.data.Dataset.from_tensor_slices(indices)\r\ndataset = dataset.shuffle(len(dataset))\r\nprint(next(iter(dataset))\r\n```\r\nWhen I wrote this code I thought `tf.data` had an optimization for shuffling an entire tensor that wouldn't create the entire shuffle buffer, but evidently it's just creating the enormous buffer in memory. I'll see if I can find a more efficient way to do this - we might end up moving everything to the `numpy` multiprocessing path to avoid it.", "I opened a PR to fix this - will continue the discussion there!" ]

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[ "Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.", "> Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.\r\n\r\nI don't think it is a problem of the version. It runs ok on colab or local machine. Only on kaggle will has this bug." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007212 / 0.011353 (-0.004141) | 0.005125 / 0.011008 (-0.005883) | 0.098460 / 0.038508 (0.059952) | 0.034040 / 0.023109 (0.010931) | 0.320203 / 0.275898 (0.044305) | 0.357787 / 0.323480 (0.034307) | 0.006000 / 0.007986 (-0.001986) | 0.005644 / 0.004328 (0.001316) | 0.072654 / 0.004250 (0.068403) | 0.049393 / 0.037052 (0.012341) | 0.345686 / 0.258489 (0.087196) | 0.362345 / 0.293841 (0.068504) | 0.036597 / 0.128546 (-0.091949) | 0.012303 / 0.075646 (-0.063343) | 0.334374 / 0.419271 (-0.084897) | 0.062010 / 0.043533 (0.018477) | 0.312547 / 0.255139 (0.057408) | 0.336021 / 0.283200 (0.052821) | 0.112304 / 0.141683 (-0.029378) | 1.446706 / 1.452155 (-0.005449) | 1.523256 / 1.492716 (0.030540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217658 / 0.018006 (0.199652) | 0.449208 / 0.000490 (0.448718) | 0.002878 / 0.000200 (0.002679) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025735 / 0.037411 (-0.011676) | 0.105876 / 0.014526 (0.091350) | 0.114887 / 0.176557 (-0.061669) | 0.170984 / 0.737135 (-0.566152) | 0.121420 / 0.296338 (-0.174918) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419670 / 0.215209 (0.204461) | 4.189453 / 2.077655 (2.111798) | 1.938236 / 1.504120 (0.434116) | 1.769747 / 1.541195 (0.228553) | 1.910919 / 1.468490 (0.442429) | 0.705046 / 4.584777 (-3.879730) | 3.783774 / 3.745712 (0.038062) | 2.096504 / 5.269862 (-3.173358) | 1.339265 / 4.565676 (-3.226412) | 0.086670 / 0.424275 (-0.337605) | 0.012243 / 0.007607 (0.004636) | 0.524701 / 0.226044 (0.298657) | 5.240689 / 2.268929 (2.971760) | 2.473622 / 55.444624 (-52.971003) | 2.170568 / 6.876477 (-4.705909) | 2.289653 / 2.142072 (0.147581) | 0.848913 / 4.805227 (-3.956314) | 0.168332 / 6.500664 (-6.332332) | 0.064926 / 0.075469 (-0.010543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193614 / 1.841788 (-0.648173) | 14.920403 / 8.074308 (6.846095) | 14.475059 / 10.191392 (4.283667) | 0.164458 / 0.680424 (-0.515966) | 0.017613 / 0.534201 (-0.516588) | 0.426311 / 0.579283 (-0.152972) | 0.431478 / 0.434364 (-0.002886) | 0.520280 / 0.540337 (-0.020057) | 0.627738 / 1.386936 (-0.759198) |\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) | 0.007458 / 0.011353 (-0.003895) | 0.005363 / 0.011008 (-0.005645) | 0.076713 / 0.038508 (0.038205) | 0.034189 / 0.023109 (0.011079) | 0.359938 / 0.275898 (0.084040) | 0.395532 / 0.323480 (0.072052) | 0.005977 / 0.007986 (-0.002008) | 0.004263 / 0.004328 (-0.000065) | 0.075971 / 0.004250 (0.071721) | 0.051924 / 0.037052 (0.014871) | 0.362818 / 0.258489 (0.104329) | 0.409897 / 0.293841 (0.116056) | 0.035494 / 0.128546 (-0.093053) | 0.012399 / 0.075646 (-0.063247) | 0.088335 / 0.419271 (-0.330937) | 0.047968 / 0.043533 (0.004435) | 0.355744 / 0.255139 (0.100606) | 0.376339 / 0.283200 (0.093139) | 0.104542 / 0.141683 (-0.037141) | 1.464826 / 1.452155 (0.012672) | 1.600665 / 1.492716 (0.107948) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220841 / 0.018006 (0.202834) | 0.446444 / 0.000490 (0.445954) | 0.000392 / 0.000200 (0.000192) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029402 / 0.037411 (-0.008009) | 0.116511 / 0.014526 (0.101986) | 0.122959 / 0.176557 (-0.053598) | 0.171674 / 0.737135 (-0.565462) | 0.129871 / 0.296338 (-0.166468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450411 / 0.215209 (0.235202) | 4.471859 / 2.077655 (2.394205) | 2.229439 / 1.504120 (0.725319) | 2.053308 / 1.541195 (0.512114) | 2.142476 / 1.468490 (0.673986) | 0.708299 / 4.584777 (-3.876478) | 3.797830 / 3.745712 (0.052118) | 2.142509 / 5.269862 (-3.127352) | 1.333357 / 4.565676 (-3.232320) | 0.086837 / 0.424275 (-0.337439) | 0.012102 / 0.007607 (0.004495) | 0.548428 / 0.226044 (0.322384) | 5.490611 / 2.268929 (3.221682) | 2.713882 / 55.444624 (-52.730742) | 2.399638 / 6.876477 (-4.476839) | 2.481549 / 2.142072 (0.339477) | 0.839812 / 4.805227 (-3.965415) | 0.168890 / 6.500664 (-6.331774) | 0.065564 / 0.075469 (-0.009906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275507 / 1.841788 (-0.566281) | 14.896343 / 8.074308 (6.822035) | 13.159701 / 10.191392 (2.968309) | 0.172065 / 0.680424 (-0.508359) | 0.017507 / 0.534201 (-0.516694) | 0.420031 / 0.579283 (-0.159252) | 0.438835 / 0.434364 (0.004471) | 0.490597 / 0.540337 (-0.049741) | 0.583952 / 1.386936 (-0.802984) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48c9755d0ae9abe4c4d6cd8c1ce76eff849f0e5c \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006567 / 0.011353 (-0.004786) | 0.004479 / 0.011008 (-0.006530) | 0.028286 / 0.038508 (-0.010222) | 0.033137 / 0.023109 (0.010028) | 0.305249 / 0.275898 (0.029351) | 0.330306 / 0.323480 (0.006826) | 0.003747 / 0.007986 (-0.004238) | 0.004409 / 0.004328 (0.000081) | 0.004742 / 0.004250 (0.000491) | 0.040780 / 0.037052 (0.003728) | 0.302879 / 0.258489 (0.044390) | 0.346880 / 0.293841 (0.053039) | 0.032908 / 0.128546 (-0.095638) | 0.010617 / 0.075646 (-0.065029) | 0.257996 / 0.419271 (-0.161275) | 0.051044 / 0.043533 (0.007511) | 0.306113 / 0.255139 (0.050974) | 0.324444 / 0.283200 (0.041244) | 0.100820 / 0.141683 (-0.040863) | 1.478402 / 1.452155 (0.026248) | 1.599398 / 1.492716 (0.106682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216540 / 0.018006 (0.198534) | 0.433480 / 0.000490 (0.432991) | 0.004032 / 0.000200 (0.003832) | 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) | 0.027807 / 0.037411 (-0.009604) | 0.107225 / 0.014526 (0.092699) | 0.120157 / 0.176557 (-0.056400) | 0.174130 / 0.737135 (-0.563005) | 0.128902 / 0.296338 (-0.167437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395996 / 0.215209 (0.180787) | 3.936254 / 2.077655 (1.858599) | 1.808864 / 1.504120 (0.304744) | 1.608935 / 1.541195 (0.067741) | 1.646427 / 1.468490 (0.177937) | 0.716026 / 4.584777 (-3.868751) | 3.815045 / 3.745712 (0.069333) | 2.271534 / 5.269862 (-2.998327) | 1.548728 / 4.565676 (-3.016948) | 0.076743 / 0.424275 (-0.347532) | 0.011575 / 0.007607 (0.003968) | 0.499202 / 0.226044 (0.273158) | 4.983754 / 2.268929 (2.714825) | 2.239319 / 55.444624 (-53.205306) | 1.919427 / 6.876477 (-4.957050) | 2.019664 / 2.142072 (-0.122408) | 0.866318 / 4.805227 (-3.938910) | 0.157309 / 6.500664 (-6.343355) | 0.063341 / 0.075469 (-0.012128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180817 / 1.841788 (-0.660971) | 14.579869 / 8.074308 (6.505561) | 14.277848 / 10.191392 (4.086456) | 0.182560 / 0.680424 (-0.497863) | 0.017402 / 0.534201 (-0.516799) | 0.411549 / 0.579283 (-0.167734) | 0.432938 / 0.434364 (-0.001426) | 0.545067 / 0.540337 (0.004730) | 0.642173 / 1.386936 (-0.744763) |\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) | 0.006753 / 0.011353 (-0.004600) | 0.004590 / 0.011008 (-0.006418) | 0.006111 / 0.038508 (-0.032397) | 0.032763 / 0.023109 (0.009654) | 0.401001 / 0.275898 (0.125103) | 0.428063 / 0.323480 (0.104583) | 0.003730 / 0.007986 (-0.004255) | 0.004617 / 0.004328 (0.000289) | 0.004770 / 0.004250 (0.000519) | 0.049718 / 0.037052 (0.012666) | 0.399724 / 0.258489 (0.141235) | 0.440292 / 0.293841 (0.146451) | 0.032846 / 0.128546 (-0.095700) | 0.010842 / 0.075646 (-0.064804) | 0.012642 / 0.419271 (-0.406630) | 0.046043 / 0.043533 (0.002510) | 0.390862 / 0.255139 (0.135723) | 0.407027 / 0.283200 (0.123828) | 0.099349 / 0.141683 (-0.042334) | 1.455739 / 1.452155 (0.003584) | 1.572214 / 1.492716 (0.079497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227186 / 0.018006 (0.209180) | 0.447404 / 0.000490 (0.446914) | 0.000400 / 0.000200 (0.000200) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029830 / 0.037411 (-0.007581) | 0.112365 / 0.014526 (0.097839) | 0.125736 / 0.176557 (-0.050821) | 0.174781 / 0.737135 (-0.562354) | 0.129439 / 0.296338 (-0.166900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444438 / 0.215209 (0.229229) | 4.459381 / 2.077655 (2.381726) | 2.264541 / 1.504120 (0.760421) | 2.075257 / 1.541195 (0.534062) | 2.181289 / 1.468490 (0.712799) | 0.725279 / 4.584777 (-3.859498) | 3.863253 / 3.745712 (0.117541) | 2.132498 / 5.269862 (-3.137364) | 1.402003 / 4.565676 (-3.163673) | 0.084268 / 0.424275 (-0.340007) | 0.011762 / 0.007607 (0.004155) | 0.556239 / 0.226044 (0.330194) | 5.617998 / 2.268929 (3.349070) | 2.754789 / 55.444624 (-52.689835) | 2.418418 / 6.876477 (-4.458059) | 2.479696 / 2.142072 (0.337624) | 0.870037 / 4.805227 (-3.935190) | 0.160480 / 6.500664 (-6.340184) | 0.064464 / 0.075469 (-0.011005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290916 / 1.841788 (-0.550872) | 14.783173 / 8.074308 (6.708865) | 13.355883 / 10.191392 (3.164491) | 0.169963 / 0.680424 (-0.510461) | 0.017657 / 0.534201 (-0.516544) | 0.409218 / 0.579283 (-0.170065) | 0.422942 / 0.434364 (-0.011422) | 0.494968 / 0.540337 (-0.045369) | 0.587044 / 1.386936 (-0.799892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2051e912d9525bc38a1caf295df0620619c488eb \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5852). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007183 / 0.011353 (-0.004169) | 0.004586 / 0.011008 (-0.006423) | 0.032668 / 0.038508 (-0.005840) | 0.040896 / 0.023109 (0.017787) | 0.358225 / 0.275898 (0.082327) | 0.395063 / 0.323480 (0.071583) | 0.004540 / 0.007986 (-0.003446) | 0.003849 / 0.004328 (-0.000480) | 0.005521 / 0.004250 (0.001271) | 0.053314 / 0.037052 (0.016262) | 0.362417 / 0.258489 (0.103928) | 0.414337 / 0.293841 (0.120496) | 0.030698 / 0.128546 (-0.097849) | 0.008823 / 0.075646 (-0.066823) | 0.303583 / 0.419271 (-0.115689) | 0.060277 / 0.043533 (0.016744) | 0.365938 / 0.255139 (0.110799) | 0.379554 / 0.283200 (0.096354) | 0.122545 / 0.141683 (-0.019138) | 1.712098 / 1.452155 (0.259943) | 1.802036 / 1.492716 (0.309319) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239508 / 0.018006 (0.221502) | 0.492194 / 0.000490 (0.491704) | 0.003280 / 0.000200 (0.003081) | 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) | 0.033301 / 0.037411 (-0.004110) | 0.125851 / 0.014526 (0.111325) | 0.137757 / 0.176557 (-0.038799) | 0.207603 / 0.737135 (-0.529533) | 0.143507 / 0.296338 (-0.152831) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470662 / 0.215209 (0.255453) | 4.736017 / 2.077655 (2.658363) | 2.154152 / 1.504120 (0.650032) | 1.954243 / 1.541195 (0.413048) | 2.080186 / 1.468490 (0.611696) | 0.622884 / 4.584777 (-3.961893) | 4.385885 / 3.745712 (0.640173) | 2.262085 / 5.269862 (-3.007776) | 1.454215 / 4.565676 (-3.111462) | 0.067342 / 0.424275 (-0.356933) | 0.012913 / 0.007607 (0.005306) | 0.600676 / 0.226044 (0.374631) | 5.915093 / 2.268929 (3.646164) | 2.664915 / 55.444624 (-52.779709) | 2.286986 / 6.876477 (-4.589490) | 2.387776 / 2.142072 (0.245704) | 0.757067 / 4.805227 (-4.048160) | 0.154625 / 6.500664 (-6.346039) | 0.074632 / 0.075469 (-0.000838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.413229 / 1.841788 (-0.428558) | 17.433012 / 8.074308 (9.358704) | 16.980340 / 10.191392 (6.788948) | 0.218943 / 0.680424 (-0.461481) | 0.020525 / 0.534201 (-0.513676) | 0.451847 / 0.579283 (-0.127436) | 0.495587 / 0.434364 (0.061223) | 0.548739 / 0.540337 (0.008402) | 0.662120 / 1.386936 (-0.724816) |\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) | 0.006775 / 0.011353 (-0.004577) | 0.004556 / 0.011008 (-0.006452) | 0.006462 / 0.038508 (-0.032046) | 0.039073 / 0.023109 (0.015964) | 0.429249 / 0.275898 (0.153351) | 0.469946 / 0.323480 (0.146467) | 0.004402 / 0.007986 (-0.003584) | 0.003798 / 0.004328 (-0.000530) | 0.005347 / 0.004250 (0.001097) | 0.053743 / 0.037052 (0.016691) | 0.434635 / 0.258489 (0.176146) | 0.475661 / 0.293841 (0.181820) | 0.029891 / 0.128546 (-0.098656) | 0.009058 / 0.075646 (-0.066588) | 0.010987 / 0.419271 (-0.408284) | 0.053877 / 0.043533 (0.010344) | 0.434428 / 0.255139 (0.179289) | 0.449637 / 0.283200 (0.166437) | 0.124331 / 0.141683 (-0.017352) | 1.736083 / 1.452155 (0.283928) | 1.831632 / 1.492716 (0.338916) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248428 / 0.018006 (0.230422) | 0.493113 / 0.000490 (0.492623) | 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) | 0.031337 / 0.037411 (-0.006074) | 0.132360 / 0.014526 (0.117834) | 0.134734 / 0.176557 (-0.041822) | 0.193811 / 0.737135 (-0.543324) | 0.146883 / 0.296338 (-0.149456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510876 / 0.215209 (0.295666) | 5.170198 / 2.077655 (3.092543) | 2.572105 / 1.504120 (1.067985) | 2.316918 / 1.541195 (0.775723) | 2.449316 / 1.468490 (0.980826) | 0.612219 / 4.584777 (-3.972558) | 4.456740 / 3.745712 (0.711028) | 2.099757 / 5.269862 (-3.170105) | 1.293017 / 4.565676 (-3.272660) | 0.067922 / 0.424275 (-0.356353) | 0.013467 / 0.007607 (0.005860) | 0.634240 / 0.226044 (0.408196) | 6.373111 / 2.268929 (4.104182) | 3.171567 / 55.444624 (-52.273057) | 2.763411 / 6.876477 (-4.113066) | 2.845557 / 2.142072 (0.703485) | 0.763431 / 4.805227 (-4.041797) | 0.155949 / 6.500664 (-6.344715) | 0.076264 / 0.075469 (0.000795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.468075 / 1.841788 (-0.373713) | 17.582354 / 8.074308 (9.508046) | 16.565964 / 10.191392 (6.374572) | 0.163779 / 0.680424 (-0.516644) | 0.020472 / 0.534201 (-0.513728) | 0.444416 / 0.579283 (-0.134867) | 0.488471 / 0.434364 (0.054107) | 0.550661 / 0.540337 (0.010323) | 0.667230 / 1.386936 (-0.719706) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3655cbf1c627c945e393641d35298a166f1e4bf5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006160 / 0.011353 (-0.005193) | 0.004093 / 0.011008 (-0.006915) | 0.056485 / 0.038508 (0.017977) | 0.033637 / 0.023109 (0.010528) | 0.296448 / 0.275898 (0.020550) | 0.332532 / 0.323480 (0.009052) | 0.003864 / 0.007986 (-0.004122) | 0.003446 / 0.004328 (-0.000883) | 0.034808 / 0.004250 (0.030558) | 0.048567 / 0.037052 (0.011514) | 0.296090 / 0.258489 (0.037601) | 0.336067 / 0.293841 (0.042226) | 0.026081 / 0.128546 (-0.102465) | 0.007875 / 0.075646 (-0.067771) | 0.286049 / 0.419271 (-0.133222) | 0.050411 / 0.043533 (0.006878) | 0.297016 / 0.255139 (0.041877) | 0.320030 / 0.283200 (0.036830) | 0.110374 / 0.141683 (-0.031308) | 1.432470 / 1.452155 (-0.019684) | 1.492479 / 1.492716 (-0.000238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262352 / 0.018006 (0.244346) | 0.557956 / 0.000490 (0.557467) | 0.010296 / 0.000200 (0.010096) | 0.000315 / 0.000054 (0.000260) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028801 / 0.037411 (-0.008611) | 0.109844 / 0.014526 (0.095318) | 0.122333 / 0.176557 (-0.054224) | 0.180571 / 0.737135 (-0.556564) | 0.125990 / 0.296338 (-0.170348) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401643 / 0.215209 (0.186434) | 4.020993 / 2.077655 (1.943338) | 1.815256 / 1.504120 (0.311136) | 1.619579 / 1.541195 (0.078384) | 1.708889 / 1.468490 (0.240398) | 0.537847 / 4.584777 (-4.046930) | 3.743331 / 3.745712 (-0.002381) | 1.779891 / 5.269862 (-3.489970) | 1.021423 / 4.565676 (-3.544253) | 0.058869 / 0.424275 (-0.365406) | 0.011826 / 0.007607 (0.004218) | 0.499665 / 0.226044 (0.273621) | 4.980928 / 2.268929 (2.712000) | 2.285664 / 55.444624 (-53.158960) | 1.936553 / 6.876477 (-4.939923) | 2.090428 / 2.142072 (-0.051645) | 0.655218 / 4.805227 (-4.150009) | 0.133178 / 6.500664 (-6.367486) | 0.062991 / 0.075469 (-0.012478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168895 / 1.841788 (-0.672892) | 14.656773 / 8.074308 (6.582465) | 13.737921 / 10.191392 (3.546529) | 0.145383 / 0.680424 (-0.535041) | 0.017614 / 0.534201 (-0.516587) | 0.386499 / 0.579283 (-0.192784) | 0.425626 / 0.434364 (-0.008738) | 0.389572 / 0.540337 (-0.150766) | 0.386753 / 1.386936 (-1.000183) |\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) | 0.005998 / 0.011353 (-0.005355) | 0.004265 / 0.011008 (-0.006743) | 0.034743 / 0.038508 (-0.003766) | 0.033929 / 0.023109 (0.010820) | 0.405535 / 0.275898 (0.129636) | 0.407235 / 0.323480 (0.083755) | 0.003972 / 0.007986 (-0.004013) | 0.003616 / 0.004328 (-0.000712) | 0.035278 / 0.004250 (0.031027) | 0.052990 / 0.037052 (0.015937) | 0.405228 / 0.258489 (0.146739) | 0.415007 / 0.293841 (0.121166) | 0.025951 / 0.128546 (-0.102595) | 0.007990 / 0.075646 (-0.067656) | 0.040492 / 0.419271 (-0.378779) | 0.049123 / 0.043533 (0.005591) | 0.399282 / 0.255139 (0.144143) | 0.384303 / 0.283200 (0.101103) | 0.115234 / 0.141683 (-0.026448) | 1.476904 / 1.452155 (0.024749) | 1.627191 / 1.492716 (0.134475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209211 / 0.018006 (0.191205) | 0.566718 / 0.000490 (0.566228) | 0.002094 / 0.000200 (0.001894) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030885 / 0.037411 (-0.006526) | 0.110777 / 0.014526 (0.096251) | 0.124382 / 0.176557 (-0.052174) | 0.175081 / 0.737135 (-0.562054) | 0.130263 / 0.296338 (-0.166075) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448091 / 0.215209 (0.232882) | 4.484404 / 2.077655 (2.406749) | 2.278438 / 1.504120 (0.774318) | 2.087933 / 1.541195 (0.546738) | 2.186709 / 1.468490 (0.718219) | 0.534822 / 4.584777 (-4.049955) | 3.778229 / 3.745712 (0.032517) | 3.312334 / 5.269862 (-1.957528) | 1.557209 / 4.565676 (-3.008467) | 0.058923 / 0.424275 (-0.365352) | 0.011350 / 0.007607 (0.003743) | 0.550470 / 0.226044 (0.324426) | 5.480347 / 2.268929 (3.211419) | 2.781709 / 55.444624 (-52.662915) | 2.478729 / 6.876477 (-4.397748) | 2.492001 / 2.142072 (0.349929) | 0.652649 / 4.805227 (-4.152578) | 0.131334 / 6.500664 (-6.369330) | 0.065619 / 0.075469 (-0.009850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253998 / 1.841788 (-0.587790) | 15.207433 / 8.074308 (7.133124) | 14.627842 / 10.191392 (4.436450) | 0.146947 / 0.680424 (-0.533477) | 0.017533 / 0.534201 (-0.516668) | 0.391627 / 0.579283 (-0.187656) | 0.431113 / 0.434364 (-0.003251) | 0.413886 / 0.540337 (-0.126451) | 0.414483 / 1.386936 (-0.972453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f4e98701590a4922050051eb0f4d63e6125723d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007741 / 0.011353 (-0.003612) | 0.004584 / 0.011008 (-0.006424) | 0.067869 / 0.038508 (0.029361) | 0.041612 / 0.023109 (0.018503) | 0.377878 / 0.275898 (0.101980) | 0.421633 / 0.323480 (0.098153) | 0.004614 / 0.007986 (-0.003371) | 0.003824 / 0.004328 (-0.000504) | 0.041479 / 0.004250 (0.037229) | 0.053309 / 0.037052 (0.016256) | 0.390147 / 0.258489 (0.131658) | 0.437706 / 0.293841 (0.143865) | 0.035951 / 0.128546 (-0.092595) | 0.009231 / 0.075646 (-0.066415) | 0.357572 / 0.419271 (-0.061699) | 0.081332 / 0.043533 (0.037799) | 0.370076 / 0.255139 (0.114937) | 0.423653 / 0.283200 (0.140453) | 0.141401 / 0.141683 (-0.000282) | 1.722744 / 1.452155 (0.270589) | 1.914668 / 1.492716 (0.421952) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256568 / 0.018006 (0.238562) | 0.512243 / 0.000490 (0.511753) | 0.019913 / 0.000200 (0.019713) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031742 / 0.037411 (-0.005670) | 0.128537 / 0.014526 (0.114011) | 0.139962 / 0.176557 (-0.036594) | 0.210711 / 0.737135 (-0.526424) | 0.147162 / 0.296338 (-0.149177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509518 / 0.215209 (0.294309) | 5.083788 / 2.077655 (3.006134) | 2.455381 / 1.504120 (0.951262) | 2.208078 / 1.541195 (0.666883) | 2.341807 / 1.468490 (0.873317) | 0.580014 / 4.584777 (-4.004763) | 4.599492 / 3.745712 (0.853780) | 2.403249 / 5.269862 (-2.866612) | 1.559177 / 4.565676 (-3.006500) | 0.072846 / 0.424275 (-0.351429) | 0.017327 / 0.007607 (0.009720) | 0.627747 / 0.226044 (0.401703) | 6.242586 / 2.268929 (3.973657) | 2.982875 / 55.444624 (-52.461750) | 2.588645 / 6.876477 (-4.287832) | 2.765915 / 2.142072 (0.623843) | 0.720455 / 4.805227 (-4.084772) | 0.157474 / 6.500664 (-6.343190) | 0.074295 / 0.075469 (-0.001174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540799 / 1.841788 (-0.300988) | 18.054632 / 8.074308 (9.980324) | 16.544036 / 10.191392 (6.352644) | 0.201423 / 0.680424 (-0.479001) | 0.020497 / 0.534201 (-0.513704) | 0.496275 / 0.579283 (-0.083008) | 0.547380 / 0.434364 (0.113017) | 0.614605 / 0.540337 (0.074267) | 0.749889 / 1.386936 (-0.637047) |\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) | 0.006963 / 0.011353 (-0.004389) | 0.004543 / 0.011008 (-0.006465) | 0.039530 / 0.038508 (0.001022) | 0.038420 / 0.023109 (0.015311) | 0.454885 / 0.275898 (0.178987) | 0.491731 / 0.323480 (0.168251) | 0.004211 / 0.007986 (-0.003775) | 0.003673 / 0.004328 (-0.000655) | 0.038735 / 0.004250 (0.034484) | 0.052085 / 0.037052 (0.015032) | 0.448924 / 0.258489 (0.190435) | 0.499254 / 0.293841 (0.205413) | 0.030069 / 0.128546 (-0.098477) | 0.009082 / 0.075646 (-0.066565) | 0.047181 / 0.419271 (-0.372090) | 0.054758 / 0.043533 (0.011225) | 0.445035 / 0.255139 (0.189896) | 0.475090 / 0.283200 (0.191891) | 0.122641 / 0.141683 (-0.019042) | 1.706514 / 1.452155 (0.254360) | 1.855726 / 1.492716 (0.363010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246028 / 0.018006 (0.228022) | 0.486382 / 0.000490 (0.485892) | 0.003038 / 0.000200 (0.002838) | 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) | 0.034298 / 0.037411 (-0.003113) | 0.135364 / 0.014526 (0.120838) | 0.146102 / 0.176557 (-0.030455) | 0.207997 / 0.737135 (-0.529139) | 0.153119 / 0.296338 (-0.143219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528758 / 0.215209 (0.313549) | 5.243303 / 2.077655 (3.165648) | 2.617194 / 1.504120 (1.113074) | 2.400740 / 1.541195 (0.859545) | 2.534692 / 1.468490 (1.066202) | 0.585825 / 4.584777 (-3.998952) | 4.879766 / 3.745712 (1.134054) | 2.377419 / 5.269862 (-2.892443) | 1.460711 / 4.565676 (-3.104966) | 0.075572 / 0.424275 (-0.348703) | 0.013650 / 0.007607 (0.006042) | 0.697103 / 0.226044 (0.471058) | 6.444984 / 2.268929 (4.176055) | 3.227662 / 55.444624 (-52.216963) | 2.875163 / 6.876477 (-4.001314) | 2.860953 / 2.142072 (0.718881) | 0.718908 / 4.805227 (-4.086319) | 0.158005 / 6.500664 (-6.342659) | 0.077581 / 0.075469 (0.002112) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.653027 / 1.841788 (-0.188760) | 18.789342 / 8.074308 (10.715034) | 16.762678 / 10.191392 (6.571286) | 0.238920 / 0.680424 (-0.441504) | 0.020698 / 0.534201 (-0.513502) | 0.512634 / 0.579283 (-0.066649) | 0.542235 / 0.434364 (0.107871) | 0.626634 / 0.540337 (0.086297) | 0.753324 / 1.386936 (-0.633612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f978ad8bec6e5e77868c6ffcc6f514354a03901d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005737 / 0.011353 (-0.005616) | 0.003767 / 0.011008 (-0.007241) | 0.097792 / 0.038508 (0.059284) | 0.028466 / 0.023109 (0.005356) | 0.317703 / 0.275898 (0.041805) | 0.359512 / 0.323480 (0.036032) | 0.003428 / 0.007986 (-0.004558) | 0.002848 / 0.004328 (-0.001481) | 0.075668 / 0.004250 (0.071418) | 0.037165 / 0.037052 (0.000113) | 0.329539 / 0.258489 (0.071050) | 0.361365 / 0.293841 (0.067524) | 0.024777 / 0.128546 (-0.103769) | 0.008324 / 0.075646 (-0.067323) | 0.317346 / 0.419271 (-0.101926) | 0.043296 / 0.043533 (-0.000237) | 0.315318 / 0.255139 (0.060179) | 0.347641 / 0.283200 (0.064441) | 0.089551 / 0.141683 (-0.052132) | 1.506335 / 1.452155 (0.054180) | 1.573931 / 1.492716 (0.081215) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208041 / 0.018006 (0.190034) | 0.428198 / 0.000490 (0.427708) | 0.002568 / 0.000200 (0.002369) | 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) | 0.023745 / 0.037411 (-0.013667) | 0.096256 / 0.014526 (0.081730) | 0.104917 / 0.176557 (-0.071639) | 0.164341 / 0.737135 (-0.572794) | 0.107972 / 0.296338 (-0.188367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453995 / 0.215209 (0.238786) | 4.546892 / 2.077655 (2.469238) | 2.185498 / 1.504120 (0.681378) | 1.989156 / 1.541195 (0.447962) | 2.053443 / 1.468490 (0.584953) | 0.559940 / 4.584777 (-4.024837) | 3.420759 / 3.745712 (-0.324954) | 1.771528 / 5.269862 (-3.498333) | 1.139692 / 4.565676 (-3.425984) | 0.067686 / 0.424275 (-0.356589) | 0.011729 / 0.007607 (0.004122) | 0.558001 / 0.226044 (0.331957) | 5.583886 / 2.268929 (3.314957) | 2.678726 / 55.444624 (-52.765899) | 2.324127 / 6.876477 (-4.552350) | 2.472805 / 2.142072 (0.330733) | 0.663163 / 4.805227 (-4.142065) | 0.134892 / 6.500664 (-6.365772) | 0.066722 / 0.075469 (-0.008747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195200 / 1.841788 (-0.646587) | 13.602517 / 8.074308 (5.528209) | 14.036344 / 10.191392 (3.844952) | 0.143759 / 0.680424 (-0.536665) | 0.017215 / 0.534201 (-0.516986) | 0.383749 / 0.579283 (-0.195534) | 0.388229 / 0.434364 (-0.046134) | 0.469366 / 0.540337 (-0.070971) | 0.560408 / 1.386936 (-0.826528) |\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) | 0.005953 / 0.011353 (-0.005400) | 0.003840 / 0.011008 (-0.007168) | 0.077481 / 0.038508 (0.038973) | 0.028318 / 0.023109 (0.005209) | 0.403991 / 0.275898 (0.128093) | 0.433374 / 0.323480 (0.109894) | 0.003572 / 0.007986 (-0.004414) | 0.003033 / 0.004328 (-0.001295) | 0.075873 / 0.004250 (0.071623) | 0.039321 / 0.037052 (0.002269) | 0.416790 / 0.258489 (0.158301) | 0.459368 / 0.293841 (0.165527) | 0.025270 / 0.128546 (-0.103276) | 0.008574 / 0.075646 (-0.067072) | 0.083376 / 0.419271 (-0.335896) | 0.043206 / 0.043533 (-0.000327) | 0.404831 / 0.255139 (0.149692) | 0.418559 / 0.283200 (0.135360) | 0.099135 / 0.141683 (-0.042548) | 1.501315 / 1.452155 (0.049160) | 1.583912 / 1.492716 (0.091195) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241510 / 0.018006 (0.223504) | 0.410473 / 0.000490 (0.409983) | 0.001857 / 0.000200 (0.001657) | 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) | 0.025366 / 0.037411 (-0.012045) | 0.103353 / 0.014526 (0.088828) | 0.107934 / 0.176557 (-0.068622) | 0.162388 / 0.737135 (-0.574747) | 0.113550 / 0.296338 (-0.182789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463529 / 0.215209 (0.248320) | 4.657688 / 2.077655 (2.580034) | 2.455088 / 1.504120 (0.950968) | 2.304833 / 1.541195 (0.763638) | 2.317520 / 1.468490 (0.849029) | 0.563395 / 4.584777 (-4.021382) | 3.408489 / 3.745712 (-0.337223) | 2.636379 / 5.269862 (-2.633482) | 1.425355 / 4.565676 (-3.140322) | 0.068335 / 0.424275 (-0.355940) | 0.011713 / 0.007607 (0.004106) | 0.550230 / 0.226044 (0.324186) | 5.519843 / 2.268929 (3.250915) | 2.864986 / 55.444624 (-52.579639) | 2.604821 / 6.876477 (-4.271655) | 2.701501 / 2.142072 (0.559428) | 0.668193 / 4.805227 (-4.137034) | 0.134739 / 6.500664 (-6.365925) | 0.067110 / 0.075469 (-0.008359) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.326358 / 1.841788 (-0.515430) | 14.184172 / 8.074308 (6.109864) | 14.139245 / 10.191392 (3.947853) | 0.151881 / 0.680424 (-0.528542) | 0.016718 / 0.534201 (-0.517483) | 0.367035 / 0.579283 (-0.212248) | 0.393512 / 0.434364 (-0.040852) | 0.441261 / 0.540337 (-0.099076) | 0.533907 / 1.386936 (-0.853029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#54098759d023f0b3e8eccd2dd98d46a1c6d19cce \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006275 / 0.011353 (-0.005078) | 0.003980 / 0.011008 (-0.007028) | 0.097617 / 0.038508 (0.059109) | 0.034089 / 0.023109 (0.010980) | 0.297381 / 0.275898 (0.021483) | 0.330106 / 0.323480 (0.006626) | 0.003838 / 0.007986 (-0.004148) | 0.004042 / 0.004328 (-0.000287) | 0.074305 / 0.004250 (0.070055) | 0.048318 / 0.037052 (0.011265) | 0.295585 / 0.258489 (0.037096) | 0.346924 / 0.293841 (0.053083) | 0.027397 / 0.128546 (-0.101150) | 0.008452 / 0.075646 (-0.067194) | 0.326837 / 0.419271 (-0.092435) | 0.049515 / 0.043533 (0.005982) | 0.303931 / 0.255139 (0.048792) | 0.317647 / 0.283200 (0.034447) | 0.098280 / 0.141683 (-0.043403) | 1.442603 / 1.452155 (-0.009552) | 1.524050 / 1.492716 (0.031334) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215095 / 0.018006 (0.197089) | 0.437662 / 0.000490 (0.437173) | 0.009771 / 0.000200 (0.009571) | 0.000401 / 0.000054 (0.000346) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027169 / 0.037411 (-0.010243) | 0.111383 / 0.014526 (0.096857) | 0.116163 / 0.176557 (-0.060394) | 0.173134 / 0.737135 (-0.564001) | 0.122376 / 0.296338 (-0.173962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398332 / 0.215209 (0.183123) | 3.974166 / 2.077655 (1.896511) | 1.793847 / 1.504120 (0.289727) | 1.615117 / 1.541195 (0.073922) | 1.660288 / 1.468490 (0.191798) | 0.523833 / 4.584777 (-4.060944) | 3.704273 / 3.745712 (-0.041439) | 1.873308 / 5.269862 (-3.396554) | 1.203546 / 4.565676 (-3.362131) | 0.064949 / 0.424275 (-0.359326) | 0.011830 / 0.007607 (0.004223) | 0.497294 / 0.226044 (0.271250) | 4.948663 / 2.268929 (2.679735) | 2.233391 / 55.444624 (-53.211234) | 1.903208 / 6.876477 (-4.973269) | 2.067908 / 2.142072 (-0.074164) | 0.644256 / 4.805227 (-4.160971) | 0.142798 / 6.500664 (-6.357866) | 0.064734 / 0.075469 (-0.010735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.172313 / 1.841788 (-0.669475) | 14.665853 / 8.074308 (6.591545) | 13.147051 / 10.191392 (2.955659) | 0.139338 / 0.680424 (-0.541086) | 0.017452 / 0.534201 (-0.516749) | 0.395660 / 0.579283 (-0.183623) | 0.410138 / 0.434364 (-0.024226) | 0.460357 / 0.540337 (-0.079980) | 0.555670 / 1.386936 (-0.831266) |\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) | 0.006247 / 0.011353 (-0.005106) | 0.004098 / 0.011008 (-0.006910) | 0.075050 / 0.038508 (0.036542) | 0.033232 / 0.023109 (0.010122) | 0.384139 / 0.275898 (0.108241) | 0.420865 / 0.323480 (0.097385) | 0.003889 / 0.007986 (-0.004096) | 0.003336 / 0.004328 (-0.000993) | 0.073837 / 0.004250 (0.069587) | 0.048775 / 0.037052 (0.011723) | 0.386373 / 0.258489 (0.127884) | 0.421718 / 0.293841 (0.127878) | 0.027553 / 0.128546 (-0.100993) | 0.008724 / 0.075646 (-0.066922) | 0.080970 / 0.419271 (-0.338302) | 0.045981 / 0.043533 (0.002448) | 0.364381 / 0.255139 (0.109242) | 0.391203 / 0.283200 (0.108004) | 0.101681 / 0.141683 (-0.040002) | 1.469533 / 1.452155 (0.017378) | 1.562016 / 1.492716 (0.069300) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222318 / 0.018006 (0.204312) | 0.441395 / 0.000490 (0.440905) | 0.000408 / 0.000200 (0.000208) | 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) | 0.030291 / 0.037411 (-0.007120) | 0.114053 / 0.014526 (0.099527) | 0.123124 / 0.176557 (-0.053433) | 0.173474 / 0.737135 (-0.563661) | 0.129946 / 0.296338 (-0.166393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430342 / 0.215209 (0.215133) | 4.309782 / 2.077655 (2.232128) | 2.110668 / 1.504120 (0.606548) | 1.922881 / 1.541195 (0.381687) | 1.993562 / 1.468490 (0.525072) | 0.523682 / 4.584777 (-4.061095) | 3.774152 / 3.745712 (0.028440) | 3.354783 / 5.269862 (-1.915079) | 1.489793 / 4.565676 (-3.075884) | 0.065169 / 0.424275 (-0.359107) | 0.011626 / 0.007607 (0.004019) | 0.539126 / 0.226044 (0.313081) | 5.372593 / 2.268929 (3.103664) | 2.570652 / 55.444624 (-52.873973) | 2.253353 / 6.876477 (-4.623123) | 2.312876 / 2.142072 (0.170804) | 0.644241 / 4.805227 (-4.160986) | 0.138326 / 6.500664 (-6.362338) | 0.064491 / 0.075469 (-0.010979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344164 / 1.841788 (-0.497624) | 15.124679 / 8.074308 (7.050371) | 14.799310 / 10.191392 (4.607918) | 0.149054 / 0.680424 (-0.531370) | 0.017564 / 0.534201 (-0.516637) | 0.394593 / 0.579283 (-0.184690) | 0.428768 / 0.434364 (-0.005596) | 0.468235 / 0.540337 (-0.072103) | 0.557384 / 1.386936 (-0.829552) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8bfac259e2b5047bb8a0cdcefc8357477ebf93c \"CML watermark\")\n", "@albertvillanova could you take a look at this one ? It directly follows the arrow formatting PR" ]