Datasets:

serhii-korobchenko

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[ "In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. " ]

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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) | 0.007755 / 0.011353 (-0.003598) | 0.004618 / 0.011008 (-0.006391) | 0.098132 / 0.038508 (0.059624) | 0.086759 / 0.023109 (0.063650) | 0.374668 / 0.275898 (0.098770) | 0.417131 / 0.323480 (0.093651) | 0.004604 / 0.007986 (-0.003382) | 0.005461 / 0.004328 (0.001132) | 0.077249 / 0.004250 (0.072999) | 0.063247 / 0.037052 (0.026195) | 0.391801 / 0.258489 (0.133312) | 0.432139 / 0.293841 (0.138298) | 0.036755 / 0.128546 (-0.091791) | 0.010011 / 0.075646 (-0.065636) | 0.346175 / 0.419271 (-0.073097) | 0.061503 / 0.043533 (0.017971) | 0.374063 / 0.255139 (0.118924) | 0.435873 / 0.283200 (0.152673) | 0.029476 / 0.141683 (-0.112207) | 1.786945 / 1.452155 (0.334790) | 1.857190 / 1.492716 (0.364474) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253939 / 0.018006 (0.235933) | 0.506847 / 0.000490 (0.506358) | 0.007278 / 0.000200 (0.007079) | 0.000451 / 0.000054 (0.000397) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032938 / 0.037411 (-0.004474) | 0.097493 / 0.014526 (0.082967) | 0.112090 / 0.176557 (-0.064467) | 0.177986 / 0.737135 (-0.559149) | 0.112060 / 0.296338 (-0.184278) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.481858 / 0.215209 (0.266649) | 4.814894 / 2.077655 (2.737239) | 2.496428 / 1.504120 (0.992308) | 2.309965 / 1.541195 (0.768770) | 2.393819 / 1.468490 (0.925329) | 0.564670 / 4.584777 (-4.020107) | 4.151222 / 3.745712 (0.405510) | 3.676115 / 5.269862 (-1.593747) | 2.346165 / 4.565676 (-2.219512) | 0.066344 / 0.424275 (-0.357931) | 0.009006 / 0.007607 (0.001399) | 0.567699 / 0.226044 (0.341654) | 5.686799 / 2.268929 (3.417871) | 3.031044 / 55.444624 (-52.413580) | 2.606259 / 6.876477 (-4.270217) | 2.864876 / 2.142072 (0.722804) | 0.681730 / 4.805227 (-4.123498) | 0.155405 / 6.500664 (-6.345259) | 0.071492 / 0.075469 (-0.003977) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.514446 / 1.841788 (-0.327341) | 22.624912 / 8.074308 (14.550604) | 16.754145 / 10.191392 (6.562753) | 0.193113 / 0.680424 (-0.487311) | 0.021808 / 0.534201 (-0.512393) | 0.468241 / 0.579283 (-0.111042) | 0.499647 / 0.434364 (0.065283) | 0.539571 / 0.540337 (-0.000766) | 0.771268 / 1.386936 (-0.615668) |\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) | 0.007562 / 0.011353 (-0.003791) | 0.004548 / 0.011008 (-0.006460) | 0.075998 / 0.038508 (0.037490) | 0.081648 / 0.023109 (0.058539) | 0.462876 / 0.275898 (0.186978) | 0.499366 / 0.323480 (0.175886) | 0.005839 / 0.007986 (-0.002147) | 0.003753 / 0.004328 (-0.000576) | 0.075918 / 0.004250 (0.071668) | 0.063233 / 0.037052 (0.026181) | 0.459024 / 0.258489 (0.200535) | 0.506388 / 0.293841 (0.212547) | 0.036179 / 0.128546 (-0.092367) | 0.009961 / 0.075646 (-0.065685) | 0.082061 / 0.419271 (-0.337211) | 0.056469 / 0.043533 (0.012936) | 0.459567 / 0.255139 (0.204428) | 0.482578 / 0.283200 (0.199378) | 0.026363 / 0.141683 (-0.115320) | 1.742247 / 1.452155 (0.290092) | 1.807166 / 1.492716 (0.314450) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.330526 / 0.018006 (0.312520) | 0.511674 / 0.000490 (0.511184) | 0.040969 / 0.000200 (0.040769) | 0.000176 / 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) | 0.035492 / 0.037411 (-0.001920) | 0.104338 / 0.014526 (0.089813) | 0.116973 / 0.176557 (-0.059583) | 0.180218 / 0.737135 (-0.556917) | 0.118801 / 0.296338 (-0.177538) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492196 / 0.215209 (0.276987) | 4.910271 / 2.077655 (2.832616) | 2.542562 / 1.504120 (1.038442) | 2.333516 / 1.541195 (0.792321) | 2.439682 / 1.468490 (0.971192) | 0.571966 / 4.584777 (-4.012811) | 4.089801 / 3.745712 (0.344089) | 3.732129 / 5.269862 (-1.537733) | 2.375887 / 4.565676 (-2.189789) | 0.067376 / 0.424275 (-0.356900) | 0.008350 / 0.007607 (0.000743) | 0.583942 / 0.226044 (0.357897) | 5.840002 / 2.268929 (3.571074) | 3.062520 / 55.444624 (-52.382104) | 2.722512 / 6.876477 (-4.153965) | 2.938307 / 2.142072 (0.796234) | 0.689459 / 4.805227 (-4.115769) | 0.155632 / 6.500664 (-6.345032) | 0.072387 / 0.075469 (-0.003082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595587 / 1.841788 (-0.246201) | 23.035478 / 8.074308 (14.961170) | 16.457675 / 10.191392 (6.266283) | 0.170819 / 0.680424 (-0.509605) | 0.022042 / 0.534201 (-0.512159) | 0.466824 / 0.579283 (-0.112459) | 0.486350 / 0.434364 (0.051986) | 0.574330 / 0.540337 (0.033993) | 0.764913 / 1.386936 (-0.622023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63 \"CML watermark\")\n" ]

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[ "Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).\r\n\r\nYou can fix the above snippet as follows:\r\n```python\r\ntrain_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={\"files\": model_training_files})\r\n```", "to anyone reaching this issue, the argument is `gen_kwargs`:\r\n```py\r\ntrain_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={\"files\": model_training_files})\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). 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) | 0.012516 / 0.011353 (0.001163) | 0.004725 / 0.011008 (-0.006283) | 0.112245 / 0.038508 (0.073736) | 0.079146 / 0.023109 (0.056037) | 0.386415 / 0.275898 (0.110517) | 0.420441 / 0.323480 (0.096961) | 0.005682 / 0.007986 (-0.002304) | 0.004169 / 0.004328 (-0.000160) | 0.077847 / 0.004250 (0.073597) | 0.055763 / 0.037052 (0.018711) | 0.385529 / 0.258489 (0.127040) | 0.422711 / 0.293841 (0.128870) | 0.047212 / 0.128546 (-0.081334) | 0.013711 / 0.075646 (-0.061935) | 0.342856 / 0.419271 (-0.076416) | 0.066788 / 0.043533 (0.023255) | 0.380728 / 0.255139 (0.125589) | 0.416241 / 0.283200 (0.133041) | 0.034676 / 0.141683 (-0.107007) | 1.679661 / 1.452155 (0.227506) | 1.838014 / 1.492716 (0.345297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219556 / 0.018006 (0.201550) | 0.524728 / 0.000490 (0.524238) | 0.005045 / 0.000200 (0.004845) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025475 / 0.037411 (-0.011936) | 0.085937 / 0.014526 (0.071412) | 0.099245 / 0.176557 (-0.077311) | 0.158995 / 0.737135 (-0.578141) | 0.101504 / 0.296338 (-0.194835) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582200 / 0.215209 (0.366991) | 5.794340 / 2.077655 (3.716685) | 2.473635 / 1.504120 (0.969515) | 2.168135 / 1.541195 (0.626941) | 2.215886 / 1.468490 (0.747396) | 0.855599 / 4.584777 (-3.729178) | 5.003067 / 3.745712 (1.257354) | 4.503566 / 5.269862 (-0.766295) | 2.912248 / 4.565676 (-1.653428) | 0.103267 / 0.424275 (-0.321008) | 0.012114 / 0.007607 (0.004507) | 0.712240 / 0.226044 (0.486196) | 7.131946 / 2.268929 (4.863017) | 3.280052 / 55.444624 (-52.164573) | 2.583472 / 6.876477 (-4.293004) | 2.820758 / 2.142072 (0.678686) | 1.132097 / 4.805227 (-3.673131) | 0.232191 / 6.500664 (-6.268473) | 0.082966 / 0.075469 (0.007497) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.581125 / 1.841788 (-0.260662) | 22.723878 / 8.074308 (14.649570) | 19.969347 / 10.191392 (9.777955) | 0.234365 / 0.680424 (-0.446059) | 0.030245 / 0.534201 (-0.503956) | 0.470843 / 0.579283 (-0.108440) | 0.558069 / 0.434364 (0.123705) | 0.534878 / 0.540337 (-0.005460) | 0.801025 / 1.386936 (-0.585911) |\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) | 0.008524 / 0.011353 (-0.002829) | 0.005083 / 0.011008 (-0.005925) | 0.078054 / 0.038508 (0.039546) | 0.082025 / 0.023109 (0.058915) | 0.458027 / 0.275898 (0.182129) | 0.498232 / 0.323480 (0.174752) | 0.005938 / 0.007986 (-0.002048) | 0.003776 / 0.004328 (-0.000553) | 0.080413 / 0.004250 (0.076163) | 0.060485 / 0.037052 (0.023433) | 0.462816 / 0.258489 (0.204327) | 0.513970 / 0.293841 (0.220129) | 0.047574 / 0.128546 (-0.080973) | 0.013424 / 0.075646 (-0.062222) | 0.087707 / 0.419271 (-0.331565) | 0.065007 / 0.043533 (0.021474) | 0.465844 / 0.255139 (0.210705) | 0.498474 / 0.283200 (0.215274) | 0.033518 / 0.141683 (-0.108164) | 1.737507 / 1.452155 (0.285352) | 1.848291 / 1.492716 (0.355574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316710 / 0.018006 (0.298703) | 0.504415 / 0.000490 (0.503925) | 0.042128 / 0.000200 (0.041928) | 0.000171 / 0.000054 (0.000117) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032097 / 0.037411 (-0.005314) | 0.099371 / 0.014526 (0.084845) | 0.109311 / 0.176557 (-0.067246) | 0.177373 / 0.737135 (-0.559762) | 0.110753 / 0.296338 (-0.185585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.688060 / 0.215209 (0.472851) | 6.255219 / 2.077655 (4.177564) | 2.696845 / 1.504120 (1.192725) | 2.395424 / 1.541195 (0.854230) | 2.414870 / 1.468490 (0.946380) | 0.865704 / 4.584777 (-3.719073) | 5.086828 / 3.745712 (1.341116) | 4.648107 / 5.269862 (-0.621754) | 3.091119 / 4.565676 (-1.474558) | 0.101787 / 0.424275 (-0.322489) | 0.008829 / 0.007607 (0.001222) | 0.772398 / 0.226044 (0.546354) | 7.700366 / 2.268929 (5.431438) | 3.608632 / 55.444624 (-51.835992) | 2.923309 / 6.876477 (-3.953168) | 2.952141 / 2.142072 (0.810069) | 1.093006 / 4.805227 (-3.712221) | 0.224363 / 6.500664 (-6.276301) | 0.074927 / 0.075469 (-0.000542) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.638414 / 1.841788 (-0.203374) | 23.486781 / 8.074308 (15.412473) | 21.129104 / 10.191392 (10.937712) | 0.259955 / 0.680424 (-0.420469) | 0.027305 / 0.534201 (-0.506895) | 0.464448 / 0.579283 (-0.114835) | 0.553737 / 0.434364 (0.119373) | 0.571318 / 0.540337 (0.030981) | 0.772917 / 1.386936 (-0.614019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"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) | 0.009093 / 0.011353 (-0.002260) | 0.005283 / 0.011008 (-0.005725) | 0.112299 / 0.038508 (0.073791) | 0.081341 / 0.023109 (0.058232) | 0.363799 / 0.275898 (0.087901) | 0.409261 / 0.323480 (0.085781) | 0.006400 / 0.007986 (-0.001586) | 0.003965 / 0.004328 (-0.000363) | 0.074389 / 0.004250 (0.070139) | 0.060654 / 0.037052 (0.023602) | 0.391046 / 0.258489 (0.132557) | 0.430514 / 0.293841 (0.136673) | 0.054900 / 0.128546 (-0.073646) | 0.017972 / 0.075646 (-0.057675) | 0.410875 / 0.419271 (-0.008396) | 0.067405 / 0.043533 (0.023873) | 0.371468 / 0.255139 (0.116329) | 0.435061 / 0.283200 (0.151861) | 0.038063 / 0.141683 (-0.103620) | 1.733509 / 1.452155 (0.281354) | 1.833899 / 1.492716 (0.341182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243230 / 0.018006 (0.225224) | 0.605636 / 0.000490 (0.605146) | 0.004890 / 0.000200 (0.004690) | 0.000098 / 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) | 0.027624 / 0.037411 (-0.009787) | 0.084799 / 0.014526 (0.070273) | 0.104405 / 0.176557 (-0.072152) | 0.165383 / 0.737135 (-0.571752) | 0.102083 / 0.296338 (-0.194255) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578334 / 0.215209 (0.363125) | 5.369520 / 2.077655 (3.291866) | 2.294174 / 1.504120 (0.790055) | 2.054195 / 1.541195 (0.513000) | 2.007304 / 1.468490 (0.538814) | 0.839283 / 4.584777 (-3.745494) | 5.262288 / 3.745712 (1.516576) | 4.363346 / 5.269862 (-0.906516) | 2.854903 / 4.565676 (-1.710773) | 0.096975 / 0.424275 (-0.327300) | 0.008237 / 0.007607 (0.000630) | 0.646746 / 0.226044 (0.420702) | 6.250621 / 2.268929 (3.981693) | 2.900377 / 55.444624 (-52.544247) | 2.283238 / 6.876477 (-4.593239) | 2.443785 / 2.142072 (0.301713) | 0.991719 / 4.805227 (-3.813508) | 0.189755 / 6.500664 (-6.310909) | 0.067906 / 0.075469 (-0.007563) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.515563 / 1.841788 (-0.326225) | 21.956499 / 8.074308 (13.882191) | 19.161750 / 10.191392 (8.970358) | 0.238199 / 0.680424 (-0.442225) | 0.026771 / 0.534201 (-0.507430) | 0.450195 / 0.579283 (-0.129088) | 0.585168 / 0.434364 (0.150804) | 0.522945 / 0.540337 (-0.017393) | 0.776244 / 1.386936 (-0.610693) |\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) | 0.007997 / 0.011353 (-0.003356) | 0.005021 / 0.011008 (-0.005988) | 0.087308 / 0.038508 (0.048800) | 0.077760 / 0.023109 (0.054650) | 0.425313 / 0.275898 (0.149415) | 0.451470 / 0.323480 (0.127990) | 0.006848 / 0.007986 (-0.001137) | 0.004812 / 0.004328 (0.000484) | 0.071198 / 0.004250 (0.066947) | 0.058325 / 0.037052 (0.021273) | 0.427411 / 0.258489 (0.168922) | 0.466069 / 0.293841 (0.172228) | 0.048686 / 0.128546 (-0.079861) | 0.011841 / 0.075646 (-0.063806) | 0.086225 / 0.419271 (-0.333047) | 0.060500 / 0.043533 (0.016967) | 0.435580 / 0.255139 (0.180441) | 0.456919 / 0.283200 (0.173719) | 0.035094 / 0.141683 (-0.106588) | 1.582805 / 1.452155 (0.130650) | 1.717838 / 1.492716 (0.225122) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.283967 / 0.018006 (0.265960) | 0.517496 / 0.000490 (0.517006) | 0.014747 / 0.000200 (0.014547) | 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) | 0.027870 / 0.037411 (-0.009541) | 0.083835 / 0.014526 (0.069309) | 0.099157 / 0.176557 (-0.077400) | 0.173210 / 0.737135 (-0.563925) | 0.094212 / 0.296338 (-0.202127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.535720 / 0.215209 (0.320511) | 5.273730 / 2.077655 (3.196075) | 2.422560 / 1.504120 (0.918440) | 2.131416 / 1.541195 (0.590222) | 2.192000 / 1.468490 (0.723510) | 0.708469 / 4.584777 (-3.876308) | 4.758092 / 3.745712 (1.012380) | 3.940729 / 5.269862 (-1.329133) | 2.553093 / 4.565676 (-2.012583) | 0.084895 / 0.424275 (-0.339380) | 0.008730 / 0.007607 (0.001123) | 0.646975 / 0.226044 (0.420930) | 6.294811 / 2.268929 (4.025883) | 3.293964 / 55.444624 (-52.150660) | 2.568985 / 6.876477 (-4.307492) | 2.743786 / 2.142072 (0.601713) | 0.899733 / 4.805227 (-3.905494) | 0.193484 / 6.500664 (-6.307181) | 0.070012 / 0.075469 (-0.005457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502255 / 1.841788 (-0.339532) | 20.690234 / 8.074308 (12.615926) | 18.375791 / 10.191392 (8.184399) | 0.200135 / 0.680424 (-0.480289) | 0.029434 / 0.534201 (-0.504767) | 0.477267 / 0.579283 (-0.102016) | 0.566869 / 0.434364 (0.132505) | 0.543756 / 0.540337 (0.003418) | 0.700476 / 1.386936 (-0.686460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/6117.diff", "html_url": "https://github.com/huggingface/datasets/pull/6117", "merged_at": "2023-08-03T14:46:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6117.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6117" }

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[ "Great idea, feel free to open a PR! :)" ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

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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) | 0.007578 / 0.011353 (-0.003775) | 0.004271 / 0.011008 (-0.006738) | 0.086607 / 0.038508 (0.048098) | 0.063209 / 0.023109 (0.040099) | 0.351724 / 0.275898 (0.075826) | 0.399261 / 0.323480 (0.075781) | 0.004767 / 0.007986 (-0.003219) | 0.003487 / 0.004328 (-0.000842) | 0.071483 / 0.004250 (0.067233) | 0.051281 / 0.037052 (0.014229) | 0.387726 / 0.258489 (0.129237) | 0.408446 / 0.293841 (0.114605) | 0.041189 / 0.128546 (-0.087357) | 0.012446 / 0.075646 (-0.063200) | 0.331147 / 0.419271 (-0.088124) | 0.056721 / 0.043533 (0.013188) | 0.361306 / 0.255139 (0.106167) | 0.409651 / 0.283200 (0.126451) | 0.035485 / 0.141683 (-0.106198) | 1.461391 / 1.452155 (0.009236) | 1.554820 / 1.492716 (0.062104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237119 / 0.018006 (0.219113) | 0.518731 / 0.000490 (0.518241) | 0.004192 / 0.000200 (0.003992) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024912 / 0.037411 (-0.012499) | 0.089420 / 0.014526 (0.074894) | 0.091209 / 0.176557 (-0.085347) | 0.152580 / 0.737135 (-0.584555) | 0.089660 / 0.296338 (-0.206678) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515223 / 0.215209 (0.300014) | 5.328359 / 2.077655 (3.250705) | 1.974326 / 1.504120 (0.470206) | 1.665216 / 1.541195 (0.124021) | 1.736040 / 1.468490 (0.267550) | 0.734746 / 4.584777 (-3.850031) | 4.186613 / 3.745712 (0.440901) | 3.535760 / 5.269862 (-1.734102) | 2.333247 / 4.565676 (-2.232429) | 0.071845 / 0.424275 (-0.352430) | 0.006147 / 0.007607 (-0.001460) | 0.546649 / 0.226044 (0.320605) | 5.452281 / 2.268929 (3.183353) | 2.512984 / 55.444624 (-52.931640) | 2.104210 / 6.876477 (-4.772267) | 2.409251 / 2.142072 (0.267178) | 0.822797 / 4.805227 (-3.982430) | 0.166648 / 6.500664 (-6.334016) | 0.056350 / 0.075469 (-0.019119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.397798 / 1.841788 (-0.443989) | 20.549399 / 8.074308 (12.475091) | 19.118168 / 10.191392 (8.926776) | 0.216361 / 0.680424 (-0.464063) | 0.027064 / 0.534201 (-0.507136) | 0.410762 / 0.579283 (-0.168521) | 0.559225 / 0.434364 (0.124861) | 0.468028 / 0.540337 (-0.072309) | 0.691520 / 1.386936 (-0.695416) |\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) | 0.006463 / 0.011353 (-0.004890) | 0.003879 / 0.011008 (-0.007130) | 0.058723 / 0.038508 (0.020215) | 0.057202 / 0.023109 (0.034092) | 0.344397 / 0.275898 (0.068499) | 0.360388 / 0.323480 (0.036908) | 0.005502 / 0.007986 (-0.002483) | 0.004101 / 0.004328 (-0.000227) | 0.058168 / 0.004250 (0.053917) | 0.059112 / 0.037052 (0.022060) | 0.362206 / 0.258489 (0.103717) | 0.386444 / 0.293841 (0.092603) | 0.036613 / 0.128546 (-0.091934) | 0.010482 / 0.075646 (-0.065165) | 0.065850 / 0.419271 (-0.353421) | 0.046528 / 0.043533 (0.002995) | 0.349568 / 0.255139 (0.094429) | 0.360181 / 0.283200 (0.076981) | 0.029030 / 0.141683 (-0.112653) | 1.314569 / 1.452155 (-0.137586) | 1.422393 / 1.492716 (-0.070324) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281554 / 0.018006 (0.263548) | 0.608018 / 0.000490 (0.607528) | 0.004568 / 0.000200 (0.004368) | 0.000182 / 0.000054 (0.000127) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023515 / 0.037411 (-0.013896) | 0.072994 / 0.014526 (0.058468) | 0.080688 / 0.176557 (-0.095868) | 0.125904 / 0.737135 (-0.611232) | 0.085457 / 0.296338 (-0.210882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471530 / 0.215209 (0.256321) | 4.796197 / 2.077655 (2.718542) | 2.189181 / 1.504120 (0.685061) | 1.886649 / 1.541195 (0.345454) | 1.871067 / 1.468490 (0.402577) | 0.661043 / 4.584777 (-3.923734) | 4.344027 / 3.745712 (0.598315) | 3.656967 / 5.269862 (-1.612895) | 2.286033 / 4.565676 (-2.279644) | 0.079146 / 0.424275 (-0.345129) | 0.006840 / 0.007607 (-0.000767) | 0.588750 / 0.226044 (0.362706) | 6.301286 / 2.268929 (4.032357) | 3.074702 / 55.444624 (-52.369923) | 2.398739 / 6.876477 (-4.477738) | 2.555057 / 2.142072 (0.412985) | 0.874189 / 4.805227 (-3.931038) | 0.191423 / 6.500664 (-6.309241) | 0.061227 / 0.075469 (-0.014242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.472763 / 1.841788 (-0.369024) | 19.441304 / 8.074308 (11.366996) | 15.974276 / 10.191392 (5.782884) | 0.172503 / 0.680424 (-0.507921) | 0.027016 / 0.534201 (-0.507185) | 0.356085 / 0.579283 (-0.223198) | 0.473251 / 0.434364 (0.038887) | 0.427949 / 0.540337 (-0.112388) | 0.588924 / 1.386936 (-0.798013) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0973da6e60ac7c1d24229ba6aa6881747b21858a \"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) | 0.006166 / 0.011353 (-0.005187) | 0.003558 / 0.011008 (-0.007450) | 0.080576 / 0.038508 (0.042068) | 0.066542 / 0.023109 (0.043432) | 0.323997 / 0.275898 (0.048099) | 0.369828 / 0.323480 (0.046348) | 0.004896 / 0.007986 (-0.003090) | 0.002909 / 0.004328 (-0.001419) | 0.062553 / 0.004250 (0.058302) | 0.049795 / 0.037052 (0.012742) | 0.321369 / 0.258489 (0.062880) | 0.422860 / 0.293841 (0.129019) | 0.027394 / 0.128546 (-0.101152) | 0.007954 / 0.075646 (-0.067693) | 0.264122 / 0.419271 (-0.155149) | 0.044881 / 0.043533 (0.001349) | 0.316702 / 0.255139 (0.061563) | 0.374718 / 0.283200 (0.091518) | 0.021728 / 0.141683 (-0.119955) | 1.394456 / 1.452155 (-0.057699) | 1.474936 / 1.492716 (-0.017780) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191902 / 0.018006 (0.173896) | 0.430468 / 0.000490 (0.429979) | 0.003790 / 0.000200 (0.003590) | 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) | 0.024974 / 0.037411 (-0.012438) | 0.073053 / 0.014526 (0.058527) | 0.083801 / 0.176557 (-0.092756) | 0.143457 / 0.737135 (-0.593678) | 0.085099 / 0.296338 (-0.211240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428411 / 0.215209 (0.213202) | 4.278077 / 2.077655 (2.200422) | 2.230039 / 1.504120 (0.725919) | 2.057191 / 1.541195 (0.515996) | 2.120109 / 1.468490 (0.651619) | 0.495242 / 4.584777 (-4.089535) | 3.031299 / 3.745712 (-0.714413) | 2.802685 / 5.269862 (-2.467176) | 1.839828 / 4.565676 (-2.725849) | 0.056875 / 0.424275 (-0.367401) | 0.006446 / 0.007607 (-0.001161) | 0.498958 / 0.226044 (0.272913) | 4.980440 / 2.268929 (2.711511) | 2.659659 / 55.444624 (-52.784965) | 2.315174 / 6.876477 (-4.561303) | 2.475920 / 2.142072 (0.333848) | 0.586946 / 4.805227 (-4.218282) | 0.124291 / 6.500664 (-6.376373) | 0.060701 / 0.075469 (-0.014768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245062 / 1.841788 (-0.596725) | 18.201444 / 8.074308 (10.127136) | 13.723271 / 10.191392 (3.531879) | 0.130203 / 0.680424 (-0.550221) | 0.016773 / 0.534201 (-0.517428) | 0.332909 / 0.579283 (-0.246374) | 0.347469 / 0.434364 (-0.086895) | 0.381364 / 0.540337 (-0.158973) | 0.541723 / 1.386936 (-0.845213) |\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) | 0.005934 / 0.011353 (-0.005419) | 0.003573 / 0.011008 (-0.007435) | 0.062195 / 0.038508 (0.023687) | 0.059026 / 0.023109 (0.035917) | 0.413993 / 0.275898 (0.138095) | 0.459552 / 0.323480 (0.136072) | 0.004610 / 0.007986 (-0.003376) | 0.002907 / 0.004328 (-0.001421) | 0.062983 / 0.004250 (0.058733) | 0.047797 / 0.037052 (0.010745) | 0.415461 / 0.258489 (0.156972) | 0.417424 / 0.293841 (0.123583) | 0.027098 / 0.128546 (-0.101449) | 0.008106 / 0.075646 (-0.067540) | 0.067600 / 0.419271 (-0.351672) | 0.041432 / 0.043533 (-0.002101) | 0.407861 / 0.255139 (0.152722) | 0.430774 / 0.283200 (0.147575) | 0.020738 / 0.141683 (-0.120945) | 1.435127 / 1.452155 (-0.017028) | 1.486961 / 1.492716 (-0.005755) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231174 / 0.018006 (0.213168) | 0.421208 / 0.000490 (0.420718) | 0.005411 / 0.000200 (0.005211) | 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) | 0.025362 / 0.037411 (-0.012049) | 0.078534 / 0.014526 (0.064008) | 0.085304 / 0.176557 (-0.091252) | 0.139048 / 0.737135 (-0.598087) | 0.087015 / 0.296338 (-0.209323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448506 / 0.215209 (0.233297) | 4.486694 / 2.077655 (2.409039) | 2.488022 / 1.504120 (0.983902) | 2.325321 / 1.541195 (0.784126) | 2.381311 / 1.468490 (0.912821) | 0.502102 / 4.584777 (-4.082675) | 3.018326 / 3.745712 (-0.727386) | 2.824922 / 5.269862 (-2.444940) | 1.857414 / 4.565676 (-2.708263) | 0.057514 / 0.424275 (-0.366761) | 0.006829 / 0.007607 (-0.000779) | 0.521939 / 0.226044 (0.295895) | 5.224393 / 2.268929 (2.955465) | 2.933132 / 55.444624 (-52.511492) | 2.661187 / 6.876477 (-4.215290) | 2.781950 / 2.142072 (0.639878) | 0.592927 / 4.805227 (-4.212300) | 0.126685 / 6.500664 (-6.373979) | 0.064188 / 0.075469 (-0.011281) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351107 / 1.841788 (-0.490681) | 18.344453 / 8.074308 (10.270145) | 13.838788 / 10.191392 (3.647396) | 0.157881 / 0.680424 (-0.522543) | 0.016636 / 0.534201 (-0.517565) | 0.331597 / 0.579283 (-0.247686) | 0.345573 / 0.434364 (-0.088791) | 0.397361 / 0.540337 (-0.142976) | 0.534289 / 1.386936 (-0.852647) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#582e722a76534904c0f3038d32ebb8db88ce9128 \"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) | 0.006399 / 0.011353 (-0.004954) | 0.003872 / 0.011008 (-0.007136) | 0.083722 / 0.038508 (0.045214) | 0.068845 / 0.023109 (0.045736) | 0.329112 / 0.275898 (0.053214) | 0.343295 / 0.323480 (0.019815) | 0.005137 / 0.007986 (-0.002849) | 0.003303 / 0.004328 (-0.001026) | 0.064495 / 0.004250 (0.060245) | 0.051448 / 0.037052 (0.014395) | 0.322554 / 0.258489 (0.064065) | 0.361934 / 0.293841 (0.068093) | 0.030821 / 0.128546 (-0.097726) | 0.008482 / 0.075646 (-0.067164) | 0.288136 / 0.419271 (-0.131135) | 0.051935 / 0.043533 (0.008402) | 0.308283 / 0.255139 (0.053144) | 0.343421 / 0.283200 (0.060221) | 0.023639 / 0.141683 (-0.118044) | 1.485442 / 1.452155 (0.033288) | 1.533282 / 1.492716 (0.040565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218163 / 0.018006 (0.200157) | 0.464473 / 0.000490 (0.463983) | 0.003097 / 0.000200 (0.002897) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028650 / 0.037411 (-0.008761) | 0.083295 / 0.014526 (0.068769) | 0.096468 / 0.176557 (-0.080088) | 0.152086 / 0.737135 (-0.585050) | 0.102586 / 0.296338 (-0.193752) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393038 / 0.215209 (0.177829) | 3.925514 / 2.077655 (1.847859) | 1.938419 / 1.504120 (0.434300) | 1.760265 / 1.541195 (0.219071) | 1.810024 / 1.468490 (0.341534) | 0.486232 / 4.584777 (-4.098545) | 3.618747 / 3.745712 (-0.126965) | 3.206950 / 5.269862 (-2.062912) | 1.999240 / 4.565676 (-2.566436) | 0.056986 / 0.424275 (-0.367289) | 0.007193 / 0.007607 (-0.000415) | 0.469313 / 0.226044 (0.243269) | 4.688670 / 2.268929 (2.419741) | 2.400332 / 55.444624 (-53.044292) | 2.074197 / 6.876477 (-4.802279) | 2.290823 / 2.142072 (0.148751) | 0.582339 / 4.805227 (-4.222888) | 0.134127 / 6.500664 (-6.366537) | 0.061061 / 0.075469 (-0.014408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272782 / 1.841788 (-0.569006) | 19.463375 / 8.074308 (11.389067) | 14.306819 / 10.191392 (4.115427) | 0.164608 / 0.680424 (-0.515816) | 0.018626 / 0.534201 (-0.515575) | 0.395225 / 0.579283 (-0.184058) | 0.408984 / 0.434364 (-0.025380) | 0.463364 / 0.540337 (-0.076974) | 0.630425 / 1.386936 (-0.756511) |\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) | 0.006465 / 0.011353 (-0.004888) | 0.003975 / 0.011008 (-0.007033) | 0.063643 / 0.038508 (0.025134) | 0.075214 / 0.023109 (0.052105) | 0.361734 / 0.275898 (0.085836) | 0.396664 / 0.323480 (0.073184) | 0.005251 / 0.007986 (-0.002735) | 0.003249 / 0.004328 (-0.001080) | 0.063841 / 0.004250 (0.059591) | 0.054504 / 0.037052 (0.017451) | 0.374791 / 0.258489 (0.116302) | 0.399205 / 0.293841 (0.105364) | 0.031355 / 0.128546 (-0.097192) | 0.008483 / 0.075646 (-0.067163) | 0.070234 / 0.419271 (-0.349037) | 0.048336 / 0.043533 (0.004803) | 0.373484 / 0.255139 (0.118345) | 0.382174 / 0.283200 (0.098974) | 0.022560 / 0.141683 (-0.119123) | 1.449799 / 1.452155 (-0.002355) | 1.525255 / 1.492716 (0.032539) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228350 / 0.018006 (0.210343) | 0.444344 / 0.000490 (0.443855) | 0.003699 / 0.000200 (0.003499) | 0.000079 / 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) | 0.030681 / 0.037411 (-0.006731) | 0.087340 / 0.014526 (0.072814) | 0.098636 / 0.176557 (-0.077920) | 0.151665 / 0.737135 (-0.585471) | 0.100840 / 0.296338 (-0.195498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417857 / 0.215209 (0.202648) | 4.168407 / 2.077655 (2.090752) | 2.201758 / 1.504120 (0.697638) | 1.997834 / 1.541195 (0.456639) | 2.127693 / 1.468490 (0.659202) | 0.486429 / 4.584777 (-4.098348) | 3.676335 / 3.745712 (-0.069378) | 3.226268 / 5.269862 (-2.043594) | 2.027255 / 4.565676 (-2.538422) | 0.056759 / 0.424275 (-0.367516) | 0.007628 / 0.007607 (0.000021) | 0.500482 / 0.226044 (0.274438) | 4.996236 / 2.268929 (2.727307) | 2.628884 / 55.444624 (-52.815740) | 2.347611 / 6.876477 (-4.528866) | 2.551328 / 2.142072 (0.409255) | 0.582449 / 4.805227 (-4.222778) | 0.132844 / 6.500664 (-6.367821) | 0.061791 / 0.075469 (-0.013678) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.373718 / 1.841788 (-0.468070) | 19.921217 / 8.074308 (11.846909) | 14.209642 / 10.191392 (4.018250) | 0.185334 / 0.680424 (-0.495090) | 0.018228 / 0.534201 (-0.515973) | 0.395549 / 0.579283 (-0.183734) | 0.404446 / 0.434364 (-0.029918) | 0.472456 / 0.540337 (-0.067882) | 0.622739 / 1.386936 (-0.764197) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) | 0.006007 / 0.011353 (-0.005346) | 0.003588 / 0.011008 (-0.007420) | 0.080334 / 0.038508 (0.041826) | 0.058932 / 0.023109 (0.035823) | 0.404613 / 0.275898 (0.128715) | 0.438377 / 0.323480 (0.114897) | 0.003468 / 0.007986 (-0.004518) | 0.003702 / 0.004328 (-0.000627) | 0.062936 / 0.004250 (0.058686) | 0.047987 / 0.037052 (0.010934) | 0.411409 / 0.258489 (0.152920) | 0.450244 / 0.293841 (0.156403) | 0.027007 / 0.128546 (-0.101539) | 0.007932 / 0.075646 (-0.067714) | 0.261390 / 0.419271 (-0.157882) | 0.044992 / 0.043533 (0.001459) | 0.409730 / 0.255139 (0.154591) | 0.433331 / 0.283200 (0.150131) | 0.020446 / 0.141683 (-0.121237) | 1.425418 / 1.452155 (-0.026736) | 1.479242 / 1.492716 (-0.013475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187375 / 0.018006 (0.169368) | 0.428532 / 0.000490 (0.428043) | 0.003406 / 0.000200 (0.003206) | 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) | 0.024390 / 0.037411 (-0.013022) | 0.072571 / 0.014526 (0.058045) | 0.083513 / 0.176557 (-0.093044) | 0.144395 / 0.737135 (-0.592741) | 0.084813 / 0.296338 (-0.211526) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409176 / 0.215209 (0.193967) | 4.078082 / 2.077655 (2.000428) | 1.913596 / 1.504120 (0.409476) | 1.718470 / 1.541195 (0.177275) | 1.753106 / 1.468490 (0.284616) | 0.494167 / 4.584777 (-4.090610) | 3.029531 / 3.745712 (-0.716181) | 2.807331 / 5.269862 (-2.462531) | 1.839471 / 4.565676 (-2.726206) | 0.057169 / 0.424275 (-0.367106) | 0.006433 / 0.007607 (-0.001175) | 0.482666 / 0.226044 (0.256621) | 4.817601 / 2.268929 (2.548673) | 2.449967 / 55.444624 (-52.994658) | 2.113891 / 6.876477 (-4.762586) | 2.399293 / 2.142072 (0.257221) | 0.578903 / 4.805227 (-4.226324) | 0.124306 / 6.500664 (-6.376358) | 0.061572 / 0.075469 (-0.013897) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254692 / 1.841788 (-0.587096) | 18.414049 / 8.074308 (10.339741) | 13.992059 / 10.191392 (3.800667) | 0.146671 / 0.680424 (-0.533753) | 0.016925 / 0.534201 (-0.517275) | 0.333124 / 0.579283 (-0.246159) | 0.348007 / 0.434364 (-0.086357) | 0.378519 / 0.540337 (-0.161819) | 0.532540 / 1.386936 (-0.854396) |\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) | 0.006050 / 0.011353 (-0.005303) | 0.003614 / 0.011008 (-0.007394) | 0.061707 / 0.038508 (0.023199) | 0.062874 / 0.023109 (0.039765) | 0.364760 / 0.275898 (0.088862) | 0.398136 / 0.323480 (0.074656) | 0.005598 / 0.007986 (-0.002388) | 0.002836 / 0.004328 (-0.001493) | 0.061880 / 0.004250 (0.057630) | 0.048165 / 0.037052 (0.011113) | 0.372656 / 0.258489 (0.114167) | 0.403967 / 0.293841 (0.110126) | 0.027046 / 0.128546 (-0.101501) | 0.008091 / 0.075646 (-0.067555) | 0.066783 / 0.419271 (-0.352489) | 0.041186 / 0.043533 (-0.002347) | 0.376009 / 0.255139 (0.120870) | 0.391769 / 0.283200 (0.108569) | 0.021020 / 0.141683 (-0.120663) | 1.514593 / 1.452155 (0.062438) | 1.548506 / 1.492716 (0.055790) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237610 / 0.018006 (0.219604) | 0.434274 / 0.000490 (0.433784) | 0.009720 / 0.000200 (0.009520) | 0.000098 / 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) | 0.025605 / 0.037411 (-0.011807) | 0.078971 / 0.014526 (0.064445) | 0.088154 / 0.176557 (-0.088403) | 0.139112 / 0.737135 (-0.598023) | 0.088890 / 0.296338 (-0.207449) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420027 / 0.215209 (0.204818) | 4.189493 / 2.077655 (2.111838) | 2.143907 / 1.504120 (0.639787) | 1.967032 / 1.541195 (0.425837) | 2.011845 / 1.468490 (0.543355) | 0.496692 / 4.584777 (-4.088085) | 3.025456 / 3.745712 (-0.720256) | 2.828436 / 5.269862 (-2.441426) | 1.860673 / 4.565676 (-2.705003) | 0.057199 / 0.424275 (-0.367076) | 0.006770 / 0.007607 (-0.000838) | 0.491281 / 0.226044 (0.265236) | 4.918065 / 2.268929 (2.649136) | 2.593172 / 55.444624 (-52.851452) | 2.250750 / 6.876477 (-4.625727) | 2.406235 / 2.142072 (0.264162) | 0.588648 / 4.805227 (-4.216579) | 0.125635 / 6.500664 (-6.375029) | 0.061697 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374065 / 1.841788 (-0.467722) | 18.439315 / 8.074308 (10.365007) | 14.031660 / 10.191392 (3.840268) | 0.153665 / 0.680424 (-0.526759) | 0.016980 / 0.534201 (-0.517221) | 0.331799 / 0.579283 (-0.247484) | 0.343201 / 0.434364 (-0.091163) | 0.392445 / 0.540337 (-0.147892) | 0.530387 / 1.386936 (-0.856549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) | 0.008189 / 0.011353 (-0.003164) | 0.004598 / 0.011008 (-0.006410) | 0.102199 / 0.038508 (0.063691) | 0.077961 / 0.023109 (0.054852) | 0.364936 / 0.275898 (0.089038) | 0.402606 / 0.323480 (0.079126) | 0.005522 / 0.007986 (-0.002464) | 0.004007 / 0.004328 (-0.000322) | 0.071560 / 0.004250 (0.067310) | 0.055818 / 0.037052 (0.018765) | 0.378394 / 0.258489 (0.119905) | 0.428990 / 0.293841 (0.135149) | 0.043142 / 0.128546 (-0.085404) | 0.013254 / 0.075646 (-0.062392) | 0.331102 / 0.419271 (-0.088170) | 0.061407 / 0.043533 (0.017875) | 0.387397 / 0.255139 (0.132258) | 0.416062 / 0.283200 (0.132862) | 0.036330 / 0.141683 (-0.105353) | 1.735352 / 1.452155 (0.283198) | 1.773329 / 1.492716 (0.280613) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188587 / 0.018006 (0.170581) | 0.519506 / 0.000490 (0.519016) | 0.004702 / 0.000200 (0.004502) | 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) | 0.027152 / 0.037411 (-0.010260) | 0.094296 / 0.014526 (0.079770) | 0.098155 / 0.176557 (-0.078402) | 0.162541 / 0.737135 (-0.574595) | 0.112092 / 0.296338 (-0.184246) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.537555 / 0.215209 (0.322346) | 5.486821 / 2.077655 (3.409166) | 2.377127 / 1.504120 (0.873008) | 2.073205 / 1.541195 (0.532011) | 2.075130 / 1.468490 (0.606640) | 0.783779 / 4.584777 (-3.800998) | 5.029524 / 3.745712 (1.283812) | 4.382724 / 5.269862 (-0.887138) | 2.836180 / 4.565676 (-1.729496) | 0.108840 / 0.424275 (-0.315435) | 0.008123 / 0.007607 (0.000516) | 0.673460 / 0.226044 (0.447416) | 6.674030 / 2.268929 (4.405102) | 3.208922 / 55.444624 (-52.235702) | 2.464908 / 6.876477 (-4.411568) | 2.661929 / 2.142072 (0.519856) | 0.962529 / 4.805227 (-3.842698) | 0.197974 / 6.500664 (-6.302690) | 0.066656 / 0.075469 (-0.008813) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430373 / 1.841788 (-0.411415) | 21.180540 / 8.074308 (13.106232) | 19.027491 / 10.191392 (8.836099) | 0.217520 / 0.680424 (-0.462904) | 0.028038 / 0.534201 (-0.506163) | 0.435266 / 0.579283 (-0.144017) | 0.529510 / 0.434364 (0.095147) | 0.511011 / 0.540337 (-0.029327) | 0.728940 / 1.386936 (-0.657996) |\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) | 0.007883 / 0.011353 (-0.003470) | 0.004448 / 0.011008 (-0.006560) | 0.071350 / 0.038508 (0.032842) | 0.075269 / 0.023109 (0.052160) | 0.396705 / 0.275898 (0.120807) | 0.457809 / 0.323480 (0.134329) | 0.005193 / 0.007986 (-0.002792) | 0.003695 / 0.004328 (-0.000633) | 0.078087 / 0.004250 (0.073836) | 0.054276 / 0.037052 (0.017224) | 0.412184 / 0.258489 (0.153695) | 0.452400 / 0.293841 (0.158559) | 0.049762 / 0.128546 (-0.078784) | 0.013206 / 0.075646 (-0.062440) | 0.085985 / 0.419271 (-0.333287) | 0.058837 / 0.043533 (0.015304) | 0.432481 / 0.255139 (0.177342) | 0.433260 / 0.283200 (0.150060) | 0.031190 / 0.141683 (-0.110493) | 1.582707 / 1.452155 (0.130552) | 1.664457 / 1.492716 (0.171741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223639 / 0.018006 (0.205633) | 0.524388 / 0.000490 (0.523899) | 0.005489 / 0.000200 (0.005289) | 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) | 0.030182 / 0.037411 (-0.007230) | 0.089309 / 0.014526 (0.074783) | 0.103306 / 0.176557 (-0.073250) | 0.162624 / 0.737135 (-0.574511) | 0.108957 / 0.296338 (-0.187381) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.577423 / 0.215209 (0.362214) | 5.900154 / 2.077655 (3.822500) | 2.687369 / 1.504120 (1.183249) | 2.513061 / 1.541195 (0.971866) | 2.506453 / 1.468490 (1.037963) | 0.830838 / 4.584777 (-3.753939) | 5.032195 / 3.745712 (1.286483) | 4.396827 / 5.269862 (-0.873035) | 2.884230 / 4.565676 (-1.681447) | 0.102239 / 0.424275 (-0.322036) | 0.008178 / 0.007607 (0.000571) | 0.710027 / 0.226044 (0.483983) | 7.149626 / 2.268929 (4.880698) | 3.403605 / 55.444624 (-52.041019) | 2.661970 / 6.876477 (-4.214506) | 2.760227 / 2.142072 (0.618154) | 1.043981 / 4.805227 (-3.761246) | 0.195028 / 6.500664 (-6.305636) | 0.065211 / 0.075469 (-0.010258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.581265 / 1.841788 (-0.260522) | 21.640230 / 8.074308 (13.565922) | 19.031860 / 10.191392 (8.840468) | 0.196903 / 0.680424 (-0.483520) | 0.027061 / 0.534201 (-0.507140) | 0.444995 / 0.579283 (-0.134288) | 0.528195 / 0.434364 (0.093831) | 0.521540 / 0.540337 (-0.018797) | 0.730204 / 1.386936 (-0.656732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n" ]

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[ "You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).", "Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else." ]

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[ "Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic." ]

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[ "Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon." ]

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[ "any idea?", "This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n\r\n`load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`", "> This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n> \r\n> `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`\r\n\r\nThanks for your help. This works." ]

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[ "This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached." ]

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[ "Thanks for reporting, @610v4nn1.\r\n\r\nIndeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.\r\n\r\nWe are adding a notification about this issue to the dataset card.\r\n\r\nSee: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 " ]

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[ "Yesterday I waited for more than 12 hours to make sure it was really **stuck** instead of proceeding too slow.", "I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.", "We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)", "The problem seems to be the `Generating train split`. Is it possible to avoid that? I have a dataset saved, just want to load it but somehow running into issues with that again.", "Hey guys, recently I ran into this problem again and I spent one whole day trying to locate the problem. Finally I found the problem seems to be with `pyarrow`'s json parser, and it seems a long-existing problem. Similar issue can be found in #2181. Anyway, my solution is to adjust the `load_dataset`'s parameter `chunksize`. You can inspect the parameter set in `datasets/packaged_modules/json/json.py`, now the actual chunksize should be very small, and you can increase the value. For me, `chunksize=10<<23` could solve the stuck problem. But I also find that too big `chunksize`, like `10 << 30`, would also cause a stuck, which is rather weird. I think I may explore this when I am free. And hope this can help those who also encounter the same problem. " ]

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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) | 0.007678 / 0.011353 (-0.003675) | 0.004233 / 0.011008 (-0.006776) | 0.095934 / 0.038508 (0.057426) | 0.064201 / 0.023109 (0.041092) | 0.345765 / 0.275898 (0.069867) | 0.383089 / 0.323480 (0.059609) | 0.004084 / 0.007986 (-0.003902) | 0.003311 / 0.004328 (-0.001017) | 0.072367 / 0.004250 (0.068117) | 0.048252 / 0.037052 (0.011200) | 0.338340 / 0.258489 (0.079851) | 0.391627 / 0.293841 (0.097786) | 0.045203 / 0.128546 (-0.083343) | 0.013494 / 0.075646 (-0.062153) | 0.314097 / 0.419271 (-0.105174) | 0.058183 / 0.043533 (0.014650) | 0.353946 / 0.255139 (0.098807) | 0.385181 / 0.283200 (0.101981) | 0.033111 / 0.141683 (-0.108572) | 1.578489 / 1.452155 (0.126335) | 1.631660 / 1.492716 (0.138944) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202592 / 0.018006 (0.184586) | 0.506450 / 0.000490 (0.505961) | 0.004630 / 0.000200 (0.004430) | 0.000105 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024761 / 0.037411 (-0.012651) | 0.086295 / 0.014526 (0.071769) | 0.094063 / 0.176557 (-0.082494) | 0.154189 / 0.737135 (-0.582947) | 0.096273 / 0.296338 (-0.200065) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.581731 / 0.215209 (0.366522) | 5.552020 / 2.077655 (3.474365) | 2.430800 / 1.504120 (0.926680) | 2.130864 / 1.541195 (0.589669) | 2.092802 / 1.468490 (0.624312) | 0.833956 / 4.584777 (-3.750821) | 4.840859 / 3.745712 (1.095147) | 4.267812 / 5.269862 (-1.002050) | 2.663245 / 4.565676 (-1.902432) | 0.093195 / 0.424275 (-0.331080) | 0.007942 / 0.007607 (0.000335) | 0.651457 / 0.226044 (0.425413) | 6.782986 / 2.268929 (4.514058) | 3.103307 / 55.444624 (-52.341318) | 2.373933 / 6.876477 (-4.502544) | 2.571613 / 2.142072 (0.429540) | 0.981389 / 4.805227 (-3.823839) | 0.199019 / 6.500664 (-6.301645) | 0.065828 / 0.075469 (-0.009641) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.429778 / 1.841788 (-0.412009) | 20.967563 / 8.074308 (12.893255) | 19.329723 / 10.191392 (9.138331) | 0.222048 / 0.680424 (-0.458376) | 0.033507 / 0.534201 (-0.500694) | 0.436801 / 0.579283 (-0.142482) | 0.530197 / 0.434364 (0.095833) | 0.491532 / 0.540337 (-0.048805) | 0.718216 / 1.386936 (-0.668720) |\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) | 0.007798 / 0.011353 (-0.003555) | 0.004748 / 0.011008 (-0.006260) | 0.070847 / 0.038508 (0.032339) | 0.069338 / 0.023109 (0.046229) | 0.400890 / 0.275898 (0.124992) | 0.429482 / 0.323480 (0.106002) | 0.006469 / 0.007986 (-0.001517) | 0.003514 / 0.004328 (-0.000814) | 0.069049 / 0.004250 (0.064798) | 0.059800 / 0.037052 (0.022748) | 0.415644 / 0.258489 (0.157155) | 0.432562 / 0.293841 (0.138721) | 0.043778 / 0.128546 (-0.084768) | 0.015141 / 0.075646 (-0.060506) | 0.081521 / 0.419271 (-0.337750) | 0.054692 / 0.043533 (0.011160) | 0.404497 / 0.255139 (0.149358) | 0.419783 / 0.283200 (0.136583) | 0.029588 / 0.141683 (-0.112094) | 1.593506 / 1.452155 (0.141351) | 1.615977 / 1.492716 (0.123261) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270981 / 0.018006 (0.252975) | 0.522074 / 0.000490 (0.521584) | 0.026568 / 0.000200 (0.026368) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031551 / 0.037411 (-0.005861) | 0.086723 / 0.014526 (0.072197) | 0.103315 / 0.176557 (-0.073242) | 0.154692 / 0.737135 (-0.582443) | 0.099472 / 0.296338 (-0.196866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.570238 / 0.215209 (0.355029) | 5.655963 / 2.077655 (3.578308) | 2.662670 / 1.504120 (1.158550) | 2.380903 / 1.541195 (0.839709) | 2.409467 / 1.468490 (0.940977) | 0.828055 / 4.584777 (-3.756722) | 4.964698 / 3.745712 (1.218986) | 4.299995 / 5.269862 (-0.969867) | 2.824162 / 4.565676 (-1.741514) | 0.095872 / 0.424275 (-0.328403) | 0.007907 / 0.007607 (0.000300) | 0.701595 / 0.226044 (0.475551) | 7.131965 / 2.268929 (4.863036) | 3.250554 / 55.444624 (-52.194070) | 2.531916 / 6.876477 (-4.344561) | 2.717908 / 2.142072 (0.575835) | 1.014479 / 4.805227 (-3.790748) | 0.223804 / 6.500664 (-6.276861) | 0.071893 / 0.075469 (-0.003576) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541702 / 1.841788 (-0.300086) | 21.668219 / 8.074308 (13.593911) | 18.916032 / 10.191392 (8.724640) | 0.205915 / 0.680424 (-0.474508) | 0.026356 / 0.534201 (-0.507845) | 0.429122 / 0.579283 (-0.150161) | 0.506110 / 0.434364 (0.071746) | 0.510148 / 0.540337 (-0.030190) | 0.724699 / 1.386936 (-0.662237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4ca93ff86551b398c979862e7be7305725a240b \"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) | 0.006884 / 0.011353 (-0.004469) | 0.004492 / 0.011008 (-0.006516) | 0.085439 / 0.038508 (0.046931) | 0.083905 / 0.023109 (0.060796) | 0.313604 / 0.275898 (0.037706) | 0.354683 / 0.323480 (0.031203) | 0.006535 / 0.007986 (-0.001451) | 0.004318 / 0.004328 (-0.000011) | 0.066129 / 0.004250 (0.061879) | 0.057568 / 0.037052 (0.020516) | 0.317162 / 0.258489 (0.058672) | 0.372501 / 0.293841 (0.078660) | 0.031059 / 0.128546 (-0.097488) | 0.009013 / 0.075646 (-0.066634) | 0.288794 / 0.419271 (-0.130478) | 0.053326 / 0.043533 (0.009793) | 0.314318 / 0.255139 (0.059179) | 0.357505 / 0.283200 (0.074305) | 0.027020 / 0.141683 (-0.114663) | 1.530653 / 1.452155 (0.078498) | 1.599782 / 1.492716 (0.107066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278788 / 0.018006 (0.260782) | 0.626822 / 0.000490 (0.626333) | 0.003780 / 0.000200 (0.003580) | 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) | 0.031703 / 0.037411 (-0.005708) | 0.085654 / 0.014526 (0.071128) | 0.754858 / 0.176557 (0.578301) | 0.212251 / 0.737135 (-0.524885) | 0.171344 / 0.296338 (-0.124994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382291 / 0.215209 (0.167082) | 3.825612 / 2.077655 (1.747958) | 1.874553 / 1.504120 (0.370433) | 1.712574 / 1.541195 (0.171379) | 1.791479 / 1.468490 (0.322989) | 0.481005 / 4.584777 (-4.103772) | 3.530559 / 3.745712 (-0.215153) | 3.395305 / 5.269862 (-1.874557) | 2.133747 / 4.565676 (-2.431930) | 0.056139 / 0.424275 (-0.368136) | 0.007424 / 0.007607 (-0.000183) | 0.458321 / 0.226044 (0.232277) | 4.577665 / 2.268929 (2.308736) | 2.380233 / 55.444624 (-53.064392) | 2.004060 / 6.876477 (-4.872417) | 2.290712 / 2.142072 (0.148639) | 0.570157 / 4.805227 (-4.235070) | 0.131670 / 6.500664 (-6.368994) | 0.060684 / 0.075469 (-0.014785) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294929 / 1.841788 (-0.546858) | 21.386663 / 8.074308 (13.312355) | 14.389440 / 10.191392 (4.198048) | 0.171177 / 0.680424 (-0.509247) | 0.018660 / 0.534201 (-0.515541) | 0.394385 / 0.579283 (-0.184898) | 0.424942 / 0.434364 (-0.009422) | 0.463618 / 0.540337 (-0.076719) | 0.651499 / 1.386936 (-0.735437) |\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) | 0.007079 / 0.011353 (-0.004274) | 0.004615 / 0.011008 (-0.006393) | 0.066300 / 0.038508 (0.027792) | 0.092636 / 0.023109 (0.069527) | 0.399080 / 0.275898 (0.123182) | 0.429873 / 0.323480 (0.106393) | 0.006689 / 0.007986 (-0.001297) | 0.004358 / 0.004328 (0.000029) | 0.067155 / 0.004250 (0.062905) | 0.064040 / 0.037052 (0.026988) | 0.399905 / 0.258489 (0.141416) | 0.448237 / 0.293841 (0.154397) | 0.031985 / 0.128546 (-0.096561) | 0.009053 / 0.075646 (-0.066593) | 0.071904 / 0.419271 (-0.347368) | 0.048759 / 0.043533 (0.005227) | 0.386797 / 0.255139 (0.131658) | 0.411240 / 0.283200 (0.128040) | 0.028568 / 0.141683 (-0.113115) | 1.501037 / 1.452155 (0.048882) | 1.594560 / 1.492716 (0.101844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300756 / 0.018006 (0.282750) | 0.631220 / 0.000490 (0.630730) | 0.010163 / 0.000200 (0.009963) | 0.000144 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033716 / 0.037411 (-0.003695) | 0.093562 / 0.014526 (0.079037) | 0.106975 / 0.176557 (-0.069582) | 0.161919 / 0.737135 (-0.575216) | 0.113397 / 0.296338 (-0.182942) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410392 / 0.215209 (0.195183) | 4.094411 / 2.077655 (2.016756) | 2.085868 / 1.504120 (0.581748) | 1.959589 / 1.541195 (0.418394) | 2.096683 / 1.468490 (0.628193) | 0.494593 / 4.584777 (-4.090184) | 3.854302 / 3.745712 (0.108590) | 3.742303 / 5.269862 (-1.527558) | 2.379983 / 4.565676 (-2.185693) | 0.058640 / 0.424275 (-0.365635) | 0.008092 / 0.007607 (0.000484) | 0.486957 / 0.226044 (0.260912) | 4.855784 / 2.268929 (2.586855) | 2.654029 / 55.444624 (-52.790595) | 2.237627 / 6.876477 (-4.638850) | 2.536955 / 2.142072 (0.394882) | 0.622398 / 4.805227 (-4.182829) | 0.139212 / 6.500664 (-6.361452) | 0.062805 / 0.075469 (-0.012664) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374862 / 1.841788 (-0.466926) | 22.797015 / 8.074308 (14.722707) | 14.393995 / 10.191392 (4.202603) | 0.196603 / 0.680424 (-0.483821) | 0.018602 / 0.534201 (-0.515599) | 0.394568 / 0.579283 (-0.184715) | 0.408792 / 0.434364 (-0.025572) | 0.486706 / 0.540337 (-0.053631) | 0.652365 / 1.386936 (-0.734571) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5713299a88f527ea162a099c2bf2cbceada8fb86 \"CML watermark\")\n" ]

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[ "Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this.\r\n\r\nIn the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type. ", "Thanks for your help!" ]

[]

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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) | 0.006706 / 0.011353 (-0.004647) | 0.004016 / 0.011008 (-0.006992) | 0.083696 / 0.038508 (0.045188) | 0.074340 / 0.023109 (0.051230) | 0.327338 / 0.275898 (0.051440) | 0.366663 / 0.323480 (0.043183) | 0.004052 / 0.007986 (-0.003934) | 0.003423 / 0.004328 (-0.000906) | 0.064576 / 0.004250 (0.060326) | 0.055037 / 0.037052 (0.017985) | 0.325089 / 0.258489 (0.066600) | 0.379986 / 0.293841 (0.086145) | 0.031614 / 0.128546 (-0.096932) | 0.008553 / 0.075646 (-0.067094) | 0.287430 / 0.419271 (-0.131841) | 0.053032 / 0.043533 (0.009499) | 0.318990 / 0.255139 (0.063851) | 0.364426 / 0.283200 (0.081226) | 0.024926 / 0.141683 (-0.116757) | 1.461835 / 1.452155 (0.009680) | 1.557172 / 1.492716 (0.064456) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212430 / 0.018006 (0.194424) | 0.512891 / 0.000490 (0.512402) | 0.004772 / 0.000200 (0.004572) | 0.000132 / 0.000054 (0.000078) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027873 / 0.037411 (-0.009538) | 0.085598 / 0.014526 (0.071072) | 0.097330 / 0.176557 (-0.079226) | 0.152235 / 0.737135 (-0.584900) | 0.097787 / 0.296338 (-0.198552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384645 / 0.215209 (0.169436) | 3.841161 / 2.077655 (1.763506) | 1.863696 / 1.504120 (0.359577) | 1.685082 / 1.541195 (0.143887) | 1.772904 / 1.468490 (0.304414) | 0.480177 / 4.584777 (-4.104599) | 3.601537 / 3.745712 (-0.144175) | 3.273647 / 5.269862 (-1.996214) | 2.014415 / 4.565676 (-2.551261) | 0.056668 / 0.424275 (-0.367607) | 0.007257 / 0.007607 (-0.000350) | 0.458194 / 0.226044 (0.232150) | 4.577311 / 2.268929 (2.308382) | 2.333983 / 55.444624 (-53.110641) | 1.964508 / 6.876477 (-4.911969) | 2.193379 / 2.142072 (0.051307) | 0.577557 / 4.805227 (-4.227670) | 0.133899 / 6.500664 (-6.366765) | 0.060804 / 0.075469 (-0.014665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249490 / 1.841788 (-0.592298) | 19.791875 / 8.074308 (11.717567) | 14.418728 / 10.191392 (4.227336) | 0.167788 / 0.680424 (-0.512636) | 0.018993 / 0.534201 (-0.515208) | 0.396141 / 0.579283 (-0.183142) | 0.412427 / 0.434364 (-0.021937) | 0.456718 / 0.540337 (-0.083619) | 0.641383 / 1.386936 (-0.745553) |\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) | 0.006546 / 0.011353 (-0.004807) | 0.004059 / 0.011008 (-0.006949) | 0.064523 / 0.038508 (0.026015) | 0.074988 / 0.023109 (0.051878) | 0.388932 / 0.275898 (0.113034) | 0.424496 / 0.323480 (0.101016) | 0.005226 / 0.007986 (-0.002760) | 0.003409 / 0.004328 (-0.000920) | 0.064284 / 0.004250 (0.060034) | 0.056829 / 0.037052 (0.019777) | 0.386457 / 0.258489 (0.127968) | 0.428063 / 0.293841 (0.134222) | 0.031411 / 0.128546 (-0.097136) | 0.008577 / 0.075646 (-0.067070) | 0.070357 / 0.419271 (-0.348915) | 0.048920 / 0.043533 (0.005388) | 0.385197 / 0.255139 (0.130058) | 0.407167 / 0.283200 (0.123967) | 0.024469 / 0.141683 (-0.117214) | 1.482733 / 1.452155 (0.030578) | 1.539027 / 1.492716 (0.046311) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227532 / 0.018006 (0.209526) | 0.448792 / 0.000490 (0.448302) | 0.004139 / 0.000200 (0.003939) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031004 / 0.037411 (-0.006408) | 0.088163 / 0.014526 (0.073637) | 0.101452 / 0.176557 (-0.075105) | 0.152907 / 0.737135 (-0.584229) | 0.102325 / 0.296338 (-0.194014) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418092 / 0.215209 (0.202883) | 4.162277 / 2.077655 (2.084623) | 2.232987 / 1.504120 (0.728867) | 2.143583 / 1.541195 (0.602388) | 2.246142 / 1.468490 (0.777652) | 0.490181 / 4.584777 (-4.094596) | 3.631514 / 3.745712 (-0.114198) | 3.315025 / 5.269862 (-1.954837) | 2.101853 / 4.565676 (-2.463823) | 0.057905 / 0.424275 (-0.366370) | 0.007686 / 0.007607 (0.000079) | 0.489965 / 0.226044 (0.263921) | 4.894375 / 2.268929 (2.625447) | 2.655459 / 55.444624 (-52.789165) | 2.262211 / 6.876477 (-4.614266) | 2.505335 / 2.142072 (0.363263) | 0.591329 / 4.805227 (-4.213898) | 0.133554 / 6.500664 (-6.367110) | 0.061922 / 0.075469 (-0.013547) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.347483 / 1.841788 (-0.494304) | 20.027011 / 8.074308 (11.952703) | 14.430737 / 10.191392 (4.239345) | 0.165767 / 0.680424 (-0.514657) | 0.018460 / 0.534201 (-0.515741) | 0.393790 / 0.579283 (-0.185494) | 0.407213 / 0.434364 (-0.027151) | 0.474459 / 0.540337 (-0.065879) | 0.635054 / 1.386936 (-0.751882) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f575111481e2e2f4d4fc9180771797f69ebcc44 \"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) | 0.007652 / 0.011353 (-0.003701) | 0.004581 / 0.011008 (-0.006427) | 0.101629 / 0.038508 (0.063121) | 0.090233 / 0.023109 (0.067124) | 0.392789 / 0.275898 (0.116891) | 0.432163 / 0.323480 (0.108683) | 0.004694 / 0.007986 (-0.003292) | 0.003927 / 0.004328 (-0.000401) | 0.076533 / 0.004250 (0.072282) | 0.064442 / 0.037052 (0.027390) | 0.397539 / 0.258489 (0.139050) | 0.441323 / 0.293841 (0.147482) | 0.036278 / 0.128546 (-0.092268) | 0.009810 / 0.075646 (-0.065836) | 0.343537 / 0.419271 (-0.075734) | 0.060273 / 0.043533 (0.016740) | 0.395023 / 0.255139 (0.139884) | 0.427210 / 0.283200 (0.144011) | 0.031717 / 0.141683 (-0.109966) | 1.771221 / 1.452155 (0.319066) | 1.896336 / 1.492716 (0.403620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235081 / 0.018006 (0.217075) | 0.512781 / 0.000490 (0.512292) | 0.004920 / 0.000200 (0.004721) | 0.000097 / 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) | 0.033525 / 0.037411 (-0.003887) | 0.104416 / 0.014526 (0.089890) | 0.115695 / 0.176557 (-0.060861) | 0.182216 / 0.737135 (-0.554919) | 0.116259 / 0.296338 (-0.180079) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454817 / 0.215209 (0.239608) | 4.527753 / 2.077655 (2.450098) | 2.222273 / 1.504120 (0.718153) | 2.038448 / 1.541195 (0.497253) | 2.179444 / 1.468490 (0.710953) | 0.573665 / 4.584777 (-4.011112) | 4.504943 / 3.745712 (0.759231) | 3.848435 / 5.269862 (-1.421427) | 2.455185 / 4.565676 (-2.110491) | 0.067985 / 0.424275 (-0.356290) | 0.008719 / 0.007607 (0.001112) | 0.552405 / 0.226044 (0.326360) | 5.515251 / 2.268929 (3.246322) | 2.851557 / 55.444624 (-52.593067) | 2.463070 / 6.876477 (-4.413407) | 2.761596 / 2.142072 (0.619524) | 0.688561 / 4.805227 (-4.116667) | 0.159946 / 6.500664 (-6.340718) | 0.075435 / 0.075469 (-0.000034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.505178 / 1.841788 (-0.336610) | 23.555236 / 8.074308 (15.480928) | 17.272759 / 10.191392 (7.081367) | 0.206495 / 0.680424 (-0.473928) | 0.021869 / 0.534201 (-0.512332) | 0.469271 / 0.579283 (-0.110012) | 0.469200 / 0.434364 (0.034837) | 0.542437 / 0.540337 (0.002100) | 0.792864 / 1.386936 (-0.594072) |\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) | 0.008151 / 0.011353 (-0.003202) | 0.004992 / 0.011008 (-0.006016) | 0.079545 / 0.038508 (0.041037) | 0.100234 / 0.023109 (0.077125) | 0.492791 / 0.275898 (0.216893) | 0.511315 / 0.323480 (0.187835) | 0.006878 / 0.007986 (-0.001108) | 0.003807 / 0.004328 (-0.000522) | 0.080876 / 0.004250 (0.076625) | 0.076734 / 0.037052 (0.039681) | 0.518247 / 0.258489 (0.259758) | 0.524202 / 0.293841 (0.230361) | 0.039896 / 0.128546 (-0.088650) | 0.016581 / 0.075646 (-0.059065) | 0.101228 / 0.419271 (-0.318043) | 0.061990 / 0.043533 (0.018457) | 0.490611 / 0.255139 (0.235472) | 0.514930 / 0.283200 (0.231730) | 0.028680 / 0.141683 (-0.113002) | 1.966215 / 1.452155 (0.514061) | 2.047757 / 1.492716 (0.555040) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286807 / 0.018006 (0.268801) | 0.506448 / 0.000490 (0.505959) | 0.005867 / 0.000200 (0.005667) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037141 / 0.037411 (-0.000270) | 0.113232 / 0.014526 (0.098706) | 0.121201 / 0.176557 (-0.055356) | 0.185472 / 0.737135 (-0.551663) | 0.122896 / 0.296338 (-0.173442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.514491 / 0.215209 (0.299282) | 4.942457 / 2.077655 (2.864802) | 2.533519 / 1.504120 (1.029399) | 2.371011 / 1.541195 (0.829817) | 2.495604 / 1.468490 (1.027114) | 0.576224 / 4.584777 (-4.008553) | 4.368584 / 3.745712 (0.622872) | 3.885598 / 5.269862 (-1.384263) | 2.443596 / 4.565676 (-2.122080) | 0.068905 / 0.424275 (-0.355371) | 0.009171 / 0.007607 (0.001564) | 0.584977 / 0.226044 (0.358932) | 5.835220 / 2.268929 (3.566291) | 3.189037 / 55.444624 (-52.255588) | 2.753228 / 6.876477 (-4.123249) | 3.009062 / 2.142072 (0.866990) | 0.690179 / 4.805227 (-4.115048) | 0.157981 / 6.500664 (-6.342683) | 0.074518 / 0.075469 (-0.000951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599907 / 1.841788 (-0.241880) | 23.853903 / 8.074308 (15.779595) | 17.419796 / 10.191392 (7.228404) | 0.204974 / 0.680424 (-0.475450) | 0.022014 / 0.534201 (-0.512187) | 0.473379 / 0.579283 (-0.105905) | 0.461346 / 0.434364 (0.026982) | 0.564881 / 0.540337 (0.024543) | 0.752933 / 1.386936 (-0.634003) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f49c9ca993fa600fae0e327636d52657328e7ffb \"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) | 0.006547 / 0.011353 (-0.004805) | 0.004020 / 0.011008 (-0.006988) | 0.086828 / 0.038508 (0.048320) | 0.072924 / 0.023109 (0.049815) | 0.312847 / 0.275898 (0.036949) | 0.344605 / 0.323480 (0.021125) | 0.004117 / 0.007986 (-0.003868) | 0.004365 / 0.004328 (0.000037) | 0.066755 / 0.004250 (0.062505) | 0.053248 / 0.037052 (0.016195) | 0.315744 / 0.258489 (0.057255) | 0.362426 / 0.293841 (0.068585) | 0.030732 / 0.128546 (-0.097814) | 0.008516 / 0.075646 (-0.067130) | 0.289927 / 0.419271 (-0.129345) | 0.052115 / 0.043533 (0.008582) | 0.308026 / 0.255139 (0.052887) | 0.343115 / 0.283200 (0.059915) | 0.024131 / 0.141683 (-0.117551) | 1.464290 / 1.452155 (0.012135) | 1.559359 / 1.492716 (0.066642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216744 / 0.018006 (0.198738) | 0.473156 / 0.000490 (0.472666) | 0.004176 / 0.000200 (0.003977) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028500 / 0.037411 (-0.008911) | 0.083892 / 0.014526 (0.069366) | 0.131851 / 0.176557 (-0.044705) | 0.162202 / 0.737135 (-0.574933) | 0.127989 / 0.296338 (-0.168349) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404555 / 0.215209 (0.189346) | 4.035989 / 2.077655 (1.958334) | 2.025174 / 1.504120 (0.521054) | 1.835785 / 1.541195 (0.294590) | 1.909819 / 1.468490 (0.441329) | 0.475352 / 4.584777 (-4.109425) | 3.548055 / 3.745712 (-0.197657) | 3.234782 / 5.269862 (-2.035080) | 2.010305 / 4.565676 (-2.555371) | 0.056507 / 0.424275 (-0.367768) | 0.007259 / 0.007607 (-0.000348) | 0.482021 / 0.226044 (0.255977) | 4.818559 / 2.268929 (2.549631) | 2.528765 / 55.444624 (-52.915860) | 2.159804 / 6.876477 (-4.716673) | 2.380640 / 2.142072 (0.238567) | 0.585005 / 4.805227 (-4.220222) | 0.133811 / 6.500664 (-6.366853) | 0.060686 / 0.075469 (-0.014783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260902 / 1.841788 (-0.580886) | 19.500215 / 8.074308 (11.425907) | 14.164698 / 10.191392 (3.973306) | 0.172492 / 0.680424 (-0.507932) | 0.018221 / 0.534201 (-0.515980) | 0.392609 / 0.579283 (-0.186674) | 0.423265 / 0.434364 (-0.011099) | 0.454705 / 0.540337 (-0.085633) | 0.639856 / 1.386936 (-0.747080) |\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) | 0.006656 / 0.011353 (-0.004697) | 0.003903 / 0.011008 (-0.007106) | 0.063780 / 0.038508 (0.025272) | 0.076848 / 0.023109 (0.053739) | 0.379429 / 0.275898 (0.103531) | 0.442554 / 0.323480 (0.119074) | 0.005327 / 0.007986 (-0.002658) | 0.003318 / 0.004328 (-0.001010) | 0.064307 / 0.004250 (0.060056) | 0.057183 / 0.037052 (0.020131) | 0.398163 / 0.258489 (0.139674) | 0.448532 / 0.293841 (0.154691) | 0.031322 / 0.128546 (-0.097224) | 0.008462 / 0.075646 (-0.067184) | 0.070354 / 0.419271 (-0.348917) | 0.048420 / 0.043533 (0.004887) | 0.368304 / 0.255139 (0.113165) | 0.428786 / 0.283200 (0.145587) | 0.023921 / 0.141683 (-0.117762) | 1.499281 / 1.452155 (0.047126) | 1.554448 / 1.492716 (0.061731) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238830 / 0.018006 (0.220824) | 0.464196 / 0.000490 (0.463706) | 0.004812 / 0.000200 (0.004613) | 0.000098 / 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) | 0.031642 / 0.037411 (-0.005770) | 0.089205 / 0.014526 (0.074679) | 0.101577 / 0.176557 (-0.074980) | 0.154993 / 0.737135 (-0.582142) | 0.102935 / 0.296338 (-0.193403) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415218 / 0.215209 (0.200009) | 4.137711 / 2.077655 (2.060056) | 2.128757 / 1.504120 (0.624637) | 1.961086 / 1.541195 (0.419891) | 2.047552 / 1.468490 (0.579061) | 0.486953 / 4.584777 (-4.097824) | 3.587851 / 3.745712 (-0.157861) | 3.280771 / 5.269862 (-1.989090) | 2.016980 / 4.565676 (-2.548697) | 0.057284 / 0.424275 (-0.366991) | 0.007705 / 0.007607 (0.000097) | 0.492242 / 0.226044 (0.266197) | 4.923213 / 2.268929 (2.654285) | 2.672528 / 55.444624 (-52.772097) | 2.292862 / 6.876477 (-4.583614) | 2.517410 / 2.142072 (0.375337) | 0.614798 / 4.805227 (-4.190429) | 0.149642 / 6.500664 (-6.351023) | 0.062898 / 0.075469 (-0.012571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.323266 / 1.841788 (-0.518522) | 19.891504 / 8.074308 (11.817196) | 14.115069 / 10.191392 (3.923677) | 0.169859 / 0.680424 (-0.510564) | 0.018538 / 0.534201 (-0.515663) | 0.398456 / 0.579283 (-0.180827) | 0.410111 / 0.434364 (-0.024253) | 0.483198 / 0.540337 (-0.057139) | 0.639283 / 1.386936 (-0.747653) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e2194f2aab6aa98686a2069ee5201b69a53c14 \"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) | 0.007731 / 0.011353 (-0.003622) | 0.004064 / 0.011008 (-0.006944) | 0.095261 / 0.038508 (0.056753) | 0.081594 / 0.023109 (0.058485) | 0.390413 / 0.275898 (0.114515) | 0.415542 / 0.323480 (0.092063) | 0.006031 / 0.007986 (-0.001954) | 0.003817 / 0.004328 (-0.000512) | 0.066381 / 0.004250 (0.062131) | 0.058262 / 0.037052 (0.021210) | 0.383626 / 0.258489 (0.125137) | 0.443237 / 0.293841 (0.149396) | 0.034358 / 0.128546 (-0.094188) | 0.010002 / 0.075646 (-0.065644) | 0.317472 / 0.419271 (-0.101800) | 0.057428 / 0.043533 (0.013895) | 0.393929 / 0.255139 (0.138790) | 0.444572 / 0.283200 (0.161373) | 0.026295 / 0.141683 (-0.115388) | 1.603639 / 1.452155 (0.151484) | 1.707750 / 1.492716 (0.215034) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222171 / 0.018006 (0.204165) | 0.491762 / 0.000490 (0.491272) | 0.003389 / 0.000200 (0.003189) | 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) | 0.029420 / 0.037411 (-0.007991) | 0.086201 / 0.014526 (0.071676) | 0.100150 / 0.176557 (-0.076406) | 0.162338 / 0.737135 (-0.574797) | 0.099349 / 0.296338 (-0.196989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445976 / 0.215209 (0.230767) | 4.460197 / 2.077655 (2.382542) | 2.211767 / 1.504120 (0.707647) | 1.988740 / 1.541195 (0.447545) | 2.052289 / 1.468490 (0.583799) | 0.570321 / 4.584777 (-4.014456) | 4.148777 / 3.745712 (0.403065) | 3.750977 / 5.269862 (-1.518885) | 2.309443 / 4.565676 (-2.256234) | 0.064552 / 0.424275 (-0.359724) | 0.008167 / 0.007607 (0.000560) | 0.523283 / 0.226044 (0.297238) | 5.349347 / 2.268929 (3.080419) | 2.710292 / 55.444624 (-52.734332) | 2.344252 / 6.876477 (-4.532225) | 2.549903 / 2.142072 (0.407831) | 0.665942 / 4.805227 (-4.139285) | 0.154108 / 6.500664 (-6.346556) | 0.070181 / 0.075469 (-0.005289) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455733 / 1.841788 (-0.386054) | 21.846958 / 8.074308 (13.772650) | 15.133865 / 10.191392 (4.942473) | 0.199009 / 0.680424 (-0.481415) | 0.021299 / 0.534201 (-0.512902) | 0.421555 / 0.579283 (-0.157729) | 0.437639 / 0.434364 (0.003275) | 0.498568 / 0.540337 (-0.041769) | 0.719649 / 1.386936 (-0.667287) |\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) | 0.007858 / 0.011353 (-0.003495) | 0.004629 / 0.011008 (-0.006380) | 0.075701 / 0.038508 (0.037193) | 0.084425 / 0.023109 (0.061316) | 0.436650 / 0.275898 (0.160752) | 0.466046 / 0.323480 (0.142566) | 0.006042 / 0.007986 (-0.001944) | 0.003834 / 0.004328 (-0.000495) | 0.074729 / 0.004250 (0.070478) | 0.065983 / 0.037052 (0.028931) | 0.447239 / 0.258489 (0.188750) | 0.466728 / 0.293841 (0.172887) | 0.035814 / 0.128546 (-0.092733) | 0.009919 / 0.075646 (-0.065727) | 0.081151 / 0.419271 (-0.338120) | 0.057256 / 0.043533 (0.013723) | 0.435609 / 0.255139 (0.180470) | 0.448901 / 0.283200 (0.165701) | 0.026325 / 0.141683 (-0.115357) | 1.745658 / 1.452155 (0.293503) | 1.804137 / 1.492716 (0.311421) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302551 / 0.018006 (0.284544) | 0.498438 / 0.000490 (0.497948) | 0.038562 / 0.000200 (0.038362) | 0.000411 / 0.000054 (0.000356) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035573 / 0.037411 (-0.001839) | 0.104957 / 0.014526 (0.090431) | 0.117208 / 0.176557 (-0.059349) | 0.178935 / 0.737135 (-0.558200) | 0.124577 / 0.296338 (-0.171761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467076 / 0.215209 (0.251867) | 4.698852 / 2.077655 (2.621197) | 2.453389 / 1.504120 (0.949269) | 2.257378 / 1.541195 (0.716183) | 2.338615 / 1.468490 (0.870125) | 0.542379 / 4.584777 (-4.042398) | 4.066895 / 3.745712 (0.321183) | 3.689540 / 5.269862 (-1.580321) | 2.268997 / 4.565676 (-2.296679) | 0.064754 / 0.424275 (-0.359521) | 0.008866 / 0.007607 (0.001259) | 0.546732 / 0.226044 (0.320687) | 5.487765 / 2.268929 (3.218836) | 2.974126 / 55.444624 (-52.470498) | 2.585492 / 6.876477 (-4.290985) | 2.754417 / 2.142072 (0.612345) | 0.652045 / 4.805227 (-4.153183) | 0.145597 / 6.500664 (-6.355067) | 0.065415 / 0.075469 (-0.010054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553970 / 1.841788 (-0.287818) | 22.300954 / 8.074308 (14.226646) | 15.640990 / 10.191392 (5.449598) | 0.170903 / 0.680424 (-0.509521) | 0.021750 / 0.534201 (-0.512451) | 0.455316 / 0.579283 (-0.123967) | 0.455051 / 0.434364 (0.020687) | 0.536174 / 0.540337 (-0.004164) | 0.735930 / 1.386936 (-0.651006) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f68139846c26b43631bd235114854f4bf6cb9954 \"CML watermark\")\n" ]

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[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6103). 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) | 0.006528 / 0.011353 (-0.004825) | 0.003909 / 0.011008 (-0.007099) | 0.083954 / 0.038508 (0.045446) | 0.070513 / 0.023109 (0.047404) | 0.344362 / 0.275898 (0.068464) | 0.370278 / 0.323480 (0.046798) | 0.005395 / 0.007986 (-0.002591) | 0.003323 / 0.004328 (-0.001005) | 0.064538 / 0.004250 (0.060288) | 0.055616 / 0.037052 (0.018564) | 0.353590 / 0.258489 (0.095101) | 0.382159 / 0.293841 (0.088318) | 0.031133 / 0.128546 (-0.097414) | 0.008429 / 0.075646 (-0.067217) | 0.288665 / 0.419271 (-0.130606) | 0.052626 / 0.043533 (0.009093) | 0.347676 / 0.255139 (0.092537) | 0.363726 / 0.283200 (0.080526) | 0.021956 / 0.141683 (-0.119727) | 1.506091 / 1.452155 (0.053936) | 1.563940 / 1.492716 (0.071223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207658 / 0.018006 (0.189652) | 0.473411 / 0.000490 (0.472922) | 0.005437 / 0.000200 (0.005237) | 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) | 0.027769 / 0.037411 (-0.009643) | 0.082566 / 0.014526 (0.068040) | 0.092700 / 0.176557 (-0.083857) | 0.152589 / 0.737135 (-0.584546) | 0.093772 / 0.296338 (-0.202566) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401072 / 0.215209 (0.185863) | 3.997922 / 2.077655 (1.920267) | 2.028223 / 1.504120 (0.524103) | 1.845229 / 1.541195 (0.304035) | 1.883980 / 1.468490 (0.415489) | 0.485112 / 4.584777 (-4.099665) | 3.657048 / 3.745712 (-0.088664) | 4.998475 / 5.269862 (-0.271386) | 3.007417 / 4.565676 (-1.558259) | 0.057003 / 0.424275 (-0.367272) | 0.007270 / 0.007607 (-0.000338) | 0.482220 / 0.226044 (0.256176) | 4.817560 / 2.268929 (2.548631) | 2.484285 / 55.444624 (-52.960340) | 2.163327 / 6.876477 (-4.713149) | 2.326412 / 2.142072 (0.184339) | 0.600349 / 4.805227 (-4.204878) | 0.134245 / 6.500664 (-6.366419) | 0.060705 / 0.075469 (-0.014764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281440 / 1.841788 (-0.560347) | 19.165591 / 8.074308 (11.091283) | 14.007728 / 10.191392 (3.816336) | 0.168367 / 0.680424 (-0.512057) | 0.018149 / 0.534201 (-0.516052) | 0.391688 / 0.579283 (-0.187595) | 0.414528 / 0.434364 (-0.019836) | 0.456964 / 0.540337 (-0.083373) | 0.613807 / 1.386936 (-0.773129) |\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 (-0.004851) | 0.003956 / 0.011008 (-0.007052) | 0.064297 / 0.038508 (0.025789) | 0.073430 / 0.023109 (0.050321) | 0.364113 / 0.275898 (0.088215) | 0.389021 / 0.323480 (0.065541) | 0.005375 / 0.007986 (-0.002611) | 0.003363 / 0.004328 (-0.000966) | 0.064404 / 0.004250 (0.060153) | 0.056664 / 0.037052 (0.019612) | 0.365504 / 0.258489 (0.107015) | 0.398477 / 0.293841 (0.104636) | 0.031739 / 0.128546 (-0.096807) | 0.008663 / 0.075646 (-0.066984) | 0.070757 / 0.419271 (-0.348515) | 0.051014 / 0.043533 (0.007481) | 0.368287 / 0.255139 (0.113148) | 0.382941 / 0.283200 (0.099742) | 0.024642 / 0.141683 (-0.117041) | 1.516721 / 1.452155 (0.064567) | 1.557625 / 1.492716 (0.064908) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208248 / 0.018006 (0.190242) | 0.443560 / 0.000490 (0.443070) | 0.004004 / 0.000200 (0.003805) | 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) | 0.031116 / 0.037411 (-0.006295) | 0.086814 / 0.014526 (0.072288) | 0.099111 / 0.176557 (-0.077445) | 0.155032 / 0.737135 (-0.582104) | 0.098938 / 0.296338 (-0.197401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413080 / 0.215209 (0.197871) | 4.115546 / 2.077655 (2.037891) | 2.162073 / 1.504120 (0.657953) | 2.008107 / 1.541195 (0.466912) | 2.052317 / 1.468490 (0.583827) | 0.485158 / 4.584777 (-4.099619) | 3.617478 / 3.745712 (-0.128234) | 5.030564 / 5.269862 (-0.239298) | 2.787812 / 4.565676 (-1.777865) | 0.057466 / 0.424275 (-0.366809) | 0.007656 / 0.007607 (0.000049) | 0.490037 / 0.226044 (0.263993) | 4.887896 / 2.268929 (2.618968) | 2.639644 / 55.444624 (-52.804981) | 2.258051 / 6.876477 (-4.618426) | 2.417573 / 2.142072 (0.275500) | 0.604473 / 4.805227 (-4.200754) | 0.134770 / 6.500664 (-6.365894) | 0.061709 / 0.075469 (-0.013760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.342500 / 1.841788 (-0.499288) | 19.354990 / 8.074308 (11.280682) | 14.161975 / 10.191392 (3.970583) | 0.157084 / 0.680424 (-0.523339) | 0.018227 / 0.534201 (-0.515974) | 0.391819 / 0.579283 (-0.187464) | 0.399157 / 0.434364 (-0.035207) | 0.460582 / 0.540337 (-0.079756) | 0.612183 / 1.386936 (-0.774753) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b20f6a82410dd47e89585bb932616a22e0eaf2e6 \"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) | 0.009318 / 0.011353 (-0.002035) | 0.005515 / 0.011008 (-0.005493) | 0.108532 / 0.038508 (0.070024) | 0.103583 / 0.023109 (0.080473) | 0.419249 / 0.275898 (0.143351) | 0.453573 / 0.323480 (0.130093) | 0.006601 / 0.007986 (-0.001384) | 0.005297 / 0.004328 (0.000968) | 0.082737 / 0.004250 (0.078487) | 0.064708 / 0.037052 (0.027656) | 0.425679 / 0.258489 (0.167190) | 0.462028 / 0.293841 (0.168187) | 0.048104 / 0.128546 (-0.080442) | 0.014069 / 0.075646 (-0.061577) | 0.377780 / 0.419271 (-0.041491) | 0.067510 / 0.043533 (0.023977) | 0.422421 / 0.255139 (0.167282) | 0.447127 / 0.283200 (0.163927) | 0.037745 / 0.141683 (-0.103938) | 1.855306 / 1.452155 (0.403152) | 1.943876 / 1.492716 (0.451160) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280161 / 0.018006 (0.262155) | 0.598001 / 0.000490 (0.597512) | 0.001130 / 0.000200 (0.000930) | 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) | 0.036064 / 0.037411 (-0.001347) | 0.113256 / 0.014526 (0.098730) | 0.120598 / 0.176557 (-0.055959) | 0.191386 / 0.737135 (-0.545750) | 0.118125 / 0.296338 (-0.178214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616887 / 0.215209 (0.401678) | 6.085498 / 2.077655 (4.007844) | 2.639428 / 1.504120 (1.135308) | 2.215444 / 1.541195 (0.674249) | 2.311990 / 1.468490 (0.843500) | 0.820539 / 4.584777 (-3.764238) | 5.306010 / 3.745712 (1.560298) | 4.731726 / 5.269862 (-0.538136) | 3.053933 / 4.565676 (-1.511744) | 0.098862 / 0.424275 (-0.325413) | 0.009456 / 0.007607 (0.001849) | 0.725455 / 0.226044 (0.499411) | 7.367385 / 2.268929 (5.098457) | 3.464921 / 55.444624 (-51.979703) | 2.833868 / 6.876477 (-4.042608) | 3.033008 / 2.142072 (0.890935) | 1.036751 / 4.805227 (-3.768476) | 0.243646 / 6.500664 (-6.257018) | 0.081079 / 0.075469 (0.005610) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584695 / 1.841788 (-0.257093) | 25.150355 / 8.074308 (17.076047) | 21.826622 / 10.191392 (11.635230) | 0.212502 / 0.680424 (-0.467921) | 0.029865 / 0.534201 (-0.504335) | 0.496814 / 0.579283 (-0.082470) | 0.611959 / 0.434364 (0.177595) | 0.550434 / 0.540337 (0.010097) | 0.800897 / 1.386936 (-0.586039) |\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) | 0.009117 / 0.011353 (-0.002236) | 0.005236 / 0.011008 (-0.005772) | 0.082402 / 0.038508 (0.043894) | 0.090578 / 0.023109 (0.067468) | 0.487302 / 0.275898 (0.211404) | 0.523639 / 0.323480 (0.200159) | 0.006684 / 0.007986 (-0.001302) | 0.004306 / 0.004328 (-0.000023) | 0.083273 / 0.004250 (0.079023) | 0.068585 / 0.037052 (0.031532) | 0.487751 / 0.258489 (0.229262) | 0.538972 / 0.293841 (0.245131) | 0.048915 / 0.128546 (-0.079632) | 0.014312 / 0.075646 (-0.061335) | 0.091863 / 0.419271 (-0.327409) | 0.066114 / 0.043533 (0.022581) | 0.483552 / 0.255139 (0.228413) | 0.522250 / 0.283200 (0.239050) | 0.038533 / 0.141683 (-0.103150) | 1.803834 / 1.452155 (0.351680) | 1.891927 / 1.492716 (0.399211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.336662 / 0.018006 (0.318656) | 0.611408 / 0.000490 (0.610918) | 0.014310 / 0.000200 (0.014110) | 0.000152 / 0.000054 (0.000097) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034755 / 0.037411 (-0.002656) | 0.101008 / 0.014526 (0.086483) | 0.124530 / 0.176557 (-0.052026) | 0.179844 / 0.737135 (-0.557292) | 0.125027 / 0.296338 (-0.171312) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618341 / 0.215209 (0.403132) | 6.146848 / 2.077655 (4.069193) | 2.893305 / 1.504120 (1.389185) | 2.608722 / 1.541195 (1.067528) | 2.671276 / 1.468490 (1.202786) | 0.860096 / 4.584777 (-3.724681) | 5.440671 / 3.745712 (1.694959) | 4.776958 / 5.269862 (-0.492903) | 3.098300 / 4.565676 (-1.467376) | 0.098664 / 0.424275 (-0.325611) | 0.009270 / 0.007607 (0.001663) | 0.712780 / 0.226044 (0.486735) | 7.199721 / 2.268929 (4.930793) | 3.620723 / 55.444624 (-51.823902) | 3.052218 / 6.876477 (-3.824259) | 3.321093 / 2.142072 (1.179021) | 1.070992 / 4.805227 (-3.734235) | 0.224091 / 6.500664 (-6.276573) | 0.083395 / 0.075469 (0.007926) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.716867 / 1.841788 (-0.124921) | 25.534617 / 8.074308 (17.460309) | 25.221014 / 10.191392 (15.029621) | 0.248098 / 0.680424 (-0.432326) | 0.029659 / 0.534201 (-0.504542) | 0.492929 / 0.579283 (-0.086355) | 0.618253 / 0.434364 (0.183889) | 0.577108 / 0.540337 (0.036771) | 0.803188 / 1.386936 (-0.583748) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#584db360eed9155e173b199ba5fc037562b7b862 \"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) | 0.006517 / 0.011353 (-0.004836) | 0.004217 / 0.011008 (-0.006792) | 0.083162 / 0.038508 (0.044654) | 0.074476 / 0.023109 (0.051367) | 0.321193 / 0.275898 (0.045295) | 0.358348 / 0.323480 (0.034868) | 0.005531 / 0.007986 (-0.002455) | 0.003621 / 0.004328 (-0.000707) | 0.063819 / 0.004250 (0.059568) | 0.056524 / 0.037052 (0.019471) | 0.322145 / 0.258489 (0.063656) | 0.371415 / 0.293841 (0.077574) | 0.030612 / 0.128546 (-0.097934) | 0.008907 / 0.075646 (-0.066739) | 0.289451 / 0.419271 (-0.129821) | 0.051959 / 0.043533 (0.008426) | 0.317729 / 0.255139 (0.062590) | 0.339750 / 0.283200 (0.056550) | 0.022430 / 0.141683 (-0.119253) | 1.487661 / 1.452155 (0.035506) | 1.554916 / 1.492716 (0.062199) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296673 / 0.018006 (0.278667) | 0.599183 / 0.000490 (0.598694) | 0.002524 / 0.000200 (0.002324) | 0.000076 / 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) | 0.027898 / 0.037411 (-0.009514) | 0.080870 / 0.014526 (0.066344) | 0.094894 / 0.176557 (-0.081662) | 0.152350 / 0.737135 (-0.584785) | 0.095765 / 0.296338 (-0.200573) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415442 / 0.215209 (0.200233) | 4.161155 / 2.077655 (2.083500) | 2.117061 / 1.504120 (0.612941) | 1.937846 / 1.541195 (0.396651) | 1.979635 / 1.468490 (0.511145) | 0.488381 / 4.584777 (-4.096396) | 3.509836 / 3.745712 (-0.235876) | 3.833074 / 5.269862 (-1.436788) | 2.307536 / 4.565676 (-2.258141) | 0.057059 / 0.424275 (-0.367216) | 0.007366 / 0.007607 (-0.000241) | 0.487752 / 0.226044 (0.261708) | 4.869406 / 2.268929 (2.600478) | 2.594775 / 55.444624 (-52.849849) | 2.191712 / 6.876477 (-4.684765) | 2.413220 / 2.142072 (0.271147) | 0.584513 / 4.805227 (-4.220714) | 0.132162 / 6.500664 (-6.368502) | 0.061059 / 0.075469 (-0.014410) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245178 / 1.841788 (-0.596610) | 20.624563 / 8.074308 (12.550255) | 14.675545 / 10.191392 (4.484153) | 0.165838 / 0.680424 (-0.514586) | 0.018700 / 0.534201 (-0.515501) | 0.392475 / 0.579283 (-0.186808) | 0.399884 / 0.434364 (-0.034480) | 0.457478 / 0.540337 (-0.082859) | 0.624553 / 1.386936 (-0.762383) |\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) | 0.006716 / 0.011353 (-0.004637) | 0.004308 / 0.011008 (-0.006700) | 0.064495 / 0.038508 (0.025987) | 0.083194 / 0.023109 (0.060085) | 0.371994 / 0.275898 (0.096096) | 0.433045 / 0.323480 (0.109566) | 0.005535 / 0.007986 (-0.002450) | 0.003469 / 0.004328 (-0.000859) | 0.064342 / 0.004250 (0.060092) | 0.059362 / 0.037052 (0.022309) | 0.393819 / 0.258489 (0.135330) | 0.442591 / 0.293841 (0.148750) | 0.031594 / 0.128546 (-0.096952) | 0.008943 / 0.075646 (-0.066703) | 0.070689 / 0.419271 (-0.348582) | 0.049219 / 0.043533 (0.005686) | 0.361568 / 0.255139 (0.106429) | 0.417085 / 0.283200 (0.133886) | 0.025112 / 0.141683 (-0.116571) | 1.497204 / 1.452155 (0.045049) | 1.552781 / 1.492716 (0.060064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325254 / 0.018006 (0.307248) | 0.528399 / 0.000490 (0.527909) | 0.007429 / 0.000200 (0.007229) | 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) | 0.029908 / 0.037411 (-0.007504) | 0.087114 / 0.014526 (0.072588) | 0.103366 / 0.176557 (-0.073191) | 0.155145 / 0.737135 (-0.581990) | 0.103458 / 0.296338 (-0.192880) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409432 / 0.215209 (0.194223) | 4.093327 / 2.077655 (2.015673) | 2.154115 / 1.504120 (0.649995) | 1.953492 / 1.541195 (0.412297) | 2.021532 / 1.468490 (0.553042) | 0.478928 / 4.584777 (-4.105849) | 3.515287 / 3.745712 (-0.230426) | 4.976239 / 5.269862 (-0.293623) | 2.832803 / 4.565676 (-1.732873) | 0.057239 / 0.424275 (-0.367036) | 0.007718 / 0.007607 (0.000111) | 0.484102 / 0.226044 (0.258057) | 4.833020 / 2.268929 (2.564092) | 2.564550 / 55.444624 (-52.880074) | 2.268969 / 6.876477 (-4.607508) | 2.513308 / 2.142072 (0.371235) | 0.582822 / 4.805227 (-4.222406) | 0.133989 / 6.500664 (-6.366675) | 0.062078 / 0.075469 (-0.013391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.393766 / 1.841788 (-0.448021) | 20.224546 / 8.074308 (12.150238) | 14.359438 / 10.191392 (4.168046) | 0.166358 / 0.680424 (-0.514066) | 0.018840 / 0.534201 (-0.515361) | 0.393206 / 0.579283 (-0.186077) | 0.404220 / 0.434364 (-0.030144) | 0.462346 / 0.540337 (-0.077992) | 0.603078 / 1.386936 (-0.783858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) | 0.006835 / 0.011353 (-0.004518) | 0.004530 / 0.011008 (-0.006478) | 0.087506 / 0.038508 (0.048997) | 0.088289 / 0.023109 (0.065180) | 0.351575 / 0.275898 (0.075677) | 0.391873 / 0.323480 (0.068393) | 0.005627 / 0.007986 (-0.002359) | 0.003735 / 0.004328 (-0.000594) | 0.065747 / 0.004250 (0.061497) | 0.058779 / 0.037052 (0.021726) | 0.358076 / 0.258489 (0.099587) | 0.408466 / 0.293841 (0.114626) | 0.031369 / 0.128546 (-0.097178) | 0.008807 / 0.075646 (-0.066839) | 0.293253 / 0.419271 (-0.126019) | 0.052950 / 0.043533 (0.009417) | 0.350411 / 0.255139 (0.095272) | 0.384827 / 0.283200 (0.101627) | 0.026219 / 0.141683 (-0.115464) | 1.464290 / 1.452155 (0.012136) | 1.549688 / 1.492716 (0.056972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270354 / 0.018006 (0.252348) | 0.593436 / 0.000490 (0.592946) | 0.003872 / 0.000200 (0.003673) | 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) | 0.031625 / 0.037411 (-0.005787) | 0.092599 / 0.014526 (0.078073) | 0.104619 / 0.176557 (-0.071938) | 0.163183 / 0.737135 (-0.573952) | 0.103245 / 0.296338 (-0.193094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390213 / 0.215209 (0.175004) | 3.894519 / 2.077655 (1.816864) | 1.905739 / 1.504120 (0.401619) | 1.728873 / 1.541195 (0.187678) | 1.838692 / 1.468490 (0.370202) | 0.484730 / 4.584777 (-4.100047) | 3.706749 / 3.745712 (-0.038963) | 5.572311 / 5.269862 (0.302449) | 3.389949 / 4.565676 (-1.175727) | 0.057315 / 0.424275 (-0.366960) | 0.007475 / 0.007607 (-0.000132) | 0.464690 / 0.226044 (0.238645) | 4.622242 / 2.268929 (2.353314) | 2.380957 / 55.444624 (-53.063667) | 2.038225 / 6.876477 (-4.838251) | 2.358881 / 2.142072 (0.216809) | 0.606358 / 4.805227 (-4.198869) | 0.133584 / 6.500664 (-6.367080) | 0.061894 / 0.075469 (-0.013575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259575 / 1.841788 (-0.582213) | 20.915216 / 8.074308 (12.840908) | 14.971952 / 10.191392 (4.780560) | 0.160206 / 0.680424 (-0.520218) | 0.018675 / 0.534201 (-0.515526) | 0.396821 / 0.579283 (-0.182462) | 0.430982 / 0.434364 (-0.003382) | 0.452895 / 0.540337 (-0.087443) | 0.647869 / 1.386936 (-0.739067) |\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) | 0.007194 / 0.011353 (-0.004158) | 0.004340 / 0.011008 (-0.006669) | 0.065125 / 0.038508 (0.026617) | 0.096243 / 0.023109 (0.073134) | 0.374361 / 0.275898 (0.098463) | 0.411863 / 0.323480 (0.088383) | 0.005813 / 0.007986 (-0.002172) | 0.003615 / 0.004328 (-0.000713) | 0.064953 / 0.004250 (0.060703) | 0.063171 / 0.037052 (0.026119) | 0.376238 / 0.258489 (0.117749) | 0.415826 / 0.293841 (0.121985) | 0.031926 / 0.128546 (-0.096620) | 0.008821 / 0.075646 (-0.066825) | 0.072150 / 0.419271 (-0.347122) | 0.049484 / 0.043533 (0.005951) | 0.369691 / 0.255139 (0.114552) | 0.390669 / 0.283200 (0.107470) | 0.025732 / 0.141683 (-0.115950) | 1.493833 / 1.452155 (0.041679) | 1.601786 / 1.492716 (0.109070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284279 / 0.018006 (0.266272) | 0.585909 / 0.000490 (0.585419) | 0.000411 / 0.000200 (0.000211) | 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) | 0.033642 / 0.037411 (-0.003769) | 0.095328 / 0.014526 (0.080802) | 0.105810 / 0.176557 (-0.070746) | 0.159779 / 0.737135 (-0.577357) | 0.108938 / 0.296338 (-0.187400) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408112 / 0.215209 (0.192902) | 4.067035 / 2.077655 (1.989380) | 2.114504 / 1.504120 (0.610384) | 1.944027 / 1.541195 (0.402832) | 2.066117 / 1.468490 (0.597627) | 0.486441 / 4.584777 (-4.098336) | 3.622659 / 3.745712 (-0.123053) | 3.399310 / 5.269862 (-1.870552) | 2.183151 / 4.565676 (-2.382525) | 0.057490 / 0.424275 (-0.366785) | 0.007955 / 0.007607 (0.000347) | 0.490221 / 0.226044 (0.264177) | 4.887301 / 2.268929 (2.618373) | 2.679806 / 55.444624 (-52.764819) | 2.258992 / 6.876477 (-4.617484) | 2.592493 / 2.142072 (0.450420) | 0.606515 / 4.805227 (-4.198712) | 0.135645 / 6.500664 (-6.365019) | 0.063956 / 0.075469 (-0.011513) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.331304 / 1.841788 (-0.510483) | 21.458611 / 8.074308 (13.384303) | 14.898964 / 10.191392 (4.707572) | 0.172110 / 0.680424 (-0.508314) | 0.018791 / 0.534201 (-0.515409) | 0.395944 / 0.579283 (-0.183339) | 0.424526 / 0.434364 (-0.009838) | 0.462517 / 0.540337 (-0.077821) | 0.610139 / 1.386936 (-0.776797) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"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) | 0.005957 / 0.011353 (-0.005396) | 0.003581 / 0.011008 (-0.007427) | 0.079624 / 0.038508 (0.041116) | 0.058004 / 0.023109 (0.034895) | 0.309345 / 0.275898 (0.033447) | 0.346653 / 0.323480 (0.023173) | 0.005420 / 0.007986 (-0.002566) | 0.002906 / 0.004328 (-0.001423) | 0.061970 / 0.004250 (0.057720) | 0.047627 / 0.037052 (0.010575) | 0.314096 / 0.258489 (0.055607) | 0.361368 / 0.293841 (0.067527) | 0.027211 / 0.128546 (-0.101335) | 0.007853 / 0.075646 (-0.067793) | 0.260202 / 0.419271 (-0.159070) | 0.045308 / 0.043533 (0.001775) | 0.312150 / 0.255139 (0.057011) | 0.341085 / 0.283200 (0.057886) | 0.021302 / 0.141683 (-0.120381) | 1.430315 / 1.452155 (-0.021840) | 1.608989 / 1.492716 (0.116273) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185289 / 0.018006 (0.167283) | 0.423318 / 0.000490 (0.422828) | 0.005741 / 0.000200 (0.005541) | 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) | 0.023777 / 0.037411 (-0.013634) | 0.071937 / 0.014526 (0.057412) | 0.079406 / 0.176557 (-0.097151) | 0.143815 / 0.737135 (-0.593320) | 0.081648 / 0.296338 (-0.214690) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431514 / 0.215209 (0.216305) | 4.314471 / 2.077655 (2.236817) | 2.305167 / 1.504120 (0.801047) | 2.137894 / 1.541195 (0.596699) | 2.161034 / 1.468490 (0.692544) | 0.511701 / 4.584777 (-4.073076) | 3.098213 / 3.745712 (-0.647499) | 4.086837 / 5.269862 (-1.183024) | 2.517184 / 4.565676 (-2.048492) | 0.058272 / 0.424275 (-0.366003) | 0.006415 / 0.007607 (-0.001192) | 0.504792 / 0.226044 (0.278747) | 5.046758 / 2.268929 (2.777829) | 2.752049 / 55.444624 (-52.692576) | 2.407707 / 6.876477 (-4.468770) | 2.532162 / 2.142072 (0.390090) | 0.597562 / 4.805227 (-4.207666) | 0.125935 / 6.500664 (-6.374729) | 0.060837 / 0.075469 (-0.014632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257048 / 1.841788 (-0.584740) | 17.877849 / 8.074308 (9.803541) | 13.904805 / 10.191392 (3.713413) | 0.131647 / 0.680424 (-0.548776) | 0.016975 / 0.534201 (-0.517226) | 0.329651 / 0.579283 (-0.249633) | 0.354358 / 0.434364 (-0.080006) | 0.377545 / 0.540337 (-0.162792) | 0.545593 / 1.386936 (-0.841343) |\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) | 0.005839 / 0.011353 (-0.005514) | 0.003580 / 0.011008 (-0.007428) | 0.062204 / 0.038508 (0.023696) | 0.057943 / 0.023109 (0.034834) | 0.400165 / 0.275898 (0.124267) | 0.427911 / 0.323480 (0.104431) | 0.004412 / 0.007986 (-0.003574) | 0.002794 / 0.004328 (-0.001534) | 0.062933 / 0.004250 (0.058683) | 0.046243 / 0.037052 (0.009191) | 0.413640 / 0.258489 (0.155151) | 0.418592 / 0.293841 (0.124751) | 0.027020 / 0.128546 (-0.101526) | 0.007927 / 0.075646 (-0.067720) | 0.067581 / 0.419271 (-0.351691) | 0.041927 / 0.043533 (-0.001606) | 0.381863 / 0.255139 (0.126724) | 0.415711 / 0.283200 (0.132511) | 0.019827 / 0.141683 (-0.121856) | 1.464049 / 1.452155 (0.011894) | 1.528387 / 1.492716 (0.035671) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224999 / 0.018006 (0.206993) | 0.419167 / 0.000490 (0.418678) | 0.000363 / 0.000200 (0.000163) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024827 / 0.037411 (-0.012585) | 0.077134 / 0.014526 (0.062608) | 0.085142 / 0.176557 (-0.091414) | 0.137400 / 0.737135 (-0.599735) | 0.086434 / 0.296338 (-0.209905) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452716 / 0.215209 (0.237507) | 4.530610 / 2.077655 (2.452955) | 2.467309 / 1.504120 (0.963189) | 2.300441 / 1.541195 (0.759246) | 2.323475 / 1.468490 (0.854985) | 0.501847 / 4.584777 (-4.082930) | 3.079432 / 3.745712 (-0.666280) | 2.793107 / 5.269862 (-2.476755) | 1.835010 / 4.565676 (-2.730666) | 0.057698 / 0.424275 (-0.366577) | 0.006756 / 0.007607 (-0.000851) | 0.529062 / 0.226044 (0.303017) | 5.287822 / 2.268929 (3.018894) | 2.908411 / 55.444624 (-52.536214) | 2.571627 / 6.876477 (-4.304850) | 2.691188 / 2.142072 (0.549116) | 0.592289 / 4.805227 (-4.212938) | 0.126091 / 6.500664 (-6.374573) | 0.062312 / 0.075469 (-0.013157) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.328854 / 1.841788 (-0.512933) | 18.185628 / 8.074308 (10.111320) | 13.858781 / 10.191392 (3.667389) | 0.142421 / 0.680424 (-0.538003) | 0.016535 / 0.534201 (-0.517666) | 0.330839 / 0.579283 (-0.248444) | 0.346559 / 0.434364 (-0.087805) | 0.389153 / 0.540337 (-0.151185) | 0.516897 / 1.386936 (-0.870039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"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) | 0.006543 / 0.011353 (-0.004810) | 0.003894 / 0.011008 (-0.007115) | 0.084742 / 0.038508 (0.046234) | 0.072942 / 0.023109 (0.049833) | 0.310722 / 0.275898 (0.034824) | 0.346806 / 0.323480 (0.023326) | 0.005373 / 0.007986 (-0.002613) | 0.003270 / 0.004328 (-0.001059) | 0.064379 / 0.004250 (0.060128) | 0.054876 / 0.037052 (0.017824) | 0.316794 / 0.258489 (0.058305) | 0.350353 / 0.293841 (0.056512) | 0.030683 / 0.128546 (-0.097863) | 0.008275 / 0.075646 (-0.067371) | 0.288747 / 0.419271 (-0.130525) | 0.051892 / 0.043533 (0.008359) | 0.315060 / 0.255139 (0.059921) | 0.331664 / 0.283200 (0.048464) | 0.023334 / 0.141683 (-0.118349) | 1.499734 / 1.452155 (0.047579) | 1.542006 / 1.492716 (0.049290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210488 / 0.018006 (0.192482) | 0.462187 / 0.000490 (0.461697) | 0.001280 / 0.000200 (0.001080) | 0.000076 / 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) | 0.027812 / 0.037411 (-0.009599) | 0.082492 / 0.014526 (0.067966) | 0.096504 / 0.176557 (-0.080053) | 0.158164 / 0.737135 (-0.578972) | 0.096678 / 0.296338 (-0.199661) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403317 / 0.215209 (0.188108) | 4.008367 / 2.077655 (1.930713) | 2.033067 / 1.504120 (0.528947) | 1.869484 / 1.541195 (0.328290) | 1.947450 / 1.468490 (0.478960) | 0.494048 / 4.584777 (-4.090729) | 3.631673 / 3.745712 (-0.114039) | 5.322167 / 5.269862 (0.052306) | 3.125570 / 4.565676 (-1.440107) | 0.057341 / 0.424275 (-0.366934) | 0.007318 / 0.007607 (-0.000289) | 0.483990 / 0.226044 (0.257945) | 4.830573 / 2.268929 (2.561645) | 2.543267 / 55.444624 (-52.901358) | 2.217890 / 6.876477 (-4.658587) | 2.435111 / 2.142072 (0.293038) | 0.597920 / 4.805227 (-4.207307) | 0.132690 / 6.500664 (-6.367974) | 0.060160 / 0.075469 (-0.015309) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247656 / 1.841788 (-0.594131) | 19.436984 / 8.074308 (11.362675) | 14.504249 / 10.191392 (4.312857) | 0.167444 / 0.680424 (-0.512980) | 0.018214 / 0.534201 (-0.515987) | 0.394790 / 0.579283 (-0.184493) | 0.413770 / 0.434364 (-0.020594) | 0.474290 / 0.540337 (-0.066048) | 0.646782 / 1.386936 (-0.740154) |\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) | 0.006575 / 0.011353 (-0.004778) | 0.003924 / 0.011008 (-0.007084) | 0.064402 / 0.038508 (0.025893) | 0.072569 / 0.023109 (0.049460) | 0.361981 / 0.275898 (0.086083) | 0.398660 / 0.323480 (0.075180) | 0.005380 / 0.007986 (-0.002605) | 0.003355 / 0.004328 (-0.000974) | 0.065173 / 0.004250 (0.060923) | 0.057120 / 0.037052 (0.020067) | 0.366347 / 0.258489 (0.107858) | 0.402723 / 0.293841 (0.108882) | 0.031258 / 0.128546 (-0.097288) | 0.008499 / 0.075646 (-0.067147) | 0.070558 / 0.419271 (-0.348714) | 0.050089 / 0.043533 (0.006556) | 0.361280 / 0.255139 (0.106141) | 0.384497 / 0.283200 (0.101297) | 0.024789 / 0.141683 (-0.116893) | 1.492577 / 1.452155 (0.040422) | 1.572242 / 1.492716 (0.079525) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228054 / 0.018006 (0.210048) | 0.448317 / 0.000490 (0.447828) | 0.000368 / 0.000200 (0.000168) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030575 / 0.037411 (-0.006836) | 0.088604 / 0.014526 (0.074078) | 0.099317 / 0.176557 (-0.077239) | 0.152455 / 0.737135 (-0.584680) | 0.100444 / 0.296338 (-0.195894) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411876 / 0.215209 (0.196667) | 4.108187 / 2.077655 (2.030532) | 2.096371 / 1.504120 (0.592251) | 1.923532 / 1.541195 (0.382337) | 1.998345 / 1.468490 (0.529855) | 0.483853 / 4.584777 (-4.100924) | 3.622433 / 3.745712 (-0.123279) | 3.254430 / 5.269862 (-2.015431) | 2.044342 / 4.565676 (-2.521334) | 0.056756 / 0.424275 (-0.367519) | 0.007720 / 0.007607 (0.000113) | 0.487656 / 0.226044 (0.261612) | 4.882024 / 2.268929 (2.613096) | 2.585008 / 55.444624 (-52.859616) | 2.229251 / 6.876477 (-4.647225) | 2.408318 / 2.142072 (0.266246) | 0.617537 / 4.805227 (-4.187691) | 0.132102 / 6.500664 (-6.368562) | 0.061694 / 0.075469 (-0.013775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362077 / 1.841788 (-0.479711) | 19.750714 / 8.074308 (11.676406) | 14.545299 / 10.191392 (4.353907) | 0.168666 / 0.680424 (-0.511758) | 0.018606 / 0.534201 (-0.515595) | 0.394760 / 0.579283 (-0.184523) | 0.410030 / 0.434364 (-0.024334) | 0.464742 / 0.540337 (-0.075596) | 0.610881 / 1.386936 (-0.776055) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) | 0.005836 / 0.011353 (-0.005517) | 0.003493 / 0.011008 (-0.007515) | 0.079877 / 0.038508 (0.041369) | 0.057299 / 0.023109 (0.034190) | 0.332945 / 0.275898 (0.057047) | 0.386615 / 0.323480 (0.063135) | 0.004437 / 0.007986 (-0.003548) | 0.002758 / 0.004328 (-0.001571) | 0.062668 / 0.004250 (0.058418) | 0.046135 / 0.037052 (0.009083) | 0.346160 / 0.258489 (0.087671) | 0.416720 / 0.293841 (0.122879) | 0.026678 / 0.128546 (-0.101868) | 0.007893 / 0.075646 (-0.067753) | 0.260427 / 0.419271 (-0.158845) | 0.044240 / 0.043533 (0.000707) | 0.328101 / 0.255139 (0.072963) | 0.380072 / 0.283200 (0.096872) | 0.020813 / 0.141683 (-0.120870) | 1.400202 / 1.452155 (-0.051952) | 1.475627 / 1.492716 (-0.017089) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174479 / 0.018006 (0.156473) | 0.413810 / 0.000490 (0.413320) | 0.003059 / 0.000200 (0.002860) | 0.000212 / 0.000054 (0.000157) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023422 / 0.037411 (-0.013990) | 0.071519 / 0.014526 (0.056993) | 0.080555 / 0.176557 (-0.096001) | 0.143825 / 0.737135 (-0.593311) | 0.081182 / 0.296338 (-0.215157) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406858 / 0.215209 (0.191648) | 4.161475 / 2.077655 (2.083820) | 1.991800 / 1.504120 (0.487680) | 1.811224 / 1.541195 (0.270030) | 1.828809 / 1.468490 (0.360318) | 0.504882 / 4.584777 (-4.079895) | 2.985010 / 3.745712 (-0.760703) | 3.984856 / 5.269862 (-1.285006) | 2.477936 / 4.565676 (-2.087740) | 0.057553 / 0.424275 (-0.366722) | 0.006436 / 0.007607 (-0.001172) | 0.488061 / 0.226044 (0.262016) | 4.805501 / 2.268929 (2.536573) | 2.446508 / 55.444624 (-52.998116) | 2.051406 / 6.876477 (-4.825071) | 2.177696 / 2.142072 (0.035623) | 0.588021 / 4.805227 (-4.217207) | 0.125118 / 6.500664 (-6.375546) | 0.060885 / 0.075469 (-0.014584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197130 / 1.841788 (-0.644658) | 17.867450 / 8.074308 (9.793142) | 13.536895 / 10.191392 (3.345503) | 0.137603 / 0.680424 (-0.542821) | 0.016706 / 0.534201 (-0.517495) | 0.327642 / 0.579283 (-0.251641) | 0.347201 / 0.434364 (-0.087163) | 0.379570 / 0.540337 (-0.160768) | 0.517825 / 1.386936 (-0.869111) |\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) | 0.005769 / 0.011353 (-0.005584) | 0.003414 / 0.011008 (-0.007594) | 0.063198 / 0.038508 (0.024690) | 0.056020 / 0.023109 (0.032911) | 0.393333 / 0.275898 (0.117435) | 0.421166 / 0.323480 (0.097686) | 0.004360 / 0.007986 (-0.003626) | 0.002860 / 0.004328 (-0.001469) | 0.062712 / 0.004250 (0.058461) | 0.045363 / 0.037052 (0.008311) | 0.413156 / 0.258489 (0.154667) | 0.422897 / 0.293841 (0.129056) | 0.027092 / 0.128546 (-0.101455) | 0.007960 / 0.075646 (-0.067687) | 0.068531 / 0.419271 (-0.350740) | 0.041402 / 0.043533 (-0.002131) | 0.377008 / 0.255139 (0.121869) | 0.409142 / 0.283200 (0.125942) | 0.019707 / 0.141683 (-0.121976) | 1.440556 / 1.452155 (-0.011599) | 1.487403 / 1.492716 (-0.005314) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224355 / 0.018006 (0.206349) | 0.397855 / 0.000490 (0.397365) | 0.000363 / 0.000200 (0.000163) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025107 / 0.037411 (-0.012305) | 0.076404 / 0.014526 (0.061878) | 0.083194 / 0.176557 (-0.093362) | 0.135347 / 0.737135 (-0.601789) | 0.084786 / 0.296338 (-0.211553) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433024 / 0.215209 (0.217815) | 4.323879 / 2.077655 (2.246224) | 2.263004 / 1.504120 (0.758884) | 2.072053 / 1.541195 (0.530858) | 2.113916 / 1.468490 (0.645426) | 0.502742 / 4.584777 (-4.082035) | 3.001716 / 3.745712 (-0.743996) | 2.777960 / 5.269862 (-2.491901) | 1.826514 / 4.565676 (-2.739162) | 0.057735 / 0.424275 (-0.366540) | 0.006671 / 0.007607 (-0.000937) | 0.503347 / 0.226044 (0.277303) | 5.037308 / 2.268929 (2.768380) | 2.679146 / 55.444624 (-52.765478) | 2.410899 / 6.876477 (-4.465577) | 2.467341 / 2.142072 (0.325268) | 0.589824 / 4.805227 (-4.215403) | 0.125529 / 6.500664 (-6.375135) | 0.061950 / 0.075469 (-0.013520) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304128 / 1.841788 (-0.537659) | 17.950215 / 8.074308 (9.875907) | 13.673768 / 10.191392 (3.482376) | 0.129863 / 0.680424 (-0.550561) | 0.016720 / 0.534201 (-0.517481) | 0.329795 / 0.579283 (-0.249488) | 0.339057 / 0.434364 (-0.095307) | 0.382279 / 0.540337 (-0.158059) | 0.507337 / 1.386936 (-0.879599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef05b6f99a2b19990c6f5e4e28d95d28781570db \"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) | 0.006199 / 0.011353 (-0.005154) | 0.003749 / 0.011008 (-0.007259) | 0.080600 / 0.038508 (0.042092) | 0.061017 / 0.023109 (0.037908) | 0.319966 / 0.275898 (0.044067) | 0.354937 / 0.323480 (0.031457) | 0.004854 / 0.007986 (-0.003131) | 0.002996 / 0.004328 (-0.001333) | 0.063100 / 0.004250 (0.058849) | 0.050063 / 0.037052 (0.013011) | 0.316744 / 0.258489 (0.058255) | 0.358001 / 0.293841 (0.064160) | 0.027503 / 0.128546 (-0.101043) | 0.007876 / 0.075646 (-0.067771) | 0.262211 / 0.419271 (-0.157060) | 0.045717 / 0.043533 (0.002184) | 0.317188 / 0.255139 (0.062049) | 0.342404 / 0.283200 (0.059205) | 0.020194 / 0.141683 (-0.121489) | 1.498672 / 1.452155 (0.046517) | 1.545479 / 1.492716 (0.052762) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210985 / 0.018006 (0.192979) | 0.433592 / 0.000490 (0.433102) | 0.002864 / 0.000200 (0.002664) | 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) | 0.023463 / 0.037411 (-0.013948) | 0.073375 / 0.014526 (0.058850) | 0.083082 / 0.176557 (-0.093475) | 0.142583 / 0.737135 (-0.594552) | 0.084267 / 0.296338 (-0.212071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412890 / 0.215209 (0.197681) | 4.131421 / 2.077655 (2.053766) | 1.969164 / 1.504120 (0.465044) | 1.772379 / 1.541195 (0.231185) | 1.834154 / 1.468490 (0.365664) | 0.496290 / 4.584777 (-4.088487) | 3.056504 / 3.745712 (-0.689208) | 3.400962 / 5.269862 (-1.868900) | 2.120575 / 4.565676 (-2.445101) | 0.056932 / 0.424275 (-0.367343) | 0.006412 / 0.007607 (-0.001195) | 0.484521 / 0.226044 (0.258477) | 4.817474 / 2.268929 (2.548545) | 2.464075 / 55.444624 (-52.980549) | 2.085056 / 6.876477 (-4.791421) | 2.324516 / 2.142072 (0.182444) | 0.592013 / 4.805227 (-4.213214) | 0.132232 / 6.500664 (-6.368432) | 0.062825 / 0.075469 (-0.012645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.228080 / 1.841788 (-0.613708) | 18.555385 / 8.074308 (10.481077) | 13.939565 / 10.191392 (3.748173) | 0.145979 / 0.680424 (-0.534445) | 0.016823 / 0.534201 (-0.517377) | 0.330569 / 0.579283 (-0.248714) | 0.358094 / 0.434364 (-0.076270) | 0.384642 / 0.540337 (-0.155696) | 0.518347 / 1.386936 (-0.868589) |\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) | 0.006198 / 0.011353 (-0.005155) | 0.003670 / 0.011008 (-0.007338) | 0.062502 / 0.038508 (0.023994) | 0.064339 / 0.023109 (0.041229) | 0.428414 / 0.275898 (0.152516) | 0.463899 / 0.323480 (0.140420) | 0.005524 / 0.007986 (-0.002462) | 0.002915 / 0.004328 (-0.001413) | 0.062521 / 0.004250 (0.058270) | 0.051182 / 0.037052 (0.014130) | 0.431144 / 0.258489 (0.172655) | 0.469465 / 0.293841 (0.175624) | 0.027463 / 0.128546 (-0.101083) | 0.007974 / 0.075646 (-0.067673) | 0.068029 / 0.419271 (-0.351242) | 0.042123 / 0.043533 (-0.001409) | 0.428667 / 0.255139 (0.173528) | 0.455917 / 0.283200 (0.172717) | 0.023264 / 0.141683 (-0.118419) | 1.426986 / 1.452155 (-0.025168) | 1.500049 / 1.492716 (0.007332) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207264 / 0.018006 (0.189258) | 0.440738 / 0.000490 (0.440248) | 0.000802 / 0.000200 (0.000602) | 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) | 0.026245 / 0.037411 (-0.011166) | 0.078749 / 0.014526 (0.064223) | 0.087873 / 0.176557 (-0.088684) | 0.141518 / 0.737135 (-0.595617) | 0.089811 / 0.296338 (-0.206527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418955 / 0.215209 (0.203746) | 4.177881 / 2.077655 (2.100226) | 2.162678 / 1.504120 (0.658558) | 1.998969 / 1.541195 (0.457775) | 2.066720 / 1.468490 (0.598230) | 0.496850 / 4.584777 (-4.087927) | 3.041179 / 3.745712 (-0.704534) | 4.126039 / 5.269862 (-1.143823) | 2.740507 / 4.565676 (-1.825169) | 0.058025 / 0.424275 (-0.366250) | 0.006846 / 0.007607 (-0.000761) | 0.493281 / 0.226044 (0.267237) | 4.930196 / 2.268929 (2.661268) | 2.685152 / 55.444624 (-52.759472) | 2.378247 / 6.876477 (-4.498230) | 2.469103 / 2.142072 (0.327031) | 0.585346 / 4.805227 (-4.219882) | 0.126099 / 6.500664 (-6.374565) | 0.062946 / 0.075469 (-0.012523) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313892 / 1.841788 (-0.527896) | 19.177117 / 8.074308 (11.102809) | 14.081321 / 10.191392 (3.889929) | 0.133948 / 0.680424 (-0.546476) | 0.017128 / 0.534201 (-0.517073) | 0.332241 / 0.579283 (-0.247042) | 0.373218 / 0.434364 (-0.061145) | 0.395308 / 0.540337 (-0.145030) | 0.529883 / 1.386936 (-0.857053) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16f7c7677942083436062b904b74643accb9bcac \"CML watermark\")\n" ]

[]

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[ "Thanks for reporting, @bilelomrani1.\r\n\r\nWe are fixing it. ", "We have fixed it. We are planning to do a patch release today." ]

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[ "Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3.\r\n\r\nIts [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception\r\n\r\n```python\r\ntry:\r\n example = self.info.features.encode_example(record) if self.info.features is not None else record\r\nexcept Exception as e:\r\n print(record)\r\n```\r\n\r\n⬇️\r\n\r\n```\r\n{'<?xml version=\"1.0\" encoding=\"UTF-8\"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'}\r\n```", "I'm getting same errors when loading this dataset", "I have figured it out. there was an option of **parquet formated files** i downloaded some from there. ", "this dataset is unfortunately no longer public", "Thanks for reporting, @IqraBaluch.\r\n\r\nWe contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.", "If anyone still needs this dataset, you could find it on kaggle here : https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset", "Thanks @Maryam-Mostafa ", "@albertvillanova don't tell 'em, we have figured it out. XD", "I noticed that some book data is missing, we can only get Books_v1_02 data. \r\nIs there any way we can get the Books_v1_00 and Books_v1_01? \r\nReally appreciate !!!", "@albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> I am not sure why the case distinction between local and remote filesystems is even necessary for DatasetDict when saving to disk. Imo this could be removed (leaving only fs.makedirs(dataset_dict_path, exist_ok=True)).\r\n\r\nIndeed. But it's better to address this in a separate PR.", "<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) | 0.007696 / 0.011353 (-0.003656) | 0.004497 / 0.011008 (-0.006511) | 0.099302 / 0.038508 (0.060794) | 0.083360 / 0.023109 (0.060251) | 0.393483 / 0.275898 (0.117585) | 0.450505 / 0.323480 (0.127025) | 0.004610 / 0.007986 (-0.003376) | 0.003637 / 0.004328 (-0.000692) | 0.075752 / 0.004250 (0.071501) | 0.064034 / 0.037052 (0.026982) | 0.397785 / 0.258489 (0.139296) | 0.462948 / 0.293841 (0.169107) | 0.035902 / 0.128546 (-0.092644) | 0.009640 / 0.075646 (-0.066007) | 0.342299 / 0.419271 (-0.076973) | 0.059586 / 0.043533 (0.016053) | 0.404918 / 0.255139 (0.149779) | 0.440889 / 0.283200 (0.157690) | 0.028981 / 0.141683 (-0.112702) | 1.775380 / 1.452155 (0.323226) | 1.866663 / 1.492716 (0.373946) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249080 / 0.018006 (0.231074) | 0.456460 / 0.000490 (0.455970) | 0.028145 / 0.000200 (0.027945) | 0.000402 / 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) | 0.030373 / 0.037411 (-0.007038) | 0.088562 / 0.014526 (0.074036) | 0.122837 / 0.176557 (-0.053720) | 0.167122 / 0.737135 (-0.570014) | 0.103953 / 0.296338 (-0.192385) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431714 / 0.215209 (0.216505) | 4.182224 / 2.077655 (2.104570) | 2.025650 / 1.504120 (0.521530) | 1.838905 / 1.541195 (0.297710) | 1.868710 / 1.468490 (0.400219) | 0.538422 / 4.584777 (-4.046355) | 4.038941 / 3.745712 (0.293228) | 3.717695 / 5.269862 (-1.552166) | 2.313197 / 4.565676 (-2.252479) | 0.061060 / 0.424275 (-0.363215) | 0.008248 / 0.007607 (0.000641) | 0.497438 / 0.226044 (0.271394) | 4.946663 / 2.268929 (2.677734) | 2.571841 / 55.444624 (-52.872784) | 2.155894 / 6.876477 (-4.720583) | 2.183180 / 2.142072 (0.041107) | 0.639810 / 4.805227 (-4.165417) | 0.153273 / 6.500664 (-6.347391) | 0.068606 / 0.075469 (-0.006863) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.376152 / 1.841788 (-0.465635) | 20.747088 / 8.074308 (12.672780) | 15.200311 / 10.191392 (5.008919) | 0.166380 / 0.680424 (-0.514043) | 0.021417 / 0.534201 (-0.512784) | 0.435677 / 0.579283 (-0.143606) | 0.460412 / 0.434364 (0.026048) | 0.509978 / 0.540337 (-0.030359) | 0.702506 / 1.386936 (-0.684430) |\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) | 0.007378 / 0.011353 (-0.003975) | 0.003938 / 0.011008 (-0.007070) | 0.067095 / 0.038508 (0.028587) | 0.082252 / 0.023109 (0.059143) | 0.420317 / 0.275898 (0.144419) | 0.477496 / 0.323480 (0.154017) | 0.006259 / 0.007986 (-0.001727) | 0.003513 / 0.004328 (-0.000816) | 0.072107 / 0.004250 (0.067856) | 0.061737 / 0.037052 (0.024684) | 0.444142 / 0.258489 (0.185653) | 0.488926 / 0.293841 (0.195085) | 0.033623 / 0.128546 (-0.094923) | 0.008091 / 0.075646 (-0.067555) | 0.073997 / 0.419271 (-0.345274) | 0.051295 / 0.043533 (0.007762) | 0.442551 / 0.255139 (0.187412) | 0.462713 / 0.283200 (0.179513) | 0.023115 / 0.141683 (-0.118568) | 1.645759 / 1.452155 (0.193604) | 1.758121 / 1.492716 (0.265405) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233450 / 0.018006 (0.215444) | 0.445384 / 0.000490 (0.444894) | 0.006412 / 0.000200 (0.006212) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032446 / 0.037411 (-0.004965) | 0.098515 / 0.014526 (0.083989) | 0.109095 / 0.176557 (-0.067462) | 0.167645 / 0.737135 (-0.569490) | 0.110403 / 0.296338 (-0.185936) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470189 / 0.215209 (0.254980) | 4.663224 / 2.077655 (2.585569) | 2.504474 / 1.504120 (1.000354) | 2.282867 / 1.541195 (0.741673) | 2.331598 / 1.468490 (0.863108) | 0.554421 / 4.584777 (-4.030356) | 4.078657 / 3.745712 (0.332945) | 3.516339 / 5.269862 (-1.753523) | 2.239134 / 4.565676 (-2.326542) | 0.062690 / 0.424275 (-0.361585) | 0.008406 / 0.007607 (0.000799) | 0.533827 / 0.226044 (0.307782) | 5.423984 / 2.268929 (3.155055) | 2.972784 / 55.444624 (-52.471840) | 2.699056 / 6.876477 (-4.177421) | 2.844403 / 2.142072 (0.702331) | 0.639194 / 4.805227 (-4.166033) | 0.142097 / 6.500664 (-6.358567) | 0.064646 / 0.075469 (-0.010823) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544640 / 1.841788 (-0.297148) | 21.453429 / 8.074308 (13.379121) | 15.610723 / 10.191392 (5.419331) | 0.207796 / 0.680424 (-0.472628) | 0.021912 / 0.534201 (-0.512289) | 0.430472 / 0.579283 (-0.148811) | 0.467530 / 0.434364 (0.033166) | 0.541339 / 0.540337 (0.001002) | 0.721976 / 1.386936 (-0.664960) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#691c4bc65888005f3aadea5c104fdbc87694882d \"CML watermark\")\n" ]

[]

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[ "Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it." ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint.", "Hi @mariosasko and/or @lhoestq, friendly pining you guys here! Let me know if there's anything else to be included within this PR, thanks! 🤗 " ]

[]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==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) | 0.012497 / 0.011353 (0.001144) | 0.005355 / 0.011008 (-0.005654) | 0.106018 / 0.038508 (0.067510) | 0.093069 / 0.023109 (0.069960) | 0.394699 / 0.275898 (0.118801) | 0.449723 / 0.323480 (0.126243) | 0.006434 / 0.007986 (-0.001552) | 0.004187 / 0.004328 (-0.000141) | 0.079620 / 0.004250 (0.075370) | 0.062513 / 0.037052 (0.025460) | 0.410305 / 0.258489 (0.151816) | 0.467231 / 0.293841 (0.173390) | 0.048130 / 0.128546 (-0.080416) | 0.013747 / 0.075646 (-0.061899) | 0.357979 / 0.419271 (-0.061293) | 0.064764 / 0.043533 (0.021231) | 0.411029 / 0.255139 (0.155890) | 0.454734 / 0.283200 (0.171534) | 0.037215 / 0.141683 (-0.104468) | 1.801331 / 1.452155 (0.349176) | 1.951628 / 1.492716 (0.458912) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231073 / 0.018006 (0.213067) | 0.564179 / 0.000490 (0.563689) | 0.000947 / 0.000200 (0.000747) | 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) | 0.030629 / 0.037411 (-0.006783) | 0.092522 / 0.014526 (0.077996) | 0.109781 / 0.176557 (-0.066775) | 0.183185 / 0.737135 (-0.553950) | 0.109679 / 0.296338 (-0.186660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.600095 / 0.215209 (0.384886) | 6.072868 / 2.077655 (3.995213) | 2.684109 / 1.504120 (1.179989) | 2.436204 / 1.541195 (0.895010) | 2.514667 / 1.468490 (1.046177) | 0.865455 / 4.584777 (-3.719322) | 5.245561 / 3.745712 (1.499849) | 5.628688 / 5.269862 (0.358826) | 3.457343 / 4.565676 (-1.108333) | 0.107563 / 0.424275 (-0.316712) | 0.008803 / 0.007607 (0.001196) | 0.754014 / 0.226044 (0.527970) | 7.341226 / 2.268929 (5.072297) | 3.482090 / 55.444624 (-51.962534) | 2.726071 / 6.876477 (-4.150406) | 3.168494 / 2.142072 (1.026422) | 1.023517 / 4.805227 (-3.781710) | 0.207440 / 6.500664 (-6.293224) | 0.073642 / 0.075469 (-0.001827) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.588636 / 1.841788 (-0.253152) | 23.305257 / 8.074308 (15.230949) | 22.071476 / 10.191392 (11.880084) | 0.242044 / 0.680424 (-0.438379) | 0.028830 / 0.534201 (-0.505371) | 0.461414 / 0.579283 (-0.117869) | 0.591024 / 0.434364 (0.156660) | 0.548984 / 0.540337 (0.008646) | 0.783318 / 1.386936 (-0.603618) |\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) | 0.008724 / 0.011353 (-0.002629) | 0.004638 / 0.011008 (-0.006371) | 0.081024 / 0.038508 (0.042516) | 0.077533 / 0.023109 (0.054423) | 0.444827 / 0.275898 (0.168929) | 0.507812 / 0.323480 (0.184332) | 0.006017 / 0.007986 (-0.001968) | 0.004204 / 0.004328 (-0.000124) | 0.082154 / 0.004250 (0.077904) | 0.063818 / 0.037052 (0.026765) | 0.463468 / 0.258489 (0.204979) | 0.536784 / 0.293841 (0.242943) | 0.046393 / 0.128546 (-0.082153) | 0.014349 / 0.075646 (-0.061298) | 0.089213 / 0.419271 (-0.330059) | 0.058313 / 0.043533 (0.014780) | 0.463674 / 0.255139 (0.208535) | 0.495865 / 0.283200 (0.212665) | 0.036586 / 0.141683 (-0.105096) | 1.801601 / 1.452155 (0.349447) | 1.871219 / 1.492716 (0.378502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273411 / 0.018006 (0.255405) | 0.531745 / 0.000490 (0.531255) | 0.000424 / 0.000200 (0.000224) | 0.000130 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037689 / 0.037411 (0.000278) | 0.109544 / 0.014526 (0.095019) | 0.124053 / 0.176557 (-0.052504) | 0.179960 / 0.737135 (-0.557175) | 0.118218 / 0.296338 (-0.178120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639859 / 0.215209 (0.424650) | 6.347385 / 2.077655 (4.269730) | 2.910188 / 1.504120 (1.406068) | 2.698821 / 1.541195 (1.157626) | 2.802652 / 1.468490 (1.334161) | 0.816109 / 4.584777 (-3.768668) | 5.190313 / 3.745712 (1.444601) | 4.642684 / 5.269862 (-0.627178) | 2.948092 / 4.565676 (-1.617584) | 0.095877 / 0.424275 (-0.328398) | 0.009631 / 0.007607 (0.002024) | 0.779136 / 0.226044 (0.553091) | 7.611586 / 2.268929 (5.342658) | 3.760804 / 55.444624 (-51.683820) | 3.139355 / 6.876477 (-3.737122) | 3.419660 / 2.142072 (1.277587) | 1.036397 / 4.805227 (-3.768831) | 0.224015 / 6.500664 (-6.276649) | 0.084037 / 0.075469 (0.008568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.710608 / 1.841788 (-0.131179) | 24.447646 / 8.074308 (16.373338) | 21.345322 / 10.191392 (11.153930) | 0.232383 / 0.680424 (-0.448040) | 0.026381 / 0.534201 (-0.507820) | 0.475995 / 0.579283 (-0.103289) | 0.611939 / 0.434364 (0.177575) | 0.541441 / 0.540337 (0.001104) | 0.742796 / 1.386936 (-0.644140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7929929525e734f7232cfc68d1d22fb8d53c54a3 \"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) | 0.006140 / 0.011353 (-0.005213) | 0.003664 / 0.011008 (-0.007344) | 0.080765 / 0.038508 (0.042257) | 0.065009 / 0.023109 (0.041900) | 0.312787 / 0.275898 (0.036889) | 0.354637 / 0.323480 (0.031157) | 0.004846 / 0.007986 (-0.003140) | 0.003019 / 0.004328 (-0.001310) | 0.062823 / 0.004250 (0.058573) | 0.050446 / 0.037052 (0.013394) | 0.314478 / 0.258489 (0.055989) | 0.360206 / 0.293841 (0.066365) | 0.027282 / 0.128546 (-0.101265) | 0.008024 / 0.075646 (-0.067622) | 0.262125 / 0.419271 (-0.157146) | 0.045793 / 0.043533 (0.002260) | 0.310508 / 0.255139 (0.055369) | 0.340899 / 0.283200 (0.057699) | 0.021850 / 0.141683 (-0.119833) | 1.510791 / 1.452155 (0.058636) | 1.570661 / 1.492716 (0.077944) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192136 / 0.018006 (0.174130) | 0.449310 / 0.000490 (0.448820) | 0.004556 / 0.000200 (0.004356) | 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) | 0.023689 / 0.037411 (-0.013722) | 0.076316 / 0.014526 (0.061791) | 0.084800 / 0.176557 (-0.091757) | 0.153154 / 0.737135 (-0.583981) | 0.086467 / 0.296338 (-0.209871) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432254 / 0.215209 (0.217045) | 4.305098 / 2.077655 (2.227443) | 2.304267 / 1.504120 (0.800147) | 2.139503 / 1.541195 (0.598309) | 2.220414 / 1.468490 (0.751924) | 0.498595 / 4.584777 (-4.086182) | 3.058593 / 3.745712 (-0.687119) | 4.324501 / 5.269862 (-0.945361) | 2.667731 / 4.565676 (-1.897946) | 0.059917 / 0.424275 (-0.364358) | 0.006829 / 0.007607 (-0.000778) | 0.504608 / 0.226044 (0.278564) | 5.044480 / 2.268929 (2.775552) | 2.753080 / 55.444624 (-52.691545) | 2.449265 / 6.876477 (-4.427212) | 2.635113 / 2.142072 (0.493040) | 0.590760 / 4.805227 (-4.214467) | 0.130133 / 6.500664 (-6.370532) | 0.062759 / 0.075469 (-0.012710) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267014 / 1.841788 (-0.574773) | 18.562890 / 8.074308 (10.488581) | 13.991257 / 10.191392 (3.799865) | 0.147108 / 0.680424 (-0.533315) | 0.017216 / 0.534201 (-0.516985) | 0.330317 / 0.579283 (-0.248966) | 0.351328 / 0.434364 (-0.083036) | 0.381097 / 0.540337 (-0.159241) | 0.558718 / 1.386936 (-0.828218) |\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) | 0.006385 / 0.011353 (-0.004967) | 0.003668 / 0.011008 (-0.007340) | 0.062581 / 0.038508 (0.024073) | 0.067006 / 0.023109 (0.043896) | 0.428465 / 0.275898 (0.152567) | 0.466106 / 0.323480 (0.142626) | 0.005806 / 0.007986 (-0.002180) | 0.003117 / 0.004328 (-0.001212) | 0.063554 / 0.004250 (0.059303) | 0.054404 / 0.037052 (0.017352) | 0.431168 / 0.258489 (0.172679) | 0.467578 / 0.293841 (0.173737) | 0.027779 / 0.128546 (-0.100767) | 0.008055 / 0.075646 (-0.067592) | 0.067718 / 0.419271 (-0.351554) | 0.043042 / 0.043533 (-0.000491) | 0.425926 / 0.255139 (0.170787) | 0.453699 / 0.283200 (0.170500) | 0.023495 / 0.141683 (-0.118187) | 1.435356 / 1.452155 (-0.016799) | 1.509340 / 1.492716 (0.016624) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242322 / 0.018006 (0.224316) | 0.446865 / 0.000490 (0.446376) | 0.001079 / 0.000200 (0.000879) | 0.000065 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025376 / 0.037411 (-0.012035) | 0.079373 / 0.014526 (0.064847) | 0.088554 / 0.176557 (-0.088002) | 0.141026 / 0.737135 (-0.596109) | 0.090666 / 0.296338 (-0.205672) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434800 / 0.215209 (0.219590) | 4.314491 / 2.077655 (2.236836) | 2.320688 / 1.504120 (0.816568) | 2.163941 / 1.541195 (0.622747) | 2.292576 / 1.468490 (0.824086) | 0.500226 / 4.584777 (-4.084551) | 3.114604 / 3.745712 (-0.631108) | 4.206997 / 5.269862 (-1.062864) | 2.461126 / 4.565676 (-2.104551) | 0.057717 / 0.424275 (-0.366558) | 0.006989 / 0.007607 (-0.000618) | 0.515623 / 0.226044 (0.289579) | 5.155301 / 2.268929 (2.886372) | 2.733589 / 55.444624 (-52.711035) | 2.542111 / 6.876477 (-4.334366) | 2.697035 / 2.142072 (0.554963) | 0.594213 / 4.805227 (-4.211014) | 0.128537 / 6.500664 (-6.372127) | 0.065223 / 0.075469 (-0.010246) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306738 / 1.841788 (-0.535050) | 19.065370 / 8.074308 (10.991062) | 14.242096 / 10.191392 (4.050704) | 0.146177 / 0.680424 (-0.534246) | 0.017186 / 0.534201 (-0.517015) | 0.337224 / 0.579283 (-0.242059) | 0.349997 / 0.434364 (-0.084367) | 0.390408 / 0.540337 (-0.149930) | 0.524597 / 1.386936 (-0.862339) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69ec36948b0ef1f194e9dcd43ec53a50b7708962 \"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) | 0.008996 / 0.011353 (-0.002357) | 0.004976 / 0.011008 (-0.006033) | 0.114495 / 0.038508 (0.075987) | 0.083958 / 0.023109 (0.060849) | 0.408395 / 0.275898 (0.132497) | 0.456757 / 0.323480 (0.133278) | 0.006396 / 0.007986 (-0.001589) | 0.004315 / 0.004328 (-0.000014) | 0.093558 / 0.004250 (0.089307) | 0.062067 / 0.037052 (0.025014) | 0.423452 / 0.258489 (0.164963) | 0.463947 / 0.293841 (0.170106) | 0.049934 / 0.128546 (-0.078613) | 0.013937 / 0.075646 (-0.061709) | 0.365809 / 0.419271 (-0.053463) | 0.067382 / 0.043533 (0.023849) | 0.418860 / 0.255139 (0.163721) | 0.463264 / 0.283200 (0.180065) | 0.034392 / 0.141683 (-0.107291) | 1.870685 / 1.452155 (0.418530) | 1.975313 / 1.492716 (0.482597) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261748 / 0.018006 (0.243742) | 0.645510 / 0.000490 (0.645020) | 0.000376 / 0.000200 (0.000176) | 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) | 0.032129 / 0.037411 (-0.005282) | 0.104309 / 0.014526 (0.089783) | 0.113154 / 0.176557 (-0.063403) | 0.186795 / 0.737135 (-0.550341) | 0.115584 / 0.296338 (-0.180755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.577755 / 0.215209 (0.362546) | 5.984988 / 2.077655 (3.907333) | 2.581967 / 1.504120 (1.077848) | 2.305744 / 1.541195 (0.764549) | 2.359618 / 1.468490 (0.891128) | 0.882892 / 4.584777 (-3.701885) | 5.755578 / 3.745712 (2.009866) | 8.718373 / 5.269862 (3.448511) | 5.217586 / 4.565676 (0.651909) | 0.099785 / 0.424275 (-0.324490) | 0.009008 / 0.007607 (0.001401) | 0.730937 / 0.226044 (0.504892) | 7.265309 / 2.268929 (4.996381) | 3.487167 / 55.444624 (-51.957457) | 2.750090 / 6.876477 (-4.126386) | 3.060198 / 2.142072 (0.918125) | 1.069945 / 4.805227 (-3.735282) | 0.227143 / 6.500664 (-6.273521) | 0.083601 / 0.075469 (0.008132) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.754375 / 1.841788 (-0.087412) | 25.448731 / 8.074308 (17.374423) | 22.385943 / 10.191392 (12.194551) | 0.249921 / 0.680424 (-0.430503) | 0.034138 / 0.534201 (-0.500063) | 0.535170 / 0.579283 (-0.044113) | 0.605474 / 0.434364 (0.171110) | 0.580025 / 0.540337 (0.039688) | 0.810537 / 1.386936 (-0.576399) |\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) | 0.009117 / 0.011353 (-0.002236) | 0.005029 / 0.011008 (-0.005979) | 0.082200 / 0.038508 (0.043691) | 0.082386 / 0.023109 (0.059277) | 0.491869 / 0.275898 (0.215971) | 0.546735 / 0.323480 (0.223255) | 0.006893 / 0.007986 (-0.001093) | 0.004571 / 0.004328 (0.000243) | 0.085361 / 0.004250 (0.081111) | 0.063342 / 0.037052 (0.026290) | 0.522522 / 0.258489 (0.264033) | 0.560784 / 0.293841 (0.266943) | 0.047685 / 0.128546 (-0.080861) | 0.017741 / 0.075646 (-0.057905) | 0.098204 / 0.419271 (-0.321067) | 0.062919 / 0.043533 (0.019386) | 0.504005 / 0.255139 (0.248866) | 0.547022 / 0.283200 (0.263823) | 0.033731 / 0.141683 (-0.107952) | 1.869765 / 1.452155 (0.417610) | 1.935867 / 1.492716 (0.443151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304756 / 0.018006 (0.286750) | 0.623647 / 0.000490 (0.623157) | 0.000508 / 0.000200 (0.000308) | 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) | 0.043627 / 0.037411 (0.006216) | 0.107183 / 0.014526 (0.092657) | 0.119304 / 0.176557 (-0.057253) | 0.192651 / 0.737135 (-0.544485) | 0.125118 / 0.296338 (-0.171221) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669980 / 0.215209 (0.454771) | 6.566068 / 2.077655 (4.488413) | 3.136271 / 1.504120 (1.632152) | 2.964643 / 1.541195 (1.423448) | 2.936772 / 1.468490 (1.468282) | 0.885205 / 4.584777 (-3.699572) | 5.539062 / 3.745712 (1.793350) | 5.006133 / 5.269862 (-0.263729) | 3.313697 / 4.565676 (-1.251979) | 0.102975 / 0.424275 (-0.321301) | 0.010759 / 0.007607 (0.003152) | 0.791176 / 0.226044 (0.565132) | 7.822195 / 2.268929 (5.553266) | 3.982315 / 55.444624 (-51.462309) | 3.357026 / 6.876477 (-3.519451) | 3.561307 / 2.142072 (1.419234) | 1.056966 / 4.805227 (-3.748261) | 0.220476 / 6.500664 (-6.280188) | 0.090535 / 0.075469 (0.015066) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.897984 / 1.841788 (0.056196) | 26.411411 / 8.074308 (18.337103) | 22.951939 / 10.191392 (12.760547) | 0.216091 / 0.680424 (-0.464333) | 0.037005 / 0.534201 (-0.497196) | 0.505585 / 0.579283 (-0.073698) | 0.617794 / 0.434364 (0.183430) | 0.604631 / 0.540337 (0.064293) | 0.826356 / 1.386936 (-0.560580) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca6342c0177adc3a1d114740444e207b8525ed6e \"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) | 0.006850 / 0.011353 (-0.004503) | 0.004062 / 0.011008 (-0.006947) | 0.086587 / 0.038508 (0.048079) | 0.079587 / 0.023109 (0.056478) | 0.353601 / 0.275898 (0.077702) | 0.396399 / 0.323480 (0.072919) | 0.004182 / 0.007986 (-0.003804) | 0.004445 / 0.004328 (0.000117) | 0.065100 / 0.004250 (0.060849) | 0.057386 / 0.037052 (0.020334) | 0.356945 / 0.258489 (0.098456) | 0.407093 / 0.293841 (0.113252) | 0.031949 / 0.128546 (-0.096597) | 0.008525 / 0.075646 (-0.067121) | 0.291310 / 0.419271 (-0.127961) | 0.053638 / 0.043533 (0.010105) | 0.359381 / 0.255139 (0.104242) | 0.399473 / 0.283200 (0.116273) | 0.025880 / 0.141683 (-0.115803) | 1.487604 / 1.452155 (0.035449) | 1.550528 / 1.492716 (0.057812) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201106 / 0.018006 (0.183099) | 0.457538 / 0.000490 (0.457048) | 0.003995 / 0.000200 (0.003795) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030365 / 0.037411 (-0.007046) | 0.088064 / 0.014526 (0.073538) | 0.096432 / 0.176557 (-0.080124) | 0.158063 / 0.737135 (-0.579072) | 0.098258 / 0.296338 (-0.198080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405351 / 0.215209 (0.190142) | 4.032639 / 2.077655 (1.954984) | 2.018357 / 1.504120 (0.514237) | 1.848493 / 1.541195 (0.307298) | 1.929401 / 1.468490 (0.460910) | 0.488729 / 4.584777 (-4.096048) | 3.586114 / 3.745712 (-0.159598) | 5.279054 / 5.269862 (0.009193) | 3.113275 / 4.565676 (-1.452402) | 0.057373 / 0.424275 (-0.366902) | 0.007416 / 0.007607 (-0.000191) | 0.485514 / 0.226044 (0.259470) | 4.854389 / 2.268929 (2.585461) | 2.493113 / 55.444624 (-52.951512) | 2.128836 / 6.876477 (-4.747641) | 2.383669 / 2.142072 (0.241596) | 0.588266 / 4.805227 (-4.216962) | 0.133603 / 6.500664 (-6.367061) | 0.061812 / 0.075469 (-0.013657) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260841 / 1.841788 (-0.580947) | 20.086954 / 8.074308 (12.012646) | 14.620932 / 10.191392 (4.429540) | 0.161525 / 0.680424 (-0.518899) | 0.018102 / 0.534201 (-0.516099) | 0.393810 / 0.579283 (-0.185473) | 0.406974 / 0.434364 (-0.027390) | 0.462732 / 0.540337 (-0.077606) | 0.634221 / 1.386936 (-0.752715) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006692 / 0.011353 (-0.004661) | 0.004068 / 0.011008 (-0.006940) | 0.068009 / 0.038508 (0.029501) | 0.081298 / 0.023109 (0.058189) | 0.363531 / 0.275898 (0.087633) | 0.408482 / 0.323480 (0.085002) | 0.005601 / 0.007986 (-0.002384) | 0.003385 / 0.004328 (-0.000943) | 0.068043 / 0.004250 (0.063792) | 0.059739 / 0.037052 (0.022687) | 0.374043 / 0.258489 (0.115553) | 0.407219 / 0.293841 (0.113378) | 0.031194 / 0.128546 (-0.097352) | 0.008630 / 0.075646 (-0.067017) | 0.073755 / 0.419271 (-0.345517) | 0.049831 / 0.043533 (0.006298) | 0.363664 / 0.255139 (0.108525) | 0.381515 / 0.283200 (0.098315) | 0.026331 / 0.141683 (-0.115352) | 1.507771 / 1.452155 (0.055617) | 1.554403 / 1.492716 (0.061686) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226309 / 0.018006 (0.208302) | 0.452428 / 0.000490 (0.451938) | 0.000937 / 0.000200 (0.000737) | 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) | 0.031899 / 0.037411 (-0.005513) | 0.092090 / 0.014526 (0.077564) | 0.100838 / 0.176557 (-0.075718) | 0.153722 / 0.737135 (-0.583413) | 0.101950 / 0.296338 (-0.194389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417879 / 0.215209 (0.202669) | 4.171939 / 2.077655 (2.094284) | 2.312937 / 1.504120 (0.808817) | 2.209991 / 1.541195 (0.668796) | 2.329469 / 1.468490 (0.860979) | 0.484576 / 4.584777 (-4.100201) | 3.659198 / 3.745712 (-0.086514) | 5.255227 / 5.269862 (-0.014634) | 3.047430 / 4.565676 (-1.518247) | 0.057029 / 0.424275 (-0.367246) | 0.007735 / 0.007607 (0.000127) | 0.499962 / 0.226044 (0.273918) | 4.991655 / 2.268929 (2.722727) | 2.755999 / 55.444624 (-52.688625) | 2.374034 / 6.876477 (-4.502443) | 2.599759 / 2.142072 (0.457687) | 0.600319 / 4.805227 (-4.204908) | 0.146176 / 6.500664 (-6.354488) | 0.062328 / 0.075469 (-0.013142) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346065 / 1.841788 (-0.495722) | 20.430343 / 8.074308 (12.356035) | 14.632959 / 10.191392 (4.441567) | 0.167007 / 0.680424 (-0.513417) | 0.018588 / 0.534201 (-0.515613) | 0.396015 / 0.579283 (-0.183268) | 0.429384 / 0.434364 (-0.004980) | 0.467746 / 0.540337 (-0.072591) | 0.615166 / 1.386936 (-0.771770) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#289bcc2ae9bf98c9414b6846ae603178a1816d3f \"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) | 0.007498 / 0.011353 (-0.003855) | 0.004158 / 0.011008 (-0.006850) | 0.087568 / 0.038508 (0.049060) | 0.083265 / 0.023109 (0.060156) | 0.378505 / 0.275898 (0.102607) | 0.399025 / 0.323480 (0.075545) | 0.006173 / 0.007986 (-0.001813) | 0.003743 / 0.004328 (-0.000586) | 0.071958 / 0.004250 (0.067707) | 0.059323 / 0.037052 (0.022271) | 0.377084 / 0.258489 (0.118595) | 0.408358 / 0.293841 (0.114517) | 0.035191 / 0.128546 (-0.093356) | 0.009408 / 0.075646 (-0.066238) | 0.312587 / 0.419271 (-0.106685) | 0.058073 / 0.043533 (0.014540) | 0.381977 / 0.255139 (0.126838) | 0.395611 / 0.283200 (0.112411) | 0.024191 / 0.141683 (-0.117491) | 1.572735 / 1.452155 (0.120581) | 1.687186 / 1.492716 (0.194470) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208886 / 0.018006 (0.190879) | 0.474625 / 0.000490 (0.474135) | 0.006261 / 0.000200 (0.006061) | 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) | 0.031401 / 0.037411 (-0.006011) | 0.086433 / 0.014526 (0.071907) | 0.108405 / 0.176557 (-0.068152) | 0.174564 / 0.737135 (-0.562571) | 0.099932 / 0.296338 (-0.196407) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407059 / 0.215209 (0.191850) | 4.102056 / 2.077655 (2.024401) | 1.975397 / 1.504120 (0.471277) | 1.807117 / 1.541195 (0.265922) | 1.908667 / 1.468490 (0.440177) | 0.525880 / 4.584777 (-4.058897) | 3.899639 / 3.745712 (0.153927) | 4.358664 / 5.269862 (-0.911198) | 2.586185 / 4.565676 (-1.979492) | 0.061967 / 0.424275 (-0.362308) | 0.007656 / 0.007607 (0.000049) | 0.504851 / 0.226044 (0.278807) | 5.004429 / 2.268929 (2.735500) | 2.515540 / 55.444624 (-52.929084) | 2.183142 / 6.876477 (-4.693334) | 2.369835 / 2.142072 (0.227763) | 0.623527 / 4.805227 (-4.181700) | 0.145105 / 6.500664 (-6.355559) | 0.063924 / 0.075469 (-0.011546) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.472661 / 1.841788 (-0.369126) | 21.781655 / 8.074308 (13.707347) | 15.628820 / 10.191392 (5.437428) | 0.182342 / 0.680424 (-0.498082) | 0.021139 / 0.534201 (-0.513062) | 0.438610 / 0.579283 (-0.140673) | 0.451343 / 0.434364 (0.016979) | 0.563320 / 0.540337 (0.022983) | 0.740976 / 1.386936 (-0.645960) |\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) | 0.007492 / 0.011353 (-0.003861) | 0.004429 / 0.011008 (-0.006579) | 0.068517 / 0.038508 (0.030008) | 0.078533 / 0.023109 (0.055424) | 0.383530 / 0.275898 (0.107632) | 0.435061 / 0.323480 (0.111581) | 0.005955 / 0.007986 (-0.002030) | 0.003645 / 0.004328 (-0.000683) | 0.068792 / 0.004250 (0.064541) | 0.062452 / 0.037052 (0.025399) | 0.408768 / 0.258489 (0.150279) | 0.438538 / 0.293841 (0.144697) | 0.032038 / 0.128546 (-0.096508) | 0.009196 / 0.075646 (-0.066450) | 0.074495 / 0.419271 (-0.344776) | 0.051322 / 0.043533 (0.007789) | 0.394458 / 0.255139 (0.139319) | 0.424763 / 0.283200 (0.141564) | 0.024890 / 0.141683 (-0.116793) | 1.568322 / 1.452155 (0.116167) | 1.703903 / 1.492716 (0.211187) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249630 / 0.018006 (0.231624) | 0.471412 / 0.000490 (0.470923) | 0.000435 / 0.000200 (0.000235) | 0.000060 / 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) | 0.033054 / 0.037411 (-0.004358) | 0.100150 / 0.014526 (0.085624) | 0.101704 / 0.176557 (-0.074853) | 0.164031 / 0.737135 (-0.573104) | 0.112497 / 0.296338 (-0.183841) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.487150 / 0.215209 (0.271941) | 4.662335 / 2.077655 (2.584681) | 2.477285 / 1.504120 (0.973165) | 2.294033 / 1.541195 (0.752838) | 2.380143 / 1.468490 (0.911653) | 0.519182 / 4.584777 (-4.065595) | 3.983589 / 3.745712 (0.237877) | 3.669895 / 5.269862 (-1.599967) | 2.267147 / 4.565676 (-2.298529) | 0.063300 / 0.424275 (-0.360975) | 0.008839 / 0.007607 (0.001232) | 0.566766 / 0.226044 (0.340721) | 5.533475 / 2.268929 (3.264546) | 3.033412 / 55.444624 (-52.411212) | 2.701793 / 6.876477 (-4.174684) | 2.899444 / 2.142072 (0.757372) | 0.614236 / 4.805227 (-4.190991) | 0.139533 / 6.500664 (-6.361131) | 0.067537 / 0.075469 (-0.007932) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.505572 / 1.841788 (-0.336216) | 22.859062 / 8.074308 (14.784754) | 15.044777 / 10.191392 (4.853385) | 0.169153 / 0.680424 (-0.511271) | 0.021027 / 0.534201 (-0.513174) | 0.447979 / 0.579283 (-0.131304) | 0.460676 / 0.434364 (0.026312) | 0.506327 / 0.540337 (-0.034010) | 0.737880 / 1.386936 (-0.649057) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db7180eb7e3ebf52b9d1f2c6629db6d92d8a29ba \"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) | 0.006118 / 0.011353 (-0.005235) | 0.003692 / 0.011008 (-0.007316) | 0.080606 / 0.038508 (0.042098) | 0.062014 / 0.023109 (0.038905) | 0.391886 / 0.275898 (0.115988) | 0.423978 / 0.323480 (0.100498) | 0.004968 / 0.007986 (-0.003017) | 0.002911 / 0.004328 (-0.001417) | 0.062867 / 0.004250 (0.058617) | 0.049493 / 0.037052 (0.012441) | 0.395656 / 0.258489 (0.137167) | 0.432406 / 0.293841 (0.138565) | 0.027242 / 0.128546 (-0.101304) | 0.007938 / 0.075646 (-0.067709) | 0.261703 / 0.419271 (-0.157569) | 0.045922 / 0.043533 (0.002389) | 0.391544 / 0.255139 (0.136405) | 0.417902 / 0.283200 (0.134703) | 0.021339 / 0.141683 (-0.120344) | 1.508391 / 1.452155 (0.056236) | 1.518970 / 1.492716 (0.026254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.181159 / 0.018006 (0.163153) | 0.431402 / 0.000490 (0.430912) | 0.003849 / 0.000200 (0.003649) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024498 / 0.037411 (-0.012914) | 0.072758 / 0.014526 (0.058233) | 0.084910 / 0.176557 (-0.091646) | 0.148314 / 0.737135 (-0.588821) | 0.085212 / 0.296338 (-0.211126) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386693 / 0.215209 (0.171484) | 3.852652 / 2.077655 (1.774997) | 1.891758 / 1.504120 (0.387638) | 1.718793 / 1.541195 (0.177598) | 1.747595 / 1.468490 (0.279104) | 0.498593 / 4.584777 (-4.086184) | 3.057907 / 3.745712 (-0.687805) | 4.728449 / 5.269862 (-0.541413) | 2.966368 / 4.565676 (-1.599308) | 0.057538 / 0.424275 (-0.366737) | 0.006415 / 0.007607 (-0.001192) | 0.461652 / 0.226044 (0.235608) | 4.625944 / 2.268929 (2.357015) | 2.306938 / 55.444624 (-53.137686) | 1.974670 / 6.876477 (-4.901806) | 2.146327 / 2.142072 (0.004254) | 0.585033 / 4.805227 (-4.220195) | 0.125936 / 6.500664 (-6.374728) | 0.062365 / 0.075469 (-0.013104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263415 / 1.841788 (-0.578373) | 18.380651 / 8.074308 (10.306343) | 13.853410 / 10.191392 (3.662018) | 0.144674 / 0.680424 (-0.535749) | 0.016833 / 0.534201 (-0.517368) | 0.330812 / 0.579283 (-0.248471) | 0.357553 / 0.434364 (-0.076810) | 0.383529 / 0.540337 (-0.156809) | 0.558923 / 1.386936 (-0.828013) |\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) | 0.006074 / 0.011353 (-0.005278) | 0.003655 / 0.011008 (-0.007353) | 0.062981 / 0.038508 (0.024473) | 0.061457 / 0.023109 (0.038348) | 0.366471 / 0.275898 (0.090573) | 0.408463 / 0.323480 (0.084983) | 0.004854 / 0.007986 (-0.003132) | 0.002916 / 0.004328 (-0.001412) | 0.062745 / 0.004250 (0.058494) | 0.051136 / 0.037052 (0.014084) | 0.380313 / 0.258489 (0.121824) | 0.416945 / 0.293841 (0.123104) | 0.027228 / 0.128546 (-0.101318) | 0.008031 / 0.075646 (-0.067615) | 0.067941 / 0.419271 (-0.351331) | 0.042886 / 0.043533 (-0.000647) | 0.370112 / 0.255139 (0.114973) | 0.397111 / 0.283200 (0.113911) | 0.023063 / 0.141683 (-0.118620) | 1.476955 / 1.452155 (0.024800) | 1.534783 / 1.492716 (0.042066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231462 / 0.018006 (0.213456) | 0.439559 / 0.000490 (0.439069) | 0.000364 / 0.000200 (0.000164) | 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) | 0.026925 / 0.037411 (-0.010486) | 0.079623 / 0.014526 (0.065097) | 0.088694 / 0.176557 (-0.087862) | 0.143163 / 0.737135 (-0.593972) | 0.089900 / 0.296338 (-0.206438) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451429 / 0.215209 (0.236220) | 4.510723 / 2.077655 (2.433069) | 2.491853 / 1.504120 (0.987733) | 2.334670 / 1.541195 (0.793475) | 2.395519 / 1.468490 (0.927029) | 0.501369 / 4.584777 (-4.083408) | 3.014019 / 3.745712 (-0.731693) | 2.809199 / 5.269862 (-2.460662) | 1.842195 / 4.565676 (-2.723481) | 0.057675 / 0.424275 (-0.366600) | 0.006742 / 0.007607 (-0.000865) | 0.524402 / 0.226044 (0.298358) | 5.245296 / 2.268929 (2.976367) | 2.957990 / 55.444624 (-52.486634) | 2.649807 / 6.876477 (-4.226670) | 2.755909 / 2.142072 (0.613836) | 0.589610 / 4.805227 (-4.215617) | 0.125708 / 6.500664 (-6.374956) | 0.062237 / 0.075469 (-0.013232) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362758 / 1.841788 (-0.479030) | 18.343694 / 8.074308 (10.269386) | 13.621521 / 10.191392 (3.430129) | 0.128866 / 0.680424 (-0.551558) | 0.016608 / 0.534201 (-0.517593) | 0.333071 / 0.579283 (-0.246212) | 0.341917 / 0.434364 (-0.092447) | 0.381075 / 0.540337 (-0.159263) | 0.512485 / 1.386936 (-0.874451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab3f0165d4a2a8ab1aee1ebc4628893e17e27387 \"CML watermark\")\n", "I forgot to mention this in the initial comment, but only one public dataset (excluding gated) uses this method - `pg19`, which I just fixed.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007838 / 0.011353 (-0.003515) | 0.004791 / 0.011008 (-0.006217) | 0.102596 / 0.038508 (0.064088) | 0.087678 / 0.023109 (0.064569) | 0.373858 / 0.275898 (0.097960) | 0.416643 / 0.323480 (0.093163) | 0.006147 / 0.007986 (-0.001839) | 0.003837 / 0.004328 (-0.000491) | 0.076706 / 0.004250 (0.072456) | 0.063449 / 0.037052 (0.026396) | 0.378392 / 0.258489 (0.119903) | 0.431768 / 0.293841 (0.137927) | 0.036648 / 0.128546 (-0.091898) | 0.010042 / 0.075646 (-0.065604) | 0.350277 / 0.419271 (-0.068995) | 0.062892 / 0.043533 (0.019359) | 0.376151 / 0.255139 (0.121012) | 0.420929 / 0.283200 (0.137729) | 0.027816 / 0.141683 (-0.113867) | 1.791607 / 1.452155 (0.339452) | 1.903045 / 1.492716 (0.410328) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224688 / 0.018006 (0.206682) | 0.491941 / 0.000490 (0.491451) | 0.004482 / 0.000200 (0.004282) | 0.000102 / 0.000054 (0.000048) |\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 (-0.003917) | 0.099855 / 0.014526 (0.085329) | 0.114593 / 0.176557 (-0.061964) | 0.190947 / 0.737135 (-0.546189) | 0.116202 / 0.296338 (-0.180136) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488581 / 0.215209 (0.273372) | 4.869531 / 2.077655 (2.791876) | 2.527920 / 1.504120 (1.023800) | 2.340021 / 1.541195 (0.798826) | 2.432661 / 1.468490 (0.964171) | 0.569646 / 4.584777 (-4.015131) | 4.392036 / 3.745712 (0.646324) | 4.987253 / 5.269862 (-0.282608) | 2.866604 / 4.565676 (-1.699073) | 0.067393 / 0.424275 (-0.356882) | 0.008759 / 0.007607 (0.001152) | 0.584327 / 0.226044 (0.358283) | 5.853000 / 2.268929 (3.584072) | 3.206721 / 55.444624 (-52.237904) | 2.730867 / 6.876477 (-4.145610) | 2.944814 / 2.142072 (0.802742) | 0.703336 / 4.805227 (-4.101891) | 0.173985 / 6.500664 (-6.326679) | 0.075333 / 0.075469 (-0.000137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.519755 / 1.841788 (-0.322033) | 22.918038 / 8.074308 (14.843730) | 17.211160 / 10.191392 (7.019768) | 0.196941 / 0.680424 (-0.483483) | 0.021833 / 0.534201 (-0.512368) | 0.476835 / 0.579283 (-0.102448) | 0.464513 / 0.434364 (0.030149) | 0.559180 / 0.540337 (0.018843) | 0.748232 / 1.386936 (-0.638704) |\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) | 0.008461 / 0.011353 (-0.002892) | 0.004799 / 0.011008 (-0.006209) | 0.077466 / 0.038508 (0.038958) | 0.103562 / 0.023109 (0.080453) | 0.453661 / 0.275898 (0.177763) | 0.531126 / 0.323480 (0.207647) | 0.006618 / 0.007986 (-0.001367) | 0.004048 / 0.004328 (-0.000280) | 0.075446 / 0.004250 (0.071196) | 0.072815 / 0.037052 (0.035762) | 0.497145 / 0.258489 (0.238656) | 0.533828 / 0.293841 (0.239987) | 0.037657 / 0.128546 (-0.090890) | 0.010139 / 0.075646 (-0.065507) | 0.083759 / 0.419271 (-0.335512) | 0.061401 / 0.043533 (0.017868) | 0.441785 / 0.255139 (0.186646) | 0.491678 / 0.283200 (0.208479) | 0.033100 / 0.141683 (-0.108583) | 1.753612 / 1.452155 (0.301458) | 1.838956 / 1.492716 (0.346240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.395023 / 0.018006 (0.377017) | 0.509362 / 0.000490 (0.508872) | 0.060742 / 0.000200 (0.060542) | 0.000545 / 0.000054 (0.000491) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039327 / 0.037411 (0.001916) | 0.117345 / 0.014526 (0.102819) | 0.124540 / 0.176557 (-0.052017) | 0.200743 / 0.737135 (-0.536392) | 0.126750 / 0.296338 (-0.169589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488597 / 0.215209 (0.273388) | 4.875534 / 2.077655 (2.797880) | 2.714364 / 1.504120 (1.210244) | 2.603707 / 1.541195 (1.062513) | 2.733547 / 1.468490 (1.265057) | 0.575183 / 4.584777 (-4.009594) | 4.126096 / 3.745712 (0.380384) | 3.853803 / 5.269862 (-1.416058) | 2.395160 / 4.565676 (-2.170516) | 0.067391 / 0.424275 (-0.356884) | 0.009108 / 0.007607 (0.001501) | 0.585865 / 0.226044 (0.359820) | 5.864878 / 2.268929 (3.595949) | 3.153369 / 55.444624 (-52.291256) | 2.759064 / 6.876477 (-4.117413) | 3.032489 / 2.142072 (0.890416) | 0.702615 / 4.805227 (-4.102613) | 0.160034 / 6.500664 (-6.340630) | 0.077294 / 0.075469 (0.001825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595069 / 1.841788 (-0.246719) | 23.231191 / 8.074308 (15.156883) | 16.365137 / 10.191392 (6.173745) | 0.188360 / 0.680424 (-0.492063) | 0.021704 / 0.534201 (-0.512497) | 0.469996 / 0.579283 (-0.109287) | 0.463255 / 0.434364 (0.028891) | 0.560506 / 0.540337 (0.020169) | 0.751006 / 1.386936 (-0.635930) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50d9a70c666ff46ff9974c47cedc77d9f88d6471 \"CML watermark\")\n", "@mariosasko How would you stream a split zip file with just [download_and_extract or download](https://github.com/huggingface/datasets/blob/main/src/datasets/download/download_manager.py#L353)? With download_custom, it is possible to combine a split zip file. Perhaps add an option in [download](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.DownloadManager.download) to combine split zips. This issue may apply to other multipart file-types.\r\n\r\nEdit - \r\nIn case asked why I use split zips, I haven't been able to upload zips larger than 50 GB to HuggingFace.\r\n\r\nEdit2 -\r\nIssue is [tackled](https://discuss.huggingface.co/t/download-custom-method-of-streamingdownloadmanager-not-implemented/28298/8) for split zips. " ]

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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) | 0.007873 / 0.011353 (-0.003480) | 0.004585 / 0.011008 (-0.006423) | 0.101622 / 0.038508 (0.063114) | 0.092459 / 0.023109 (0.069350) | 0.365157 / 0.275898 (0.089259) | 0.405943 / 0.323480 (0.082463) | 0.006229 / 0.007986 (-0.001756) | 0.003811 / 0.004328 (-0.000518) | 0.073831 / 0.004250 (0.069580) | 0.065097 / 0.037052 (0.028045) | 0.378912 / 0.258489 (0.120423) | 0.422174 / 0.293841 (0.128333) | 0.036244 / 0.128546 (-0.092302) | 0.009677 / 0.075646 (-0.065970) | 0.345164 / 0.419271 (-0.074107) | 0.061632 / 0.043533 (0.018099) | 0.370350 / 0.255139 (0.115211) | 0.418245 / 0.283200 (0.135046) | 0.027272 / 0.141683 (-0.114411) | 1.774047 / 1.452155 (0.321892) | 1.880278 / 1.492716 (0.387562) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217238 / 0.018006 (0.199231) | 0.489560 / 0.000490 (0.489071) | 0.004013 / 0.000200 (0.003813) | 0.000092 / 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) | 0.034139 / 0.037411 (-0.003272) | 0.103831 / 0.014526 (0.089305) | 0.114353 / 0.176557 (-0.062204) | 0.182034 / 0.737135 (-0.555102) | 0.116171 / 0.296338 (-0.180168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448658 / 0.215209 (0.233449) | 4.520849 / 2.077655 (2.443195) | 2.216121 / 1.504120 (0.712001) | 2.034596 / 1.541195 (0.493402) | 2.193216 / 1.468490 (0.724725) | 0.568166 / 4.584777 (-4.016611) | 4.133587 / 3.745712 (0.387875) | 4.641117 / 5.269862 (-0.628744) | 2.772913 / 4.565676 (-1.792764) | 0.067664 / 0.424275 (-0.356611) | 0.008719 / 0.007607 (0.001112) | 0.547723 / 0.226044 (0.321678) | 5.438325 / 2.268929 (3.169397) | 2.877667 / 55.444624 (-52.566958) | 2.477503 / 6.876477 (-4.398974) | 2.688209 / 2.142072 (0.546136) | 0.692593 / 4.805227 (-4.112634) | 0.154549 / 6.500664 (-6.346115) | 0.073286 / 0.075469 (-0.002183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.610927 / 1.841788 (-0.230861) | 23.413345 / 8.074308 (15.339037) | 16.851819 / 10.191392 (6.660427) | 0.170076 / 0.680424 (-0.510348) | 0.021428 / 0.534201 (-0.512773) | 0.468184 / 0.579283 (-0.111099) | 0.491820 / 0.434364 (0.057456) | 0.553453 / 0.540337 (0.013115) | 0.762303 / 1.386936 (-0.624633) |\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) | 0.008033 / 0.011353 (-0.003320) | 0.004638 / 0.011008 (-0.006370) | 0.077044 / 0.038508 (0.038536) | 0.096529 / 0.023109 (0.073420) | 0.428735 / 0.275898 (0.152837) | 0.477303 / 0.323480 (0.153823) | 0.006040 / 0.007986 (-0.001946) | 0.003808 / 0.004328 (-0.000521) | 0.076042 / 0.004250 (0.071791) | 0.066123 / 0.037052 (0.029071) | 0.445482 / 0.258489 (0.186993) | 0.481350 / 0.293841 (0.187509) | 0.036951 / 0.128546 (-0.091595) | 0.009944 / 0.075646 (-0.065703) | 0.082731 / 0.419271 (-0.336541) | 0.057490 / 0.043533 (0.013958) | 0.432668 / 0.255139 (0.177529) | 0.461146 / 0.283200 (0.177947) | 0.027330 / 0.141683 (-0.114353) | 1.784195 / 1.452155 (0.332040) | 1.834776 / 1.492716 (0.342059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254104 / 0.018006 (0.236097) | 0.475810 / 0.000490 (0.475321) | 0.000459 / 0.000200 (0.000259) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037058 / 0.037411 (-0.000353) | 0.114962 / 0.014526 (0.100436) | 0.123725 / 0.176557 (-0.052832) | 0.188885 / 0.737135 (-0.548251) | 0.125668 / 0.296338 (-0.170670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492627 / 0.215209 (0.277418) | 4.900625 / 2.077655 (2.822970) | 2.546349 / 1.504120 (1.042229) | 2.360350 / 1.541195 (0.819155) | 2.477975 / 1.468490 (1.009485) | 0.574042 / 4.584777 (-4.010735) | 4.408414 / 3.745712 (0.662702) | 3.836640 / 5.269862 (-1.433222) | 2.438450 / 4.565676 (-2.127227) | 0.067706 / 0.424275 (-0.356569) | 0.009165 / 0.007607 (0.001558) | 0.580313 / 0.226044 (0.354269) | 5.798211 / 2.268929 (3.529283) | 3.098480 / 55.444624 (-52.346145) | 2.740180 / 6.876477 (-4.136296) | 2.984548 / 2.142072 (0.842476) | 0.702550 / 4.805227 (-4.102677) | 0.158248 / 6.500664 (-6.342416) | 0.073999 / 0.075469 (-0.001470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.636034 / 1.841788 (-0.205754) | 24.068000 / 8.074308 (15.993692) | 17.123987 / 10.191392 (6.932595) | 0.210101 / 0.680424 (-0.470323) | 0.022555 / 0.534201 (-0.511646) | 0.509354 / 0.579283 (-0.069929) | 0.540739 / 0.434364 (0.106375) | 0.546048 / 0.540337 (0.005711) | 0.719155 / 1.386936 (-0.667781) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40530382ba98f54445de8820943b1236d4a4704f \"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) | 0.007342 / 0.011353 (-0.004010) | 0.004579 / 0.011008 (-0.006429) | 0.087050 / 0.038508 (0.048542) | 0.089001 / 0.023109 (0.065892) | 0.307319 / 0.275898 (0.031421) | 0.377573 / 0.323480 (0.054093) | 0.006472 / 0.007986 (-0.001514) | 0.004287 / 0.004328 (-0.000041) | 0.067226 / 0.004250 (0.062976) | 0.063147 / 0.037052 (0.026094) | 0.314541 / 0.258489 (0.056052) | 0.369919 / 0.293841 (0.076078) | 0.031283 / 0.128546 (-0.097263) | 0.009175 / 0.075646 (-0.066471) | 0.289211 / 0.419271 (-0.130061) | 0.053444 / 0.043533 (0.009911) | 0.307308 / 0.255139 (0.052169) | 0.346221 / 0.283200 (0.063021) | 0.027948 / 0.141683 (-0.113735) | 1.475177 / 1.452155 (0.023022) | 1.575971 / 1.492716 (0.083255) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291092 / 0.018006 (0.273086) | 0.696951 / 0.000490 (0.696461) | 0.005211 / 0.000200 (0.005011) | 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) | 0.031787 / 0.037411 (-0.005625) | 0.084382 / 0.014526 (0.069857) | 0.106474 / 0.176557 (-0.070083) | 0.161472 / 0.737135 (-0.575663) | 0.108650 / 0.296338 (-0.187688) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379656 / 0.215209 (0.164447) | 3.784072 / 2.077655 (1.706417) | 1.826580 / 1.504120 (0.322460) | 1.654916 / 1.541195 (0.113721) | 1.730698 / 1.468490 (0.262208) | 0.478003 / 4.584777 (-4.106774) | 3.564920 / 3.745712 (-0.180792) | 5.824873 / 5.269862 (0.555012) | 3.454563 / 4.565676 (-1.111113) | 0.056646 / 0.424275 (-0.367629) | 0.007410 / 0.007607 (-0.000197) | 0.461781 / 0.226044 (0.235737) | 4.600928 / 2.268929 (2.331999) | 2.351887 / 55.444624 (-53.092738) | 1.986470 / 6.876477 (-4.890007) | 2.311623 / 2.142072 (0.169551) | 0.571247 / 4.805227 (-4.233980) | 0.132191 / 6.500664 (-6.368473) | 0.059943 / 0.075469 (-0.015526) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253142 / 1.841788 (-0.588646) | 21.294983 / 8.074308 (13.220675) | 14.522429 / 10.191392 (4.331037) | 0.166663 / 0.680424 (-0.513761) | 0.019694 / 0.534201 (-0.514507) | 0.395908 / 0.579283 (-0.183375) | 0.413283 / 0.434364 (-0.021081) | 0.457739 / 0.540337 (-0.082599) | 0.664361 / 1.386936 (-0.722575) |\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) | 0.007228 / 0.011353 (-0.004124) | 0.004941 / 0.011008 (-0.006067) | 0.065381 / 0.038508 (0.026873) | 0.090790 / 0.023109 (0.067681) | 0.391315 / 0.275898 (0.115417) | 0.416518 / 0.323480 (0.093038) | 0.007015 / 0.007986 (-0.000970) | 0.004417 / 0.004328 (0.000089) | 0.067235 / 0.004250 (0.062985) | 0.068092 / 0.037052 (0.031039) | 0.403031 / 0.258489 (0.144542) | 0.434013 / 0.293841 (0.140172) | 0.032004 / 0.128546 (-0.096542) | 0.009242 / 0.075646 (-0.066404) | 0.071222 / 0.419271 (-0.348050) | 0.054207 / 0.043533 (0.010674) | 0.386198 / 0.255139 (0.131059) | 0.404350 / 0.283200 (0.121150) | 0.036284 / 0.141683 (-0.105399) | 1.488814 / 1.452155 (0.036660) | 1.587785 / 1.492716 (0.095069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313760 / 0.018006 (0.295754) | 0.747778 / 0.000490 (0.747289) | 0.003307 / 0.000200 (0.003107) | 0.000113 / 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) | 0.034321 / 0.037411 (-0.003090) | 0.088266 / 0.014526 (0.073740) | 0.112874 / 0.176557 (-0.063682) | 0.171554 / 0.737135 (-0.565581) | 0.111356 / 0.296338 (-0.184982) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422624 / 0.215209 (0.207415) | 4.212079 / 2.077655 (2.134425) | 2.242742 / 1.504120 (0.738622) | 2.072555 / 1.541195 (0.531360) | 2.192648 / 1.468490 (0.724158) | 0.488214 / 4.584777 (-4.096563) | 3.597013 / 3.745712 (-0.148699) | 3.477556 / 5.269862 (-1.792305) | 2.184340 / 4.565676 (-2.381337) | 0.057170 / 0.424275 (-0.367105) | 0.007772 / 0.007607 (0.000165) | 0.499455 / 0.226044 (0.273411) | 4.988953 / 2.268929 (2.720024) | 2.797894 / 55.444624 (-52.646731) | 2.402215 / 6.876477 (-4.474262) | 2.725069 / 2.142072 (0.582997) | 0.596213 / 4.805227 (-4.209014) | 0.136564 / 6.500664 (-6.364100) | 0.061799 / 0.075469 (-0.013670) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.360739 / 1.841788 (-0.481049) | 21.846457 / 8.074308 (13.772149) | 14.568842 / 10.191392 (4.377450) | 0.168980 / 0.680424 (-0.511444) | 0.018795 / 0.534201 (-0.515406) | 0.396173 / 0.579283 (-0.183110) | 0.418651 / 0.434364 (-0.015713) | 0.480042 / 0.540337 (-0.060295) | 0.650803 / 1.386936 (-0.736133) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d460304487d4daab0a64ca0ca707e896367ca1 \"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) | 0.006640 / 0.011353 (-0.004713) | 0.004077 / 0.011008 (-0.006931) | 0.084905 / 0.038508 (0.046397) | 0.074004 / 0.023109 (0.050895) | 0.315968 / 0.275898 (0.040070) | 0.351594 / 0.323480 (0.028114) | 0.005623 / 0.007986 (-0.002362) | 0.003476 / 0.004328 (-0.000852) | 0.065089 / 0.004250 (0.060839) | 0.054683 / 0.037052 (0.017631) | 0.314983 / 0.258489 (0.056494) | 0.371776 / 0.293841 (0.077935) | 0.031727 / 0.128546 (-0.096819) | 0.008786 / 0.075646 (-0.066860) | 0.289905 / 0.419271 (-0.129367) | 0.053340 / 0.043533 (0.009807) | 0.311802 / 0.255139 (0.056663) | 0.351927 / 0.283200 (0.068727) | 0.024453 / 0.141683 (-0.117229) | 1.491727 / 1.452155 (0.039572) | 1.585027 / 1.492716 (0.092310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238757 / 0.018006 (0.220750) | 0.557691 / 0.000490 (0.557202) | 0.005158 / 0.000200 (0.004958) | 0.000204 / 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) | 0.028435 / 0.037411 (-0.008977) | 0.082219 / 0.014526 (0.067693) | 0.096932 / 0.176557 (-0.079625) | 0.153802 / 0.737135 (-0.583333) | 0.098338 / 0.296338 (-0.198001) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383448 / 0.215209 (0.168238) | 3.816074 / 2.077655 (1.738420) | 1.835111 / 1.504120 (0.330991) | 1.662326 / 1.541195 (0.121131) | 1.720202 / 1.468490 (0.251712) | 0.483107 / 4.584777 (-4.101669) | 3.648528 / 3.745712 (-0.097184) | 4.020929 / 5.269862 (-1.248932) | 2.433141 / 4.565676 (-2.132536) | 0.057081 / 0.424275 (-0.367194) | 0.007303 / 0.007607 (-0.000304) | 0.461366 / 0.226044 (0.235322) | 4.609090 / 2.268929 (2.340162) | 2.355940 / 55.444624 (-53.088684) | 1.989833 / 6.876477 (-4.886644) | 2.201451 / 2.142072 (0.059378) | 0.586156 / 4.805227 (-4.219071) | 0.133486 / 6.500664 (-6.367178) | 0.060062 / 0.075469 (-0.015407) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247845 / 1.841788 (-0.593942) | 19.624252 / 8.074308 (11.549944) | 14.305975 / 10.191392 (4.114583) | 0.168687 / 0.680424 (-0.511737) | 0.018075 / 0.534201 (-0.516126) | 0.393859 / 0.579283 (-0.185424) | 0.407272 / 0.434364 (-0.027092) | 0.463760 / 0.540337 (-0.076578) | 0.629930 / 1.386936 (-0.757006) |\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) | 0.006760 / 0.011353 (-0.004593) | 0.004345 / 0.011008 (-0.006663) | 0.064379 / 0.038508 (0.025871) | 0.078295 / 0.023109 (0.055186) | 0.364532 / 0.275898 (0.088633) | 0.395852 / 0.323480 (0.072372) | 0.005659 / 0.007986 (-0.002327) | 0.003515 / 0.004328 (-0.000813) | 0.065030 / 0.004250 (0.060780) | 0.059950 / 0.037052 (0.022898) | 0.375420 / 0.258489 (0.116931) | 0.411579 / 0.293841 (0.117738) | 0.031575 / 0.128546 (-0.096972) | 0.008737 / 0.075646 (-0.066910) | 0.070350 / 0.419271 (-0.348922) | 0.050607 / 0.043533 (0.007075) | 0.359785 / 0.255139 (0.104646) | 0.382638 / 0.283200 (0.099438) | 0.025533 / 0.141683 (-0.116150) | 1.564379 / 1.452155 (0.112225) | 1.620642 / 1.492716 (0.127925) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212779 / 0.018006 (0.194773) | 0.563827 / 0.000490 (0.563337) | 0.003767 / 0.000200 (0.003567) | 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) | 0.030275 / 0.037411 (-0.007136) | 0.088108 / 0.014526 (0.073582) | 0.102454 / 0.176557 (-0.074103) | 0.156107 / 0.737135 (-0.581028) | 0.103961 / 0.296338 (-0.192378) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421395 / 0.215209 (0.206186) | 4.204935 / 2.077655 (2.127280) | 2.144929 / 1.504120 (0.640809) | 1.999341 / 1.541195 (0.458147) | 2.066966 / 1.468490 (0.598476) | 0.486135 / 4.584777 (-4.098642) | 3.628139 / 3.745712 (-0.117573) | 5.652683 / 5.269862 (0.382821) | 3.216721 / 4.565676 (-1.348956) | 0.057513 / 0.424275 (-0.366762) | 0.007553 / 0.007607 (-0.000055) | 0.494470 / 0.226044 (0.268426) | 4.949343 / 2.268929 (2.680414) | 2.654222 / 55.444624 (-52.790402) | 2.322257 / 6.876477 (-4.554220) | 2.555633 / 2.142072 (0.413561) | 0.588355 / 4.805227 (-4.216872) | 0.134481 / 6.500664 (-6.366183) | 0.062415 / 0.075469 (-0.013054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.377578 / 1.841788 (-0.464209) | 19.805201 / 8.074308 (11.730893) | 14.128536 / 10.191392 (3.937144) | 0.164343 / 0.680424 (-0.516081) | 0.018553 / 0.534201 (-0.515648) | 0.398191 / 0.579283 (-0.181093) | 0.414268 / 0.434364 (-0.020096) | 0.462270 / 0.540337 (-0.078068) | 0.608497 / 1.386936 (-0.778439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3af05ba487f361fae90a4c80af72de5c4ed70162 \"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) | 0.006966 / 0.011353 (-0.004387) | 0.004339 / 0.011008 (-0.006669) | 0.086682 / 0.038508 (0.048174) | 0.086143 / 0.023109 (0.063034) | 0.316106 / 0.275898 (0.040208) | 0.351422 / 0.323480 (0.027942) | 0.005916 / 0.007986 (-0.002069) | 0.003630 / 0.004328 (-0.000698) | 0.066980 / 0.004250 (0.062730) | 0.060031 / 0.037052 (0.022979) | 0.317487 / 0.258489 (0.058998) | 0.356280 / 0.293841 (0.062439) | 0.031816 / 0.128546 (-0.096730) | 0.008797 / 0.075646 (-0.066849) | 0.289848 / 0.419271 (-0.129424) | 0.055431 / 0.043533 (0.011898) | 0.318881 / 0.255139 (0.063742) | 0.332315 / 0.283200 (0.049116) | 0.025946 / 0.141683 (-0.115737) | 1.472904 / 1.452155 (0.020749) | 1.577973 / 1.492716 (0.085257) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239056 / 0.018006 (0.221050) | 0.565406 / 0.000490 (0.564917) | 0.003606 / 0.000200 (0.003406) | 0.000080 / 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) | 0.029771 / 0.037411 (-0.007640) | 0.085534 / 0.014526 (0.071008) | 0.107008 / 0.176557 (-0.069548) | 0.631583 / 0.737135 (-0.105552) | 0.104210 / 0.296338 (-0.192128) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390675 / 0.215209 (0.175466) | 3.898746 / 2.077655 (1.821091) | 1.933048 / 1.504120 (0.428928) | 1.792162 / 1.541195 (0.250967) | 1.958045 / 1.468490 (0.489555) | 0.488632 / 4.584777 (-4.096144) | 3.696306 / 3.745712 (-0.049406) | 3.454600 / 5.269862 (-1.815262) | 2.176292 / 4.565676 (-2.389385) | 0.057617 / 0.424275 (-0.366658) | 0.007603 / 0.007607 (-0.000004) | 0.467843 / 0.226044 (0.241798) | 4.672928 / 2.268929 (2.404000) | 2.441096 / 55.444624 (-53.003529) | 2.133506 / 6.876477 (-4.742970) | 2.431167 / 2.142072 (0.289095) | 0.588567 / 4.805227 (-4.216661) | 0.136070 / 6.500664 (-6.364594) | 0.063395 / 0.075469 (-0.012074) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255003 / 1.841788 (-0.586784) | 20.587656 / 8.074308 (12.513348) | 15.147817 / 10.191392 (4.956425) | 0.152039 / 0.680424 (-0.528384) | 0.018815 / 0.534201 (-0.515386) | 0.397458 / 0.579283 (-0.181825) | 0.431433 / 0.434364 (-0.002931) | 0.487890 / 0.540337 (-0.052448) | 0.675367 / 1.386936 (-0.711569) |\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) | 0.007209 / 0.011353 (-0.004144) | 0.004372 / 0.011008 (-0.006636) | 0.066288 / 0.038508 (0.027780) | 0.091776 / 0.023109 (0.068667) | 0.390724 / 0.275898 (0.114826) | 0.434711 / 0.323480 (0.111231) | 0.005790 / 0.007986 (-0.002196) | 0.003562 / 0.004328 (-0.000767) | 0.066155 / 0.004250 (0.061904) | 0.062459 / 0.037052 (0.025406) | 0.406622 / 0.258489 (0.148133) | 0.433976 / 0.293841 (0.140135) | 0.032590 / 0.128546 (-0.095957) | 0.008856 / 0.075646 (-0.066790) | 0.072327 / 0.419271 (-0.346945) | 0.049958 / 0.043533 (0.006426) | 0.400164 / 0.255139 (0.145025) | 0.413339 / 0.283200 (0.130139) | 0.025283 / 0.141683 (-0.116399) | 1.487668 / 1.452155 (0.035514) | 1.537679 / 1.492716 (0.044962) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257814 / 0.018006 (0.239808) | 0.571741 / 0.000490 (0.571251) | 0.000412 / 0.000200 (0.000212) | 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) | 0.033893 / 0.037411 (-0.003518) | 0.094533 / 0.014526 (0.080008) | 0.105876 / 0.176557 (-0.070680) | 0.158675 / 0.737135 (-0.578460) | 0.107790 / 0.296338 (-0.188548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425796 / 0.215209 (0.210587) | 4.229159 / 2.077655 (2.151505) | 2.239613 / 1.504120 (0.735493) | 2.073830 / 1.541195 (0.532635) | 2.185508 / 1.468490 (0.717018) | 0.483984 / 4.584777 (-4.100793) | 3.645575 / 3.745712 (-0.100137) | 3.454767 / 5.269862 (-1.815095) | 2.141387 / 4.565676 (-2.424290) | 0.057570 / 0.424275 (-0.366705) | 0.007901 / 0.007607 (0.000294) | 0.501160 / 0.226044 (0.275116) | 5.012283 / 2.268929 (2.743355) | 2.701267 / 55.444624 (-52.743357) | 2.465409 / 6.876477 (-4.411068) | 2.696812 / 2.142072 (0.554739) | 0.587160 / 4.805227 (-4.218067) | 0.134175 / 6.500664 (-6.366489) | 0.062028 / 0.075469 (-0.013441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345632 / 1.841788 (-0.496155) | 21.077279 / 8.074308 (13.002971) | 14.700826 / 10.191392 (4.509434) | 0.156191 / 0.680424 (-0.524233) | 0.018991 / 0.534201 (-0.515210) | 0.400413 / 0.579283 (-0.178870) | 0.420597 / 0.434364 (-0.013767) | 0.486534 / 0.540337 (-0.053804) | 0.646606 / 1.386936 (-0.740330) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5bb8fabb135ca8adf47151ad3de050e3a258ccab \"CML watermark\")\n" ]

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[ "Thanks for reporting. We've merged a PR with a fix." ]

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[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]

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[ "Thanks for reporting! A PR with a fix has just been merged." ]

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[ "Hi @mariosasko unless someone's already working on it, I guess I can tackle it!", "Hi! Sure, feel free to tackle this.", "#self-assign", "I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?" ]

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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) | 0.006031 / 0.011353 (-0.005322) | 0.003579 / 0.011008 (-0.007429) | 0.080862 / 0.038508 (0.042354) | 0.056660 / 0.023109 (0.033551) | 0.388285 / 0.275898 (0.112387) | 0.422270 / 0.323480 (0.098790) | 0.004651 / 0.007986 (-0.003335) | 0.002895 / 0.004328 (-0.001433) | 0.062767 / 0.004250 (0.058517) | 0.046491 / 0.037052 (0.009438) | 0.389918 / 0.258489 (0.131428) | 0.434650 / 0.293841 (0.140809) | 0.027265 / 0.128546 (-0.101281) | 0.007946 / 0.075646 (-0.067701) | 0.261207 / 0.419271 (-0.158065) | 0.045057 / 0.043533 (0.001525) | 0.391977 / 0.255139 (0.136838) | 0.418525 / 0.283200 (0.135326) | 0.020705 / 0.141683 (-0.120978) | 1.459271 / 1.452155 (0.007116) | 1.516935 / 1.492716 (0.024218) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174659 / 0.018006 (0.156653) | 0.429627 / 0.000490 (0.429137) | 0.003714 / 0.000200 (0.003514) | 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) | 0.023255 / 0.037411 (-0.014156) | 0.073463 / 0.014526 (0.058937) | 0.083000 / 0.176557 (-0.093557) | 0.146704 / 0.737135 (-0.590431) | 0.084419 / 0.296338 (-0.211919) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392222 / 0.215209 (0.177013) | 3.902620 / 2.077655 (1.824966) | 1.903056 / 1.504120 (0.398936) | 1.753423 / 1.541195 (0.212228) | 1.874547 / 1.468490 (0.406057) | 0.495947 / 4.584777 (-4.088829) | 3.084680 / 3.745712 (-0.661032) | 4.235064 / 5.269862 (-1.034797) | 2.626840 / 4.565676 (-1.938837) | 0.057273 / 0.424275 (-0.367002) | 0.006457 / 0.007607 (-0.001150) | 0.466018 / 0.226044 (0.239974) | 4.648264 / 2.268929 (2.379335) | 2.520293 / 55.444624 (-52.924331) | 2.339393 / 6.876477 (-4.537083) | 2.538848 / 2.142072 (0.396775) | 0.592018 / 4.805227 (-4.213210) | 0.125041 / 6.500664 (-6.375623) | 0.061038 / 0.075469 (-0.014431) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244285 / 1.841788 (-0.597503) | 18.411576 / 8.074308 (10.337268) | 13.850100 / 10.191392 (3.658708) | 0.131904 / 0.680424 (-0.548520) | 0.016824 / 0.534201 (-0.517377) | 0.328931 / 0.579283 (-0.250352) | 0.364801 / 0.434364 (-0.069563) | 0.376298 / 0.540337 (-0.164039) | 0.525045 / 1.386936 (-0.861891) |\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) | 0.006059 / 0.011353 (-0.005294) | 0.003693 / 0.011008 (-0.007315) | 0.062982 / 0.038508 (0.024473) | 0.062155 / 0.023109 (0.039046) | 0.389467 / 0.275898 (0.113568) | 0.437046 / 0.323480 (0.113566) | 0.004823 / 0.007986 (-0.003163) | 0.002935 / 0.004328 (-0.001393) | 0.062679 / 0.004250 (0.058429) | 0.049676 / 0.037052 (0.012623) | 0.418054 / 0.258489 (0.159565) | 0.442467 / 0.293841 (0.148626) | 0.027652 / 0.128546 (-0.100895) | 0.008146 / 0.075646 (-0.067501) | 0.069414 / 0.419271 (-0.349858) | 0.042884 / 0.043533 (-0.000649) | 0.387167 / 0.255139 (0.132028) | 0.418684 / 0.283200 (0.135484) | 0.022419 / 0.141683 (-0.119264) | 1.460606 / 1.452155 (0.008451) | 1.514204 / 1.492716 (0.021487) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200523 / 0.018006 (0.182517) | 0.415970 / 0.000490 (0.415481) | 0.003202 / 0.000200 (0.003002) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025836 / 0.037411 (-0.011575) | 0.078859 / 0.014526 (0.064333) | 0.088523 / 0.176557 (-0.088034) | 0.141572 / 0.737135 (-0.595563) | 0.090258 / 0.296338 (-0.206080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416548 / 0.215209 (0.201339) | 4.155278 / 2.077655 (2.077623) | 2.126683 / 1.504120 (0.622563) | 1.963762 / 1.541195 (0.422568) | 2.029018 / 1.468490 (0.560528) | 0.499005 / 4.584777 (-4.085772) | 3.063503 / 3.745712 (-0.682209) | 4.250800 / 5.269862 (-1.019061) | 2.642634 / 4.565676 (-1.923043) | 0.057815 / 0.424275 (-0.366460) | 0.006784 / 0.007607 (-0.000823) | 0.492481 / 0.226044 (0.266437) | 4.914306 / 2.268929 (2.645377) | 2.601582 / 55.444624 (-52.843042) | 2.337863 / 6.876477 (-4.538614) | 2.462854 / 2.142072 (0.320782) | 0.593738 / 4.805227 (-4.211489) | 0.127030 / 6.500664 (-6.373634) | 0.064206 / 0.075469 (-0.011263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.326919 / 1.841788 (-0.514868) | 18.728929 / 8.074308 (10.654621) | 13.903681 / 10.191392 (3.712289) | 0.162670 / 0.680424 (-0.517754) | 0.016913 / 0.534201 (-0.517288) | 0.337504 / 0.579283 (-0.241779) | 0.339786 / 0.434364 (-0.094577) | 0.384955 / 0.540337 (-0.155383) | 0.514358 / 1.386936 (-0.872578) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5c31b492c45e01c6f4593ada2b84517a75a5c7c \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6085). 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) | 0.007610 / 0.011353 (-0.003743) | 0.004616 / 0.011008 (-0.006392) | 0.100330 / 0.038508 (0.061821) | 0.084450 / 0.023109 (0.061341) | 0.386610 / 0.275898 (0.110712) | 0.418479 / 0.323480 (0.094999) | 0.006085 / 0.007986 (-0.001900) | 0.003800 / 0.004328 (-0.000529) | 0.076248 / 0.004250 (0.071997) | 0.065175 / 0.037052 (0.028122) | 0.387154 / 0.258489 (0.128665) | 0.425484 / 0.293841 (0.131643) | 0.035946 / 0.128546 (-0.092601) | 0.009901 / 0.075646 (-0.065745) | 0.343015 / 0.419271 (-0.076256) | 0.060965 / 0.043533 (0.017432) | 0.390585 / 0.255139 (0.135446) | 0.405873 / 0.283200 (0.122673) | 0.026929 / 0.141683 (-0.114754) | 1.767916 / 1.452155 (0.315761) | 1.893431 / 1.492716 (0.400715) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237888 / 0.018006 (0.219882) | 0.503949 / 0.000490 (0.503459) | 0.004769 / 0.000200 (0.004570) | 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) | 0.031553 / 0.037411 (-0.005859) | 0.096950 / 0.014526 (0.082424) | 0.110374 / 0.176557 (-0.066183) | 0.176754 / 0.737135 (-0.560381) | 0.111703 / 0.296338 (-0.184635) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449232 / 0.215209 (0.234023) | 4.510247 / 2.077655 (2.432592) | 2.188547 / 1.504120 (0.684427) | 2.007530 / 1.541195 (0.466335) | 2.095650 / 1.468490 (0.627160) | 0.563262 / 4.584777 (-4.021515) | 4.062412 / 3.745712 (0.316700) | 6.338350 / 5.269862 (1.068489) | 3.844669 / 4.565676 (-0.721008) | 0.064517 / 0.424275 (-0.359758) | 0.008536 / 0.007607 (0.000929) | 0.553872 / 0.226044 (0.327828) | 5.530311 / 2.268929 (3.261383) | 2.835109 / 55.444624 (-52.609516) | 2.493900 / 6.876477 (-4.382577) | 2.728412 / 2.142072 (0.586340) | 0.680161 / 4.805227 (-4.125066) | 0.155831 / 6.500664 (-6.344833) | 0.070359 / 0.075469 (-0.005110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504852 / 1.841788 (-0.336936) | 22.806335 / 8.074308 (14.732027) | 16.598386 / 10.191392 (6.406994) | 0.207857 / 0.680424 (-0.472566) | 0.021425 / 0.534201 (-0.512776) | 0.474069 / 0.579283 (-0.105214) | 0.472263 / 0.434364 (0.037899) | 0.542195 / 0.540337 (0.001858) | 0.782871 / 1.386936 (-0.604065) |\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) | 0.007443 / 0.011353 (-0.003910) | 0.004465 / 0.011008 (-0.006544) | 0.076268 / 0.038508 (0.037759) | 0.086607 / 0.023109 (0.063498) | 0.443295 / 0.275898 (0.167397) | 0.472819 / 0.323480 (0.149339) | 0.005841 / 0.007986 (-0.002144) | 0.003727 / 0.004328 (-0.000602) | 0.076015 / 0.004250 (0.071765) | 0.063188 / 0.037052 (0.026136) | 0.450555 / 0.258489 (0.192066) | 0.478532 / 0.293841 (0.184691) | 0.036258 / 0.128546 (-0.092288) | 0.009869 / 0.075646 (-0.065777) | 0.083786 / 0.419271 (-0.335486) | 0.056546 / 0.043533 (0.013013) | 0.449647 / 0.255139 (0.194508) | 0.457588 / 0.283200 (0.174389) | 0.027197 / 0.141683 (-0.114486) | 1.769991 / 1.452155 (0.317836) | 1.859905 / 1.492716 (0.367189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268637 / 0.018006 (0.250631) | 0.492860 / 0.000490 (0.492370) | 0.008574 / 0.000200 (0.008374) | 0.000140 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037679 / 0.037411 (0.000268) | 0.108258 / 0.014526 (0.093733) | 0.117850 / 0.176557 (-0.058707) | 0.181611 / 0.737135 (-0.555524) | 0.120901 / 0.296338 (-0.175437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485780 / 0.215209 (0.270571) | 4.851289 / 2.077655 (2.773635) | 2.486068 / 1.504120 (0.981948) | 2.299417 / 1.541195 (0.758222) | 2.387093 / 1.468490 (0.918603) | 0.568826 / 4.584777 (-4.015951) | 4.163426 / 3.745712 (0.417713) | 6.224964 / 5.269862 (0.955102) | 3.255619 / 4.565676 (-1.310058) | 0.067081 / 0.424275 (-0.357194) | 0.009065 / 0.007607 (0.001458) | 0.580449 / 0.226044 (0.354405) | 5.786394 / 2.268929 (3.517465) | 3.057780 / 55.444624 (-52.386844) | 2.764339 / 6.876477 (-4.112138) | 2.880718 / 2.142072 (0.738645) | 0.681376 / 4.805227 (-4.123851) | 0.157858 / 6.500664 (-6.342806) | 0.072481 / 0.075469 (-0.002988) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.590704 / 1.841788 (-0.251083) | 23.141929 / 8.074308 (15.067620) | 17.001141 / 10.191392 (6.809749) | 0.203790 / 0.680424 (-0.476634) | 0.021766 / 0.534201 (-0.512435) | 0.475309 / 0.579283 (-0.103974) | 0.466448 / 0.434364 (0.032084) | 0.551470 / 0.540337 (0.011132) | 0.727876 / 1.386936 (-0.659060) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b19eea7fc5cf484e8cdf41d6ae035f94d8a671 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6083). 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) | 0.006049 / 0.011353 (-0.005304) | 0.003698 / 0.011008 (-0.007310) | 0.080614 / 0.038508 (0.042106) | 0.060955 / 0.023109 (0.037846) | 0.337119 / 0.275898 (0.061221) | 0.369544 / 0.323480 (0.046064) | 0.004681 / 0.007986 (-0.003305) | 0.002892 / 0.004328 (-0.001436) | 0.062907 / 0.004250 (0.058657) | 0.049235 / 0.037052 (0.012183) | 0.338842 / 0.258489 (0.080353) | 0.371172 / 0.293841 (0.077331) | 0.027016 / 0.128546 (-0.101530) | 0.007940 / 0.075646 (-0.067706) | 0.260902 / 0.419271 (-0.158369) | 0.044566 / 0.043533 (0.001034) | 0.342354 / 0.255139 (0.087215) | 0.359829 / 0.283200 (0.076629) | 0.020801 / 0.141683 (-0.120881) | 1.444111 / 1.452155 (-0.008044) | 1.515595 / 1.492716 (0.022879) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183446 / 0.018006 (0.165439) | 0.437071 / 0.000490 (0.436581) | 0.003124 / 0.000200 (0.002924) | 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) | 0.023760 / 0.037411 (-0.013651) | 0.072812 / 0.014526 (0.058286) | 0.082790 / 0.176557 (-0.093766) | 0.146330 / 0.737135 (-0.590805) | 0.084469 / 0.296338 (-0.211870) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395215 / 0.215209 (0.180006) | 3.953023 / 2.077655 (1.875369) | 1.914268 / 1.504120 (0.410148) | 1.710195 / 1.541195 (0.169001) | 1.782594 / 1.468490 (0.314104) | 0.503651 / 4.584777 (-4.081126) | 3.039656 / 3.745712 (-0.706056) | 4.364691 / 5.269862 (-0.905171) | 2.597762 / 4.565676 (-1.967915) | 0.057384 / 0.424275 (-0.366891) | 0.006419 / 0.007607 (-0.001188) | 0.467214 / 0.226044 (0.241169) | 4.661425 / 2.268929 (2.392497) | 2.341957 / 55.444624 (-53.102667) | 1.977598 / 6.876477 (-4.898878) | 2.178005 / 2.142072 (0.035933) | 0.588492 / 4.805227 (-4.216735) | 0.124972 / 6.500664 (-6.375692) | 0.060902 / 0.075469 (-0.014567) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243092 / 1.841788 (-0.598695) | 18.369971 / 8.074308 (10.295663) | 13.939700 / 10.191392 (3.748308) | 0.149275 / 0.680424 (-0.531149) | 0.016873 / 0.534201 (-0.517328) | 0.334245 / 0.579283 (-0.245038) | 0.353832 / 0.434364 (-0.080532) | 0.382720 / 0.540337 (-0.157617) | 0.534634 / 1.386936 (-0.852302) |\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) | 0.005933 / 0.011353 (-0.005420) | 0.003695 / 0.011008 (-0.007313) | 0.063457 / 0.038508 (0.024949) | 0.062347 / 0.023109 (0.039238) | 0.412370 / 0.275898 (0.136472) | 0.450399 / 0.323480 (0.126920) | 0.004627 / 0.007986 (-0.003358) | 0.002822 / 0.004328 (-0.001507) | 0.063819 / 0.004250 (0.059569) | 0.049154 / 0.037052 (0.012101) | 0.428196 / 0.258489 (0.169707) | 0.464109 / 0.293841 (0.170268) | 0.026967 / 0.128546 (-0.101579) | 0.007876 / 0.075646 (-0.067770) | 0.068479 / 0.419271 (-0.350793) | 0.041080 / 0.043533 (-0.002453) | 0.399817 / 0.255139 (0.144678) | 0.426900 / 0.283200 (0.143701) | 0.019931 / 0.141683 (-0.121752) | 1.461642 / 1.452155 (0.009487) | 1.529314 / 1.492716 (0.036598) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230256 / 0.018006 (0.212249) | 0.423442 / 0.000490 (0.422952) | 0.002492 / 0.000200 (0.002292) | 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) | 0.025798 / 0.037411 (-0.011613) | 0.077361 / 0.014526 (0.062836) | 0.088454 / 0.176557 (-0.088102) | 0.142137 / 0.737135 (-0.594998) | 0.088213 / 0.296338 (-0.208125) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417656 / 0.215209 (0.202447) | 4.157095 / 2.077655 (2.079440) | 2.132863 / 1.504120 (0.628743) | 1.967220 / 1.541195 (0.426025) | 2.020505 / 1.468490 (0.552015) | 0.496835 / 4.584777 (-4.087942) | 2.989251 / 3.745712 (-0.756462) | 2.849315 / 5.269862 (-2.420546) | 1.848941 / 4.565676 (-2.716736) | 0.057307 / 0.424275 (-0.366968) | 0.006825 / 0.007607 (-0.000782) | 0.489103 / 0.226044 (0.263059) | 4.904776 / 2.268929 (2.635847) | 2.593914 / 55.444624 (-52.850710) | 2.253384 / 6.876477 (-4.623093) | 2.426384 / 2.142072 (0.284312) | 0.592467 / 4.805227 (-4.212760) | 0.126122 / 6.500664 (-6.374542) | 0.063160 / 0.075469 (-0.012309) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313020 / 1.841788 (-0.528768) | 18.343984 / 8.074308 (10.269676) | 13.763060 / 10.191392 (3.571668) | 0.146312 / 0.680424 (-0.534111) | 0.016980 / 0.534201 (-0.517221) | 0.339572 / 0.579283 (-0.239711) | 0.351310 / 0.434364 (-0.083054) | 0.397616 / 0.540337 (-0.142721) | 0.536879 / 1.386936 (-0.850057) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#73bed12ecda17d1573fd3bf73ed5db24d3622f86 \"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) | 0.009979 / 0.011353 (-0.001374) | 0.005024 / 0.011008 (-0.005984) | 0.096566 / 0.038508 (0.058058) | 0.081181 / 0.023109 (0.058072) | 0.398415 / 0.275898 (0.122517) | 0.513971 / 0.323480 (0.190491) | 0.006716 / 0.007986 (-0.001269) | 0.004350 / 0.004328 (0.000022) | 0.071418 / 0.004250 (0.067168) | 0.065002 / 0.037052 (0.027949) | 0.424791 / 0.258489 (0.166302) | 0.442369 / 0.293841 (0.148528) | 0.054540 / 0.128546 (-0.074007) | 0.014067 / 0.075646 (-0.061580) | 0.368930 / 0.419271 (-0.050341) | 0.082468 / 0.043533 (0.038935) | 0.419875 / 0.255139 (0.164736) | 0.508308 / 0.283200 (0.225108) | 0.050411 / 0.141683 (-0.091272) | 1.582271 / 1.452155 (0.130116) | 1.842033 / 1.492716 (0.349317) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290427 / 0.018006 (0.272420) | 0.594736 / 0.000490 (0.594246) | 0.007058 / 0.000200 (0.006858) | 0.000149 / 0.000054 (0.000095) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027085 / 0.037411 (-0.010326) | 0.087626 / 0.014526 (0.073101) | 0.094299 / 0.176557 (-0.082257) | 0.160169 / 0.737135 (-0.576966) | 0.101474 / 0.296338 (-0.194864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.545845 / 0.215209 (0.330636) | 5.674389 / 2.077655 (3.596734) | 2.489065 / 1.504120 (0.984945) | 2.166674 / 1.541195 (0.625479) | 2.166925 / 1.468490 (0.698434) | 0.791244 / 4.584777 (-3.793533) | 4.944878 / 3.745712 (1.199165) | 4.121628 / 5.269862 (-1.148234) | 2.701262 / 4.565676 (-1.864415) | 0.087609 / 0.424275 (-0.336666) | 0.006945 / 0.007607 (-0.000662) | 0.668478 / 0.226044 (0.442434) | 6.552813 / 2.268929 (4.283885) | 3.164698 / 55.444624 (-52.279927) | 2.447333 / 6.876477 (-4.429144) | 2.608271 / 2.142072 (0.466198) | 0.954202 / 4.805227 (-3.851025) | 0.187730 / 6.500664 (-6.312934) | 0.063229 / 0.075469 (-0.012240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.461042 / 1.841788 (-0.380746) | 21.601409 / 8.074308 (13.527101) | 18.553604 / 10.191392 (8.362212) | 0.234571 / 0.680424 (-0.445853) | 0.027119 / 0.534201 (-0.507082) | 0.423448 / 0.579283 (-0.155835) | 0.556397 / 0.434364 (0.122033) | 0.493958 / 0.540337 (-0.046379) | 0.711345 / 1.386936 (-0.675591) |\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) | 0.008637 / 0.011353 (-0.002716) | 0.014450 / 0.011008 (0.003442) | 0.084135 / 0.038508 (0.045627) | 0.080513 / 0.023109 (0.057403) | 0.557941 / 0.275898 (0.282042) | 0.563199 / 0.323480 (0.239719) | 0.006475 / 0.007986 (-0.001510) | 0.004407 / 0.004328 (0.000078) | 0.088537 / 0.004250 (0.084287) | 0.060871 / 0.037052 (0.023819) | 0.593077 / 0.258489 (0.334588) | 0.615572 / 0.293841 (0.321732) | 0.050157 / 0.128546 (-0.078389) | 0.014313 / 0.075646 (-0.061333) | 0.091784 / 0.419271 (-0.327487) | 0.065649 / 0.043533 (0.022116) | 0.532569 / 0.255139 (0.277430) | 0.580775 / 0.283200 (0.297575) | 0.036434 / 0.141683 (-0.105249) | 2.080051 / 1.452155 (0.627896) | 1.907430 / 1.492716 (0.414713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.297763 / 0.018006 (0.279757) | 0.670408 / 0.000490 (0.669918) | 0.000467 / 0.000200 (0.000267) | 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) | 0.030297 / 0.037411 (-0.007114) | 0.100310 / 0.014526 (0.085784) | 0.113158 / 0.176557 (-0.063398) | 0.149599 / 0.737135 (-0.587536) | 0.102620 / 0.296338 (-0.193718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616588 / 0.215209 (0.401379) | 6.572262 / 2.077655 (4.494608) | 2.830748 / 1.504120 (1.326628) | 2.478441 / 1.541195 (0.937246) | 2.573017 / 1.468490 (1.104527) | 0.844154 / 4.584777 (-3.740623) | 5.161625 / 3.745712 (1.415913) | 4.541114 / 5.269862 (-0.728748) | 2.907804 / 4.565676 (-1.657872) | 0.097044 / 0.424275 (-0.327231) | 0.008692 / 0.007607 (0.001085) | 0.806640 / 0.226044 (0.580595) | 7.620521 / 2.268929 (5.351593) | 3.587100 / 55.444624 (-51.857524) | 2.901319 / 6.876477 (-3.975157) | 3.091288 / 2.142072 (0.949215) | 1.056109 / 4.805227 (-3.749118) | 0.209860 / 6.500664 (-6.290804) | 0.079575 / 0.075469 (0.004106) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.966194 / 1.841788 (0.124407) | 28.040515 / 8.074308 (19.966207) | 25.848647 / 10.191392 (15.657255) | 0.255472 / 0.680424 (-0.424951) | 0.036154 / 0.534201 (-0.498046) | 0.515168 / 0.579283 (-0.064115) | 0.696092 / 0.434364 (0.261728) | 0.602712 / 0.540337 (0.062374) | 0.781091 / 1.386936 (-0.605845) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f641aca7fbb1f21da48c087a5c10e76f4c6be35 \"CML watermark\")\n" ]

[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6082). 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) | 0.007215 / 0.011353 (-0.004138) | 0.004101 / 0.011008 (-0.006907) | 0.085884 / 0.038508 (0.047376) | 0.085375 / 0.023109 (0.062266) | 0.351610 / 0.275898 (0.075712) | 0.399284 / 0.323480 (0.075804) | 0.005598 / 0.007986 (-0.002388) | 0.003405 / 0.004328 (-0.000923) | 0.064906 / 0.004250 (0.060656) | 0.059000 / 0.037052 (0.021948) | 0.354589 / 0.258489 (0.096100) | 0.406070 / 0.293841 (0.112229) | 0.031627 / 0.128546 (-0.096919) | 0.008597 / 0.075646 (-0.067049) | 0.291050 / 0.419271 (-0.128221) | 0.054120 / 0.043533 (0.010587) | 0.366242 / 0.255139 (0.111103) | 0.375975 / 0.283200 (0.092776) | 0.025608 / 0.141683 (-0.116074) | 1.473514 / 1.452155 (0.021359) | 1.543226 / 1.492716 (0.050510) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198068 / 0.018006 (0.180062) | 0.450583 / 0.000490 (0.450093) | 0.005368 / 0.000200 (0.005168) | 0.000102 / 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) | 0.028323 / 0.037411 (-0.009089) | 0.089058 / 0.014526 (0.074533) | 0.097718 / 0.176557 (-0.078839) | 0.154546 / 0.737135 (-0.582590) | 0.098224 / 0.296338 (-0.198115) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386292 / 0.215209 (0.171083) | 3.846222 / 2.077655 (1.768567) | 1.858695 / 1.504120 (0.354575) | 1.685885 / 1.541195 (0.144690) | 1.790727 / 1.468490 (0.322237) | 0.486771 / 4.584777 (-4.098006) | 3.658363 / 3.745712 (-0.087349) | 5.345236 / 5.269862 (0.075374) | 3.215942 / 4.565676 (-1.349734) | 0.057580 / 0.424275 (-0.366695) | 0.007382 / 0.007607 (-0.000225) | 0.464174 / 0.226044 (0.238129) | 4.640848 / 2.268929 (2.371920) | 2.383152 / 55.444624 (-53.061472) | 2.013288 / 6.876477 (-4.863188) | 2.244142 / 2.142072 (0.102069) | 0.585408 / 4.805227 (-4.219819) | 0.134698 / 6.500664 (-6.365966) | 0.060641 / 0.075469 (-0.014828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258414 / 1.841788 (-0.583374) | 19.825848 / 8.074308 (11.751540) | 14.644025 / 10.191392 (4.452633) | 0.169198 / 0.680424 (-0.511226) | 0.018180 / 0.534201 (-0.516021) | 0.395100 / 0.579283 (-0.184183) | 0.411543 / 0.434364 (-0.022821) | 0.463364 / 0.540337 (-0.076973) | 0.628613 / 1.386936 (-0.758323) |\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) | 0.006860 / 0.011353 (-0.004493) | 0.003981 / 0.011008 (-0.007027) | 0.065589 / 0.038508 (0.027081) | 0.082460 / 0.023109 (0.059350) | 0.362980 / 0.275898 (0.087082) | 0.394837 / 0.323480 (0.071357) | 0.005298 / 0.007986 (-0.002688) | 0.003372 / 0.004328 (-0.000957) | 0.064918 / 0.004250 (0.060667) | 0.058033 / 0.037052 (0.020981) | 0.367259 / 0.258489 (0.108770) | 0.403122 / 0.293841 (0.109281) | 0.031566 / 0.128546 (-0.096980) | 0.008583 / 0.075646 (-0.067063) | 0.071287 / 0.419271 (-0.347984) | 0.049586 / 0.043533 (0.006053) | 0.359252 / 0.255139 (0.104113) | 0.378519 / 0.283200 (0.095319) | 0.023412 / 0.141683 (-0.118271) | 1.494522 / 1.452155 (0.042367) | 1.559176 / 1.492716 (0.066460) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228396 / 0.018006 (0.210390) | 0.441865 / 0.000490 (0.441375) | 0.000395 / 0.000200 (0.000195) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031169 / 0.037411 (-0.006242) | 0.093427 / 0.014526 (0.078901) | 0.100673 / 0.176557 (-0.075883) | 0.152817 / 0.737135 (-0.584319) | 0.102226 / 0.296338 (-0.194112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437032 / 0.215209 (0.221823) | 4.376078 / 2.077655 (2.298423) | 2.346928 / 1.504120 (0.842808) | 2.168573 / 1.541195 (0.627378) | 2.261024 / 1.468490 (0.792534) | 0.497080 / 4.584777 (-4.087697) | 3.594402 / 3.745712 (-0.151310) | 5.090361 / 5.269862 (-0.179501) | 3.034750 / 4.565676 (-1.530927) | 0.058538 / 0.424275 (-0.365737) | 0.007892 / 0.007607 (0.000285) | 0.517643 / 0.226044 (0.291598) | 5.173174 / 2.268929 (2.904246) | 2.825917 / 55.444624 (-52.618708) | 2.542593 / 6.876477 (-4.333884) | 2.716290 / 2.142072 (0.574218) | 0.598253 / 4.805227 (-4.206974) | 0.135610 / 6.500664 (-6.365054) | 0.062113 / 0.075469 (-0.013356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.389554 / 1.841788 (-0.452233) | 20.412868 / 8.074308 (12.338560) | 14.539988 / 10.191392 (4.348596) | 0.162046 / 0.680424 (-0.518378) | 0.018508 / 0.534201 (-0.515693) | 0.398840 / 0.579283 (-0.180443) | 0.400902 / 0.434364 (-0.033462) | 0.463647 / 0.540337 (-0.076691) | 0.612921 / 1.386936 (-0.774015) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45bef1810d9341ba4cb27547d748fddb97843792 \"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) | 0.005943 / 0.011353 (-0.005410) | 0.003582 / 0.011008 (-0.007426) | 0.080030 / 0.038508 (0.041522) | 0.057458 / 0.023109 (0.034349) | 0.390783 / 0.275898 (0.114885) | 0.430926 / 0.323480 (0.107446) | 0.003207 / 0.007986 (-0.004778) | 0.003592 / 0.004328 (-0.000737) | 0.062468 / 0.004250 (0.058217) | 0.046739 / 0.037052 (0.009687) | 0.394343 / 0.258489 (0.135854) | 0.435912 / 0.293841 (0.142071) | 0.026812 / 0.128546 (-0.101734) | 0.007954 / 0.075646 (-0.067692) | 0.261415 / 0.419271 (-0.157857) | 0.044665 / 0.043533 (0.001132) | 0.403454 / 0.255139 (0.148315) | 0.418946 / 0.283200 (0.135747) | 0.022247 / 0.141683 (-0.119436) | 1.456387 / 1.452155 (0.004232) | 1.508234 / 1.492716 (0.015518) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182487 / 0.018006 (0.164480) | 0.416343 / 0.000490 (0.415854) | 0.001404 / 0.000200 (0.001204) | 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) | 0.023643 / 0.037411 (-0.013768) | 0.071798 / 0.014526 (0.057272) | 0.083623 / 0.176557 (-0.092933) | 0.146023 / 0.737135 (-0.591112) | 0.083094 / 0.296338 (-0.213245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417484 / 0.215209 (0.202275) | 4.157393 / 2.077655 (2.079738) | 1.950438 / 1.504120 (0.446318) | 1.766639 / 1.541195 (0.225444) | 1.807382 / 1.468490 (0.338892) | 0.496061 / 4.584777 (-4.088716) | 2.975001 / 3.745712 (-0.770711) | 3.340608 / 5.269862 (-1.929254) | 2.236293 / 4.565676 (-2.329384) | 0.056946 / 0.424275 (-0.367329) | 0.006506 / 0.007607 (-0.001101) | 0.480377 / 0.226044 (0.254332) | 4.788525 / 2.268929 (2.519597) | 2.430139 / 55.444624 (-53.014485) | 2.154145 / 6.876477 (-4.722332) | 2.321623 / 2.142072 (0.179551) | 0.584040 / 4.805227 (-4.221188) | 0.124508 / 6.500664 (-6.376156) | 0.060828 / 0.075469 (-0.014641) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201641 / 1.841788 (-0.640146) | 18.066232 / 8.074308 (9.991924) | 14.022304 / 10.191392 (3.830912) | 0.146573 / 0.680424 (-0.533850) | 0.016892 / 0.534201 (-0.517308) | 0.333259 / 0.579283 (-0.246024) | 0.357795 / 0.434364 (-0.076568) | 0.391265 / 0.540337 (-0.149072) | 0.551378 / 1.386936 (-0.835558) |\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) | 0.005706 / 0.011353 (-0.005647) | 0.003448 / 0.011008 (-0.007560) | 0.063146 / 0.038508 (0.024638) | 0.056292 / 0.023109 (0.033183) | 0.355533 / 0.275898 (0.079635) | 0.394996 / 0.323480 (0.071517) | 0.004270 / 0.007986 (-0.003716) | 0.002790 / 0.004328 (-0.001538) | 0.063033 / 0.004250 (0.058783) | 0.044684 / 0.037052 (0.007631) | 0.370621 / 0.258489 (0.112132) | 0.401074 / 0.293841 (0.107233) | 0.026737 / 0.128546 (-0.101809) | 0.007872 / 0.075646 (-0.067774) | 0.068815 / 0.419271 (-0.350457) | 0.040976 / 0.043533 (-0.002557) | 0.370733 / 0.255139 (0.115594) | 0.387418 / 0.283200 (0.104218) | 0.018854 / 0.141683 (-0.122829) | 1.479834 / 1.452155 (0.027680) | 1.536388 / 1.492716 (0.043672) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222125 / 0.018006 (0.204119) | 0.408007 / 0.000490 (0.407517) | 0.000367 / 0.000200 (0.000167) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025100 / 0.037411 (-0.012311) | 0.076617 / 0.014526 (0.062091) | 0.088311 / 0.176557 (-0.088246) | 0.143785 / 0.737135 (-0.593350) | 0.088349 / 0.296338 (-0.207989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419246 / 0.215209 (0.204037) | 4.172413 / 2.077655 (2.094759) | 2.199355 / 1.504120 (0.695235) | 2.025158 / 1.541195 (0.483963) | 2.074491 / 1.468490 (0.606001) | 0.495893 / 4.584777 (-4.088884) | 2.998858 / 3.745712 (-0.746854) | 2.770531 / 5.269862 (-2.499331) | 1.817497 / 4.565676 (-2.748179) | 0.057317 / 0.424275 (-0.366958) | 0.006723 / 0.007607 (-0.000884) | 0.491062 / 0.226044 (0.265017) | 4.906155 / 2.268929 (2.637226) | 2.654916 / 55.444624 (-52.789708) | 2.299873 / 6.876477 (-4.576604) | 2.451438 / 2.142072 (0.309366) | 0.585048 / 4.805227 (-4.220179) | 0.124778 / 6.500664 (-6.375886) | 0.062067 / 0.075469 (-0.013402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.298239 / 1.841788 (-0.543549) | 18.090238 / 8.074308 (10.015930) | 13.822568 / 10.191392 (3.631176) | 0.130560 / 0.680424 (-0.549864) | 0.016662 / 0.534201 (-0.517539) | 0.333337 / 0.579283 (-0.245946) | 0.348493 / 0.434364 (-0.085871) | 0.386049 / 0.540337 (-0.154289) | 0.511156 / 1.386936 (-0.875780) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) | 0.006014 / 0.011353 (-0.005339) | 0.003623 / 0.011008 (-0.007385) | 0.080500 / 0.038508 (0.041992) | 0.057713 / 0.023109 (0.034603) | 0.325976 / 0.275898 (0.050078) | 0.359986 / 0.323480 (0.036506) | 0.004709 / 0.007986 (-0.003277) | 0.002933 / 0.004328 (-0.001395) | 0.063457 / 0.004250 (0.059207) | 0.047514 / 0.037052 (0.010462) | 0.331629 / 0.258489 (0.073140) | 0.382048 / 0.293841 (0.088207) | 0.026949 / 0.128546 (-0.101597) | 0.008043 / 0.075646 (-0.067604) | 0.262152 / 0.419271 (-0.157119) | 0.045271 / 0.043533 (0.001738) | 0.333355 / 0.255139 (0.078216) | 0.347996 / 0.283200 (0.064796) | 0.020814 / 0.141683 (-0.120868) | 1.460723 / 1.452155 (0.008568) | 1.488845 / 1.492716 (-0.003872) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193735 / 0.018006 (0.175728) | 0.431433 / 0.000490 (0.430943) | 0.002494 / 0.000200 (0.002294) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023762 / 0.037411 (-0.013650) | 0.072680 / 0.014526 (0.058154) | 0.081687 / 0.176557 (-0.094869) | 0.143224 / 0.737135 (-0.593911) | 0.083083 / 0.296338 (-0.213255) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397393 / 0.215209 (0.182184) | 3.954643 / 2.077655 (1.876989) | 1.950038 / 1.504120 (0.445919) | 1.760551 / 1.541195 (0.219357) | 1.871165 / 1.468490 (0.402675) | 0.508645 / 4.584777 (-4.076132) | 3.114379 / 3.745712 (-0.631333) | 3.474554 / 5.269862 (-1.795307) | 2.090126 / 4.565676 (-2.475551) | 0.058008 / 0.424275 (-0.366267) | 0.006465 / 0.007607 (-0.001142) | 0.475009 / 0.226044 (0.248965) | 4.767981 / 2.268929 (2.499052) | 2.372050 / 55.444624 (-53.072574) | 2.038094 / 6.876477 (-4.838383) | 2.072819 / 2.142072 (-0.069253) | 0.591913 / 4.805227 (-4.213314) | 0.125002 / 6.500664 (-6.375662) | 0.060055 / 0.075469 (-0.015414) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234171 / 1.841788 (-0.607617) | 18.121476 / 8.074308 (10.047168) | 13.727313 / 10.191392 (3.535921) | 0.136021 / 0.680424 (-0.544402) | 0.016505 / 0.534201 (-0.517696) | 0.331400 / 0.579283 (-0.247883) | 0.346019 / 0.434364 (-0.088345) | 0.378985 / 0.540337 (-0.161353) | 0.522606 / 1.386936 (-0.864330) |\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) | 0.006035 / 0.011353 (-0.005318) | 0.003584 / 0.011008 (-0.007425) | 0.061953 / 0.038508 (0.023445) | 0.059416 / 0.023109 (0.036307) | 0.359380 / 0.275898 (0.083482) | 0.396842 / 0.323480 (0.073363) | 0.004716 / 0.007986 (-0.003269) | 0.002825 / 0.004328 (-0.001504) | 0.061697 / 0.004250 (0.057447) | 0.049009 / 0.037052 (0.011956) | 0.363099 / 0.258489 (0.104610) | 0.403672 / 0.293841 (0.109831) | 0.027722 / 0.128546 (-0.100824) | 0.007966 / 0.075646 (-0.067680) | 0.067455 / 0.419271 (-0.351816) | 0.042530 / 0.043533 (-0.001003) | 0.361257 / 0.255139 (0.106118) | 0.388957 / 0.283200 (0.105758) | 0.021845 / 0.141683 (-0.119838) | 1.431989 / 1.452155 (-0.020166) | 1.503131 / 1.492716 (0.010415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241493 / 0.018006 (0.223487) | 0.429319 / 0.000490 (0.428829) | 0.002604 / 0.000200 (0.002404) | 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) | 0.026227 / 0.037411 (-0.011184) | 0.077177 / 0.014526 (0.062651) | 0.085840 / 0.176557 (-0.090717) | 0.142280 / 0.737135 (-0.594855) | 0.088465 / 0.296338 (-0.207873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434912 / 0.215209 (0.219703) | 4.339664 / 2.077655 (2.262009) | 2.242495 / 1.504120 (0.738375) | 2.091353 / 1.541195 (0.550159) | 2.161425 / 1.468490 (0.692935) | 0.501647 / 4.584777 (-4.083130) | 3.075326 / 3.745712 (-0.670386) | 4.091557 / 5.269862 (-1.178304) | 2.776425 / 4.565676 (-1.789251) | 0.057338 / 0.424275 (-0.366937) | 0.006767 / 0.007607 (-0.000840) | 0.506882 / 0.226044 (0.280837) | 5.059074 / 2.268929 (2.790146) | 2.706665 / 55.444624 (-52.737959) | 2.370253 / 6.876477 (-4.506224) | 2.505421 / 2.142072 (0.363348) | 0.590289 / 4.805227 (-4.214938) | 0.125990 / 6.500664 (-6.374674) | 0.062778 / 0.075469 (-0.012691) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.361287 / 1.841788 (-0.480501) | 18.500726 / 8.074308 (10.426418) | 13.844459 / 10.191392 (3.653067) | 0.144416 / 0.680424 (-0.536008) | 0.016987 / 0.534201 (-0.517214) | 0.336237 / 0.579283 (-0.243046) | 0.357116 / 0.434364 (-0.077248) | 0.402062 / 0.540337 (-0.138275) | 0.543066 / 1.386936 (-0.843870) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) | 0.007559 / 0.011353 (-0.003794) | 0.004379 / 0.011008 (-0.006629) | 0.089702 / 0.038508 (0.051194) | 0.065104 / 0.023109 (0.041995) | 0.362016 / 0.275898 (0.086118) | 0.376768 / 0.323480 (0.053288) | 0.006538 / 0.007986 (-0.001447) | 0.004167 / 0.004328 (-0.000161) | 0.074138 / 0.004250 (0.069888) | 0.052753 / 0.037052 (0.015701) | 0.366367 / 0.258489 (0.107878) | 0.389121 / 0.293841 (0.095280) | 0.042820 / 0.128546 (-0.085727) | 0.012560 / 0.075646 (-0.063086) | 0.359235 / 0.419271 (-0.060037) | 0.074250 / 0.043533 (0.030718) | 0.384051 / 0.255139 (0.128912) | 0.385450 / 0.283200 (0.102250) | 0.046270 / 0.141683 (-0.095413) | 1.593275 / 1.452155 (0.141120) | 1.704207 / 1.492716 (0.211490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249390 / 0.018006 (0.231384) | 0.614347 / 0.000490 (0.613857) | 0.012641 / 0.000200 (0.012441) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029099 / 0.037411 (-0.008312) | 0.090966 / 0.014526 (0.076440) | 0.102273 / 0.176557 (-0.074284) | 0.167564 / 0.737135 (-0.569571) | 0.106118 / 0.296338 (-0.190220) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.536122 / 0.215209 (0.320913) | 5.448464 / 2.077655 (3.370809) | 2.461977 / 1.504120 (0.957857) | 2.081506 / 1.541195 (0.540311) | 2.091509 / 1.468490 (0.623019) | 0.810307 / 4.584777 (-3.774470) | 5.161304 / 3.745712 (1.415592) | 4.525070 / 5.269862 (-0.744792) | 2.886313 / 4.565676 (-1.679363) | 0.093992 / 0.424275 (-0.330283) | 0.008516 / 0.007607 (0.000909) | 0.691978 / 0.226044 (0.465934) | 6.834665 / 2.268929 (4.565737) | 3.284355 / 55.444624 (-52.160270) | 2.496803 / 6.876477 (-4.379674) | 2.814387 / 2.142072 (0.672315) | 0.985300 / 4.805227 (-3.819928) | 0.210343 / 6.500664 (-6.290321) | 0.075459 / 0.075469 (-0.000010) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.436073 / 1.841788 (-0.405714) | 22.722401 / 8.074308 (14.648093) | 19.988521 / 10.191392 (9.797129) | 0.229757 / 0.680424 (-0.450667) | 0.029672 / 0.534201 (-0.504529) | 0.479914 / 0.579283 (-0.099369) | 0.605106 / 0.434364 (0.170743) | 0.511668 / 0.540337 (-0.028670) | 0.800281 / 1.386936 (-0.586655) |\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) | 0.008665 / 0.011353 (-0.002688) | 0.006009 / 0.011008 (-0.004999) | 0.073377 / 0.038508 (0.034869) | 0.077188 / 0.023109 (0.054079) | 0.451422 / 0.275898 (0.175524) | 0.484640 / 0.323480 (0.161160) | 0.006266 / 0.007986 (-0.001719) | 0.004129 / 0.004328 (-0.000200) | 0.063102 / 0.004250 (0.058851) | 0.064653 / 0.037052 (0.027601) | 0.439521 / 0.258489 (0.181032) | 0.458964 / 0.293841 (0.165123) | 0.046018 / 0.128546 (-0.082528) | 0.014109 / 0.075646 (-0.061537) | 0.095727 / 0.419271 (-0.323544) | 0.070133 / 0.043533 (0.026600) | 0.440143 / 0.255139 (0.185004) | 0.502468 / 0.283200 (0.219269) | 0.034582 / 0.141683 (-0.107101) | 1.656282 / 1.452155 (0.204127) | 1.784641 / 1.492716 (0.291925) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303111 / 0.018006 (0.285105) | 0.599194 / 0.000490 (0.598705) | 0.000411 / 0.000200 (0.000211) | 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) | 0.033061 / 0.037411 (-0.004350) | 0.096073 / 0.014526 (0.081548) | 0.095347 / 0.176557 (-0.081209) | 0.161004 / 0.737135 (-0.576131) | 0.111544 / 0.296338 (-0.184794) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615695 / 0.215209 (0.400486) | 5.794243 / 2.077655 (3.716588) | 2.594720 / 1.504120 (1.090600) | 2.566255 / 1.541195 (1.025060) | 2.573653 / 1.468490 (1.105163) | 0.873653 / 4.584777 (-3.711124) | 5.353323 / 3.745712 (1.607611) | 4.604974 / 5.269862 (-0.664887) | 2.901282 / 4.565676 (-1.664394) | 0.099614 / 0.424275 (-0.324661) | 0.010368 / 0.007607 (0.002761) | 0.775490 / 0.226044 (0.549446) | 7.245449 / 2.268929 (4.976520) | 3.740165 / 55.444624 (-51.704459) | 2.986132 / 6.876477 (-3.890345) | 3.092510 / 2.142072 (0.950438) | 1.022461 / 4.805227 (-3.782766) | 0.212137 / 6.500664 (-6.288527) | 0.084534 / 0.075469 (0.009065) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687983 / 1.841788 (-0.153805) | 23.491808 / 8.074308 (15.417500) | 20.722165 / 10.191392 (10.530773) | 0.231011 / 0.680424 (-0.449413) | 0.028309 / 0.534201 (-0.505892) | 0.436911 / 0.579283 (-0.142372) | 0.583126 / 0.434364 (0.148762) | 0.559712 / 0.540337 (0.019374) | 0.820645 / 1.386936 (-0.566291) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) | 0.006538 / 0.011353 (-0.004815) | 0.003952 / 0.011008 (-0.007056) | 0.084183 / 0.038508 (0.045675) | 0.070616 / 0.023109 (0.047507) | 0.320491 / 0.275898 (0.044593) | 0.352021 / 0.323480 (0.028541) | 0.005330 / 0.007986 (-0.002656) | 0.003400 / 0.004328 (-0.000928) | 0.066392 / 0.004250 (0.062141) | 0.052529 / 0.037052 (0.015477) | 0.329581 / 0.258489 (0.071092) | 0.374437 / 0.293841 (0.080596) | 0.031379 / 0.128546 (-0.097167) | 0.008576 / 0.075646 (-0.067070) | 0.288621 / 0.419271 (-0.130650) | 0.052748 / 0.043533 (0.009215) | 0.319911 / 0.255139 (0.064772) | 0.358169 / 0.283200 (0.074970) | 0.023128 / 0.141683 (-0.118555) | 1.479578 / 1.452155 (0.027424) | 1.566351 / 1.492716 (0.073635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217616 / 0.018006 (0.199610) | 0.471546 / 0.000490 (0.471056) | 0.003880 / 0.000200 (0.003680) | 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) | 0.027716 / 0.037411 (-0.009696) | 0.081718 / 0.014526 (0.067192) | 0.095457 / 0.176557 (-0.081100) | 0.150746 / 0.737135 (-0.586389) | 0.096061 / 0.296338 (-0.200277) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406811 / 0.215209 (0.191602) | 4.062757 / 2.077655 (1.985103) | 2.060658 / 1.504120 (0.556538) | 1.870944 / 1.541195 (0.329749) | 1.908984 / 1.468490 (0.440493) | 0.489053 / 4.584777 (-4.095724) | 3.571038 / 3.745712 (-0.174674) | 3.255351 / 5.269862 (-2.014511) | 2.007078 / 4.565676 (-2.558599) | 0.057078 / 0.424275 (-0.367197) | 0.007240 / 0.007607 (-0.000367) | 0.485641 / 0.226044 (0.259596) | 4.841657 / 2.268929 (2.572729) | 2.569676 / 55.444624 (-52.874949) | 2.151119 / 6.876477 (-4.725357) | 2.330337 / 2.142072 (0.188265) | 0.581721 / 4.805227 (-4.223506) | 0.132591 / 6.500664 (-6.368073) | 0.060491 / 0.075469 (-0.014978) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237699 / 1.841788 (-0.604089) | 19.460306 / 8.074308 (11.385998) | 14.123006 / 10.191392 (3.931614) | 0.155669 / 0.680424 (-0.524754) | 0.018385 / 0.534201 (-0.515816) | 0.393330 / 0.579283 (-0.185953) | 0.408890 / 0.434364 (-0.025474) | 0.457348 / 0.540337 (-0.082989) | 0.640293 / 1.386936 (-0.746643) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006582 / 0.011353 (-0.004771) | 0.003950 / 0.011008 (-0.007059) | 0.064636 / 0.038508 (0.026128) | 0.077651 / 0.023109 (0.054541) | 0.365505 / 0.275898 (0.089607) | 0.393370 / 0.323480 (0.069890) | 0.005466 / 0.007986 (-0.002520) | 0.003314 / 0.004328 (-0.001014) | 0.064960 / 0.004250 (0.060710) | 0.057355 / 0.037052 (0.020302) | 0.377773 / 0.258489 (0.119284) | 0.408394 / 0.293841 (0.114553) | 0.031698 / 0.128546 (-0.096848) | 0.008575 / 0.075646 (-0.067071) | 0.070390 / 0.419271 (-0.348881) | 0.050035 / 0.043533 (0.006502) | 0.360461 / 0.255139 (0.105323) | 0.384862 / 0.283200 (0.101662) | 0.025380 / 0.141683 (-0.116303) | 1.484429 / 1.452155 (0.032275) | 1.542944 / 1.492716 (0.050227) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190193 / 0.018006 (0.172187) | 0.468996 / 0.000490 (0.468506) | 0.003012 / 0.000200 (0.002812) | 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) | 0.031488 / 0.037411 (-0.005923) | 0.088673 / 0.014526 (0.074147) | 0.101886 / 0.176557 (-0.074670) | 0.156774 / 0.737135 (-0.580361) | 0.102818 / 0.296338 (-0.193520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428019 / 0.215209 (0.212810) | 4.271369 / 2.077655 (2.193714) | 2.271530 / 1.504120 (0.767410) | 2.085172 / 1.541195 (0.543977) | 2.143439 / 1.468490 (0.674949) | 0.493468 / 4.584777 (-4.091309) | 3.569030 / 3.745712 (-0.176683) | 4.777962 / 5.269862 (-0.491900) | 2.872115 / 4.565676 (-1.693562) | 0.058200 / 0.424275 (-0.366075) | 0.007657 / 0.007607 (0.000050) | 0.502874 / 0.226044 (0.276830) | 5.026721 / 2.268929 (2.757792) | 2.734301 / 55.444624 (-52.710324) | 2.396072 / 6.876477 (-4.480405) | 2.574322 / 2.142072 (0.432249) | 0.593855 / 4.805227 (-4.211373) | 0.135134 / 6.500664 (-6.365530) | 0.061491 / 0.075469 (-0.013978) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.320522 / 1.841788 (-0.521265) | 19.933221 / 8.074308 (11.858912) | 14.055921 / 10.191392 (3.864529) | 0.149620 / 0.680424 (-0.530804) | 0.018590 / 0.534201 (-0.515611) | 0.399550 / 0.579283 (-0.179733) | 0.410463 / 0.434364 (-0.023901) | 0.469872 / 0.540337 (-0.070465) | 0.616481 / 1.386936 (-0.770455) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"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) | 0.006680 / 0.011353 (-0.004673) | 0.003987 / 0.011008 (-0.007021) | 0.084677 / 0.038508 (0.046169) | 0.076800 / 0.023109 (0.053691) | 0.358338 / 0.275898 (0.082440) | 0.386573 / 0.323480 (0.063094) | 0.005370 / 0.007986 (-0.002616) | 0.003323 / 0.004328 (-0.001005) | 0.064238 / 0.004250 (0.059988) | 0.057859 / 0.037052 (0.020806) | 0.355408 / 0.258489 (0.096919) | 0.388302 / 0.293841 (0.094461) | 0.030784 / 0.128546 (-0.097762) | 0.008381 / 0.075646 (-0.067266) | 0.287971 / 0.419271 (-0.131300) | 0.053078 / 0.043533 (0.009545) | 0.352719 / 0.255139 (0.097580) | 0.370319 / 0.283200 (0.087119) | 0.023064 / 0.141683 (-0.118619) | 1.480661 / 1.452155 (0.028507) | 1.555711 / 1.492716 (0.062995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211289 / 0.018006 (0.193283) | 0.466957 / 0.000490 (0.466467) | 0.003760 / 0.000200 (0.003561) | 0.000076 / 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) | 0.028552 / 0.037411 (-0.008859) | 0.084469 / 0.014526 (0.069943) | 0.096027 / 0.176557 (-0.080529) | 0.152170 / 0.737135 (-0.584965) | 0.096513 / 0.296338 (-0.199825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382940 / 0.215209 (0.167731) | 3.841735 / 2.077655 (1.764080) | 1.850575 / 1.504120 (0.346455) | 1.676554 / 1.541195 (0.135360) | 1.765241 / 1.468490 (0.296751) | 0.482131 / 4.584777 (-4.102646) | 3.512739 / 3.745712 (-0.232973) | 3.977042 / 5.269862 (-1.292820) | 2.387568 / 4.565676 (-2.178109) | 0.056657 / 0.424275 (-0.367618) | 0.007283 / 0.007607 (-0.000324) | 0.468193 / 0.226044 (0.242149) | 4.704077 / 2.268929 (2.435149) | 2.373467 / 55.444624 (-53.071157) | 2.002470 / 6.876477 (-4.874007) | 2.228280 / 2.142072 (0.086208) | 0.576908 / 4.805227 (-4.228320) | 0.132000 / 6.500664 (-6.368664) | 0.060544 / 0.075469 (-0.014926) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256168 / 1.841788 (-0.585619) | 19.965458 / 8.074308 (11.891150) | 14.521435 / 10.191392 (4.330043) | 0.159156 / 0.680424 (-0.521268) | 0.018170 / 0.534201 (-0.516031) | 0.393019 / 0.579283 (-0.186264) | 0.415002 / 0.434364 (-0.019362) | 0.471810 / 0.540337 (-0.068528) | 0.658907 / 1.386936 (-0.728029) |\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) | 0.006836 / 0.011353 (-0.004517) | 0.004067 / 0.011008 (-0.006942) | 0.066242 / 0.038508 (0.027734) | 0.078601 / 0.023109 (0.055491) | 0.369371 / 0.275898 (0.093473) | 0.402026 / 0.323480 (0.078546) | 0.006097 / 0.007986 (-0.001889) | 0.003337 / 0.004328 (-0.000991) | 0.065854 / 0.004250 (0.061603) | 0.057665 / 0.037052 (0.020612) | 0.379709 / 0.258489 (0.121219) | 0.406868 / 0.293841 (0.113027) | 0.031946 / 0.128546 (-0.096600) | 0.008691 / 0.075646 (-0.066955) | 0.071430 / 0.419271 (-0.347841) | 0.049518 / 0.043533 (0.005986) | 0.370439 / 0.255139 (0.115300) | 0.389235 / 0.283200 (0.106036) | 0.023730 / 0.141683 (-0.117953) | 1.509035 / 1.452155 (0.056880) | 1.548890 / 1.492716 (0.056173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229264 / 0.018006 (0.211258) | 0.445801 / 0.000490 (0.445312) | 0.000363 / 0.000200 (0.000163) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032377 / 0.037411 (-0.005034) | 0.091082 / 0.014526 (0.076556) | 0.104816 / 0.176557 (-0.071740) | 0.161040 / 0.737135 (-0.576095) | 0.105165 / 0.296338 (-0.191173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411012 / 0.215209 (0.195803) | 4.097256 / 2.077655 (2.019602) | 2.088686 / 1.504120 (0.584566) | 1.934429 / 1.541195 (0.393234) | 2.027387 / 1.468490 (0.558896) | 0.476262 / 4.584777 (-4.108515) | 3.518416 / 3.745712 (-0.227296) | 3.260919 / 5.269862 (-2.008943) | 2.041441 / 4.565676 (-2.524235) | 0.056302 / 0.424275 (-0.367973) | 0.007750 / 0.007607 (0.000143) | 0.489966 / 0.226044 (0.263922) | 4.915844 / 2.268929 (2.646916) | 2.617001 / 55.444624 (-52.827623) | 2.333557 / 6.876477 (-4.542920) | 2.484530 / 2.142072 (0.342458) | 0.572009 / 4.805227 (-4.233219) | 0.142557 / 6.500664 (-6.358107) | 0.066711 / 0.075469 (-0.008758) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359929 / 1.841788 (-0.481859) | 20.332252 / 8.074308 (12.257943) | 14.585842 / 10.191392 (4.394450) | 0.170498 / 0.680424 (-0.509926) | 0.018450 / 0.534201 (-0.515751) | 0.395449 / 0.579283 (-0.183834) | 0.409666 / 0.434364 (-0.024698) | 0.467937 / 0.540337 (-0.072401) | 0.616078 / 1.386936 (-0.770858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a888bc94dc6bce7815e3061a28e718097f4b8b9e \"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) | 0.006458 / 0.011353 (-0.004894) | 0.003895 / 0.011008 (-0.007114) | 0.084280 / 0.038508 (0.045772) | 0.071304 / 0.023109 (0.048195) | 0.313910 / 0.275898 (0.038012) | 0.344070 / 0.323480 (0.020590) | 0.005413 / 0.007986 (-0.002573) | 0.003308 / 0.004328 (-0.001021) | 0.064570 / 0.004250 (0.060320) | 0.056824 / 0.037052 (0.019771) | 0.321102 / 0.258489 (0.062613) | 0.355834 / 0.293841 (0.061993) | 0.031252 / 0.128546 (-0.097294) | 0.008427 / 0.075646 (-0.067219) | 0.287348 / 0.419271 (-0.131924) | 0.053261 / 0.043533 (0.009728) | 0.324892 / 0.255139 (0.069753) | 0.335847 / 0.283200 (0.052647) | 0.023453 / 0.141683 (-0.118230) | 1.485456 / 1.452155 (0.033301) | 1.531329 / 1.492716 (0.038612) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201924 / 0.018006 (0.183918) | 0.447188 / 0.000490 (0.446698) | 0.005543 / 0.000200 (0.005343) | 0.000086 / 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) | 0.027586 / 0.037411 (-0.009825) | 0.082412 / 0.014526 (0.067886) | 0.094851 / 0.176557 (-0.081706) | 0.151331 / 0.737135 (-0.585804) | 0.094475 / 0.296338 (-0.201863) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399004 / 0.215209 (0.183795) | 3.974652 / 2.077655 (1.896997) | 1.991909 / 1.504120 (0.487789) | 1.811684 / 1.541195 (0.270489) | 1.869774 / 1.468490 (0.401283) | 0.487745 / 4.584777 (-4.097032) | 3.558945 / 3.745712 (-0.186768) | 5.530468 / 5.269862 (0.260606) | 3.293147 / 4.565676 (-1.272529) | 0.057531 / 0.424275 (-0.366744) | 0.007212 / 0.007607 (-0.000395) | 0.470325 / 0.226044 (0.244281) | 4.701652 / 2.268929 (2.432723) | 2.453020 / 55.444624 (-52.991605) | 2.110152 / 6.876477 (-4.766325) | 2.314669 / 2.142072 (0.172597) | 0.615039 / 4.805227 (-4.190189) | 0.133229 / 6.500664 (-6.367435) | 0.060821 / 0.075469 (-0.014648) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296708 / 1.841788 (-0.545079) | 18.717251 / 8.074308 (10.642943) | 14.325305 / 10.191392 (4.133913) | 0.147680 / 0.680424 (-0.532744) | 0.018312 / 0.534201 (-0.515889) | 0.392766 / 0.579283 (-0.186517) | 0.403319 / 0.434364 (-0.031045) | 0.453696 / 0.540337 (-0.086641) | 0.622564 / 1.386936 (-0.764372) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006483 / 0.011353 (-0.004870) | 0.004018 / 0.011008 (-0.006991) | 0.064436 / 0.038508 (0.025928) | 0.072365 / 0.023109 (0.049256) | 0.387532 / 0.275898 (0.111634) | 0.418175 / 0.323480 (0.094695) | 0.005453 / 0.007986 (-0.002533) | 0.003368 / 0.004328 (-0.000961) | 0.064896 / 0.004250 (0.060645) | 0.057018 / 0.037052 (0.019966) | 0.406596 / 0.258489 (0.148107) | 0.431194 / 0.293841 (0.137353) | 0.031788 / 0.128546 (-0.096759) | 0.008532 / 0.075646 (-0.067114) | 0.070605 / 0.419271 (-0.348666) | 0.053317 / 0.043533 (0.009785) | 0.391930 / 0.255139 (0.136791) | 0.406071 / 0.283200 (0.122872) | 0.028652 / 0.141683 (-0.113030) | 1.487677 / 1.452155 (0.035522) | 1.546071 / 1.492716 (0.053355) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220063 / 0.018006 (0.202056) | 0.441111 / 0.000490 (0.440621) | 0.006066 / 0.000200 (0.005867) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035179 / 0.037411 (-0.002232) | 0.096745 / 0.014526 (0.082219) | 0.108171 / 0.176557 (-0.068386) | 0.164590 / 0.737135 (-0.572545) | 0.109425 / 0.296338 (-0.186913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408101 / 0.215209 (0.192892) | 4.062961 / 2.077655 (1.985306) | 2.101849 / 1.504120 (0.597730) | 1.935919 / 1.541195 (0.394724) | 1.993749 / 1.468490 (0.525259) | 0.487788 / 4.584777 (-4.096989) | 3.533972 / 3.745712 (-0.211740) | 3.218448 / 5.269862 (-2.051414) | 2.002322 / 4.565676 (-2.563355) | 0.057371 / 0.424275 (-0.366904) | 0.007704 / 0.007607 (0.000097) | 0.491695 / 0.226044 (0.265650) | 4.905009 / 2.268929 (2.636080) | 2.597879 / 55.444624 (-52.846745) | 2.252086 / 6.876477 (-4.624391) | 2.434439 / 2.142072 (0.292367) | 0.583071 / 4.805227 (-4.222156) | 0.133765 / 6.500664 (-6.366899) | 0.061276 / 0.075469 (-0.014193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.403111 / 1.841788 (-0.438676) | 19.218886 / 8.074308 (11.144578) | 13.981775 / 10.191392 (3.790383) | 0.167784 / 0.680424 (-0.512640) | 0.018401 / 0.534201 (-0.515800) | 0.392038 / 0.579283 (-0.187245) | 0.414776 / 0.434364 (-0.019587) | 0.476221 / 0.540337 (-0.064117) | 0.632724 / 1.386936 (-0.754212) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"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) | 0.007595 / 0.011353 (-0.003758) | 0.004540 / 0.011008 (-0.006468) | 0.099350 / 0.038508 (0.060842) | 0.087062 / 0.023109 (0.063953) | 0.415980 / 0.275898 (0.140082) | 0.466390 / 0.323480 (0.142910) | 0.005958 / 0.007986 (-0.002027) | 0.003671 / 0.004328 (-0.000657) | 0.075714 / 0.004250 (0.071463) | 0.066062 / 0.037052 (0.029010) | 0.426527 / 0.258489 (0.168038) | 0.473282 / 0.293841 (0.179441) | 0.035669 / 0.128546 (-0.092878) | 0.009729 / 0.075646 (-0.065918) | 0.344035 / 0.419271 (-0.075237) | 0.061153 / 0.043533 (0.017620) | 0.428607 / 0.255139 (0.173468) | 0.445951 / 0.283200 (0.162752) | 0.026373 / 0.141683 (-0.115310) | 1.788725 / 1.452155 (0.336570) | 1.871055 / 1.492716 (0.378339) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230606 / 0.018006 (0.212600) | 0.489835 / 0.000490 (0.489345) | 0.005669 / 0.000200 (0.005469) | 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) | 0.032197 / 0.037411 (-0.005214) | 0.099571 / 0.014526 (0.085045) | 0.112686 / 0.176557 (-0.063871) | 0.179478 / 0.737135 (-0.557658) | 0.112670 / 0.296338 (-0.183668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449606 / 0.215209 (0.234397) | 4.503356 / 2.077655 (2.425701) | 2.190480 / 1.504120 (0.686361) | 1.986054 / 1.541195 (0.444860) | 2.071594 / 1.468490 (0.603104) | 0.566301 / 4.584777 (-4.018475) | 4.088460 / 3.745712 (0.342748) | 4.840100 / 5.269862 (-0.429761) | 2.857697 / 4.565676 (-1.707980) | 0.066718 / 0.424275 (-0.357557) | 0.008642 / 0.007607 (0.001034) | 0.539785 / 0.226044 (0.313740) | 5.383252 / 2.268929 (3.114323) | 2.878177 / 55.444624 (-52.566447) | 2.374577 / 6.876477 (-4.501899) | 2.590500 / 2.142072 (0.448428) | 0.675196 / 4.805227 (-4.130031) | 0.153544 / 6.500664 (-6.347120) | 0.070958 / 0.075469 (-0.004511) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.490403 / 1.841788 (-0.351385) | 22.085740 / 8.074308 (14.011432) | 16.588093 / 10.191392 (6.396701) | 0.188598 / 0.680424 (-0.491826) | 0.021567 / 0.534201 (-0.512634) | 0.472594 / 0.579283 (-0.106689) | 0.472903 / 0.434364 (0.038539) | 0.545305 / 0.540337 (0.004968) | 0.736399 / 1.386936 (-0.650537) |\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) | 0.007635 / 0.011353 (-0.003718) | 0.004731 / 0.011008 (-0.006277) | 0.076482 / 0.038508 (0.037974) | 0.083666 / 0.023109 (0.060557) | 0.469596 / 0.275898 (0.193698) | 0.493068 / 0.323480 (0.169588) | 0.006014 / 0.007986 (-0.001971) | 0.003902 / 0.004328 (-0.000426) | 0.077142 / 0.004250 (0.072891) | 0.064355 / 0.037052 (0.027303) | 0.468859 / 0.258489 (0.210370) | 0.504002 / 0.293841 (0.210161) | 0.037606 / 0.128546 (-0.090940) | 0.010141 / 0.075646 (-0.065505) | 0.083790 / 0.419271 (-0.335482) | 0.060923 / 0.043533 (0.017390) | 0.464752 / 0.255139 (0.209613) | 0.500464 / 0.283200 (0.217264) | 0.031183 / 0.141683 (-0.110499) | 1.779294 / 1.452155 (0.327139) | 1.870848 / 1.492716 (0.378131) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246567 / 0.018006 (0.228560) | 0.477182 / 0.000490 (0.476693) | 0.000426 / 0.000200 (0.000226) | 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) | 0.035377 / 0.037411 (-0.002034) | 0.106042 / 0.014526 (0.091516) | 0.119237 / 0.176557 (-0.057320) | 0.182145 / 0.737135 (-0.554991) | 0.119537 / 0.296338 (-0.176801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491352 / 0.215209 (0.276143) | 4.824220 / 2.077655 (2.746565) | 2.652039 / 1.504120 (1.147919) | 2.535310 / 1.541195 (0.994116) | 2.620009 / 1.468490 (1.151519) | 0.567865 / 4.584777 (-4.016912) | 4.158795 / 3.745712 (0.413082) | 6.042582 / 5.269862 (0.772721) | 3.957193 / 4.565676 (-0.608484) | 0.066647 / 0.424275 (-0.357628) | 0.008893 / 0.007607 (0.001285) | 0.570137 / 0.226044 (0.344093) | 5.687126 / 2.268929 (3.418198) | 3.137605 / 55.444624 (-52.307019) | 2.655979 / 6.876477 (-4.220498) | 2.893338 / 2.142072 (0.751265) | 0.698388 / 4.805227 (-4.106840) | 0.154897 / 6.500664 (-6.345767) | 0.071208 / 0.075469 (-0.004261) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.619346 / 1.841788 (-0.222441) | 22.782510 / 8.074308 (14.708202) | 16.317395 / 10.191392 (6.126003) | 0.197630 / 0.680424 (-0.482794) | 0.021795 / 0.534201 (-0.512406) | 0.466982 / 0.579283 (-0.112302) | 0.468609 / 0.434364 (0.034245) | 0.574380 / 0.540337 (0.034043) | 0.759827 / 1.386936 (-0.627109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n" ]

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[ "When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ", "Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(**kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n", "Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```", "PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.", "I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```", "`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.", "Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.", "Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?", "Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.", "One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!", "**Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n", "I'm facing a similar situation in the local VS Code. \r\n\r\nDatasets version 2.14.4\r\nTorch 2.0.1+cu118\r\n\r\nSame code runs without issues in Colab\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\nsample = next(iter(dataset[\"train\"]))\r\n```\r\n\r\nis stuck for minutes. If I interrupt, I get\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[5], line 5\r\n 1 from datasets import load_dataset\r\n 3 dataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\n----> 5 sample = next(iter(dataset[\"train\"]))\r\n 6 print(sample[\"text\"])\r\n 7 sample[\"image\"]\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353), in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255), in ArrowExamplesIterable.__iter__(self)\r\n 253 def __iter__(self):\r\n 254 formatter = PythonFormatter()\r\n--> 255 for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n 256 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n...\r\n-> 1130 return self._sslobj.read(len, buffer)\r\n 1131 else:\r\n 1132 return self._sslobj.read(len)\r\n```", "@osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet.", "Thank you reporting this and sharing the solution, I ran into this as well!" ]

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[ "Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ", "Ok thank you for your answer", "I'm closing this as a duplicate of #5380" ]

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[ "The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.", "Hi @mariosasko !\r\n\r\nI agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.", "Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?", "I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`.\r\n\r\nHere is also a bunch of stack traces when I interrupted the process:\r\n\r\n<details>\r\n <summary>stack trace 1</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in <listcomp>\r\n [\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 2</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 291, in _cast_to_python_objects\r\n if config.JAX_AVAILABLE and \"jax\" in sys.modules:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 3</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 298, in _cast_to_python_objects\r\n if obj.ndim == 0:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\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) | 0.008191 / 0.011353 (-0.003162) | 0.004669 / 0.011008 (-0.006339) | 0.101315 / 0.038508 (0.062807) | 0.090235 / 0.023109 (0.067126) | 0.381265 / 0.275898 (0.105367) | 0.418266 / 0.323480 (0.094786) | 0.006292 / 0.007986 (-0.001693) | 0.003979 / 0.004328 (-0.000349) | 0.075946 / 0.004250 (0.071696) | 0.070678 / 0.037052 (0.033625) | 0.378006 / 0.258489 (0.119517) | 0.425825 / 0.293841 (0.131984) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.345687 / 0.419271 (-0.073584) | 0.063846 / 0.043533 (0.020313) | 0.386003 / 0.255139 (0.130864) | 0.400875 / 0.283200 (0.117675) | 0.027806 / 0.141683 (-0.113877) | 1.814810 / 1.452155 (0.362655) | 1.879897 / 1.492716 (0.387180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218684 / 0.018006 (0.200677) | 0.501715 / 0.000490 (0.501225) | 0.004808 / 0.000200 (0.004608) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035494 / 0.037411 (-0.001917) | 0.100949 / 0.014526 (0.086423) | 0.114639 / 0.176557 (-0.061917) | 0.188908 / 0.737135 (-0.548227) | 0.115794 / 0.296338 (-0.180545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462537 / 0.215209 (0.247328) | 4.612469 / 2.077655 (2.534814) | 2.298065 / 1.504120 (0.793945) | 2.088738 / 1.541195 (0.547543) | 2.188072 / 1.468490 (0.719582) | 0.565412 / 4.584777 (-4.019364) | 4.180394 / 3.745712 (0.434681) | 3.848696 / 5.269862 (-1.421165) | 2.391381 / 4.565676 (-2.174296) | 0.067647 / 0.424275 (-0.356628) | 0.008847 / 0.007607 (0.001240) | 0.553288 / 0.226044 (0.327243) | 5.517962 / 2.268929 (3.249033) | 2.866622 / 55.444624 (-52.578002) | 2.439025 / 6.876477 (-4.437452) | 2.740156 / 2.142072 (0.598084) | 0.694796 / 4.805227 (-4.110431) | 0.159022 / 6.500664 (-6.341642) | 0.074471 / 0.075469 (-0.000998) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534979 / 1.841788 (-0.306808) | 23.297273 / 8.074308 (15.222965) | 16.859178 / 10.191392 (6.667786) | 0.207594 / 0.680424 (-0.472830) | 0.021990 / 0.534201 (-0.512211) | 0.472059 / 0.579283 (-0.107224) | 0.497632 / 0.434364 (0.063268) | 0.565672 / 0.540337 (0.025335) | 0.772485 / 1.386936 (-0.614451) |\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) | 0.007777 / 0.011353 (-0.003576) | 0.004679 / 0.011008 (-0.006329) | 0.077317 / 0.038508 (0.038809) | 0.087433 / 0.023109 (0.064324) | 0.437389 / 0.275898 (0.161491) | 0.479562 / 0.323480 (0.156082) | 0.006137 / 0.007986 (-0.001849) | 0.003938 / 0.004328 (-0.000390) | 0.074769 / 0.004250 (0.070518) | 0.066605 / 0.037052 (0.029553) | 0.454865 / 0.258489 (0.196376) | 0.485103 / 0.293841 (0.191262) | 0.036540 / 0.128546 (-0.092006) | 0.009983 / 0.075646 (-0.065664) | 0.083566 / 0.419271 (-0.335706) | 0.059527 / 0.043533 (0.015994) | 0.449154 / 0.255139 (0.194015) | 0.462542 / 0.283200 (0.179342) | 0.027581 / 0.141683 (-0.114102) | 1.776720 / 1.452155 (0.324565) | 1.847920 / 1.492716 (0.355204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246792 / 0.018006 (0.228786) | 0.494513 / 0.000490 (0.494024) | 0.004376 / 0.000200 (0.004176) | 0.000115 / 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) | 0.037837 / 0.037411 (0.000426) | 0.112752 / 0.014526 (0.098226) | 0.121742 / 0.176557 (-0.054815) | 0.189365 / 0.737135 (-0.547770) | 0.124366 / 0.296338 (-0.171973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492890 / 0.215209 (0.277681) | 4.920270 / 2.077655 (2.842615) | 2.565350 / 1.504120 (1.061230) | 2.378679 / 1.541195 (0.837484) | 2.483794 / 1.468490 (1.015304) | 0.579623 / 4.584777 (-4.005154) | 4.195924 / 3.745712 (0.450212) | 3.903382 / 5.269862 (-1.366479) | 2.466884 / 4.565676 (-2.098793) | 0.064145 / 0.424275 (-0.360130) | 0.008695 / 0.007607 (0.001088) | 0.579300 / 0.226044 (0.353256) | 5.809064 / 2.268929 (3.540136) | 3.145393 / 55.444624 (-52.299232) | 2.832760 / 6.876477 (-4.043717) | 3.020460 / 2.142072 (0.878388) | 0.700235 / 4.805227 (-4.104992) | 0.161262 / 6.500664 (-6.339402) | 0.076484 / 0.075469 (0.001015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606504 / 1.841788 (-0.235284) | 23.747863 / 8.074308 (15.673555) | 17.281712 / 10.191392 (7.090320) | 0.203874 / 0.680424 (-0.476550) | 0.021839 / 0.534201 (-0.512362) | 0.472365 / 0.579283 (-0.106918) | 0.475150 / 0.434364 (0.040786) | 0.571713 / 0.540337 (0.031376) | 0.759210 / 1.386936 (-0.627726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n", "> Some questions: won't this have an impact on downloading time, once we do not longer compress the payload? What is the advantage of this approach over the one with block_size: 0?\r\n\r\nSurely, but this prevents random access which is needed at multiple places in the code (eg to check the compression type).\r\nGithub isn't a good place for big files anyway so we should be fine" ]

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[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]

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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) | 0.006616 / 0.011353 (-0.004737) | 0.003915 / 0.011008 (-0.007093) | 0.083271 / 0.038508 (0.044763) | 0.072595 / 0.023109 (0.049485) | 0.307224 / 0.275898 (0.031326) | 0.337244 / 0.323480 (0.013764) | 0.005296 / 0.007986 (-0.002690) | 0.003325 / 0.004328 (-0.001003) | 0.064589 / 0.004250 (0.060339) | 0.056369 / 0.037052 (0.019316) | 0.310829 / 0.258489 (0.052340) | 0.345563 / 0.293841 (0.051722) | 0.030551 / 0.128546 (-0.097995) | 0.008519 / 0.075646 (-0.067127) | 0.286368 / 0.419271 (-0.132903) | 0.052498 / 0.043533 (0.008966) | 0.308735 / 0.255139 (0.053596) | 0.329234 / 0.283200 (0.046034) | 0.022588 / 0.141683 (-0.119095) | 1.453135 / 1.452155 (0.000981) | 1.525956 / 1.492716 (0.033239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199417 / 0.018006 (0.181410) | 0.454621 / 0.000490 (0.454131) | 0.004928 / 0.000200 (0.004728) | 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) | 0.028436 / 0.037411 (-0.008975) | 0.083722 / 0.014526 (0.069196) | 0.095162 / 0.176557 (-0.081395) | 0.153434 / 0.737135 (-0.583702) | 0.099480 / 0.296338 (-0.196859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384647 / 0.215209 (0.169438) | 3.838406 / 2.077655 (1.760751) | 1.891267 / 1.504120 (0.387148) | 1.751432 / 1.541195 (0.210238) | 1.737443 / 1.468490 (0.268953) | 0.487758 / 4.584777 (-4.097019) | 3.635925 / 3.745712 (-0.109787) | 5.208718 / 5.269862 (-0.061144) | 3.029374 / 4.565676 (-1.536302) | 0.057613 / 0.424275 (-0.366662) | 0.007177 / 0.007607 (-0.000430) | 0.455596 / 0.226044 (0.229552) | 4.559969 / 2.268929 (2.291040) | 2.325321 / 55.444624 (-53.119303) | 2.034924 / 6.876477 (-4.841552) | 2.163869 / 2.142072 (0.021796) | 0.583477 / 4.805227 (-4.221750) | 0.132870 / 6.500664 (-6.367795) | 0.059618 / 0.075469 (-0.015851) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263751 / 1.841788 (-0.578037) | 19.740004 / 8.074308 (11.665696) | 14.410980 / 10.191392 (4.219588) | 0.170367 / 0.680424 (-0.510057) | 0.018225 / 0.534201 (-0.515976) | 0.390101 / 0.579283 (-0.189182) | 0.404298 / 0.434364 (-0.030066) | 0.455295 / 0.540337 (-0.085043) | 0.621179 / 1.386936 (-0.765757) |\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) | 0.006580 / 0.011353 (-0.004773) | 0.004078 / 0.011008 (-0.006930) | 0.065842 / 0.038508 (0.027334) | 0.074494 / 0.023109 (0.051385) | 0.403644 / 0.275898 (0.127746) | 0.430204 / 0.323480 (0.106724) | 0.005343 / 0.007986 (-0.002643) | 0.003366 / 0.004328 (-0.000963) | 0.064858 / 0.004250 (0.060607) | 0.056252 / 0.037052 (0.019200) | 0.412556 / 0.258489 (0.154067) | 0.434099 / 0.293841 (0.140258) | 0.031518 / 0.128546 (-0.097028) | 0.008543 / 0.075646 (-0.067104) | 0.071658 / 0.419271 (-0.347613) | 0.049962 / 0.043533 (0.006430) | 0.398511 / 0.255139 (0.143372) | 0.415908 / 0.283200 (0.132708) | 0.025011 / 0.141683 (-0.116672) | 1.492350 / 1.452155 (0.040195) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204971 / 0.018006 (0.186964) | 0.439965 / 0.000490 (0.439475) | 0.002071 / 0.000200 (0.001872) | 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) | 0.031673 / 0.037411 (-0.005738) | 0.087529 / 0.014526 (0.073004) | 0.099882 / 0.176557 (-0.076675) | 0.156994 / 0.737135 (-0.580141) | 0.101421 / 0.296338 (-0.194918) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407480 / 0.215209 (0.192271) | 4.069123 / 2.077655 (1.991468) | 2.081288 / 1.504120 (0.577169) | 1.920367 / 1.541195 (0.379172) | 1.981053 / 1.468490 (0.512563) | 0.481995 / 4.584777 (-4.102782) | 3.546486 / 3.745712 (-0.199226) | 5.133150 / 5.269862 (-0.136712) | 3.056444 / 4.565676 (-1.509232) | 0.056650 / 0.424275 (-0.367625) | 0.007746 / 0.007607 (0.000139) | 0.490891 / 0.226044 (0.264847) | 4.902160 / 2.268929 (2.633232) | 2.564726 / 55.444624 (-52.879899) | 2.234988 / 6.876477 (-4.641489) | 2.387656 / 2.142072 (0.245583) | 0.576315 / 4.805227 (-4.228912) | 0.132065 / 6.500664 (-6.368599) | 0.060728 / 0.075469 (-0.014741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.370568 / 1.841788 (-0.471220) | 19.883159 / 8.074308 (11.808851) | 14.442066 / 10.191392 (4.250674) | 0.150119 / 0.680424 (-0.530305) | 0.018359 / 0.534201 (-0.515842) | 0.394128 / 0.579283 (-0.185155) | 0.411697 / 0.434364 (-0.022667) | 0.460580 / 0.540337 (-0.079757) | 0.608490 / 1.386936 (-0.778446) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#035d0cf842b82b14059999baa78e8d158dfbed12 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "merging now if you don't mind - this way I can make a patch release" ]

[]

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[ "Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)." ]

[]

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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) | 0.007617 / 0.011353 (-0.003736) | 0.004580 / 0.011008 (-0.006428) | 0.100913 / 0.038508 (0.062405) | 0.087703 / 0.023109 (0.064594) | 0.424159 / 0.275898 (0.148261) | 0.467195 / 0.323480 (0.143715) | 0.006890 / 0.007986 (-0.001096) | 0.003765 / 0.004328 (-0.000564) | 0.077513 / 0.004250 (0.073262) | 0.064889 / 0.037052 (0.027837) | 0.422349 / 0.258489 (0.163860) | 0.477391 / 0.293841 (0.183550) | 0.036025 / 0.128546 (-0.092522) | 0.009939 / 0.075646 (-0.065707) | 0.342409 / 0.419271 (-0.076862) | 0.061568 / 0.043533 (0.018035) | 0.431070 / 0.255139 (0.175931) | 0.462008 / 0.283200 (0.178809) | 0.027480 / 0.141683 (-0.114203) | 1.802271 / 1.452155 (0.350116) | 1.861336 / 1.492716 (0.368620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255806 / 0.018006 (0.237800) | 0.507969 / 0.000490 (0.507479) | 0.010060 / 0.000200 (0.009860) | 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) | 0.032286 / 0.037411 (-0.005125) | 0.104468 / 0.014526 (0.089942) | 0.112707 / 0.176557 (-0.063850) | 0.181285 / 0.737135 (-0.555850) | 0.113180 / 0.296338 (-0.183158) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449265 / 0.215209 (0.234056) | 4.465941 / 2.077655 (2.388287) | 2.177889 / 1.504120 (0.673769) | 1.969864 / 1.541195 (0.428669) | 2.077502 / 1.468490 (0.609011) | 0.561607 / 4.584777 (-4.023170) | 4.281873 / 3.745712 (0.536161) | 4.975352 / 5.269862 (-0.294510) | 2.907121 / 4.565676 (-1.658555) | 0.070205 / 0.424275 (-0.354070) | 0.009164 / 0.007607 (0.001557) | 0.581921 / 0.226044 (0.355876) | 5.538667 / 2.268929 (3.269739) | 2.798853 / 55.444624 (-52.645771) | 2.314015 / 6.876477 (-4.562462) | 2.584836 / 2.142072 (0.442763) | 0.672333 / 4.805227 (-4.132894) | 0.153828 / 6.500664 (-6.346836) | 0.069757 / 0.075469 (-0.005712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559670 / 1.841788 (-0.282118) | 23.994639 / 8.074308 (15.920331) | 16.856160 / 10.191392 (6.664768) | 0.195555 / 0.680424 (-0.484869) | 0.021586 / 0.534201 (-0.512615) | 0.469295 / 0.579283 (-0.109989) | 0.481582 / 0.434364 (0.047218) | 0.588667 / 0.540337 (0.048329) | 0.734347 / 1.386936 (-0.652589) |\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) | 0.009614 / 0.011353 (-0.001739) | 0.004616 / 0.011008 (-0.006392) | 0.077223 / 0.038508 (0.038715) | 0.103074 / 0.023109 (0.079965) | 0.447834 / 0.275898 (0.171936) | 0.524696 / 0.323480 (0.201216) | 0.007120 / 0.007986 (-0.000866) | 0.003890 / 0.004328 (-0.000438) | 0.076406 / 0.004250 (0.072156) | 0.073488 / 0.037052 (0.036436) | 0.466221 / 0.258489 (0.207732) | 0.532206 / 0.293841 (0.238365) | 0.037596 / 0.128546 (-0.090950) | 0.010029 / 0.075646 (-0.065617) | 0.084313 / 0.419271 (-0.334959) | 0.060088 / 0.043533 (0.016555) | 0.437792 / 0.255139 (0.182653) | 0.512850 / 0.283200 (0.229650) | 0.032424 / 0.141683 (-0.109259) | 1.762130 / 1.452155 (0.309975) | 1.946097 / 1.492716 (0.453381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250774 / 0.018006 (0.232768) | 0.506869 / 0.000490 (0.506379) | 0.008232 / 0.000200 (0.008032) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037779 / 0.037411 (0.000368) | 0.111933 / 0.014526 (0.097407) | 0.122385 / 0.176557 (-0.054172) | 0.190372 / 0.737135 (-0.546763) | 0.122472 / 0.296338 (-0.173866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488502 / 0.215209 (0.273293) | 4.878114 / 2.077655 (2.800459) | 2.504144 / 1.504120 (1.000024) | 2.321077 / 1.541195 (0.779883) | 2.416797 / 1.468490 (0.948307) | 0.583582 / 4.584777 (-4.001195) | 4.277896 / 3.745712 (0.532184) | 3.874780 / 5.269862 (-1.395082) | 2.540099 / 4.565676 (-2.025577) | 0.068734 / 0.424275 (-0.355541) | 0.009158 / 0.007607 (0.001550) | 0.578401 / 0.226044 (0.352357) | 5.763354 / 2.268929 (3.494426) | 3.167771 / 55.444624 (-52.276853) | 2.675220 / 6.876477 (-4.201257) | 2.920927 / 2.142072 (0.778855) | 0.673948 / 4.805227 (-4.131280) | 0.157908 / 6.500664 (-6.342756) | 0.071672 / 0.075469 (-0.003797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.635120 / 1.841788 (-0.206668) | 24.853480 / 8.074308 (16.779172) | 17.162978 / 10.191392 (6.971586) | 0.209577 / 0.680424 (-0.470847) | 0.030110 / 0.534201 (-0.504091) | 0.546970 / 0.579283 (-0.032313) | 0.581912 / 0.434364 (0.147548) | 0.571460 / 0.540337 (0.031123) | 0.823411 / 1.386936 (-0.563525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"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) | 0.006674 / 0.011353 (-0.004679) | 0.004198 / 0.011008 (-0.006810) | 0.084859 / 0.038508 (0.046351) | 0.076065 / 0.023109 (0.052955) | 0.316065 / 0.275898 (0.040167) | 0.352097 / 0.323480 (0.028617) | 0.005610 / 0.007986 (-0.002376) | 0.003600 / 0.004328 (-0.000729) | 0.064921 / 0.004250 (0.060671) | 0.054493 / 0.037052 (0.017441) | 0.318125 / 0.258489 (0.059636) | 0.370183 / 0.293841 (0.076342) | 0.031141 / 0.128546 (-0.097405) | 0.008755 / 0.075646 (-0.066891) | 0.288241 / 0.419271 (-0.131030) | 0.052379 / 0.043533 (0.008846) | 0.328147 / 0.255139 (0.073008) | 0.347548 / 0.283200 (0.064348) | 0.024393 / 0.141683 (-0.117290) | 1.480646 / 1.452155 (0.028492) | 1.575867 / 1.492716 (0.083151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268978 / 0.018006 (0.250971) | 0.586470 / 0.000490 (0.585980) | 0.003190 / 0.000200 (0.002990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030595 / 0.037411 (-0.006816) | 0.083037 / 0.014526 (0.068511) | 0.103706 / 0.176557 (-0.072850) | 0.164104 / 0.737135 (-0.573031) | 0.104536 / 0.296338 (-0.191802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382274 / 0.215209 (0.167065) | 3.811878 / 2.077655 (1.734223) | 1.840098 / 1.504120 (0.335978) | 1.670949 / 1.541195 (0.129754) | 1.763755 / 1.468490 (0.295264) | 0.479526 / 4.584777 (-4.105251) | 3.544443 / 3.745712 (-0.201269) | 3.263004 / 5.269862 (-2.006858) | 2.092801 / 4.565676 (-2.472875) | 0.057167 / 0.424275 (-0.367108) | 0.007450 / 0.007607 (-0.000157) | 0.463731 / 0.226044 (0.237686) | 4.624630 / 2.268929 (2.355701) | 2.327078 / 55.444624 (-53.117546) | 1.977734 / 6.876477 (-4.898743) | 2.237152 / 2.142072 (0.095079) | 0.573210 / 4.805227 (-4.232018) | 0.132095 / 6.500664 (-6.368569) | 0.060283 / 0.075469 (-0.015186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243404 / 1.841788 (-0.598384) | 20.306778 / 8.074308 (12.232470) | 14.561660 / 10.191392 (4.370268) | 0.170826 / 0.680424 (-0.509598) | 0.018574 / 0.534201 (-0.515627) | 0.392367 / 0.579283 (-0.186916) | 0.402918 / 0.434364 (-0.031446) | 0.476629 / 0.540337 (-0.063708) | 0.653709 / 1.386936 (-0.733227) |\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) | 0.006562 / 0.011353 (-0.004791) | 0.004092 / 0.011008 (-0.006916) | 0.065951 / 0.038508 (0.027443) | 0.078090 / 0.023109 (0.054981) | 0.369679 / 0.275898 (0.093781) | 0.411442 / 0.323480 (0.087962) | 0.005646 / 0.007986 (-0.002339) | 0.003537 / 0.004328 (-0.000791) | 0.066024 / 0.004250 (0.061773) | 0.058947 / 0.037052 (0.021895) | 0.389219 / 0.258489 (0.130730) | 0.414200 / 0.293841 (0.120359) | 0.030372 / 0.128546 (-0.098174) | 0.008631 / 0.075646 (-0.067015) | 0.071692 / 0.419271 (-0.347580) | 0.048035 / 0.043533 (0.004502) | 0.376960 / 0.255139 (0.121821) | 0.389847 / 0.283200 (0.106648) | 0.023940 / 0.141683 (-0.117743) | 1.487633 / 1.452155 (0.035479) | 1.561680 / 1.492716 (0.068964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301467 / 0.018006 (0.283461) | 0.544159 / 0.000490 (0.543669) | 0.000408 / 0.000200 (0.000208) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030939 / 0.037411 (-0.006472) | 0.087432 / 0.014526 (0.072906) | 0.103263 / 0.176557 (-0.073293) | 0.154551 / 0.737135 (-0.582585) | 0.104631 / 0.296338 (-0.191707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422348 / 0.215209 (0.207139) | 4.206003 / 2.077655 (2.128348) | 2.212619 / 1.504120 (0.708499) | 2.049616 / 1.541195 (0.508421) | 2.139093 / 1.468490 (0.670603) | 0.489647 / 4.584777 (-4.095130) | 3.523291 / 3.745712 (-0.222422) | 3.277657 / 5.269862 (-1.992205) | 2.111353 / 4.565676 (-2.454324) | 0.057597 / 0.424275 (-0.366679) | 0.007675 / 0.007607 (0.000068) | 0.493068 / 0.226044 (0.267023) | 4.939493 / 2.268929 (2.670565) | 2.695995 / 55.444624 (-52.748630) | 2.374904 / 6.876477 (-4.501573) | 2.600110 / 2.142072 (0.458038) | 0.586306 / 4.805227 (-4.218921) | 0.134137 / 6.500664 (-6.366527) | 0.061897 / 0.075469 (-0.013572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330628 / 1.841788 (-0.511160) | 20.557964 / 8.074308 (12.483656) | 14.251632 / 10.191392 (4.060240) | 0.148772 / 0.680424 (-0.531652) | 0.018383 / 0.534201 (-0.515817) | 0.392552 / 0.579283 (-0.186731) | 0.403959 / 0.434364 (-0.030405) | 0.462154 / 0.540337 (-0.078184) | 0.608832 / 1.386936 (-0.778104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"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) | 0.007659 / 0.011353 (-0.003694) | 0.004500 / 0.011008 (-0.006508) | 0.100379 / 0.038508 (0.061871) | 0.079731 / 0.023109 (0.056622) | 0.381788 / 0.275898 (0.105890) | 0.416524 / 0.323480 (0.093044) | 0.004446 / 0.007986 (-0.003539) | 0.003752 / 0.004328 (-0.000577) | 0.074956 / 0.004250 (0.070706) | 0.062885 / 0.037052 (0.025832) | 0.383849 / 0.258489 (0.125360) | 0.433906 / 0.293841 (0.140065) | 0.036079 / 0.128546 (-0.092468) | 0.009927 / 0.075646 (-0.065719) | 0.343879 / 0.419271 (-0.075393) | 0.061055 / 0.043533 (0.017523) | 0.376703 / 0.255139 (0.121564) | 0.428111 / 0.283200 (0.144911) | 0.028667 / 0.141683 (-0.113016) | 1.777755 / 1.452155 (0.325600) | 1.878283 / 1.492716 (0.385567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220829 / 0.018006 (0.202823) | 0.506406 / 0.000490 (0.505916) | 0.005550 / 0.000200 (0.005350) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034928 / 0.037411 (-0.002483) | 0.103873 / 0.014526 (0.089347) | 0.114352 / 0.176557 (-0.062204) | 0.188218 / 0.737135 (-0.548918) | 0.117343 / 0.296338 (-0.178995) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459148 / 0.215209 (0.243939) | 4.582092 / 2.077655 (2.504437) | 2.275603 / 1.504120 (0.771483) | 2.058155 / 1.541195 (0.516960) | 2.163886 / 1.468490 (0.695396) | 0.573033 / 4.584777 (-4.011744) | 4.414891 / 3.745712 (0.669178) | 7.280433 / 5.269862 (2.010572) | 4.119414 / 4.565676 (-0.446262) | 0.067432 / 0.424275 (-0.356843) | 0.008687 / 0.007607 (0.001080) | 0.556029 / 0.226044 (0.329984) | 5.557192 / 2.268929 (3.288264) | 2.921596 / 55.444624 (-52.523028) | 2.520249 / 6.876477 (-4.356228) | 2.778965 / 2.142072 (0.636893) | 0.684765 / 4.805227 (-4.120462) | 0.159228 / 6.500664 (-6.341436) | 0.074015 / 0.075469 (-0.001454) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534470 / 1.841788 (-0.307318) | 23.630693 / 8.074308 (15.556385) | 17.058142 / 10.191392 (6.866750) | 0.200909 / 0.680424 (-0.479515) | 0.021637 / 0.534201 (-0.512564) | 0.467417 / 0.579283 (-0.111866) | 0.460456 / 0.434364 (0.026092) | 0.541131 / 0.540337 (0.000793) | 0.728560 / 1.386936 (-0.658376) |\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) | 0.007625 / 0.011353 (-0.003727) | 0.004495 / 0.011008 (-0.006513) | 0.076373 / 0.038508 (0.037865) | 0.085260 / 0.023109 (0.062151) | 0.475778 / 0.275898 (0.199880) | 0.504604 / 0.323480 (0.181124) | 0.006733 / 0.007986 (-0.001253) | 0.003751 / 0.004328 (-0.000578) | 0.074993 / 0.004250 (0.070743) | 0.064704 / 0.037052 (0.027652) | 0.490072 / 0.258489 (0.231583) | 0.507560 / 0.293841 (0.213719) | 0.036765 / 0.128546 (-0.091781) | 0.009955 / 0.075646 (-0.065692) | 0.082452 / 0.419271 (-0.336820) | 0.057131 / 0.043533 (0.013598) | 0.467664 / 0.255139 (0.212525) | 0.482143 / 0.283200 (0.198943) | 0.025396 / 0.141683 (-0.116287) | 1.807587 / 1.452155 (0.355433) | 1.853355 / 1.492716 (0.360639) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250543 / 0.018006 (0.232537) | 0.495685 / 0.000490 (0.495196) | 0.000415 / 0.000200 (0.000215) | 0.000063 / 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) | 0.035795 / 0.037411 (-0.001616) | 0.105954 / 0.014526 (0.091428) | 0.120158 / 0.176557 (-0.056399) | 0.181714 / 0.737135 (-0.555422) | 0.121242 / 0.296338 (-0.175097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488241 / 0.215209 (0.273032) | 4.866916 / 2.077655 (2.789262) | 2.531530 / 1.504120 (1.027410) | 2.360642 / 1.541195 (0.819448) | 2.457320 / 1.468490 (0.988830) | 0.571224 / 4.584777 (-4.013553) | 4.339042 / 3.745712 (0.593330) | 3.672812 / 5.269862 (-1.597050) | 2.364535 / 4.565676 (-2.201142) | 0.067004 / 0.424275 (-0.357271) | 0.009019 / 0.007607 (0.001412) | 0.563751 / 0.226044 (0.337707) | 5.664917 / 2.268929 (3.395989) | 3.043316 / 55.444624 (-52.401308) | 2.682722 / 6.876477 (-4.193755) | 2.863482 / 2.142072 (0.721409) | 0.666171 / 4.805227 (-4.139056) | 0.151862 / 6.500664 (-6.348802) | 0.071199 / 0.075469 (-0.004271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.601880 / 1.841788 (-0.239907) | 23.069073 / 8.074308 (14.994765) | 16.918377 / 10.191392 (6.726985) | 0.173614 / 0.680424 (-0.506810) | 0.021843 / 0.534201 (-0.512358) | 0.470531 / 0.579283 (-0.108753) | 0.471152 / 0.434364 (0.036788) | 0.550968 / 0.540337 (0.010631) | 0.718869 / 1.386936 (-0.668067) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9e6eea46fc9503765c125395e30e26c1ae2e084 \"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) | 0.007530 / 0.011353 (-0.003823) | 0.004151 / 0.011008 (-0.006858) | 0.098490 / 0.038508 (0.059982) | 0.086955 / 0.023109 (0.063846) | 0.362133 / 0.275898 (0.086235) | 0.391402 / 0.323480 (0.067922) | 0.006274 / 0.007986 (-0.001712) | 0.003711 / 0.004328 (-0.000618) | 0.073519 / 0.004250 (0.069269) | 0.066170 / 0.037052 (0.029118) | 0.379057 / 0.258489 (0.120568) | 0.398132 / 0.293841 (0.104291) | 0.033936 / 0.128546 (-0.094610) | 0.009977 / 0.075646 (-0.065670) | 0.323766 / 0.419271 (-0.095505) | 0.078615 / 0.043533 (0.035082) | 0.352403 / 0.255139 (0.097264) | 0.386607 / 0.283200 (0.103407) | 0.036579 / 0.141683 (-0.105103) | 1.691899 / 1.452155 (0.239745) | 1.819396 / 1.492716 (0.326680) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216888 / 0.018006 (0.198882) | 0.465781 / 0.000490 (0.465291) | 0.006197 / 0.000200 (0.005997) | 0.000086 / 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) | 0.032870 / 0.037411 (-0.004542) | 0.096026 / 0.014526 (0.081500) | 0.111093 / 0.176557 (-0.065464) | 0.185982 / 0.737135 (-0.551154) | 0.106967 / 0.296338 (-0.189371) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441567 / 0.215209 (0.226358) | 4.353813 / 2.077655 (2.276158) | 2.176034 / 1.504120 (0.671914) | 1.969631 / 1.541195 (0.428437) | 2.048821 / 1.468490 (0.580330) | 0.549144 / 4.584777 (-4.035633) | 4.016166 / 3.745712 (0.270453) | 3.764249 / 5.269862 (-1.505613) | 2.293995 / 4.565676 (-2.271681) | 0.065227 / 0.424275 (-0.359048) | 0.008303 / 0.007607 (0.000695) | 0.513783 / 0.226044 (0.287738) | 5.247617 / 2.268929 (2.978689) | 2.782114 / 55.444624 (-52.662510) | 2.342776 / 6.876477 (-4.533701) | 2.621569 / 2.142072 (0.479497) | 0.679336 / 4.805227 (-4.125891) | 0.152061 / 6.500664 (-6.348603) | 0.070294 / 0.075469 (-0.005175) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471778 / 1.841788 (-0.370010) | 22.714904 / 8.074308 (14.640596) | 15.607991 / 10.191392 (5.416599) | 0.172592 / 0.680424 (-0.507832) | 0.021799 / 0.534201 (-0.512402) | 0.462740 / 0.579283 (-0.116543) | 0.490885 / 0.434364 (0.056521) | 0.552997 / 0.540337 (0.012660) | 0.763784 / 1.386936 (-0.623152) |\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) | 0.007466 / 0.011353 (-0.003886) | 0.004322 / 0.011008 (-0.006686) | 0.074331 / 0.038508 (0.035823) | 0.085315 / 0.023109 (0.062206) | 0.409284 / 0.275898 (0.133386) | 0.464584 / 0.323480 (0.141104) | 0.005651 / 0.007986 (-0.002335) | 0.003577 / 0.004328 (-0.000751) | 0.070250 / 0.004250 (0.066000) | 0.059780 / 0.037052 (0.022727) | 0.419668 / 0.258489 (0.161179) | 0.462984 / 0.293841 (0.169143) | 0.034159 / 0.128546 (-0.094387) | 0.008999 / 0.075646 (-0.066647) | 0.076302 / 0.419271 (-0.342969) | 0.052274 / 0.043533 (0.008741) | 0.425938 / 0.255139 (0.170799) | 0.430399 / 0.283200 (0.147200) | 0.025017 / 0.141683 (-0.116666) | 1.680697 / 1.452155 (0.228542) | 1.774677 / 1.492716 (0.281960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291514 / 0.018006 (0.273508) | 0.461175 / 0.000490 (0.460685) | 0.023061 / 0.000200 (0.022861) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033950 / 0.037411 (-0.003462) | 0.100032 / 0.014526 (0.085506) | 0.118308 / 0.176557 (-0.058249) | 0.183601 / 0.737135 (-0.553535) | 0.116936 / 0.296338 (-0.179402) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478779 / 0.215209 (0.263570) | 4.709505 / 2.077655 (2.631850) | 2.457442 / 1.504120 (0.953322) | 2.213737 / 1.541195 (0.672542) | 2.340642 / 1.468490 (0.872152) | 0.567187 / 4.584777 (-4.017590) | 3.923061 / 3.745712 (0.177349) | 3.752989 / 5.269862 (-1.516873) | 2.324028 / 4.565676 (-2.241649) | 0.064471 / 0.424275 (-0.359804) | 0.008845 / 0.007607 (0.001238) | 0.547447 / 0.226044 (0.321402) | 5.599435 / 2.268929 (3.330506) | 2.980547 / 55.444624 (-52.464077) | 2.754908 / 6.876477 (-4.121569) | 2.832978 / 2.142072 (0.690906) | 0.635059 / 4.805227 (-4.170168) | 0.153478 / 6.500664 (-6.347187) | 0.067146 / 0.075469 (-0.008323) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.555588 / 1.841788 (-0.286200) | 22.828906 / 8.074308 (14.754597) | 16.211008 / 10.191392 (6.019616) | 0.168009 / 0.680424 (-0.512415) | 0.021966 / 0.534201 (-0.512235) | 0.464872 / 0.579283 (-0.114411) | 0.460429 / 0.434364 (0.026065) | 0.530498 / 0.540337 (-0.009839) | 0.705020 / 1.386936 (-0.681916) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#deb9e703237c8310c5a6db04f54d54368e951edd \"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) | 0.005964 / 0.011353 (-0.005389) | 0.003644 / 0.011008 (-0.007364) | 0.079607 / 0.038508 (0.041099) | 0.058387 / 0.023109 (0.035278) | 0.312226 / 0.275898 (0.036328) | 0.349206 / 0.323480 (0.025726) | 0.004715 / 0.007986 (-0.003271) | 0.002869 / 0.004328 (-0.001460) | 0.061668 / 0.004250 (0.057417) | 0.045694 / 0.037052 (0.008642) | 0.313516 / 0.258489 (0.055027) | 0.357543 / 0.293841 (0.063702) | 0.027179 / 0.128546 (-0.101367) | 0.007961 / 0.075646 (-0.067686) | 0.262473 / 0.419271 (-0.156798) | 0.045588 / 0.043533 (0.002055) | 0.313102 / 0.255139 (0.057963) | 0.368686 / 0.283200 (0.085486) | 0.020556 / 0.141683 (-0.121127) | 1.447258 / 1.452155 (-0.004897) | 1.527319 / 1.492716 (0.034602) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199417 / 0.018006 (0.181411) | 0.422155 / 0.000490 (0.421665) | 0.004972 / 0.000200 (0.004772) | 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) | 0.023539 / 0.037411 (-0.013872) | 0.073055 / 0.014526 (0.058529) | 0.083631 / 0.176557 (-0.092926) | 0.145923 / 0.737135 (-0.591212) | 0.083820 / 0.296338 (-0.212518) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396305 / 0.215209 (0.181096) | 3.967065 / 2.077655 (1.889410) | 2.101109 / 1.504120 (0.596989) | 1.958817 / 1.541195 (0.417622) | 2.037894 / 1.468490 (0.569404) | 0.496955 / 4.584777 (-4.087822) | 3.078948 / 3.745712 (-0.666764) | 3.363655 / 5.269862 (-1.906207) | 2.087659 / 4.565676 (-2.478018) | 0.057171 / 0.424275 (-0.367104) | 0.006410 / 0.007607 (-0.001197) | 0.470535 / 0.226044 (0.244491) | 4.715259 / 2.268929 (2.446330) | 2.355510 / 55.444624 (-53.089114) | 2.025270 / 6.876477 (-4.851207) | 2.210401 / 2.142072 (0.068329) | 0.580538 / 4.805227 (-4.224689) | 0.125068 / 6.500664 (-6.375596) | 0.059871 / 0.075469 (-0.015598) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245468 / 1.841788 (-0.596320) | 18.322042 / 8.074308 (10.247734) | 13.609726 / 10.191392 (3.418334) | 0.143623 / 0.680424 (-0.536801) | 0.017068 / 0.534201 (-0.517133) | 0.330758 / 0.579283 (-0.248525) | 0.339946 / 0.434364 (-0.094418) | 0.377861 / 0.540337 (-0.162476) | 0.524593 / 1.386936 (-0.862343) |\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) | 0.006049 / 0.011353 (-0.005304) | 0.003737 / 0.011008 (-0.007271) | 0.062816 / 0.038508 (0.024308) | 0.063768 / 0.023109 (0.040658) | 0.362001 / 0.275898 (0.086103) | 0.395251 / 0.323480 (0.071772) | 0.004823 / 0.007986 (-0.003163) | 0.002881 / 0.004328 (-0.001447) | 0.061987 / 0.004250 (0.057737) | 0.049950 / 0.037052 (0.012898) | 0.362442 / 0.258489 (0.103953) | 0.399321 / 0.293841 (0.105480) | 0.027616 / 0.128546 (-0.100930) | 0.007965 / 0.075646 (-0.067681) | 0.068584 / 0.419271 (-0.350687) | 0.044700 / 0.043533 (0.001168) | 0.361011 / 0.255139 (0.105872) | 0.386007 / 0.283200 (0.102807) | 0.024621 / 0.141683 (-0.117061) | 1.441497 / 1.452155 (-0.010657) | 1.533145 / 1.492716 (0.040429) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223446 / 0.018006 (0.205440) | 0.411147 / 0.000490 (0.410657) | 0.001821 / 0.000200 (0.001621) | 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) | 0.025661 / 0.037411 (-0.011751) | 0.077838 / 0.014526 (0.063313) | 0.086148 / 0.176557 (-0.090408) | 0.140386 / 0.737135 (-0.596750) | 0.088793 / 0.296338 (-0.207546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425209 / 0.215209 (0.210000) | 4.250723 / 2.077655 (2.173068) | 2.403437 / 1.504120 (0.899317) | 2.283584 / 1.541195 (0.742390) | 2.326870 / 1.468490 (0.858380) | 0.504781 / 4.584777 (-4.079996) | 3.017042 / 3.745712 (-0.728670) | 4.643068 / 5.269862 (-0.626794) | 2.535710 / 4.565676 (-2.029967) | 0.058520 / 0.424275 (-0.365755) | 0.006766 / 0.007607 (-0.000841) | 0.500664 / 0.226044 (0.274620) | 5.017073 / 2.268929 (2.748145) | 2.668661 / 55.444624 (-52.775963) | 2.335486 / 6.876477 (-4.540991) | 2.486518 / 2.142072 (0.344445) | 0.598795 / 4.805227 (-4.206432) | 0.126395 / 6.500664 (-6.374269) | 0.063154 / 0.075469 (-0.012315) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.358059 / 1.841788 (-0.483728) | 18.615724 / 8.074308 (10.541416) | 13.670934 / 10.191392 (3.479542) | 0.134650 / 0.680424 (-0.545774) | 0.016941 / 0.534201 (-0.517260) | 0.335215 / 0.579283 (-0.244068) | 0.356118 / 0.434364 (-0.078246) | 0.393109 / 0.540337 (-0.147228) | 0.534165 / 1.386936 (-0.852771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7d3b557665f34e84cd151ffe9d80b45a19fe33 \"CML watermark\")\n" ]

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[ "Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?", "Hi @lhoestq ! Thank you so much 🙌 \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a `fsspec.implementations.arrow.ArrowFSWrapper` [to make it](https://arrow.apache.org/docs/python/filesystems.html#using-arrow-filesystems-with-fsspec) `fsspec` compatible). I also register it as an entrypoint with `fsspec` so that it's the one that gets automatically resolved when looking for filesystems for the `s3` protocol\r\n\r\nIn my case the `storage_option` that seemed not getting piped through was the filesystem's `endpoint_override` that I use in some tests to point at a mock S3 bucket" ]

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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) | 0.008473 / 0.011353 (-0.002880) | 0.004734 / 0.011008 (-0.006274) | 0.103895 / 0.038508 (0.065387) | 0.071838 / 0.023109 (0.048729) | 0.379949 / 0.275898 (0.104051) | 0.397375 / 0.323480 (0.073895) | 0.006695 / 0.007986 (-0.001290) | 0.004536 / 0.004328 (0.000207) | 0.076151 / 0.004250 (0.071901) | 0.058690 / 0.037052 (0.021638) | 0.379937 / 0.258489 (0.121448) | 0.411833 / 0.293841 (0.117992) | 0.046805 / 0.128546 (-0.081741) | 0.013689 / 0.075646 (-0.061958) | 0.327896 / 0.419271 (-0.091375) | 0.063873 / 0.043533 (0.020340) | 0.378451 / 0.255139 (0.123312) | 0.398725 / 0.283200 (0.115525) | 0.034961 / 0.141683 (-0.106722) | 1.604999 / 1.452155 (0.152845) | 1.748370 / 1.492716 (0.255654) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224634 / 0.018006 (0.206628) | 0.548468 / 0.000490 (0.547979) | 0.005049 / 0.000200 (0.004849) | 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) | 0.028144 / 0.037411 (-0.009267) | 0.092184 / 0.014526 (0.077659) | 0.102987 / 0.176557 (-0.073570) | 0.176987 / 0.737135 (-0.560149) | 0.103093 / 0.296338 (-0.193246) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578410 / 0.215209 (0.363201) | 5.664781 / 2.077655 (3.587126) | 2.487763 / 1.504120 (0.983643) | 2.254213 / 1.541195 (0.713018) | 2.239693 / 1.468490 (0.771202) | 0.810380 / 4.584777 (-3.774397) | 5.036540 / 3.745712 (1.290828) | 7.064695 / 5.269862 (1.794834) | 4.215101 / 4.565676 (-0.350575) | 0.089792 / 0.424275 (-0.334483) | 0.008487 / 0.007607 (0.000879) | 0.692292 / 0.226044 (0.466248) | 6.780226 / 2.268929 (4.511297) | 3.245510 / 55.444624 (-52.199114) | 2.575984 / 6.876477 (-4.300493) | 2.747546 / 2.142072 (0.605473) | 0.956604 / 4.805227 (-3.848623) | 0.198937 / 6.500664 (-6.301727) | 0.070849 / 0.075469 (-0.004620) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.536469 / 1.841788 (-0.305319) | 21.750583 / 8.074308 (13.676275) | 20.559532 / 10.191392 (10.368140) | 0.241244 / 0.680424 (-0.439180) | 0.030078 / 0.534201 (-0.504123) | 0.462204 / 0.579283 (-0.117079) | 0.600103 / 0.434364 (0.165739) | 0.535074 / 0.540337 (-0.005264) | 0.764427 / 1.386936 (-0.622509) |\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) | 0.009712 / 0.011353 (-0.001641) | 0.005036 / 0.011008 (-0.005972) | 0.073683 / 0.038508 (0.035175) | 0.078684 / 0.023109 (0.055574) | 0.445096 / 0.275898 (0.169198) | 0.496233 / 0.323480 (0.172754) | 0.006231 / 0.007986 (-0.001755) | 0.004720 / 0.004328 (0.000392) | 0.076444 / 0.004250 (0.072194) | 0.060932 / 0.037052 (0.023880) | 0.505727 / 0.258489 (0.247238) | 0.498702 / 0.293841 (0.204861) | 0.047115 / 0.128546 (-0.081431) | 0.014028 / 0.075646 (-0.061618) | 0.099292 / 0.419271 (-0.319980) | 0.061571 / 0.043533 (0.018038) | 0.468435 / 0.255139 (0.213296) | 0.481747 / 0.283200 (0.198547) | 0.033962 / 0.141683 (-0.107721) | 1.665397 / 1.452155 (0.213242) | 1.830488 / 1.492716 (0.337772) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268217 / 0.018006 (0.250211) | 0.555123 / 0.000490 (0.554633) | 0.000451 / 0.000200 (0.000251) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034262 / 0.037411 (-0.003150) | 0.107807 / 0.014526 (0.093281) | 0.115631 / 0.176557 (-0.060926) | 0.175914 / 0.737135 (-0.561221) | 0.118775 / 0.296338 (-0.177564) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583260 / 0.215209 (0.368051) | 5.934976 / 2.077655 (3.857321) | 2.752304 / 1.504120 (1.248184) | 2.382746 / 1.541195 (0.841551) | 2.389402 / 1.468490 (0.920912) | 0.794213 / 4.584777 (-3.790564) | 5.215269 / 3.745712 (1.469557) | 7.083595 / 5.269862 (1.813733) | 3.776136 / 4.565676 (-0.789540) | 0.091141 / 0.424275 (-0.333135) | 0.008803 / 0.007607 (0.001196) | 0.726510 / 0.226044 (0.500465) | 6.926860 / 2.268929 (4.657931) | 3.475612 / 55.444624 (-51.969012) | 2.730237 / 6.876477 (-4.146240) | 2.879145 / 2.142072 (0.737073) | 0.959956 / 4.805227 (-3.845271) | 0.189812 / 6.500664 (-6.310852) | 0.071624 / 0.075469 (-0.003845) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748184 / 1.841788 (-0.093603) | 23.764520 / 8.074308 (15.690212) | 19.502461 / 10.191392 (9.311069) | 0.233987 / 0.680424 (-0.446437) | 0.028116 / 0.534201 (-0.506085) | 0.478838 / 0.579283 (-0.100445) | 0.560952 / 0.434364 (0.126588) | 0.529902 / 0.540337 (-0.010435) | 0.735095 / 1.386936 (-0.651841) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dda3e389212f44117a40b44bb0cdf358cfd9f71e \"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) | 0.006735 / 0.011353 (-0.004618) | 0.004131 / 0.011008 (-0.006878) | 0.085619 / 0.038508 (0.047111) | 0.076973 / 0.023109 (0.053864) | 0.315175 / 0.275898 (0.039277) | 0.354703 / 0.323480 (0.031223) | 0.005409 / 0.007986 (-0.002577) | 0.003438 / 0.004328 (-0.000891) | 0.064773 / 0.004250 (0.060523) | 0.056117 / 0.037052 (0.019064) | 0.313825 / 0.258489 (0.055336) | 0.354654 / 0.293841 (0.060813) | 0.031384 / 0.128546 (-0.097163) | 0.008537 / 0.075646 (-0.067109) | 0.288528 / 0.419271 (-0.130744) | 0.053036 / 0.043533 (0.009504) | 0.312213 / 0.255139 (0.057074) | 0.335952 / 0.283200 (0.052752) | 0.023165 / 0.141683 (-0.118518) | 1.497559 / 1.452155 (0.045404) | 1.561949 / 1.492716 (0.069233) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212558 / 0.018006 (0.194552) | 0.456555 / 0.000490 (0.456065) | 0.000334 / 0.000200 (0.000134) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028571 / 0.037411 (-0.008840) | 0.085154 / 0.014526 (0.070628) | 0.095961 / 0.176557 (-0.080596) | 0.153041 / 0.737135 (-0.584094) | 0.099234 / 0.296338 (-0.197105) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.381796 / 0.215209 (0.166587) | 3.806948 / 2.077655 (1.729294) | 1.829597 / 1.504120 (0.325477) | 1.659065 / 1.541195 (0.117870) | 1.738524 / 1.468490 (0.270034) | 0.483379 / 4.584777 (-4.101398) | 3.540648 / 3.745712 (-0.205064) | 3.269188 / 5.269862 (-2.000673) | 2.042113 / 4.565676 (-2.523564) | 0.056905 / 0.424275 (-0.367370) | 0.007235 / 0.007607 (-0.000373) | 0.460581 / 0.226044 (0.234537) | 4.597451 / 2.268929 (2.328522) | 2.334284 / 55.444624 (-53.110340) | 1.960026 / 6.876477 (-4.916450) | 2.172118 / 2.142072 (0.030045) | 0.576758 / 4.805227 (-4.228470) | 0.131196 / 6.500664 (-6.369468) | 0.060053 / 0.075469 (-0.015417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289466 / 1.841788 (-0.552322) | 19.713059 / 8.074308 (11.638750) | 14.292390 / 10.191392 (4.100998) | 0.146199 / 0.680424 (-0.534225) | 0.018123 / 0.534201 (-0.516078) | 0.392492 / 0.579283 (-0.186791) | 0.416544 / 0.434364 (-0.017820) | 0.457166 / 0.540337 (-0.083171) | 0.645490 / 1.386936 (-0.741446) |\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) | 0.006508 / 0.011353 (-0.004845) | 0.004010 / 0.011008 (-0.006998) | 0.065201 / 0.038508 (0.026693) | 0.076322 / 0.023109 (0.053213) | 0.364198 / 0.275898 (0.088300) | 0.398251 / 0.323480 (0.074771) | 0.005328 / 0.007986 (-0.002658) | 0.003298 / 0.004328 (-0.001031) | 0.064378 / 0.004250 (0.060128) | 0.056053 / 0.037052 (0.019000) | 0.365431 / 0.258489 (0.106942) | 0.402777 / 0.293841 (0.108936) | 0.031014 / 0.128546 (-0.097532) | 0.008507 / 0.075646 (-0.067140) | 0.071471 / 0.419271 (-0.347801) | 0.048300 / 0.043533 (0.004768) | 0.359700 / 0.255139 (0.104561) | 0.382244 / 0.283200 (0.099044) | 0.023783 / 0.141683 (-0.117900) | 1.517518 / 1.452155 (0.065363) | 1.569732 / 1.492716 (0.077015) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257447 / 0.018006 (0.239440) | 0.452598 / 0.000490 (0.452109) | 0.015187 / 0.000200 (0.014987) | 0.000164 / 0.000054 (0.000109) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030958 / 0.037411 (-0.006454) | 0.090066 / 0.014526 (0.075540) | 0.101120 / 0.176557 (-0.075437) | 0.154295 / 0.737135 (-0.582840) | 0.103582 / 0.296338 (-0.192756) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415945 / 0.215209 (0.200736) | 4.146464 / 2.077655 (2.068809) | 2.121414 / 1.504120 (0.617294) | 1.956885 / 1.541195 (0.415690) | 2.047955 / 1.468490 (0.579465) | 0.486334 / 4.584777 (-4.098443) | 3.506263 / 3.745712 (-0.239449) | 4.942274 / 5.269862 (-0.327587) | 2.907836 / 4.565676 (-1.657841) | 0.057344 / 0.424275 (-0.366931) | 0.007813 / 0.007607 (0.000206) | 0.497888 / 0.226044 (0.271844) | 4.978017 / 2.268929 (2.709089) | 2.600447 / 55.444624 (-52.844177) | 2.335050 / 6.876477 (-4.541427) | 2.480373 / 2.142072 (0.338301) | 0.597954 / 4.805227 (-4.207274) | 0.134794 / 6.500664 (-6.365870) | 0.062605 / 0.075469 (-0.012864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344390 / 1.841788 (-0.497398) | 20.020067 / 8.074308 (11.945759) | 14.344626 / 10.191392 (4.153234) | 0.172101 / 0.680424 (-0.508322) | 0.018549 / 0.534201 (-0.515652) | 0.393589 / 0.579283 (-0.185694) | 0.438401 / 0.434364 (0.004037) | 0.463800 / 0.540337 (-0.076537) | 0.618269 / 1.386936 (-0.768667) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0177910b32712f28d147879395e511207e39958 \"CML watermark\")\n" ]

[]

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[ "You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n", "This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.", "The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.", "The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed", "All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.\r\n\r\nIf that's not the case, we need a full reproducer (not snippets) with the environment info.", "I have resolved the error after including a collate function as indicated in the Quick Start session of the Datasets docs.:\r\n\r\nHere is what I did:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef preprocess_train(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef collate_fn(examples):\r\n images = []\r\n labels = []\r\n for example in examples:\r\n images.append((example[\"pixel_values\"]))\r\n labels.append(example[\"label\"])\r\n\r\n pixel_values = torch.stack(images)\r\n labels = torch.tensor(labels)\r\n return {\"pixel_values\": pixel_values, \"label\": labels}\r\n\r\ntrain_dataset = image_dataset[\"train\"].with_transform(preprocess_train)\r\nval_dataset = image_dataset[\"validation\"].with_transform(preprocess_val)\r\n\r\nimage_datasets = {\r\n \"train\": train_dataset,\r\n \"val\": val_dataset\r\n}\r\n\r\nsamplers = {\r\n \"train\": data.RandomSampler(train_dataset),\r\n \"val\": data.SequentialSampler(val_dataset),\r\n}\r\n\r\ndataloaders = {\r\n x: data.DataLoader(\r\n image_datasets[x],\r\n collate_fn=collate_fn,\r\n batch_size=batch_size,\r\n sampler=samplers[x],\r\n num_workers=args.num_workers,\r\n worker_init_fn=utils.set_seed_for_worker,\r\n generator=g,\r\n pin_memory=True,\r\n )\r\n for x in [\"train\", \"val\"]\r\n}\r\n\r\ntrain_loader, val_loader = dataloaders[\"train\"], dataloaders[\"val\"]\r\n```\r\nEverything runs fine without any bug now. " ]

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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) | 0.006573 / 0.011353 (-0.004780) | 0.004014 / 0.011008 (-0.006994) | 0.084999 / 0.038508 (0.046491) | 0.074965 / 0.023109 (0.051855) | 0.313294 / 0.275898 (0.037396) | 0.349678 / 0.323480 (0.026198) | 0.005401 / 0.007986 (-0.002585) | 0.003401 / 0.004328 (-0.000927) | 0.065363 / 0.004250 (0.061112) | 0.057159 / 0.037052 (0.020107) | 0.313260 / 0.258489 (0.054771) | 0.354654 / 0.293841 (0.060813) | 0.030895 / 0.128546 (-0.097651) | 0.008605 / 0.075646 (-0.067042) | 0.289190 / 0.419271 (-0.130081) | 0.052474 / 0.043533 (0.008942) | 0.316193 / 0.255139 (0.061054) | 0.339966 / 0.283200 (0.056767) | 0.024112 / 0.141683 (-0.117571) | 1.515606 / 1.452155 (0.063452) | 1.571428 / 1.492716 (0.078711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203284 / 0.018006 (0.185278) | 0.452720 / 0.000490 (0.452230) | 0.003891 / 0.000200 (0.003691) | 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) | 0.028992 / 0.037411 (-0.008419) | 0.083170 / 0.014526 (0.068644) | 0.097739 / 0.176557 (-0.078817) | 0.153401 / 0.737135 (-0.583734) | 0.098628 / 0.296338 (-0.197711) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390190 / 0.215209 (0.174981) | 3.901272 / 2.077655 (1.823617) | 1.887194 / 1.504120 (0.383074) | 1.723696 / 1.541195 (0.182501) | 1.800537 / 1.468490 (0.332047) | 0.481758 / 4.584777 (-4.103019) | 3.605098 / 3.745712 (-0.140614) | 3.304482 / 5.269862 (-1.965380) | 2.053515 / 4.565676 (-2.512161) | 0.056997 / 0.424275 (-0.367278) | 0.007347 / 0.007607 (-0.000260) | 0.461367 / 0.226044 (0.235323) | 4.606152 / 2.268929 (2.337223) | 2.470048 / 55.444624 (-52.974576) | 2.060019 / 6.876477 (-4.816458) | 2.320507 / 2.142072 (0.178435) | 0.575050 / 4.805227 (-4.230178) | 0.133030 / 6.500664 (-6.367634) | 0.061508 / 0.075469 (-0.013962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275430 / 1.841788 (-0.566357) | 19.725453 / 8.074308 (11.651145) | 14.396360 / 10.191392 (4.204968) | 0.157980 / 0.680424 (-0.522443) | 0.018516 / 0.534201 (-0.515685) | 0.394717 / 0.579283 (-0.184566) | 0.404948 / 0.434364 (-0.029415) | 0.474001 / 0.540337 (-0.066336) | 0.668463 / 1.386936 (-0.718474) |\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) | 0.006697 / 0.011353 (-0.004656) | 0.004206 / 0.011008 (-0.006802) | 0.065458 / 0.038508 (0.026950) | 0.075845 / 0.023109 (0.052735) | 0.365051 / 0.275898 (0.089153) | 0.400919 / 0.323480 (0.077439) | 0.005347 / 0.007986 (-0.002638) | 0.003386 / 0.004328 (-0.000943) | 0.065398 / 0.004250 (0.061148) | 0.057320 / 0.037052 (0.020268) | 0.379161 / 0.258489 (0.120672) | 0.406892 / 0.293841 (0.113051) | 0.031986 / 0.128546 (-0.096560) | 0.008674 / 0.075646 (-0.066972) | 0.071723 / 0.419271 (-0.347549) | 0.049897 / 0.043533 (0.006364) | 0.372034 / 0.255139 (0.116895) | 0.394293 / 0.283200 (0.111094) | 0.023681 / 0.141683 (-0.118002) | 1.479793 / 1.452155 (0.027639) | 1.553105 / 1.492716 (0.060389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233660 / 0.018006 (0.215654) | 0.454412 / 0.000490 (0.453923) | 0.004473 / 0.000200 (0.004273) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031115 / 0.037411 (-0.006296) | 0.090541 / 0.014526 (0.076015) | 0.104363 / 0.176557 (-0.072193) | 0.161022 / 0.737135 (-0.576114) | 0.105114 / 0.296338 (-0.191225) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427668 / 0.215209 (0.212459) | 4.263145 / 2.077655 (2.185490) | 2.247043 / 1.504120 (0.742923) | 2.082554 / 1.541195 (0.541360) | 2.170505 / 1.468490 (0.702015) | 0.491802 / 4.584777 (-4.092975) | 3.587295 / 3.745712 (-0.158417) | 3.344697 / 5.269862 (-1.925165) | 2.060529 / 4.565676 (-2.505148) | 0.057829 / 0.424275 (-0.366446) | 0.007780 / 0.007607 (0.000173) | 0.503374 / 0.226044 (0.277330) | 5.034742 / 2.268929 (2.765814) | 2.701957 / 55.444624 (-52.742667) | 2.479002 / 6.876477 (-4.397474) | 2.622055 / 2.142072 (0.479982) | 0.591363 / 4.805227 (-4.213864) | 0.133834 / 6.500664 (-6.366830) | 0.062276 / 0.075469 (-0.013193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338788 / 1.841788 (-0.503000) | 20.333599 / 8.074308 (12.259291) | 14.783196 / 10.191392 (4.591804) | 0.168695 / 0.680424 (-0.511729) | 0.018478 / 0.534201 (-0.515723) | 0.397398 / 0.579283 (-0.181885) | 0.409900 / 0.434364 (-0.024464) | 0.475315 / 0.540337 (-0.065023) | 0.644267 / 1.386936 (-0.742669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb0b324e0bae4c93bb5509b2f0731bc346adb21b \"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) | 0.007315 / 0.011353 (-0.004038) | 0.004294 / 0.011008 (-0.006714) | 0.100300 / 0.038508 (0.061792) | 0.077780 / 0.023109 (0.054670) | 0.353728 / 0.275898 (0.077830) | 0.400538 / 0.323480 (0.077058) | 0.005807 / 0.007986 (-0.002178) | 0.003649 / 0.004328 (-0.000680) | 0.077548 / 0.004250 (0.073297) | 0.058834 / 0.037052 (0.021781) | 0.352064 / 0.258489 (0.093574) | 0.399951 / 0.293841 (0.106110) | 0.036472 / 0.128546 (-0.092074) | 0.008653 / 0.075646 (-0.066994) | 0.323089 / 0.419271 (-0.096182) | 0.075127 / 0.043533 (0.031594) | 0.334412 / 0.255139 (0.079273) | 0.375718 / 0.283200 (0.092519) | 0.027915 / 0.141683 (-0.113768) | 1.698795 / 1.452155 (0.246640) | 1.781447 / 1.492716 (0.288730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216111 / 0.018006 (0.198104) | 0.507706 / 0.000490 (0.507216) | 0.000851 / 0.000200 (0.000651) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030451 / 0.037411 (-0.006960) | 0.087488 / 0.014526 (0.072962) | 0.105094 / 0.176557 (-0.071462) | 0.168130 / 0.737135 (-0.569006) | 0.106791 / 0.296338 (-0.189547) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426291 / 0.215209 (0.211082) | 4.281046 / 2.077655 (2.203391) | 2.162268 / 1.504120 (0.658148) | 1.909503 / 1.541195 (0.368309) | 1.943165 / 1.468490 (0.474675) | 0.516667 / 4.584777 (-4.068110) | 4.113218 / 3.745712 (0.367506) | 5.931372 / 5.269862 (0.661510) | 3.563521 / 4.565676 (-1.002155) | 0.062415 / 0.424275 (-0.361860) | 0.007577 / 0.007607 (-0.000030) | 0.534588 / 0.226044 (0.308543) | 5.183490 / 2.268929 (2.914561) | 2.790662 / 55.444624 (-52.653962) | 2.258630 / 6.876477 (-4.617846) | 2.499930 / 2.142072 (0.357857) | 0.606154 / 4.805227 (-4.199073) | 0.136093 / 6.500664 (-6.364571) | 0.061151 / 0.075469 (-0.014318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.398392 / 1.841788 (-0.443396) | 21.482150 / 8.074308 (13.407842) | 15.477336 / 10.191392 (5.285944) | 0.192878 / 0.680424 (-0.487546) | 0.021764 / 0.534201 (-0.512437) | 0.437149 / 0.579283 (-0.142134) | 0.439976 / 0.434364 (0.005612) | 0.514498 / 0.540337 (-0.025840) | 0.762642 / 1.386936 (-0.624294) |\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) | 0.007504 / 0.011353 (-0.003849) | 0.004526 / 0.011008 (-0.006482) | 0.071008 / 0.038508 (0.032500) | 0.078305 / 0.023109 (0.055195) | 0.436160 / 0.275898 (0.160262) | 0.439048 / 0.323480 (0.115568) | 0.006061 / 0.007986 (-0.001925) | 0.003681 / 0.004328 (-0.000648) | 0.069445 / 0.004250 (0.065195) | 0.059258 / 0.037052 (0.022206) | 0.437745 / 0.258489 (0.179256) | 0.464247 / 0.293841 (0.170406) | 0.033286 / 0.128546 (-0.095260) | 0.009846 / 0.075646 (-0.065800) | 0.076330 / 0.419271 (-0.342941) | 0.051919 / 0.043533 (0.008386) | 0.432817 / 0.255139 (0.177678) | 0.426295 / 0.283200 (0.143095) | 0.029818 / 0.141683 (-0.111865) | 1.747640 / 1.452155 (0.295485) | 1.726653 / 1.492716 (0.233937) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251253 / 0.018006 (0.233247) | 0.483394 / 0.000490 (0.482904) | 0.003992 / 0.000200 (0.003793) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032180 / 0.037411 (-0.005231) | 0.095425 / 0.014526 (0.080900) | 0.105908 / 0.176557 (-0.070648) | 0.164732 / 0.737135 (-0.572403) | 0.115903 / 0.296338 (-0.180435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469467 / 0.215209 (0.254258) | 4.633239 / 2.077655 (2.555584) | 2.517557 / 1.504120 (1.013437) | 2.352726 / 1.541195 (0.811531) | 2.314618 / 1.468490 (0.846128) | 0.548446 / 4.584777 (-4.036331) | 3.908797 / 3.745712 (0.163085) | 3.525941 / 5.269862 (-1.743921) | 2.178858 / 4.565676 (-2.386819) | 0.057614 / 0.424275 (-0.366661) | 0.008604 / 0.007607 (0.000997) | 0.554756 / 0.226044 (0.328711) | 5.325635 / 2.268929 (3.056706) | 3.014266 / 55.444624 (-52.430359) | 2.844165 / 6.876477 (-4.032312) | 2.903019 / 2.142072 (0.760947) | 0.617750 / 4.805227 (-4.187478) | 0.144259 / 6.500664 (-6.356405) | 0.065944 / 0.075469 (-0.009525) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504625 / 1.841788 (-0.337163) | 22.400787 / 8.074308 (14.326479) | 15.223702 / 10.191392 (5.032310) | 0.213357 / 0.680424 (-0.467067) | 0.019310 / 0.534201 (-0.514891) | 0.456596 / 0.579283 (-0.122687) | 0.473811 / 0.434364 (0.039447) | 0.517800 / 0.540337 (-0.022537) | 0.792468 / 1.386936 (-0.594468) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#03750f4a4c664125c7de910be004710b181dd354 \"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) | 0.007420 / 0.011353 (-0.003933) | 0.004502 / 0.011008 (-0.006506) | 0.097882 / 0.038508 (0.059374) | 0.079084 / 0.023109 (0.055975) | 0.361797 / 0.275898 (0.085899) | 0.416563 / 0.323480 (0.093083) | 0.006106 / 0.007986 (-0.001879) | 0.003803 / 0.004328 (-0.000526) | 0.074669 / 0.004250 (0.070418) | 0.062168 / 0.037052 (0.025116) | 0.378844 / 0.258489 (0.120355) | 0.426601 / 0.293841 (0.132760) | 0.035619 / 0.128546 (-0.092927) | 0.009686 / 0.075646 (-0.065960) | 0.336481 / 0.419271 (-0.082790) | 0.065553 / 0.043533 (0.022021) | 0.362501 / 0.255139 (0.107362) | 0.399752 / 0.283200 (0.116552) | 0.028685 / 0.141683 (-0.112998) | 1.683495 / 1.452155 (0.231340) | 1.786105 / 1.492716 (0.293388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220792 / 0.018006 (0.202786) | 0.501936 / 0.000490 (0.501447) | 0.000389 / 0.000200 (0.000189) | 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) | 0.032180 / 0.037411 (-0.005232) | 0.093079 / 0.014526 (0.078553) | 0.107967 / 0.176557 (-0.068589) | 0.171747 / 0.737135 (-0.565389) | 0.107920 / 0.296338 (-0.188418) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444431 / 0.215209 (0.229222) | 4.454934 / 2.077655 (2.377279) | 2.140265 / 1.504120 (0.636145) | 1.960126 / 1.541195 (0.418931) | 2.049649 / 1.468490 (0.581158) | 0.557861 / 4.584777 (-4.026916) | 4.046240 / 3.745712 (0.300528) | 4.513748 / 5.269862 (-0.756114) | 2.593643 / 4.565676 (-1.972034) | 0.066795 / 0.424275 (-0.357480) | 0.008302 / 0.007607 (0.000694) | 0.535643 / 0.226044 (0.309599) | 5.299429 / 2.268929 (3.030500) | 2.656019 / 55.444624 (-52.788606) | 2.281214 / 6.876477 (-4.595263) | 2.302910 / 2.142072 (0.160837) | 0.661696 / 4.805227 (-4.143532) | 0.149787 / 6.500664 (-6.350877) | 0.069609 / 0.075469 (-0.005860) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.509842 / 1.841788 (-0.331946) | 21.717504 / 8.074308 (13.643196) | 15.825102 / 10.191392 (5.633710) | 0.168115 / 0.680424 (-0.512309) | 0.021637 / 0.534201 (-0.512564) | 0.454270 / 0.579283 (-0.125013) | 0.458531 / 0.434364 (0.024167) | 0.523052 / 0.540337 (-0.017285) | 0.711219 / 1.386936 (-0.675717) |\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) | 0.007189 / 0.011353 (-0.004164) | 0.004437 / 0.011008 (-0.006571) | 0.075111 / 0.038508 (0.036603) | 0.079245 / 0.023109 (0.056136) | 0.423169 / 0.275898 (0.147270) | 0.455007 / 0.323480 (0.131527) | 0.006076 / 0.007986 (-0.001909) | 0.003819 / 0.004328 (-0.000509) | 0.074976 / 0.004250 (0.070726) | 0.062127 / 0.037052 (0.025075) | 0.456809 / 0.258489 (0.198320) | 0.474707 / 0.293841 (0.180867) | 0.036221 / 0.128546 (-0.092325) | 0.009428 / 0.075646 (-0.066218) | 0.082842 / 0.419271 (-0.336429) | 0.057086 / 0.043533 (0.013553) | 0.436121 / 0.255139 (0.180982) | 0.453934 / 0.283200 (0.170734) | 0.026045 / 0.141683 (-0.115638) | 1.789782 / 1.452155 (0.337627) | 1.820934 / 1.492716 (0.328218) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230790 / 0.018006 (0.212784) | 0.497987 / 0.000490 (0.497497) | 0.002775 / 0.000200 (0.002575) | 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) | 0.034418 / 0.037411 (-0.002994) | 0.105567 / 0.014526 (0.091041) | 0.113134 / 0.176557 (-0.063423) | 0.173742 / 0.737135 (-0.563394) | 0.115936 / 0.296338 (-0.180403) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502259 / 0.215209 (0.287050) | 4.969877 / 2.077655 (2.892222) | 2.684860 / 1.504120 (1.180740) | 2.484386 / 1.541195 (0.943192) | 2.543061 / 1.468490 (1.074571) | 0.545733 / 4.584777 (-4.039044) | 4.029660 / 3.745712 (0.283948) | 5.927883 / 5.269862 (0.658021) | 3.528372 / 4.565676 (-1.037305) | 0.065957 / 0.424275 (-0.358318) | 0.008933 / 0.007607 (0.001326) | 0.601630 / 0.226044 (0.375585) | 5.825872 / 2.268929 (3.556944) | 3.230721 / 55.444624 (-52.213904) | 2.891308 / 6.876477 (-3.985169) | 3.054994 / 2.142072 (0.912922) | 0.665480 / 4.805227 (-4.139747) | 0.154815 / 6.500664 (-6.345849) | 0.072997 / 0.075469 (-0.002472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549892 / 1.841788 (-0.291896) | 22.337484 / 8.074308 (14.263176) | 16.308286 / 10.191392 (6.116894) | 0.189594 / 0.680424 (-0.490830) | 0.021844 / 0.534201 (-0.512357) | 0.456958 / 0.579283 (-0.122325) | 0.459957 / 0.434364 (0.025593) | 0.529014 / 0.540337 (-0.011323) | 0.700359 / 1.386936 (-0.686577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e4df86b5fb0bc164433ce615af641ec3ba437e \"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) | 0.009050 / 0.011353 (-0.002303) | 0.004968 / 0.011008 (-0.006040) | 0.114315 / 0.038508 (0.075807) | 0.084475 / 0.023109 (0.061366) | 0.426325 / 0.275898 (0.150427) | 0.457870 / 0.323480 (0.134390) | 0.007076 / 0.007986 (-0.000910) | 0.004635 / 0.004328 (0.000307) | 0.082950 / 0.004250 (0.078700) | 0.065414 / 0.037052 (0.028361) | 0.441936 / 0.258489 (0.183447) | 0.476983 / 0.293841 (0.183142) | 0.048575 / 0.128546 (-0.079972) | 0.013929 / 0.075646 (-0.061717) | 0.377498 / 0.419271 (-0.041774) | 0.081503 / 0.043533 (0.037970) | 0.426706 / 0.255139 (0.171567) | 0.460374 / 0.283200 (0.177175) | 0.046052 / 0.141683 (-0.095631) | 1.894896 / 1.452155 (0.442741) | 1.998639 / 1.492716 (0.505923) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313267 / 0.018006 (0.295261) | 0.607501 / 0.000490 (0.607012) | 0.003369 / 0.000200 (0.003169) | 0.000102 / 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) | 0.032266 / 0.037411 (-0.005145) | 0.120138 / 0.014526 (0.105613) | 0.115044 / 0.176557 (-0.061513) | 0.181374 / 0.737135 (-0.555761) | 0.114681 / 0.296338 (-0.181657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648039 / 0.215209 (0.432830) | 6.005048 / 2.077655 (3.927394) | 2.674524 / 1.504120 (1.170404) | 2.284831 / 1.541195 (0.743637) | 2.360150 / 1.468490 (0.891660) | 0.888021 / 4.584777 (-3.696756) | 5.419840 / 3.745712 (1.674128) | 4.825816 / 5.269862 (-0.444046) | 3.140876 / 4.565676 (-1.424801) | 0.099511 / 0.424275 (-0.324764) | 0.009176 / 0.007607 (0.001569) | 0.735646 / 0.226044 (0.509602) | 7.224026 / 2.268929 (4.955097) | 3.551146 / 55.444624 (-51.893478) | 2.844374 / 6.876477 (-4.032103) | 3.145307 / 2.142072 (1.003235) | 1.077636 / 4.805227 (-3.727591) | 0.217754 / 6.500664 (-6.282910) | 0.081755 / 0.075469 (0.006286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670956 / 1.841788 (-0.170831) | 25.524961 / 8.074308 (17.450653) | 23.061596 / 10.191392 (12.870204) | 0.247524 / 0.680424 (-0.432899) | 0.031712 / 0.534201 (-0.502489) | 0.513049 / 0.579283 (-0.066234) | 0.614568 / 0.434364 (0.180204) | 0.574669 / 0.540337 (0.034331) | 0.816621 / 1.386936 (-0.570315) |\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) | 0.009384 / 0.011353 (-0.001969) | 0.004959 / 0.011008 (-0.006049) | 0.084782 / 0.038508 (0.046274) | 0.098086 / 0.023109 (0.074977) | 0.544395 / 0.275898 (0.268497) | 0.585157 / 0.323480 (0.261677) | 0.006507 / 0.007986 (-0.001479) | 0.004151 / 0.004328 (-0.000178) | 0.088596 / 0.004250 (0.084345) | 0.069149 / 0.037052 (0.032097) | 0.533109 / 0.258489 (0.274620) | 0.604117 / 0.293841 (0.310276) | 0.047685 / 0.128546 (-0.080861) | 0.013651 / 0.075646 (-0.061996) | 0.096566 / 0.419271 (-0.322705) | 0.062022 / 0.043533 (0.018489) | 0.561897 / 0.255139 (0.306758) | 0.617636 / 0.283200 (0.334436) | 0.034636 / 0.141683 (-0.107047) | 1.854667 / 1.452155 (0.402512) | 1.908923 / 1.492716 (0.416207) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260633 / 0.018006 (0.242627) | 0.622268 / 0.000490 (0.621778) | 0.002116 / 0.000200 (0.001916) | 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) | 0.035161 / 0.037411 (-0.002250) | 0.103707 / 0.014526 (0.089181) | 0.115467 / 0.176557 (-0.061090) | 0.180077 / 0.737135 (-0.557059) | 0.118871 / 0.296338 (-0.177467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.628481 / 0.215209 (0.413271) | 6.304929 / 2.077655 (4.227275) | 3.027775 / 1.504120 (1.523655) | 2.753880 / 1.541195 (1.212686) | 2.820442 / 1.468490 (1.351952) | 0.851103 / 4.584777 (-3.733674) | 5.427383 / 3.745712 (1.681670) | 7.434310 / 5.269862 (2.164449) | 4.418790 / 4.565676 (-0.146887) | 0.101733 / 0.424275 (-0.322542) | 0.009701 / 0.007607 (0.002094) | 0.763033 / 0.226044 (0.536989) | 7.497927 / 2.268929 (5.228998) | 3.735335 / 55.444624 (-51.709290) | 3.149200 / 6.876477 (-3.727277) | 3.306214 / 2.142072 (1.164141) | 1.085440 / 4.805227 (-3.719787) | 0.207562 / 6.500664 (-6.293102) | 0.078091 / 0.075469 (0.002622) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.820097 / 1.841788 (-0.021691) | 25.525539 / 8.074308 (17.451231) | 21.874219 / 10.191392 (11.682827) | 0.228391 / 0.680424 (-0.452033) | 0.029584 / 0.534201 (-0.504617) | 0.511546 / 0.579283 (-0.067737) | 0.602719 / 0.434364 (0.168355) | 0.581874 / 0.540337 (0.041537) | 0.802861 / 1.386936 (-0.584075) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6063ea2069c8b5641b983ba2c1d39b60afe7c00a \"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) | 0.006578 / 0.011353 (-0.004775) | 0.003953 / 0.011008 (-0.007055) | 0.084417 / 0.038508 (0.045908) | 0.076729 / 0.023109 (0.053620) | 0.315369 / 0.275898 (0.039471) | 0.347012 / 0.323480 (0.023533) | 0.005299 / 0.007986 (-0.002686) | 0.003321 / 0.004328 (-0.001007) | 0.063954 / 0.004250 (0.059704) | 0.055810 / 0.037052 (0.018758) | 0.317651 / 0.258489 (0.059162) | 0.352603 / 0.293841 (0.058762) | 0.031355 / 0.128546 (-0.097192) | 0.008493 / 0.075646 (-0.067153) | 0.287295 / 0.419271 (-0.131977) | 0.052716 / 0.043533 (0.009183) | 0.316410 / 0.255139 (0.061271) | 0.328893 / 0.283200 (0.045693) | 0.024005 / 0.141683 (-0.117678) | 1.520333 / 1.452155 (0.068178) | 1.601268 / 1.492716 (0.108552) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205144 / 0.018006 (0.187138) | 0.459160 / 0.000490 (0.458670) | 0.000321 / 0.000200 (0.000121) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027503 / 0.037411 (-0.009908) | 0.081476 / 0.014526 (0.066950) | 0.096759 / 0.176557 (-0.079798) | 0.157888 / 0.737135 (-0.579247) | 0.094592 / 0.296338 (-0.201746) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384762 / 0.215209 (0.169553) | 3.843503 / 2.077655 (1.765849) | 1.921685 / 1.504120 (0.417565) | 1.752441 / 1.541195 (0.211246) | 1.822105 / 1.468490 (0.353615) | 0.480243 / 4.584777 (-4.104534) | 3.577220 / 3.745712 (-0.168492) | 5.047560 / 5.269862 (-0.222302) | 2.988008 / 4.565676 (-1.577669) | 0.056430 / 0.424275 (-0.367845) | 0.007180 / 0.007607 (-0.000427) | 0.458113 / 0.226044 (0.232069) | 4.584096 / 2.268929 (2.315168) | 2.395307 / 55.444624 (-53.049317) | 2.080530 / 6.876477 (-4.795947) | 2.239000 / 2.142072 (0.096927) | 0.575822 / 4.805227 (-4.229405) | 0.133303 / 6.500664 (-6.367361) | 0.059449 / 0.075469 (-0.016020) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256496 / 1.841788 (-0.585291) | 19.651614 / 8.074308 (11.577306) | 14.232480 / 10.191392 (4.041088) | 0.146461 / 0.680424 (-0.533963) | 0.018632 / 0.534201 (-0.515569) | 0.399844 / 0.579283 (-0.179439) | 0.411225 / 0.434364 (-0.023139) | 0.458203 / 0.540337 (-0.082135) | 0.669916 / 1.386936 (-0.717020) |\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) | 0.006463 / 0.011353 (-0.004890) | 0.003898 / 0.011008 (-0.007110) | 0.064037 / 0.038508 (0.025529) | 0.071982 / 0.023109 (0.048873) | 0.361936 / 0.275898 (0.086038) | 0.393165 / 0.323480 (0.069685) | 0.005207 / 0.007986 (-0.002779) | 0.003231 / 0.004328 (-0.001098) | 0.064318 / 0.004250 (0.060068) | 0.055776 / 0.037052 (0.018724) | 0.383087 / 0.258489 (0.124598) | 0.402428 / 0.293841 (0.108587) | 0.031587 / 0.128546 (-0.096959) | 0.008527 / 0.075646 (-0.067119) | 0.070495 / 0.419271 (-0.348777) | 0.048806 / 0.043533 (0.005273) | 0.369932 / 0.255139 (0.114793) | 0.385268 / 0.283200 (0.102068) | 0.023183 / 0.141683 (-0.118500) | 1.491175 / 1.452155 (0.039020) | 1.534191 / 1.492716 (0.041475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224526 / 0.018006 (0.206520) | 0.445460 / 0.000490 (0.444970) | 0.003612 / 0.000200 (0.003412) | 0.000089 / 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) | 0.029829 / 0.037411 (-0.007583) | 0.087951 / 0.014526 (0.073425) | 0.100069 / 0.176557 (-0.076487) | 0.154944 / 0.737135 (-0.582192) | 0.101271 / 0.296338 (-0.195067) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412385 / 0.215209 (0.197175) | 4.108038 / 2.077655 (2.030384) | 2.163578 / 1.504120 (0.659459) | 2.031934 / 1.541195 (0.490740) | 2.155857 / 1.468490 (0.687367) | 0.481132 / 4.584777 (-4.103645) | 3.620868 / 3.745712 (-0.124844) | 5.222175 / 5.269862 (-0.047687) | 3.115637 / 4.565676 (-1.450039) | 0.056480 / 0.424275 (-0.367795) | 0.007761 / 0.007607 (0.000154) | 0.483553 / 0.226044 (0.257509) | 4.830087 / 2.268929 (2.561159) | 2.629919 / 55.444624 (-52.814705) | 2.327551 / 6.876477 (-4.548926) | 2.539934 / 2.142072 (0.397861) | 0.587963 / 4.805227 (-4.217265) | 0.131085 / 6.500664 (-6.369579) | 0.060807 / 0.075469 (-0.014662) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350003 / 1.841788 (-0.491785) | 19.491713 / 8.074308 (11.417405) | 14.030429 / 10.191392 (3.839037) | 0.174762 / 0.680424 (-0.505662) | 0.018523 / 0.534201 (-0.515678) | 0.394946 / 0.579283 (-0.184337) | 0.407652 / 0.434364 (-0.026712) | 0.465806 / 0.540337 (-0.074531) | 0.605417 / 1.386936 (-0.781519) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc85979df3a39657079fdf0844c7e64547507f1a \"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) | 0.006235 / 0.011353 (-0.005118) | 0.003675 / 0.011008 (-0.007333) | 0.080680 / 0.038508 (0.042171) | 0.064378 / 0.023109 (0.041268) | 0.394312 / 0.275898 (0.118414) | 0.428143 / 0.323480 (0.104663) | 0.004794 / 0.007986 (-0.003191) | 0.002899 / 0.004328 (-0.001429) | 0.062592 / 0.004250 (0.058342) | 0.050957 / 0.037052 (0.013904) | 0.396831 / 0.258489 (0.138342) | 0.438280 / 0.293841 (0.144439) | 0.027743 / 0.128546 (-0.100804) | 0.008068 / 0.075646 (-0.067578) | 0.262541 / 0.419271 (-0.156730) | 0.060837 / 0.043533 (0.017304) | 0.397941 / 0.255139 (0.142802) | 0.417012 / 0.283200 (0.133813) | 0.030153 / 0.141683 (-0.111530) | 1.477115 / 1.452155 (0.024960) | 1.516642 / 1.492716 (0.023926) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178032 / 0.018006 (0.160026) | 0.445775 / 0.000490 (0.445286) | 0.004275 / 0.000200 (0.004075) | 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) | 0.025025 / 0.037411 (-0.012386) | 0.074113 / 0.014526 (0.059587) | 0.083814 / 0.176557 (-0.092743) | 0.148860 / 0.737135 (-0.588275) | 0.085408 / 0.296338 (-0.210931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393714 / 0.215209 (0.178505) | 3.936589 / 2.077655 (1.858934) | 1.910501 / 1.504120 (0.406381) | 1.729670 / 1.541195 (0.188475) | 1.777647 / 1.468490 (0.309156) | 0.499532 / 4.584777 (-4.085245) | 3.002385 / 3.745712 (-0.743327) | 2.906916 / 5.269862 (-2.362945) | 1.883321 / 4.565676 (-2.682356) | 0.057546 / 0.424275 (-0.366730) | 0.006492 / 0.007607 (-0.001115) | 0.463605 / 0.226044 (0.237560) | 4.620215 / 2.268929 (2.351287) | 2.399021 / 55.444624 (-53.045603) | 2.182962 / 6.876477 (-4.693514) | 2.357344 / 2.142072 (0.215272) | 0.583946 / 4.805227 (-4.221282) | 0.124644 / 6.500664 (-6.376021) | 0.060831 / 0.075469 (-0.014638) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276412 / 1.841788 (-0.565375) | 18.462522 / 8.074308 (10.388214) | 13.877375 / 10.191392 (3.685983) | 0.150584 / 0.680424 (-0.529840) | 0.016675 / 0.534201 (-0.517526) | 0.331711 / 0.579283 (-0.247573) | 0.366659 / 0.434364 (-0.067705) | 0.396400 / 0.540337 (-0.143938) | 0.555418 / 1.386936 (-0.831518) |\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) | 0.005995 / 0.011353 (-0.005358) | 0.003610 / 0.011008 (-0.007399) | 0.061802 / 0.038508 (0.023294) | 0.059265 / 0.023109 (0.036156) | 0.392628 / 0.275898 (0.116730) | 0.413143 / 0.323480 (0.089663) | 0.004687 / 0.007986 (-0.003299) | 0.002843 / 0.004328 (-0.001486) | 0.061932 / 0.004250 (0.057682) | 0.049466 / 0.037052 (0.012413) | 0.402718 / 0.258489 (0.144229) | 0.415039 / 0.293841 (0.121198) | 0.027352 / 0.128546 (-0.101194) | 0.007965 / 0.075646 (-0.067682) | 0.067456 / 0.419271 (-0.351815) | 0.042336 / 0.043533 (-0.001196) | 0.405543 / 0.255139 (0.150404) | 0.403209 / 0.283200 (0.120010) | 0.021459 / 0.141683 (-0.120224) | 1.442861 / 1.452155 (-0.009293) | 1.491213 / 1.492716 (-0.001503) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248225 / 0.018006 (0.230219) | 0.434174 / 0.000490 (0.433684) | 0.001973 / 0.000200 (0.001773) | 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) | 0.025475 / 0.037411 (-0.011936) | 0.077865 / 0.014526 (0.063339) | 0.086980 / 0.176557 (-0.089577) | 0.143682 / 0.737135 (-0.593453) | 0.088634 / 0.296338 (-0.207705) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417591 / 0.215209 (0.202382) | 4.168700 / 2.077655 (2.091045) | 2.335743 / 1.504120 (0.831623) | 2.208174 / 1.541195 (0.666980) | 2.256658 / 1.468490 (0.788168) | 0.503164 / 4.584777 (-4.081613) | 3.026667 / 3.745712 (-0.719045) | 4.496675 / 5.269862 (-0.773187) | 2.741049 / 4.565676 (-1.824628) | 0.057781 / 0.424275 (-0.366494) | 0.006810 / 0.007607 (-0.000797) | 0.490803 / 0.226044 (0.264759) | 4.914369 / 2.268929 (2.645441) | 2.594250 / 55.444624 (-52.850375) | 2.274552 / 6.876477 (-4.601925) | 2.397529 / 2.142072 (0.255456) | 0.593008 / 4.805227 (-4.212220) | 0.126194 / 6.500664 (-6.374470) | 0.062261 / 0.075469 (-0.013208) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.357561 / 1.841788 (-0.484227) | 18.622995 / 8.074308 (10.548687) | 14.142569 / 10.191392 (3.951177) | 0.146527 / 0.680424 (-0.533897) | 0.016863 / 0.534201 (-0.517338) | 0.336219 / 0.579283 (-0.243064) | 0.348650 / 0.434364 (-0.085714) | 0.385958 / 0.540337 (-0.154380) | 0.517958 / 1.386936 (-0.868978) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3da7a5a7d0d0415476ecebb0458e7c60df24445 \"CML watermark\")\n" ]

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[ "Hi ! I opened https://github.com/huggingface/datasets/pull/6067 to add the missing `_lock`\r\n\r\nWe'll do a patch release soon, but feel free to install `datasets` from source in the meantime", "I have tested the latest main, it does not work.\r\n\r\nI add more logs to reproduce this issue, it looks like a multi threading bug:\r\n\r\n```python\r\n@contextmanager\r\ndef ensure_lock(tqdm_class, lock_name=\"\"):\r\n \"\"\"get (create if necessary) and then restore `tqdm_class`'s lock\"\"\"\r\n import os\r\n import threading\r\n print(os.getpid(), threading.get_ident(), \"ensure_lock\", tqdm_class, lock_name)\r\n old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock\r\n lock = old_lock or tqdm_class.get_lock() # maybe create a new lock\r\n lock = getattr(lock, lock_name, lock) # maybe subtype\r\n tqdm_class.set_lock(lock)\r\n print(os.getpid(), threading.get_ident(), \"set_lock\")\r\n yield lock\r\n if old_lock is None:\r\n print(os.getpid(), threading.get_ident(), \"del tqdm_class\")\r\n del tqdm_class._lock\r\n else:\r\n tqdm_class.set_lock(old_lock)\r\n```\r\noutput\r\n```\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11568967680 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11568967680 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11568967680 del tqdm_class\r\n```\r\n\r\nThread `11638370304` del the _lock from tqdm_class first, then thread `11568967680` del _lock failed.", "Maybe it is a bug of tqdm? I think simply use `try ... except AttributeError ...` wraps `del tqdm_class._lock` should work.", "Yes it looks like a bug on their end indeed, do you want to open a PR on tqdm ?\r\n\r\nLet me see if I can find a workaround in the meantime", "I opened https://github.com/huggingface/datasets/pull/6068 if you want to try it out", "> I opened #6068 if you want to try it out\r\n\r\nThis fix works! Thanks.", "Awesome ! closing this then :)\r\nWe'll do a patch release today or tomorrow" ]

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[ "Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we want to maintain.\r\n\r\ncc @lhoestq for the 2nd opinion", "@mariosasko I was going to quote the Zen of Python in the other direction :) To me, this actually is much more explicit than the current behavior of guessing pyarrow types based on the raw dictionary return values. Explicit typehinting is increasingly the de facto way to deal with this dynamic type serialization - plus it feels like a clearer fit to me than separating out the mapper function from the feature column definition in the call to the actual `.map()`. Another benefit is providing typehinting support for clients that use mypy or other static typecheckers to detect return mismatches.\r\n\r\nBut will leave it to you and @lhoestq to see if it's something you'd like in core versus a support package.", "I meant that explicitly specifying the target features (the `features` param) is cleaner (easier to track) than relying on type hints.", "Passing features= to `map()` is richer and more explicit. Also I don't think users would guess that such API exist.\r\n\r\nOther libraries like dask also infer the type from the output or requires the typing to be specified using the `meta` argument", "Point about discoverability is a fair one, would certainly need some docs around it. All good! Will close this out and keep in our extension utilities." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6064). 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) | 0.006704 / 0.011353 (-0.004649) | 0.004208 / 0.011008 (-0.006800) | 0.085895 / 0.038508 (0.047387) | 0.079303 / 0.023109 (0.056193) | 0.353430 / 0.275898 (0.077532) | 0.390814 / 0.323480 (0.067334) | 0.006565 / 0.007986 (-0.001420) | 0.003588 / 0.004328 (-0.000740) | 0.065249 / 0.004250 (0.060999) | 0.059772 / 0.037052 (0.022720) | 0.356315 / 0.258489 (0.097826) | 0.404812 / 0.293841 (0.110971) | 0.031127 / 0.128546 (-0.097419) | 0.008656 / 0.075646 (-0.066991) | 0.288734 / 0.419271 (-0.130537) | 0.053157 / 0.043533 (0.009625) | 0.354651 / 0.255139 (0.099512) | 0.370590 / 0.283200 (0.087391) | 0.024944 / 0.141683 (-0.116738) | 1.472393 / 1.452155 (0.020238) | 1.548946 / 1.492716 (0.056229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223430 / 0.018006 (0.205424) | 0.567359 / 0.000490 (0.566870) | 0.006744 / 0.000200 (0.006544) | 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) | 0.030174 / 0.037411 (-0.007237) | 0.084865 / 0.014526 (0.070339) | 0.098986 / 0.176557 (-0.077571) | 0.161458 / 0.737135 (-0.575678) | 0.099198 / 0.296338 (-0.197141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404324 / 0.215209 (0.189115) | 4.043744 / 2.077655 (1.966090) | 1.972834 / 1.504120 (0.468714) | 1.801634 / 1.541195 (0.260439) | 1.891198 / 1.468490 (0.422708) | 0.488511 / 4.584777 (-4.096266) | 3.566890 / 3.745712 (-0.178823) | 3.369415 / 5.269862 (-1.900447) | 2.054995 / 4.565676 (-2.510682) | 0.057225 / 0.424275 (-0.367050) | 0.007360 / 0.007607 (-0.000247) | 0.471813 / 0.226044 (0.245769) | 4.734397 / 2.268929 (2.465468) | 2.526585 / 55.444624 (-52.918039) | 2.230535 / 6.876477 (-4.645942) | 2.434403 / 2.142072 (0.292330) | 0.630090 / 4.805227 (-4.175137) | 0.138544 / 6.500664 (-6.362120) | 0.060099 / 0.075469 (-0.015370) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260951 / 1.841788 (-0.580837) | 20.051513 / 8.074308 (11.977204) | 14.675938 / 10.191392 (4.484546) | 0.169535 / 0.680424 (-0.510889) | 0.018574 / 0.534201 (-0.515627) | 0.394255 / 0.579283 (-0.185028) | 0.412713 / 0.434364 (-0.021651) | 0.475891 / 0.540337 (-0.064446) | 0.658223 / 1.386936 (-0.728713) |\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) | 0.006969 / 0.011353 (-0.004384) | 0.004417 / 0.011008 (-0.006591) | 0.064399 / 0.038508 (0.025891) | 0.082928 / 0.023109 (0.059819) | 0.402285 / 0.275898 (0.126387) | 0.440032 / 0.323480 (0.116552) | 0.005896 / 0.007986 (-0.002090) | 0.003580 / 0.004328 (-0.000749) | 0.065340 / 0.004250 (0.061090) | 0.060363 / 0.037052 (0.023311) | 0.417413 / 0.258489 (0.158924) | 0.448527 / 0.293841 (0.154686) | 0.032238 / 0.128546 (-0.096308) | 0.008820 / 0.075646 (-0.066826) | 0.071516 / 0.419271 (-0.347755) | 0.050614 / 0.043533 (0.007081) | 0.406565 / 0.255139 (0.151426) | 0.422527 / 0.283200 (0.139328) | 0.025866 / 0.141683 (-0.115817) | 1.512256 / 1.452155 (0.060101) | 1.568433 / 1.492716 (0.075717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266521 / 0.018006 (0.248515) | 0.564524 / 0.000490 (0.564034) | 0.005236 / 0.000200 (0.005036) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031998 / 0.037411 (-0.005413) | 0.090754 / 0.014526 (0.076229) | 0.105954 / 0.176557 (-0.070602) | 0.164506 / 0.737135 (-0.572629) | 0.108792 / 0.296338 (-0.187546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422044 / 0.215209 (0.206835) | 4.204449 / 2.077655 (2.126795) | 2.232060 / 1.504120 (0.727940) | 2.060389 / 1.541195 (0.519194) | 2.152723 / 1.468490 (0.684233) | 0.488456 / 4.584777 (-4.096321) | 3.591102 / 3.745712 (-0.154611) | 5.250401 / 5.269862 (-0.019461) | 3.060259 / 4.565676 (-1.505417) | 0.057558 / 0.424275 (-0.366717) | 0.007881 / 0.007607 (0.000274) | 0.508631 / 0.226044 (0.282587) | 5.064857 / 2.268929 (2.795928) | 2.719068 / 55.444624 (-52.725556) | 2.389992 / 6.876477 (-4.486485) | 2.595073 / 2.142072 (0.453000) | 0.590179 / 4.805227 (-4.215048) | 0.136149 / 6.500664 (-6.364515) | 0.062546 / 0.075469 (-0.012923) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369252 / 1.841788 (-0.472535) | 20.637580 / 8.074308 (12.563272) | 14.217129 / 10.191392 (4.025737) | 0.195464 / 0.680424 (-0.484960) | 0.018452 / 0.534201 (-0.515749) | 0.397044 / 0.579283 (-0.182239) | 0.401127 / 0.434364 (-0.033237) | 0.465033 / 0.540337 (-0.075305) | 0.613484 / 1.386936 (-0.773452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9f1651128e50e7887f5e8eaaf6b55fe4cd84fdc \"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) | 0.006793 / 0.011353 (-0.004559) | 0.004374 / 0.011008 (-0.006635) | 0.084958 / 0.038508 (0.046450) | 0.080440 / 0.023109 (0.057331) | 0.317951 / 0.275898 (0.042053) | 0.376133 / 0.323480 (0.052653) | 0.005775 / 0.007986 (-0.002211) | 0.003644 / 0.004328 (-0.000684) | 0.064823 / 0.004250 (0.060573) | 0.059442 / 0.037052 (0.022390) | 0.319636 / 0.258489 (0.061147) | 0.389668 / 0.293841 (0.095827) | 0.031181 / 0.128546 (-0.097365) | 0.008725 / 0.075646 (-0.066921) | 0.288514 / 0.419271 (-0.130757) | 0.053466 / 0.043533 (0.009933) | 0.323131 / 0.255139 (0.067992) | 0.345276 / 0.283200 (0.062076) | 0.025046 / 0.141683 (-0.116637) | 1.491659 / 1.452155 (0.039504) | 1.562105 / 1.492716 (0.069389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286325 / 0.018006 (0.268319) | 0.578021 / 0.000490 (0.577531) | 0.007240 / 0.000200 (0.007040) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030163 / 0.037411 (-0.007248) | 0.082100 / 0.014526 (0.067574) | 0.098331 / 0.176557 (-0.078225) | 0.160517 / 0.737135 (-0.576618) | 0.098479 / 0.296338 (-0.197859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401782 / 0.215209 (0.186573) | 4.006330 / 2.077655 (1.928675) | 2.033841 / 1.504120 (0.529721) | 1.853248 / 1.541195 (0.312053) | 1.980046 / 1.468490 (0.511556) | 0.480636 / 4.584777 (-4.104141) | 3.684482 / 3.745712 (-0.061231) | 5.601940 / 5.269862 (0.332079) | 3.369683 / 4.565676 (-1.195993) | 0.057105 / 0.424275 (-0.367170) | 0.007462 / 0.007607 (-0.000145) | 0.474860 / 0.226044 (0.248815) | 4.749624 / 2.268929 (2.480695) | 2.492084 / 55.444624 (-52.952540) | 2.157985 / 6.876477 (-4.718491) | 2.420997 / 2.142072 (0.278925) | 0.574718 / 4.805227 (-4.230509) | 0.134672 / 6.500664 (-6.365992) | 0.061677 / 0.075469 (-0.013792) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284151 / 1.841788 (-0.557637) | 20.186823 / 8.074308 (12.112515) | 14.247024 / 10.191392 (4.055632) | 0.171606 / 0.680424 (-0.508818) | 0.018619 / 0.534201 (-0.515582) | 0.394156 / 0.579283 (-0.185127) | 0.424684 / 0.434364 (-0.009679) | 0.476056 / 0.540337 (-0.064281) | 0.668751 / 1.386936 (-0.718185) |\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) | 0.006807 / 0.011353 (-0.004546) | 0.004142 / 0.011008 (-0.006867) | 0.065503 / 0.038508 (0.026995) | 0.083232 / 0.023109 (0.060122) | 0.378278 / 0.275898 (0.102380) | 0.410191 / 0.323480 (0.086711) | 0.005660 / 0.007986 (-0.002326) | 0.003486 / 0.004328 (-0.000842) | 0.066109 / 0.004250 (0.061859) | 0.059654 / 0.037052 (0.022601) | 0.375965 / 0.258489 (0.117476) | 0.420046 / 0.293841 (0.126205) | 0.031587 / 0.128546 (-0.096959) | 0.008693 / 0.075646 (-0.066953) | 0.071121 / 0.419271 (-0.348151) | 0.049468 / 0.043533 (0.005935) | 0.373785 / 0.255139 (0.118646) | 0.395577 / 0.283200 (0.112377) | 0.024138 / 0.141683 (-0.117545) | 1.465451 / 1.452155 (0.013297) | 1.547565 / 1.492716 (0.054849) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325241 / 0.018006 (0.307234) | 0.532415 / 0.000490 (0.531925) | 0.004755 / 0.000200 (0.004555) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033472 / 0.037411 (-0.003939) | 0.090574 / 0.014526 (0.076048) | 0.106712 / 0.176557 (-0.069845) | 0.164353 / 0.737135 (-0.572783) | 0.109344 / 0.296338 (-0.186994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420161 / 0.215209 (0.204952) | 4.192334 / 2.077655 (2.114679) | 2.178181 / 1.504120 (0.674061) | 2.017405 / 1.541195 (0.476211) | 2.182783 / 1.468490 (0.714293) | 0.484037 / 4.584777 (-4.100740) | 3.641911 / 3.745712 (-0.103801) | 5.543874 / 5.269862 (0.274013) | 3.440084 / 4.565676 (-1.125593) | 0.056662 / 0.424275 (-0.367614) | 0.007773 / 0.007607 (0.000166) | 0.498357 / 0.226044 (0.272313) | 4.951315 / 2.268929 (2.682386) | 2.656732 / 55.444624 (-52.787892) | 2.370566 / 6.876477 (-4.505910) | 2.682289 / 2.142072 (0.540217) | 0.598479 / 4.805227 (-4.206749) | 0.151546 / 6.500664 (-6.349118) | 0.063278 / 0.075469 (-0.012191) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.385897 / 1.841788 (-0.455891) | 20.961851 / 8.074308 (12.887543) | 14.465688 / 10.191392 (4.274296) | 0.166156 / 0.680424 (-0.514268) | 0.018848 / 0.534201 (-0.515353) | 0.401712 / 0.579283 (-0.177571) | 0.416674 / 0.434364 (-0.017690) | 0.471834 / 0.540337 (-0.068503) | 0.622463 / 1.386936 (-0.764473) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e3ab9bc6ae8cc42f7e7d01afbd2637d51c3faf6 \"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) | 0.007703 / 0.011353 (-0.003650) | 0.004699 / 0.011008 (-0.006309) | 0.090195 / 0.038508 (0.051687) | 0.119165 / 0.023109 (0.096056) | 0.361435 / 0.275898 (0.085537) | 0.404429 / 0.323480 (0.080949) | 0.006172 / 0.007986 (-0.001814) | 0.003932 / 0.004328 (-0.000397) | 0.068384 / 0.004250 (0.064133) | 0.066730 / 0.037052 (0.029678) | 0.360978 / 0.258489 (0.102489) | 0.401301 / 0.293841 (0.107460) | 0.032836 / 0.128546 (-0.095710) | 0.010821 / 0.075646 (-0.064825) | 0.294526 / 0.419271 (-0.124745) | 0.068751 / 0.043533 (0.025218) | 0.368427 / 0.255139 (0.113288) | 0.376969 / 0.283200 (0.093770) | 0.040538 / 0.141683 (-0.101145) | 1.509966 / 1.452155 (0.057811) | 1.564885 / 1.492716 (0.072169) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292243 / 0.018006 (0.274237) | 0.662067 / 0.000490 (0.661577) | 0.004966 / 0.000200 (0.004766) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029050 / 0.037411 (-0.008361) | 0.099880 / 0.014526 (0.085354) | 0.109277 / 0.176557 (-0.067280) | 0.167877 / 0.737135 (-0.569258) | 0.110770 / 0.296338 (-0.185569) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395742 / 0.215209 (0.180533) | 3.944152 / 2.077655 (1.866498) | 1.875295 / 1.504120 (0.371175) | 1.705088 / 1.541195 (0.163893) | 1.884443 / 1.468490 (0.415953) | 0.497243 / 4.584777 (-4.087534) | 3.749287 / 3.745712 (0.003575) | 4.418826 / 5.269862 (-0.851035) | 2.481149 / 4.565676 (-2.084528) | 0.058260 / 0.424275 (-0.366015) | 0.007744 / 0.007607 (0.000137) | 0.472531 / 0.226044 (0.246486) | 4.716022 / 2.268929 (2.447094) | 2.480446 / 55.444624 (-52.964179) | 2.163098 / 6.876477 (-4.713379) | 2.217555 / 2.142072 (0.075482) | 0.601965 / 4.805227 (-4.203262) | 0.139364 / 6.500664 (-6.361301) | 0.067097 / 0.075469 (-0.008372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330537 / 1.841788 (-0.511251) | 22.176270 / 8.074308 (14.101962) | 16.224981 / 10.191392 (6.033589) | 0.173708 / 0.680424 (-0.506715) | 0.019402 / 0.534201 (-0.514799) | 0.401994 / 0.579283 (-0.177289) | 0.432597 / 0.434364 (-0.001767) | 0.489933 / 0.540337 (-0.050404) | 0.672334 / 1.386936 (-0.714602) |\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) | 0.008622 / 0.011353 (-0.002731) | 0.004609 / 0.011008 (-0.006399) | 0.067791 / 0.038508 (0.029283) | 0.112770 / 0.023109 (0.089661) | 0.380939 / 0.275898 (0.105041) | 0.416940 / 0.323480 (0.093460) | 0.006170 / 0.007986 (-0.001815) | 0.003876 / 0.004328 (-0.000452) | 0.066227 / 0.004250 (0.061976) | 0.073132 / 0.037052 (0.036080) | 0.390120 / 0.258489 (0.131631) | 0.420893 / 0.293841 (0.127052) | 0.033235 / 0.128546 (-0.095311) | 0.009659 / 0.075646 (-0.065987) | 0.072668 / 0.419271 (-0.346604) | 0.051333 / 0.043533 (0.007801) | 0.393828 / 0.255139 (0.138689) | 0.412376 / 0.283200 (0.129176) | 0.027760 / 0.141683 (-0.113923) | 1.494369 / 1.452155 (0.042214) | 1.592862 / 1.492716 (0.100145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.345376 / 0.018006 (0.327369) | 0.609399 / 0.000490 (0.608909) | 0.000546 / 0.000200 (0.000346) | 0.000061 / 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) | 0.035601 / 0.037411 (-0.001810) | 0.106527 / 0.014526 (0.092001) | 0.114388 / 0.176557 (-0.062168) | 0.175607 / 0.737135 (-0.561529) | 0.113009 / 0.296338 (-0.183329) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417237 / 0.215209 (0.202028) | 4.136329 / 2.077655 (2.058675) | 2.147134 / 1.504120 (0.643014) | 2.009501 / 1.541195 (0.468306) | 2.139499 / 1.468490 (0.671009) | 0.491593 / 4.584777 (-4.093184) | 3.766734 / 3.745712 (0.021022) | 5.652446 / 5.269862 (0.382585) | 3.021654 / 4.565676 (-1.544022) | 0.058458 / 0.424275 (-0.365817) | 0.008271 / 0.007607 (0.000664) | 0.488229 / 0.226044 (0.262184) | 4.861343 / 2.268929 (2.592415) | 2.694142 / 55.444624 (-52.750482) | 2.489130 / 6.876477 (-4.387346) | 2.679376 / 2.142072 (0.537304) | 0.589959 / 4.805227 (-4.215268) | 0.137939 / 6.500664 (-6.362725) | 0.066833 / 0.075469 (-0.008636) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.444871 / 1.841788 (-0.396916) | 22.874961 / 8.074308 (14.800653) | 15.842130 / 10.191392 (5.650738) | 0.175529 / 0.680424 (-0.504895) | 0.019024 / 0.534201 (-0.515177) | 0.406551 / 0.579283 (-0.172732) | 0.430335 / 0.434364 (-0.004029) | 0.475750 / 0.540337 (-0.064587) | 0.624836 / 1.386936 (-0.762100) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"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) | 0.006068 / 0.011353 (-0.005285) | 0.003694 / 0.011008 (-0.007315) | 0.080321 / 0.038508 (0.041813) | 0.061738 / 0.023109 (0.038629) | 0.329675 / 0.275898 (0.053777) | 0.364008 / 0.323480 (0.040528) | 0.004722 / 0.007986 (-0.003263) | 0.002857 / 0.004328 (-0.001471) | 0.062447 / 0.004250 (0.058197) | 0.047006 / 0.037052 (0.009953) | 0.335730 / 0.258489 (0.077241) | 0.373047 / 0.293841 (0.079206) | 0.027273 / 0.128546 (-0.101274) | 0.007979 / 0.075646 (-0.067667) | 0.262693 / 0.419271 (-0.156579) | 0.045416 / 0.043533 (0.001883) | 0.340774 / 0.255139 (0.085635) | 0.359667 / 0.283200 (0.076468) | 0.020848 / 0.141683 (-0.120835) | 1.450110 / 1.452155 (-0.002045) | 1.489511 / 1.492716 (-0.003206) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185090 / 0.018006 (0.167084) | 0.429823 / 0.000490 (0.429334) | 0.000703 / 0.000200 (0.000503) | 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) | 0.024398 / 0.037411 (-0.013013) | 0.072983 / 0.014526 (0.058457) | 0.084012 / 0.176557 (-0.092544) | 0.146160 / 0.737135 (-0.590975) | 0.084068 / 0.296338 (-0.212270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432204 / 0.215209 (0.216995) | 4.320593 / 2.077655 (2.242939) | 2.261260 / 1.504120 (0.757140) | 2.087148 / 1.541195 (0.545954) | 2.144520 / 1.468490 (0.676029) | 0.501477 / 4.584777 (-4.083300) | 3.119557 / 3.745712 (-0.626156) | 3.572527 / 5.269862 (-1.697335) | 2.208836 / 4.565676 (-2.356840) | 0.057232 / 0.424275 (-0.367043) | 0.006494 / 0.007607 (-0.001113) | 0.508135 / 0.226044 (0.282091) | 5.090416 / 2.268929 (2.821488) | 2.739800 / 55.444624 (-52.704824) | 2.416105 / 6.876477 (-4.460372) | 2.616037 / 2.142072 (0.473965) | 0.583730 / 4.805227 (-4.221497) | 0.124312 / 6.500664 (-6.376352) | 0.060760 / 0.075469 (-0.014709) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256097 / 1.841788 (-0.585691) | 18.326073 / 8.074308 (10.251765) | 13.859173 / 10.191392 (3.667781) | 0.143639 / 0.680424 (-0.536785) | 0.016649 / 0.534201 (-0.517552) | 0.331671 / 0.579283 (-0.247612) | 0.365370 / 0.434364 (-0.068994) | 0.392753 / 0.540337 (-0.147584) | 0.549302 / 1.386936 (-0.837634) |\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) | 0.006054 / 0.011353 (-0.005299) | 0.003641 / 0.011008 (-0.007367) | 0.063109 / 0.038508 (0.024601) | 0.060482 / 0.023109 (0.037372) | 0.404047 / 0.275898 (0.128149) | 0.425436 / 0.323480 (0.101956) | 0.004603 / 0.007986 (-0.003382) | 0.002905 / 0.004328 (-0.001423) | 0.063207 / 0.004250 (0.058956) | 0.048248 / 0.037052 (0.011196) | 0.404325 / 0.258489 (0.145836) | 0.432652 / 0.293841 (0.138811) | 0.027630 / 0.128546 (-0.100916) | 0.008062 / 0.075646 (-0.067584) | 0.068367 / 0.419271 (-0.350905) | 0.042169 / 0.043533 (-0.001364) | 0.384903 / 0.255139 (0.129764) | 0.418617 / 0.283200 (0.135417) | 0.020767 / 0.141683 (-0.120915) | 1.463606 / 1.452155 (0.011451) | 1.512081 / 1.492716 (0.019365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229601 / 0.018006 (0.211594) | 0.417878 / 0.000490 (0.417388) | 0.000373 / 0.000200 (0.000173) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026354 / 0.037411 (-0.011057) | 0.078100 / 0.014526 (0.063574) | 0.087122 / 0.176557 (-0.089434) | 0.140017 / 0.737135 (-0.597118) | 0.089923 / 0.296338 (-0.206415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422405 / 0.215209 (0.207196) | 4.237383 / 2.077655 (2.159728) | 2.161104 / 1.504120 (0.656984) | 1.982337 / 1.541195 (0.441142) | 2.050216 / 1.468490 (0.581726) | 0.499281 / 4.584777 (-4.085496) | 2.996953 / 3.745712 (-0.748759) | 5.027069 / 5.269862 (-0.242792) | 2.804703 / 4.565676 (-1.760974) | 0.057707 / 0.424275 (-0.366568) | 0.006809 / 0.007607 (-0.000798) | 0.495196 / 0.226044 (0.269152) | 4.946593 / 2.268929 (2.677665) | 2.598965 / 55.444624 (-52.845660) | 2.349871 / 6.876477 (-4.526606) | 2.451665 / 2.142072 (0.309593) | 0.592314 / 4.805227 (-4.212913) | 0.125685 / 6.500664 (-6.374979) | 0.063252 / 0.075469 (-0.012217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.325422 / 1.841788 (-0.516366) | 18.521059 / 8.074308 (10.446751) | 14.046757 / 10.191392 (3.855365) | 0.133009 / 0.680424 (-0.547415) | 0.017097 / 0.534201 (-0.517104) | 0.339804 / 0.579283 (-0.239479) | 0.345464 / 0.434364 (-0.088900) | 0.387623 / 0.540337 (-0.152714) | 0.519880 / 1.386936 (-0.867056) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"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) | 0.008671 / 0.011353 (-0.002682) | 0.004681 / 0.011008 (-0.006327) | 0.107517 / 0.038508 (0.069008) | 0.078846 / 0.023109 (0.055737) | 0.449745 / 0.275898 (0.173847) | 0.504075 / 0.323480 (0.180596) | 0.005837 / 0.007986 (-0.002148) | 0.004031 / 0.004328 (-0.000297) | 0.092021 / 0.004250 (0.087771) | 0.065954 / 0.037052 (0.028902) | 0.442082 / 0.258489 (0.183593) | 0.529349 / 0.293841 (0.235508) | 0.052527 / 0.128546 (-0.076019) | 0.013854 / 0.075646 (-0.061792) | 0.367315 / 0.419271 (-0.051956) | 0.068731 / 0.043533 (0.025199) | 0.494733 / 0.255139 (0.239594) | 0.472801 / 0.283200 (0.189601) | 0.036791 / 0.141683 (-0.104892) | 1.877648 / 1.452155 (0.425493) | 1.928399 / 1.492716 (0.435683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231910 / 0.018006 (0.213904) | 0.553464 / 0.000490 (0.552974) | 0.011915 / 0.000200 (0.011715) | 0.000378 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028232 / 0.037411 (-0.009179) | 0.091441 / 0.014526 (0.076916) | 0.110394 / 0.176557 (-0.066162) | 0.187638 / 0.737135 (-0.549497) | 0.111810 / 0.296338 (-0.184529) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599987 / 0.215209 (0.384778) | 6.008709 / 2.077655 (3.931054) | 2.518769 / 1.504120 (1.014650) | 2.197029 / 1.541195 (0.655834) | 2.217165 / 1.468490 (0.748675) | 0.894939 / 4.584777 (-3.689837) | 5.001217 / 3.745712 (1.255505) | 4.636482 / 5.269862 (-0.633379) | 3.237613 / 4.565676 (-1.328063) | 0.104227 / 0.424275 (-0.320048) | 0.008504 / 0.007607 (0.000897) | 0.750190 / 0.226044 (0.524145) | 7.514571 / 2.268929 (5.245642) | 3.358003 / 55.444624 (-52.086621) | 2.585649 / 6.876477 (-4.290827) | 2.731129 / 2.142072 (0.589056) | 1.088828 / 4.805227 (-3.716400) | 0.217308 / 6.500664 (-6.283356) | 0.076410 / 0.075469 (0.000941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620087 / 1.841788 (-0.221701) | 23.145743 / 8.074308 (15.071435) | 20.583403 / 10.191392 (10.392011) | 0.225467 / 0.680424 (-0.454956) | 0.029063 / 0.534201 (-0.505138) | 0.480563 / 0.579283 (-0.098720) | 0.539083 / 0.434364 (0.104719) | 0.563787 / 0.540337 (0.023449) | 0.782902 / 1.386936 (-0.604034) |\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) | 0.010113 / 0.011353 (-0.001239) | 0.004997 / 0.011008 (-0.006011) | 0.082974 / 0.038508 (0.044466) | 0.090375 / 0.023109 (0.067266) | 0.440273 / 0.275898 (0.164375) | 0.476939 / 0.323480 (0.153459) | 0.005955 / 0.007986 (-0.002031) | 0.004375 / 0.004328 (0.000046) | 0.080459 / 0.004250 (0.076209) | 0.061787 / 0.037052 (0.024734) | 0.477211 / 0.258489 (0.218722) | 0.487164 / 0.293841 (0.193323) | 0.054198 / 0.128546 (-0.074348) | 0.013945 / 0.075646 (-0.061701) | 0.093006 / 0.419271 (-0.326266) | 0.062685 / 0.043533 (0.019152) | 0.461373 / 0.255139 (0.206234) | 0.475766 / 0.283200 (0.192567) | 0.032059 / 0.141683 (-0.109623) | 1.857989 / 1.452155 (0.405834) | 1.837993 / 1.492716 (0.345277) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243048 / 0.018006 (0.225042) | 0.535850 / 0.000490 (0.535360) | 0.007204 / 0.000200 (0.007004) | 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) | 0.032584 / 0.037411 (-0.004827) | 0.098151 / 0.014526 (0.083625) | 0.109691 / 0.176557 (-0.066866) | 0.172803 / 0.737135 (-0.564333) | 0.110469 / 0.296338 (-0.185869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635086 / 0.215209 (0.419877) | 6.500864 / 2.077655 (4.423210) | 2.996727 / 1.504120 (1.492607) | 2.537218 / 1.541195 (0.996023) | 2.572310 / 1.468490 (1.103820) | 0.870868 / 4.584777 (-3.713909) | 4.989744 / 3.745712 (1.244032) | 4.422174 / 5.269862 (-0.847687) | 2.935874 / 4.565676 (-1.629803) | 0.097118 / 0.424275 (-0.327157) | 0.009360 / 0.007607 (0.001753) | 0.790447 / 0.226044 (0.564403) | 7.859519 / 2.268929 (5.590591) | 3.975616 / 55.444624 (-51.469009) | 3.018271 / 6.876477 (-3.858206) | 3.111173 / 2.142072 (0.969101) | 1.085577 / 4.805227 (-3.719651) | 0.225719 / 6.500664 (-6.274945) | 0.080576 / 0.075469 (0.005107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.802284 / 1.841788 (-0.039504) | 23.487921 / 8.074308 (15.413613) | 20.595171 / 10.191392 (10.403779) | 0.196610 / 0.680424 (-0.483814) | 0.027483 / 0.534201 (-0.506718) | 0.485840 / 0.579283 (-0.093443) | 0.542661 / 0.434364 (0.108297) | 0.580602 / 0.540337 (0.040265) | 0.768195 / 1.386936 (-0.618741) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"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) | 0.008340 / 0.011353 (-0.003013) | 0.005053 / 0.011008 (-0.005955) | 0.103294 / 0.038508 (0.064786) | 0.069417 / 0.023109 (0.046308) | 0.436922 / 0.275898 (0.161024) | 0.461348 / 0.323480 (0.137868) | 0.006030 / 0.007986 (-0.001955) | 0.003727 / 0.004328 (-0.000601) | 0.076384 / 0.004250 (0.072134) | 0.056742 / 0.037052 (0.019689) | 0.439996 / 0.258489 (0.181507) | 0.469417 / 0.293841 (0.175577) | 0.044343 / 0.128546 (-0.084203) | 0.012634 / 0.075646 (-0.063013) | 0.359746 / 0.419271 (-0.059525) | 0.064842 / 0.043533 (0.021309) | 0.425960 / 0.255139 (0.170821) | 0.458568 / 0.283200 (0.175368) | 0.039802 / 0.141683 (-0.101881) | 1.687320 / 1.452155 (0.235165) | 1.806212 / 1.492716 (0.313496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255484 / 0.018006 (0.237478) | 0.563039 / 0.000490 (0.562549) | 0.000445 / 0.000200 (0.000245) | 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) | 0.027511 / 0.037411 (-0.009900) | 0.089185 / 0.014526 (0.074659) | 0.098397 / 0.176557 (-0.078160) | 0.163897 / 0.737135 (-0.573238) | 0.099905 / 0.296338 (-0.196434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.612737 / 0.215209 (0.397528) | 6.209948 / 2.077655 (4.132294) | 2.756060 / 1.504120 (1.251940) | 2.402115 / 1.541195 (0.860920) | 2.422665 / 1.468490 (0.954175) | 0.834799 / 4.584777 (-3.749977) | 5.251699 / 3.745712 (1.505986) | 5.554141 / 5.269862 (0.284280) | 3.254699 / 4.565676 (-1.310977) | 0.095697 / 0.424275 (-0.328578) | 0.009406 / 0.007607 (0.001799) | 0.729025 / 0.226044 (0.502980) | 7.195521 / 2.268929 (4.926593) | 3.360264 / 55.444624 (-52.084361) | 2.696764 / 6.876477 (-4.179713) | 2.702796 / 2.142072 (0.560724) | 0.974420 / 4.805227 (-3.830808) | 0.195215 / 6.500664 (-6.305450) | 0.069754 / 0.075469 (-0.005715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553458 / 1.841788 (-0.288330) | 21.972436 / 8.074308 (13.898128) | 20.027392 / 10.191392 (9.836000) | 0.216950 / 0.680424 (-0.463474) | 0.032196 / 0.534201 (-0.502005) | 0.449884 / 0.579283 (-0.129399) | 0.586213 / 0.434364 (0.151849) | 0.537227 / 0.540337 (-0.003111) | 0.751022 / 1.386936 (-0.635914) |\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) | 0.007859 / 0.011353 (-0.003493) | 0.004762 / 0.011008 (-0.006246) | 0.086023 / 0.038508 (0.047515) | 0.069218 / 0.023109 (0.046109) | 0.449312 / 0.275898 (0.173414) | 0.481687 / 0.323480 (0.158207) | 0.006318 / 0.007986 (-0.001668) | 0.004063 / 0.004328 (-0.000266) | 0.076917 / 0.004250 (0.072667) | 0.058034 / 0.037052 (0.020981) | 0.474265 / 0.258489 (0.215775) | 0.497736 / 0.293841 (0.203895) | 0.044587 / 0.128546 (-0.083959) | 0.013880 / 0.075646 (-0.061766) | 0.089233 / 0.419271 (-0.330038) | 0.058760 / 0.043533 (0.015227) | 0.439515 / 0.255139 (0.184376) | 0.473246 / 0.283200 (0.190047) | 0.042968 / 0.141683 (-0.098715) | 1.802647 / 1.452155 (0.350493) | 1.778563 / 1.492716 (0.285847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343741 / 0.018006 (0.325735) | 0.567409 / 0.000490 (0.566919) | 0.029727 / 0.000200 (0.029527) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031021 / 0.037411 (-0.006390) | 0.096659 / 0.014526 (0.082133) | 0.103341 / 0.176557 (-0.073215) | 0.169893 / 0.737135 (-0.567242) | 0.103280 / 0.296338 (-0.193058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584724 / 0.215209 (0.369515) | 5.792596 / 2.077655 (3.714941) | 2.683133 / 1.504120 (1.179013) | 2.367837 / 1.541195 (0.826643) | 2.378567 / 1.468490 (0.910076) | 0.803427 / 4.584777 (-3.781350) | 5.179017 / 3.745712 (1.433305) | 4.446323 / 5.269862 (-0.823538) | 2.771731 / 4.565676 (-1.793945) | 0.100943 / 0.424275 (-0.323332) | 0.009875 / 0.007607 (0.002268) | 0.725260 / 0.226044 (0.499216) | 7.149728 / 2.268929 (4.880800) | 3.646438 / 55.444624 (-51.798187) | 2.793858 / 6.876477 (-4.082618) | 2.971966 / 2.142072 (0.829894) | 0.998147 / 4.805227 (-3.807080) | 0.198004 / 6.500664 (-6.302660) | 0.072581 / 0.075469 (-0.002888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696737 / 1.841788 (-0.145051) | 22.615193 / 8.074308 (14.540884) | 20.272421 / 10.191392 (10.081029) | 0.237459 / 0.680424 (-0.442965) | 0.034774 / 0.534201 (-0.499427) | 0.484649 / 0.579283 (-0.094634) | 0.590263 / 0.434364 (0.155899) | 0.547833 / 0.540337 (0.007495) | 0.762109 / 1.386936 (-0.624827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bc3628b5a8f71ad7cfc014d8ba5e798f26becb7 \"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) | 0.011183 / 0.011353 (-0.000170) | 0.005267 / 0.011008 (-0.005741) | 0.108506 / 0.038508 (0.069997) | 0.083541 / 0.023109 (0.060431) | 0.452189 / 0.275898 (0.176291) | 0.496229 / 0.323480 (0.172749) | 0.004951 / 0.007986 (-0.003035) | 0.004452 / 0.004328 (0.000124) | 0.085133 / 0.004250 (0.080883) | 0.061291 / 0.037052 (0.024239) | 0.450453 / 0.258489 (0.191964) | 0.506456 / 0.293841 (0.212616) | 0.049784 / 0.128546 (-0.078762) | 0.014738 / 0.075646 (-0.060908) | 0.372603 / 0.419271 (-0.046669) | 0.065223 / 0.043533 (0.021690) | 0.467872 / 0.255139 (0.212733) | 0.500062 / 0.283200 (0.216862) | 0.040911 / 0.141683 (-0.100772) | 1.852970 / 1.452155 (0.400816) | 2.016996 / 1.492716 (0.524280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262620 / 0.018006 (0.244614) | 0.593925 / 0.000490 (0.593435) | 0.000413 / 0.000200 (0.000213) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035713 / 0.037411 (-0.001698) | 0.111403 / 0.014526 (0.096878) | 0.117259 / 0.176557 (-0.059298) | 0.201545 / 0.737135 (-0.535590) | 0.133111 / 0.296338 (-0.163228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.597318 / 0.215209 (0.382109) | 5.882691 / 2.077655 (3.805036) | 2.572203 / 1.504120 (1.068083) | 2.248016 / 1.541195 (0.706821) | 2.359103 / 1.468490 (0.890613) | 0.852023 / 4.584777 (-3.732754) | 5.270831 / 3.745712 (1.525119) | 4.712915 / 5.269862 (-0.556947) | 3.124295 / 4.565676 (-1.441381) | 0.092045 / 0.424275 (-0.332230) | 0.007834 / 0.007607 (0.000227) | 0.695711 / 0.226044 (0.469666) | 7.011760 / 2.268929 (4.742831) | 3.333300 / 55.444624 (-52.111325) | 2.745889 / 6.876477 (-4.130587) | 3.153458 / 2.142072 (1.011385) | 1.011089 / 4.805227 (-3.794139) | 0.207467 / 6.500664 (-6.293197) | 0.079802 / 0.075469 (0.004333) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.703784 / 1.841788 (-0.138003) | 24.414340 / 8.074308 (16.340032) | 22.534528 / 10.191392 (12.343136) | 0.276129 / 0.680424 (-0.404295) | 0.027954 / 0.534201 (-0.506247) | 0.484261 / 0.579283 (-0.095022) | 0.605316 / 0.434364 (0.170952) | 0.557219 / 0.540337 (0.016882) | 0.802209 / 1.386936 (-0.584727) |\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) | 0.009109 / 0.011353 (-0.002244) | 0.005376 / 0.011008 (-0.005632) | 0.085141 / 0.038508 (0.046633) | 0.100560 / 0.023109 (0.077450) | 0.482673 / 0.275898 (0.206775) | 0.551582 / 0.323480 (0.228103) | 0.006756 / 0.007986 (-0.001229) | 0.004171 / 0.004328 (-0.000158) | 0.084184 / 0.004250 (0.079933) | 0.069283 / 0.037052 (0.032230) | 0.517722 / 0.258489 (0.259233) | 0.542641 / 0.293841 (0.248801) | 0.047790 / 0.128546 (-0.080756) | 0.014063 / 0.075646 (-0.061583) | 0.110591 / 0.419271 (-0.308680) | 0.064373 / 0.043533 (0.020840) | 0.496636 / 0.255139 (0.241497) | 0.551906 / 0.283200 (0.268707) | 0.046187 / 0.141683 (-0.095496) | 1.864836 / 1.452155 (0.412681) | 1.923765 / 1.492716 (0.431049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286558 / 0.018006 (0.268552) | 0.610353 / 0.000490 (0.609863) | 0.012647 / 0.000200 (0.012447) | 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) | 0.037099 / 0.037411 (-0.000313) | 0.108608 / 0.014526 (0.094082) | 0.120386 / 0.176557 (-0.056170) | 0.183450 / 0.737135 (-0.553686) | 0.124860 / 0.296338 (-0.171479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629006 / 0.215209 (0.413797) | 6.309206 / 2.077655 (4.231551) | 2.878558 / 1.504120 (1.374438) | 2.616093 / 1.541195 (1.074898) | 2.668096 / 1.468490 (1.199606) | 0.865732 / 4.584777 (-3.719045) | 5.312433 / 3.745712 (1.566721) | 4.799352 / 5.269862 (-0.470509) | 3.142207 / 4.565676 (-1.423469) | 0.099591 / 0.424275 (-0.324684) | 0.009159 / 0.007607 (0.001552) | 0.730999 / 0.226044 (0.504954) | 7.486442 / 2.268929 (5.217513) | 3.657699 / 55.444624 (-51.786925) | 3.080094 / 6.876477 (-3.796383) | 3.320976 / 2.142072 (1.178904) | 1.089324 / 4.805227 (-3.715904) | 0.222831 / 6.500664 (-6.277833) | 0.083976 / 0.075469 (0.008507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793181 / 1.841788 (-0.048607) | 25.307444 / 8.074308 (17.233136) | 21.321713 / 10.191392 (11.130321) | 0.216326 / 0.680424 (-0.464098) | 0.034298 / 0.534201 (-0.499903) | 0.497173 / 0.579283 (-0.082110) | 0.643550 / 0.434364 (0.209186) | 0.581213 / 0.540337 (0.040876) | 0.830973 / 1.386936 (-0.555963) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24875bb8494c3a7803182b08c70747b1b1a6bf4d \"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) | 0.006886 / 0.011353 (-0.004467) | 0.004267 / 0.011008 (-0.006741) | 0.086182 / 0.038508 (0.047674) | 0.083405 / 0.023109 (0.060296) | 0.313717 / 0.275898 (0.037819) | 0.351476 / 0.323480 (0.027996) | 0.005702 / 0.007986 (-0.002284) | 0.003802 / 0.004328 (-0.000526) | 0.065759 / 0.004250 (0.061508) | 0.060056 / 0.037052 (0.023003) | 0.315871 / 0.258489 (0.057382) | 0.364520 / 0.293841 (0.070679) | 0.032067 / 0.128546 (-0.096479) | 0.008679 / 0.075646 (-0.066967) | 0.294968 / 0.419271 (-0.124303) | 0.054684 / 0.043533 (0.011152) | 0.314124 / 0.255139 (0.058985) | 0.337312 / 0.283200 (0.054113) | 0.025051 / 0.141683 (-0.116632) | 1.505242 / 1.452155 (0.053087) | 1.608263 / 1.492716 (0.115547) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266562 / 0.018006 (0.248556) | 0.579887 / 0.000490 (0.579397) | 0.004161 / 0.000200 (0.003961) | 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) | 0.031153 / 0.037411 (-0.006258) | 0.087703 / 0.014526 (0.073177) | 0.103864 / 0.176557 (-0.072693) | 0.159032 / 0.737135 (-0.578104) | 0.102482 / 0.296338 (-0.193857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405805 / 0.215209 (0.190596) | 4.050669 / 2.077655 (1.973014) | 2.064384 / 1.504120 (0.560264) | 1.892825 / 1.541195 (0.351630) | 2.001083 / 1.468490 (0.532593) | 0.478174 / 4.584777 (-4.106603) | 3.542580 / 3.745712 (-0.203132) | 3.319205 / 5.269862 (-1.950656) | 2.075868 / 4.565676 (-2.489808) | 0.057345 / 0.424275 (-0.366930) | 0.007459 / 0.007607 (-0.000148) | 0.483564 / 0.226044 (0.257520) | 4.827746 / 2.268929 (2.558818) | 2.579541 / 55.444624 (-52.865083) | 2.205125 / 6.876477 (-4.671352) | 2.489206 / 2.142072 (0.347133) | 0.575843 / 4.805227 (-4.229384) | 0.133010 / 6.500664 (-6.367654) | 0.061082 / 0.075469 (-0.014387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286059 / 1.841788 (-0.555729) | 20.575173 / 8.074308 (12.500865) | 14.351692 / 10.191392 (4.160300) | 0.150401 / 0.680424 (-0.530022) | 0.018678 / 0.534201 (-0.515523) | 0.397860 / 0.579283 (-0.181423) | 0.419474 / 0.434364 (-0.014890) | 0.474492 / 0.540337 (-0.065846) | 0.659510 / 1.386936 (-0.727426) |\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) | 0.006948 / 0.011353 (-0.004405) | 0.004305 / 0.011008 (-0.006703) | 0.064220 / 0.038508 (0.025712) | 0.083251 / 0.023109 (0.060142) | 0.388148 / 0.275898 (0.112250) | 0.417834 / 0.323480 (0.094354) | 0.005762 / 0.007986 (-0.002224) | 0.003803 / 0.004328 (-0.000525) | 0.066365 / 0.004250 (0.062114) | 0.061808 / 0.037052 (0.024756) | 0.390889 / 0.258489 (0.132400) | 0.430619 / 0.293841 (0.136778) | 0.031777 / 0.128546 (-0.096770) | 0.008781 / 0.075646 (-0.066865) | 0.070844 / 0.419271 (-0.348427) | 0.050552 / 0.043533 (0.007019) | 0.378420 / 0.255139 (0.123281) | 0.403273 / 0.283200 (0.120074) | 0.024578 / 0.141683 (-0.117105) | 1.494790 / 1.452155 (0.042636) | 1.549408 / 1.492716 (0.056692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302668 / 0.018006 (0.284662) | 0.542235 / 0.000490 (0.541746) | 0.001847 / 0.000200 (0.001647) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031947 / 0.037411 (-0.005465) | 0.092220 / 0.014526 (0.077694) | 0.104525 / 0.176557 (-0.072031) | 0.162000 / 0.737135 (-0.575135) | 0.106795 / 0.296338 (-0.189543) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412035 / 0.215209 (0.196826) | 4.106527 / 2.077655 (2.028872) | 2.111529 / 1.504120 (0.607409) | 1.953201 / 1.541195 (0.412006) | 2.079258 / 1.468490 (0.610768) | 0.479562 / 4.584777 (-4.105215) | 3.606256 / 3.745712 (-0.139456) | 5.175250 / 5.269862 (-0.094612) | 3.292465 / 4.565676 (-1.273212) | 0.057726 / 0.424275 (-0.366549) | 0.008247 / 0.007607 (0.000640) | 0.486143 / 0.226044 (0.260098) | 4.859051 / 2.268929 (2.590123) | 2.675629 / 55.444624 (-52.768995) | 2.267448 / 6.876477 (-4.609029) | 2.567639 / 2.142072 (0.425567) | 0.580822 / 4.805227 (-4.224406) | 0.134942 / 6.500664 (-6.365722) | 0.063825 / 0.075469 (-0.011644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334421 / 1.841788 (-0.507367) | 20.481428 / 8.074308 (12.407120) | 14.227943 / 10.191392 (4.036551) | 0.170711 / 0.680424 (-0.509713) | 0.018212 / 0.534201 (-0.515989) | 0.397212 / 0.579283 (-0.182071) | 0.411934 / 0.434364 (-0.022430) | 0.478019 / 0.540337 (-0.062319) | 0.645434 / 1.386936 (-0.741502) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3d3f10886e23a65cce3bfd939b8ec0d5a5c2c1 \"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) | 0.007700 / 0.011353 (-0.003653) | 0.004680 / 0.011008 (-0.006328) | 0.098812 / 0.038508 (0.060304) | 0.085062 / 0.023109 (0.061952) | 0.371472 / 0.275898 (0.095574) | 0.412552 / 0.323480 (0.089072) | 0.004700 / 0.007986 (-0.003285) | 0.003765 / 0.004328 (-0.000564) | 0.074267 / 0.004250 (0.070017) | 0.063003 / 0.037052 (0.025951) | 0.391842 / 0.258489 (0.133353) | 0.436955 / 0.293841 (0.143114) | 0.035291 / 0.128546 (-0.093255) | 0.009309 / 0.075646 (-0.066338) | 0.313097 / 0.419271 (-0.106174) | 0.060098 / 0.043533 (0.016565) | 0.350726 / 0.255139 (0.095587) | 0.402692 / 0.283200 (0.119493) | 0.029321 / 0.141683 (-0.112361) | 1.671806 / 1.452155 (0.219651) | 1.743760 / 1.492716 (0.251044) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242281 / 0.018006 (0.224275) | 0.505054 / 0.000490 (0.504564) | 0.006595 / 0.000200 (0.006395) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032174 / 0.037411 (-0.005238) | 0.094483 / 0.014526 (0.079957) | 0.108527 / 0.176557 (-0.068030) | 0.178983 / 0.737135 (-0.558152) | 0.113766 / 0.296338 (-0.182572) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419764 / 0.215209 (0.204555) | 4.282650 / 2.077655 (2.204995) | 2.075325 / 1.504120 (0.571205) | 1.897668 / 1.541195 (0.356473) | 2.027109 / 1.468490 (0.558619) | 0.519983 / 4.584777 (-4.064794) | 4.134603 / 3.745712 (0.388891) | 6.586711 / 5.269862 (1.316849) | 3.811726 / 4.565676 (-0.753951) | 0.058628 / 0.424275 (-0.365647) | 0.007586 / 0.007607 (-0.000021) | 0.502180 / 0.226044 (0.276136) | 5.101588 / 2.268929 (2.832660) | 2.534295 / 55.444624 (-52.910330) | 2.220170 / 6.876477 (-4.656307) | 2.441110 / 2.142072 (0.299038) | 0.644775 / 4.805227 (-4.160452) | 0.144716 / 6.500664 (-6.355948) | 0.067018 / 0.075469 (-0.008451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.431279 / 1.841788 (-0.410508) | 21.947814 / 8.074308 (13.873506) | 15.548236 / 10.191392 (5.356844) | 0.174774 / 0.680424 (-0.505650) | 0.021182 / 0.534201 (-0.513019) | 0.441320 / 0.579283 (-0.137963) | 0.476685 / 0.434364 (0.042321) | 0.506277 / 0.540337 (-0.034060) | 0.809943 / 1.386936 (-0.576993) |\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) | 0.007172 / 0.011353 (-0.004181) | 0.004358 / 0.011008 (-0.006650) | 0.068604 / 0.038508 (0.030096) | 0.083956 / 0.023109 (0.060847) | 0.402579 / 0.275898 (0.126681) | 0.444714 / 0.323480 (0.121235) | 0.005940 / 0.007986 (-0.002046) | 0.003607 / 0.004328 (-0.000722) | 0.073134 / 0.004250 (0.068883) | 0.061722 / 0.037052 (0.024669) | 0.410957 / 0.258489 (0.152468) | 0.458819 / 0.293841 (0.164978) | 0.033710 / 0.128546 (-0.094836) | 0.010230 / 0.075646 (-0.065417) | 0.084678 / 0.419271 (-0.334593) | 0.058203 / 0.043533 (0.014670) | 0.444972 / 0.255139 (0.189833) | 0.470962 / 0.283200 (0.187763) | 0.029222 / 0.141683 (-0.112461) | 1.671460 / 1.452155 (0.219306) | 1.759471 / 1.492716 (0.266754) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238894 / 0.018006 (0.220888) | 0.493605 / 0.000490 (0.493115) | 0.001979 / 0.000200 (0.001780) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036498 / 0.037411 (-0.000913) | 0.095245 / 0.014526 (0.080719) | 0.112147 / 0.176557 (-0.064409) | 0.171128 / 0.737135 (-0.566007) | 0.115295 / 0.296338 (-0.181044) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461067 / 0.215209 (0.245858) | 4.723932 / 2.077655 (2.646277) | 2.432697 / 1.504120 (0.928578) | 2.237302 / 1.541195 (0.696107) | 2.351320 / 1.468490 (0.882830) | 0.509963 / 4.584777 (-4.074813) | 4.194817 / 3.745712 (0.449105) | 6.689529 / 5.269862 (1.419667) | 3.351198 / 4.565676 (-1.214478) | 0.064563 / 0.424275 (-0.359712) | 0.008605 / 0.007607 (0.000998) | 0.575590 / 0.226044 (0.349546) | 5.644179 / 2.268929 (3.375250) | 3.021375 / 55.444624 (-52.423249) | 2.595305 / 6.876477 (-4.281172) | 2.839228 / 2.142072 (0.697156) | 0.657148 / 4.805227 (-4.148079) | 0.144831 / 6.500664 (-6.355834) | 0.067882 / 0.075469 (-0.007587) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595580 / 1.841788 (-0.246208) | 22.431609 / 8.074308 (14.357301) | 15.700845 / 10.191392 (5.509453) | 0.164675 / 0.680424 (-0.515749) | 0.021322 / 0.534201 (-0.512879) | 0.455270 / 0.579283 (-0.124013) | 0.451547 / 0.434364 (0.017183) | 0.520955 / 0.540337 (-0.019383) | 0.687803 / 1.386936 (-0.699133) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"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) | 0.008171 / 0.011353 (-0.003182) | 0.005563 / 0.011008 (-0.005445) | 0.102265 / 0.038508 (0.063757) | 0.074755 / 0.023109 (0.051646) | 0.431317 / 0.275898 (0.155419) | 0.472179 / 0.323480 (0.148699) | 0.006153 / 0.007986 (-0.001833) | 0.003832 / 0.004328 (-0.000496) | 0.078480 / 0.004250 (0.074230) | 0.056250 / 0.037052 (0.019197) | 0.432938 / 0.258489 (0.174449) | 0.480983 / 0.293841 (0.187142) | 0.048861 / 0.128546 (-0.079685) | 0.016252 / 0.075646 (-0.059394) | 0.343508 / 0.419271 (-0.075763) | 0.065057 / 0.043533 (0.021524) | 0.468418 / 0.255139 (0.213279) | 0.463692 / 0.283200 (0.180492) | 0.032912 / 0.141683 (-0.108771) | 1.795194 / 1.452155 (0.343039) | 1.833047 / 1.492716 (0.340331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197980 / 0.018006 (0.179974) | 0.500662 / 0.000490 (0.500172) | 0.007380 / 0.000200 (0.007181) | 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) | 0.028323 / 0.037411 (-0.009089) | 0.089817 / 0.014526 (0.075291) | 0.102923 / 0.176557 (-0.073633) | 0.173851 / 0.737135 (-0.563284) | 0.104006 / 0.296338 (-0.192333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580277 / 0.215209 (0.365068) | 5.878739 / 2.077655 (3.801085) | 2.404673 / 1.504120 (0.900553) | 2.071765 / 1.541195 (0.530571) | 2.106024 / 1.468490 (0.637534) | 0.855217 / 4.584777 (-3.729560) | 4.918602 / 3.745712 (1.172890) | 5.354984 / 5.269862 (0.085122) | 3.141288 / 4.565676 (-1.424389) | 0.099553 / 0.424275 (-0.324723) | 0.008152 / 0.007607 (0.000545) | 0.709857 / 0.226044 (0.483813) | 7.144602 / 2.268929 (4.875673) | 3.137637 / 55.444624 (-52.306987) | 2.379851 / 6.876477 (-4.496626) | 2.346426 / 2.142072 (0.204353) | 1.033416 / 4.805227 (-3.771811) | 0.213120 / 6.500664 (-6.287544) | 0.076037 / 0.075469 (0.000568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.597742 / 1.841788 (-0.244046) | 21.745366 / 8.074308 (13.671058) | 20.830698 / 10.191392 (10.639306) | 0.238727 / 0.680424 (-0.441697) | 0.027923 / 0.534201 (-0.506278) | 0.466073 / 0.579283 (-0.113210) | 0.548647 / 0.434364 (0.114283) | 0.549245 / 0.540337 (0.008908) | 0.977148 / 1.386936 (-0.409788) |\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) | 0.008252 / 0.011353 (-0.003101) | 0.004653 / 0.011008 (-0.006356) | 0.084012 / 0.038508 (0.045504) | 0.077418 / 0.023109 (0.054309) | 0.440748 / 0.275898 (0.164850) | 0.464279 / 0.323480 (0.140799) | 0.005762 / 0.007986 (-0.002224) | 0.004909 / 0.004328 (0.000581) | 0.086441 / 0.004250 (0.082190) | 0.057883 / 0.037052 (0.020831) | 0.466655 / 0.258489 (0.208166) | 0.479751 / 0.293841 (0.185910) | 0.047166 / 0.128546 (-0.081380) | 0.014480 / 0.075646 (-0.061166) | 0.092599 / 0.419271 (-0.326672) | 0.062454 / 0.043533 (0.018921) | 0.449753 / 0.255139 (0.194614) | 0.461876 / 0.283200 (0.178676) | 0.034828 / 0.141683 (-0.106855) | 1.752249 / 1.452155 (0.300095) | 1.865449 / 1.492716 (0.372732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245028 / 0.018006 (0.227022) | 0.509564 / 0.000490 (0.509074) | 0.003930 / 0.000200 (0.003730) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034746 / 0.037411 (-0.002665) | 0.096563 / 0.014526 (0.082037) | 0.107581 / 0.176557 (-0.068975) | 0.184952 / 0.737135 (-0.552184) | 0.108747 / 0.296338 (-0.187591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613091 / 0.215209 (0.397882) | 5.994985 / 2.077655 (3.917330) | 2.711276 / 1.504120 (1.207156) | 2.415862 / 1.541195 (0.874668) | 2.391055 / 1.468490 (0.922565) | 0.868723 / 4.584777 (-3.716054) | 4.953992 / 3.745712 (1.208280) | 4.606542 / 5.269862 (-0.663319) | 2.942162 / 4.565676 (-1.623515) | 0.102737 / 0.424275 (-0.321538) | 0.008634 / 0.007607 (0.001027) | 0.722122 / 0.226044 (0.496078) | 7.245097 / 2.268929 (4.976168) | 3.428232 / 55.444624 (-52.016393) | 2.709539 / 6.876477 (-4.166938) | 2.857956 / 2.142072 (0.715884) | 1.045594 / 4.805227 (-3.759634) | 0.213344 / 6.500664 (-6.287320) | 0.073601 / 0.075469 (-0.001868) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651954 / 1.841788 (-0.189834) | 22.458646 / 8.074308 (14.384338) | 19.583203 / 10.191392 (9.391811) | 0.246932 / 0.680424 (-0.433492) | 0.025730 / 0.534201 (-0.508471) | 0.473475 / 0.579283 (-0.105808) | 0.521411 / 0.434364 (0.087047) | 0.562038 / 0.540337 (0.021700) | 0.767673 / 1.386936 (-0.619263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n", "The CI error is unrelated.", "<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) | 0.006649 / 0.011353 (-0.004703) | 0.003963 / 0.011008 (-0.007045) | 0.084564 / 0.038508 (0.046056) | 0.075668 / 0.023109 (0.052559) | 0.314233 / 0.275898 (0.038335) | 0.343320 / 0.323480 (0.019841) | 0.005405 / 0.007986 (-0.002581) | 0.003356 / 0.004328 (-0.000973) | 0.065094 / 0.004250 (0.060844) | 0.058774 / 0.037052 (0.021722) | 0.320772 / 0.258489 (0.062283) | 0.353546 / 0.293841 (0.059705) | 0.030921 / 0.128546 (-0.097625) | 0.008463 / 0.075646 (-0.067184) | 0.287490 / 0.419271 (-0.131781) | 0.053188 / 0.043533 (0.009656) | 0.324023 / 0.255139 (0.068884) | 0.337828 / 0.283200 (0.054628) | 0.024764 / 0.141683 (-0.116918) | 1.458028 / 1.452155 (0.005873) | 1.521615 / 1.492716 (0.028899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209360 / 0.018006 (0.191353) | 0.461331 / 0.000490 (0.460841) | 0.000386 / 0.000200 (0.000186) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028405 / 0.037411 (-0.009006) | 0.081074 / 0.014526 (0.066548) | 0.094868 / 0.176557 (-0.081689) | 0.151050 / 0.737135 (-0.586085) | 0.095854 / 0.296338 (-0.200484) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393957 / 0.215209 (0.178748) | 3.938649 / 2.077655 (1.860994) | 1.938190 / 1.504120 (0.434070) | 1.766458 / 1.541195 (0.225263) | 1.818028 / 1.468490 (0.349538) | 0.483926 / 4.584777 (-4.100851) | 3.641957 / 3.745712 (-0.103755) | 4.883845 / 5.269862 (-0.386016) | 2.960300 / 4.565676 (-1.605377) | 0.057227 / 0.424275 (-0.367048) | 0.007285 / 0.007607 (-0.000322) | 0.475928 / 0.226044 (0.249884) | 4.756757 / 2.268929 (2.487828) | 2.502659 / 55.444624 (-52.941966) | 2.178067 / 6.876477 (-4.698410) | 2.378298 / 2.142072 (0.236226) | 0.578639 / 4.805227 (-4.226588) | 0.132512 / 6.500664 (-6.368152) | 0.059656 / 0.075469 (-0.015813) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272673 / 1.841788 (-0.569115) | 19.266884 / 8.074308 (11.192576) | 14.272930 / 10.191392 (4.081538) | 0.165897 / 0.680424 (-0.514527) | 0.018436 / 0.534201 (-0.515765) | 0.395177 / 0.579283 (-0.184107) | 0.420134 / 0.434364 (-0.014229) | 0.460781 / 0.540337 (-0.079557) | 0.645376 / 1.386936 (-0.741560) |\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) | 0.006504 / 0.011353 (-0.004849) | 0.003942 / 0.011008 (-0.007066) | 0.064936 / 0.038508 (0.026428) | 0.075015 / 0.023109 (0.051905) | 0.396871 / 0.275898 (0.120973) | 0.423448 / 0.323480 (0.099968) | 0.005239 / 0.007986 (-0.002747) | 0.003265 / 0.004328 (-0.001063) | 0.064910 / 0.004250 (0.060660) | 0.055006 / 0.037052 (0.017953) | 0.392818 / 0.258489 (0.134329) | 0.429735 / 0.293841 (0.135894) | 0.031847 / 0.128546 (-0.096699) | 0.008626 / 0.075646 (-0.067021) | 0.071591 / 0.419271 (-0.347681) | 0.049006 / 0.043533 (0.005473) | 0.384913 / 0.255139 (0.129774) | 0.408969 / 0.283200 (0.125769) | 0.023573 / 0.141683 (-0.118110) | 1.490271 / 1.452155 (0.038117) | 1.564620 / 1.492716 (0.071904) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225917 / 0.018006 (0.207911) | 0.450369 / 0.000490 (0.449880) | 0.000375 / 0.000200 (0.000175) | 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) | 0.031196 / 0.037411 (-0.006215) | 0.090486 / 0.014526 (0.075960) | 0.102326 / 0.176557 (-0.074231) | 0.157483 / 0.737135 (-0.579653) | 0.103670 / 0.296338 (-0.192668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417577 / 0.215209 (0.202368) | 4.170798 / 2.077655 (2.093143) | 2.123689 / 1.504120 (0.619569) | 1.948231 / 1.541195 (0.407037) | 2.040277 / 1.468490 (0.571787) | 0.497919 / 4.584777 (-4.086858) | 3.633270 / 3.745712 (-0.112442) | 4.851698 / 5.269862 (-0.418164) | 2.691992 / 4.565676 (-1.873684) | 0.058641 / 0.424275 (-0.365634) | 0.007719 / 0.007607 (0.000112) | 0.500652 / 0.226044 (0.274607) | 4.988657 / 2.268929 (2.719728) | 2.604488 / 55.444624 (-52.840136) | 2.329829 / 6.876477 (-4.546648) | 2.468239 / 2.142072 (0.326167) | 0.598724 / 4.805227 (-4.206503) | 0.135959 / 6.500664 (-6.364706) | 0.061088 / 0.075469 (-0.014381) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352107 / 1.841788 (-0.489681) | 19.973976 / 8.074308 (11.899668) | 14.292812 / 10.191392 (4.101420) | 0.163855 / 0.680424 (-0.516568) | 0.018402 / 0.534201 (-0.515799) | 0.393128 / 0.579283 (-0.186155) | 0.407379 / 0.434364 (-0.026985) | 0.462324 / 0.540337 (-0.078013) | 0.607501 / 1.386936 (-0.779435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n" ]

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[ "Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or-fairscale/7229/7) didn't help.", "If it does the `map` twice then it means the hash of your map function is not some same between your two processes.\r\n\r\nCan you make sure your map functions have the same hash in different processes ?\r\n\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nprint(Hasher.hash(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)))\r\nprint(Hasher.hash(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16)))\r\n```\r\n\r\nYou can also set the fingerprint used to reload the resulting dataset by passing `new_finegrprint=` in `map`, see https://huggingface.co/docs/datasets/v2.13.1/en/about_cache#the-cache. This will force the different processes to use the same fingerprint used to locate the resulting dataset in the cache.", "Thanks for help! I find the fingerprint between processes don't have same hash:\r\n```\r\nRank 0: Gpu 0 cut_reorder_keys fingerprint c7f47f40e9a67657\r\nRank 0: Gpu 0 random_shift fingerprint 240a0ce79831e7d4\r\n\r\nRank 1: Gpu 1 cut_reorder_keys fingerprint 20edd3d9cf284001\r\nRank 1: Gpu 1 random_shift fingerprint 819f7c1c18e7733f\r\n```\r\nBut my functions only process the example one by one and don't need rank or other arguments. After all it can work in the test for dataset and dataloader.\r\nI'll try to set `new_fingerprint` to see if it works and figure out the reason of different hash.", "I finally figure it out. The fingerprint of the function will change if other key-value pairs change in `args` even the `args.num_stations_list` is not changed.\r\n\r\n```python\r\nlambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)\r\n```\r\n\r\nMy `args` contains the key `rank` which refers the rank of its GPU, so the fingerprints change among the GPUs.\r\nI use `partial` in `functools` to generate a partial function that fixs the argument `num_stations_list=args.num_stations_list`, and the fingerprint of this partial function keeps among the GPUs. Finally I can reuse the mapped cache." ]

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[ "This can also mean one of the files was not downloaded correctly.\r\n\r\nWe log an erroneous file's name before raising the reader's error, so this is how you can find the problematic file. Then, you should delete it and call `load_dataset` again.\r\n\r\n(I checked all the uploaded files, and they seem to be valid Parquet files, so I don't think this is a bug on their side)\r\n" ]

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[ "Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\nand share the output?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ", "If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?", "> If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nYou are right. I was having some other issue earlier that I need more input from you guys to overcome, and when I overcame it the yaml was just legacy from before. I'll update the PR. ", "> If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nI remembered what it was, and why I needed the yaml. I needed it so it could remember the progress for a particular num_shards setup, as different num_shards would lead to different number of splits, and a user might switch between them while uploading, and I did not want the index to be conflated with one of another num_shards setup. \r\n\r\nAny idea how we deal with that without a yaml?", "If the user changes the num_shards parameters then we should re-upload everything.\r\n\r\nIt happens that the num_shards is part of the parquet file names, so it restarts the upload from scratch without having to write additional logic :)" ]

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[ "A duplicate of https://github.com/huggingface/datasets/issues/5929" ]

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[ "This would break a lot of existing code, so we can't really do this." ]

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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) | 0.006658 / 0.011353 (-0.004695) | 0.004347 / 0.011008 (-0.006661) | 0.084179 / 0.038508 (0.045671) | 0.080842 / 0.023109 (0.057733) | 0.321642 / 0.275898 (0.045744) | 0.348758 / 0.323480 (0.025278) | 0.005624 / 0.007986 (-0.002362) | 0.003479 / 0.004328 (-0.000850) | 0.065125 / 0.004250 (0.060875) | 0.057624 / 0.037052 (0.020572) | 0.323643 / 0.258489 (0.065154) | 0.360939 / 0.293841 (0.067098) | 0.031005 / 0.128546 (-0.097541) | 0.008618 / 0.075646 (-0.067028) | 0.287443 / 0.419271 (-0.131828) | 0.052640 / 0.043533 (0.009107) | 0.316947 / 0.255139 (0.061808) | 0.330292 / 0.283200 (0.047093) | 0.024393 / 0.141683 (-0.117289) | 1.476734 / 1.452155 (0.024579) | 1.534505 / 1.492716 (0.041789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273808 / 0.018006 (0.255802) | 0.591146 / 0.000490 (0.590656) | 0.000322 / 0.000200 (0.000122) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029992 / 0.037411 (-0.007419) | 0.086654 / 0.014526 (0.072129) | 0.098590 / 0.176557 (-0.077967) | 0.157225 / 0.737135 (-0.579910) | 0.101816 / 0.296338 (-0.194522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382578 / 0.215209 (0.167368) | 3.803576 / 2.077655 (1.725922) | 1.875136 / 1.504120 (0.371016) | 1.704207 / 1.541195 (0.163012) | 1.765146 / 1.468490 (0.296656) | 0.482802 / 4.584777 (-4.101975) | 3.571772 / 3.745712 (-0.173940) | 3.245626 / 5.269862 (-2.024235) | 2.051612 / 4.565676 (-2.514064) | 0.056539 / 0.424275 (-0.367736) | 0.007199 / 0.007607 (-0.000408) | 0.462445 / 0.226044 (0.236401) | 4.623800 / 2.268929 (2.354872) | 2.318948 / 55.444624 (-53.125677) | 1.971442 / 6.876477 (-4.905035) | 2.225444 / 2.142072 (0.083371) | 0.575205 / 4.805227 (-4.230022) | 0.129243 / 6.500664 (-6.371421) | 0.059036 / 0.075469 (-0.016433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266827 / 1.841788 (-0.574960) | 20.323419 / 8.074308 (12.249110) | 14.577603 / 10.191392 (4.386210) | 0.162131 / 0.680424 (-0.518293) | 0.018529 / 0.534201 (-0.515672) | 0.395046 / 0.579283 (-0.184237) | 0.410870 / 0.434364 (-0.023494) | 0.455782 / 0.540337 (-0.084556) | 0.662851 / 1.386936 (-0.724085) |\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) | 0.006867 / 0.011353 (-0.004486) | 0.004197 / 0.011008 (-0.006811) | 0.066060 / 0.038508 (0.027552) | 0.084145 / 0.023109 (0.061036) | 0.366740 / 0.275898 (0.090842) | 0.402362 / 0.323480 (0.078882) | 0.005785 / 0.007986 (-0.002200) | 0.003551 / 0.004328 (-0.000778) | 0.066177 / 0.004250 (0.061926) | 0.061521 / 0.037052 (0.024468) | 0.377807 / 0.258489 (0.119318) | 0.413490 / 0.293841 (0.119649) | 0.031918 / 0.128546 (-0.096628) | 0.008767 / 0.075646 (-0.066879) | 0.071437 / 0.419271 (-0.347835) | 0.049237 / 0.043533 (0.005704) | 0.365929 / 0.255139 (0.110790) | 0.393545 / 0.283200 (0.110346) | 0.024054 / 0.141683 (-0.117628) | 1.524599 / 1.452155 (0.072445) | 1.576592 / 1.492716 (0.083876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315181 / 0.018006 (0.297174) | 0.535501 / 0.000490 (0.535011) | 0.000410 / 0.000200 (0.000210) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032915 / 0.037411 (-0.004497) | 0.089310 / 0.014526 (0.074784) | 0.105136 / 0.176557 (-0.071421) | 0.158572 / 0.737135 (-0.578563) | 0.106850 / 0.296338 (-0.189489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419343 / 0.215209 (0.204134) | 4.200166 / 2.077655 (2.122511) | 2.180234 / 1.504120 (0.676114) | 2.016885 / 1.541195 (0.475690) | 2.131480 / 1.468490 (0.662990) | 0.484681 / 4.584777 (-4.100096) | 3.613535 / 3.745712 (-0.132177) | 5.762111 / 5.269862 (0.492249) | 3.190590 / 4.565676 (-1.375086) | 0.057403 / 0.424275 (-0.366872) | 0.007862 / 0.007607 (0.000255) | 0.490857 / 0.226044 (0.264813) | 4.911241 / 2.268929 (2.642313) | 2.650787 / 55.444624 (-52.793838) | 2.317060 / 6.876477 (-4.559416) | 2.579677 / 2.142072 (0.437605) | 0.587388 / 4.805227 (-4.217840) | 0.148109 / 6.500664 (-6.352555) | 0.061435 / 0.075469 (-0.014034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322181 / 1.841788 (-0.519606) | 20.647184 / 8.074308 (12.572875) | 14.907635 / 10.191392 (4.716243) | 0.156330 / 0.680424 (-0.524094) | 0.018719 / 0.534201 (-0.515482) | 0.397636 / 0.579283 (-0.181647) | 0.414107 / 0.434364 (-0.020257) | 0.460812 / 0.540337 (-0.079526) | 0.609568 / 1.386936 (-0.777368) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n", "This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ", "what would be the change i need to make? ", "@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n", "The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged", "I just merged the other PR so you should be good 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) | 0.006303 / 0.011353 (-0.005049) | 0.003746 / 0.011008 (-0.007262) | 0.081083 / 0.038508 (0.042575) | 0.067973 / 0.023109 (0.044864) | 0.322221 / 0.275898 (0.046323) | 0.359432 / 0.323480 (0.035952) | 0.004891 / 0.007986 (-0.003095) | 0.002988 / 0.004328 (-0.001341) | 0.064068 / 0.004250 (0.059818) | 0.052042 / 0.037052 (0.014990) | 0.323387 / 0.258489 (0.064898) | 0.390416 / 0.293841 (0.096575) | 0.028090 / 0.128546 (-0.100457) | 0.008009 / 0.075646 (-0.067638) | 0.262288 / 0.419271 (-0.156984) | 0.044986 / 0.043533 (0.001453) | 0.322319 / 0.255139 (0.067180) | 0.345323 / 0.283200 (0.062123) | 0.021798 / 0.141683 (-0.119885) | 1.417259 / 1.452155 (-0.034895) | 1.490050 / 1.492716 (-0.002667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195902 / 0.018006 (0.177896) | 0.490808 / 0.000490 (0.490318) | 0.002969 / 0.000200 (0.002770) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025221 / 0.037411 (-0.012190) | 0.075341 / 0.014526 (0.060815) | 0.086703 / 0.176557 (-0.089853) | 0.146953 / 0.737135 (-0.590182) | 0.086610 / 0.296338 (-0.209728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434890 / 0.215209 (0.219681) | 4.352283 / 2.077655 (2.274629) | 2.293098 / 1.504120 (0.788979) | 2.123023 / 1.541195 (0.581829) | 2.179722 / 1.468490 (0.711232) | 0.503851 / 4.584777 (-4.080926) | 3.087991 / 3.745712 (-0.657721) | 2.898689 / 5.269862 (-2.371173) | 1.902813 / 4.565676 (-2.662864) | 0.058079 / 0.424275 (-0.366196) | 0.006600 / 0.007607 (-0.001007) | 0.509243 / 0.226044 (0.283199) | 5.080204 / 2.268929 (2.811275) | 2.753594 / 55.444624 (-52.691030) | 2.417385 / 6.876477 (-4.459091) | 2.635470 / 2.142072 (0.493398) | 0.591059 / 4.805227 (-4.214168) | 0.126360 / 6.500664 (-6.374304) | 0.062108 / 0.075469 (-0.013361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254398 / 1.841788 (-0.587390) | 18.866729 / 8.074308 (10.792420) | 14.120008 / 10.191392 (3.928616) | 0.152388 / 0.680424 (-0.528035) | 0.016997 / 0.534201 (-0.517204) | 0.336435 / 0.579283 (-0.242848) | 0.364612 / 0.434364 (-0.069752) | 0.391434 / 0.540337 (-0.148903) | 0.567180 / 1.386936 (-0.819756) |\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) | 0.006477 / 0.011353 (-0.004876) | 0.003723 / 0.011008 (-0.007285) | 0.062712 / 0.038508 (0.024204) | 0.069380 / 0.023109 (0.046271) | 0.393394 / 0.275898 (0.117496) | 0.446903 / 0.323480 (0.123423) | 0.004833 / 0.007986 (-0.003153) | 0.002946 / 0.004328 (-0.001382) | 0.062076 / 0.004250 (0.057826) | 0.051589 / 0.037052 (0.014537) | 0.388536 / 0.258489 (0.130047) | 0.451406 / 0.293841 (0.157565) | 0.027824 / 0.128546 (-0.100722) | 0.008040 / 0.075646 (-0.067606) | 0.067085 / 0.419271 (-0.352187) | 0.042269 / 0.043533 (-0.001264) | 0.363419 / 0.255139 (0.108280) | 0.435201 / 0.283200 (0.152001) | 0.021275 / 0.141683 (-0.120408) | 1.439838 / 1.452155 (-0.012316) | 1.477279 / 1.492716 (-0.015437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229667 / 0.018006 (0.211661) | 0.434101 / 0.000490 (0.433611) | 0.000652 / 0.000200 (0.000452) | 0.000060 / 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) | 0.026141 / 0.037411 (-0.011271) | 0.078950 / 0.014526 (0.064424) | 0.090143 / 0.176557 (-0.086413) | 0.143941 / 0.737135 (-0.593195) | 0.090465 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432042 / 0.215209 (0.216833) | 4.322134 / 2.077655 (2.244479) | 2.242897 / 1.504120 (0.738777) | 2.076351 / 1.541195 (0.535157) | 2.166739 / 1.468490 (0.698249) | 0.500833 / 4.584777 (-4.083944) | 3.140117 / 3.745712 (-0.605595) | 4.383050 / 5.269862 (-0.886812) | 2.548245 / 4.565676 (-2.017432) | 0.057521 / 0.424275 (-0.366754) | 0.006946 / 0.007607 (-0.000662) | 0.509613 / 0.226044 (0.283569) | 5.114052 / 2.268929 (2.845123) | 2.682112 / 55.444624 (-52.762512) | 2.362385 / 6.876477 (-4.514092) | 2.531787 / 2.142072 (0.389715) | 0.595085 / 4.805227 (-4.210142) | 0.130198 / 6.500664 (-6.370466) | 0.064057 / 0.075469 (-0.011412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346254 / 1.841788 (-0.495534) | 19.036911 / 8.074308 (10.962603) | 14.478689 / 10.191392 (4.287297) | 0.147541 / 0.680424 (-0.532883) | 0.016851 / 0.534201 (-0.517350) | 0.333901 / 0.579283 (-0.245382) | 0.380012 / 0.434364 (-0.054352) | 0.396021 / 0.540337 (-0.144317) | 0.540612 / 1.386936 (-0.846324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n", "CI failure is unrelated. Merging.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009498 / 0.011353 (-0.001855) | 0.005639 / 0.011008 (-0.005369) | 0.108731 / 0.038508 (0.070223) | 0.094052 / 0.023109 (0.070943) | 0.454375 / 0.275898 (0.178477) | 0.486852 / 0.323480 (0.163372) | 0.006627 / 0.007986 (-0.001359) | 0.004712 / 0.004328 (0.000383) | 0.082006 / 0.004250 (0.077756) | 0.079394 / 0.037052 (0.042342) | 0.450982 / 0.258489 (0.192493) | 0.502659 / 0.293841 (0.208818) | 0.049741 / 0.128546 (-0.078806) | 0.014482 / 0.075646 (-0.061164) | 0.362661 / 0.419271 (-0.056611) | 0.068225 / 0.043533 (0.024692) | 0.456219 / 0.255139 (0.201080) | 0.483919 / 0.283200 (0.200719) | 0.044490 / 0.141683 (-0.097193) | 1.809420 / 1.452155 (0.357265) | 1.908859 / 1.492716 (0.416143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267350 / 0.018006 (0.249344) | 0.600403 / 0.000490 (0.599913) | 0.003665 / 0.000200 (0.003465) | 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) | 0.032499 / 0.037411 (-0.004912) | 0.104829 / 0.014526 (0.090303) | 0.115809 / 0.176557 (-0.060747) | 0.191561 / 0.737135 (-0.545574) | 0.113454 / 0.296338 (-0.182885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599165 / 0.215209 (0.383956) | 5.802947 / 2.077655 (3.725292) | 2.477330 / 1.504120 (0.973210) | 2.231147 / 1.541195 (0.689952) | 2.365688 / 1.468490 (0.897197) | 0.853912 / 4.584777 (-3.730865) | 5.529472 / 3.745712 (1.783760) | 6.145286 / 5.269862 (0.875424) | 3.415871 / 4.565676 (-1.149805) | 0.099889 / 0.424275 (-0.324386) | 0.008933 / 0.007607 (0.001325) | 0.704643 / 0.226044 (0.478598) | 7.178101 / 2.268929 (4.909173) | 3.367120 / 55.444624 (-52.077504) | 2.795177 / 6.876477 (-4.081300) | 2.796798 / 2.142072 (0.654726) | 1.039097 / 4.805227 (-3.766130) | 0.232784 / 6.500664 (-6.267881) | 0.083608 / 0.075469 (0.008138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646827 / 1.841788 (-0.194961) | 25.003419 / 8.074308 (16.929111) | 22.165746 / 10.191392 (11.974354) | 0.246179 / 0.680424 (-0.434245) | 0.029304 / 0.534201 (-0.504897) | 0.500767 / 0.579283 (-0.078516) | 0.606501 / 0.434364 (0.172137) | 0.564092 / 0.540337 (0.023755) | 0.857568 / 1.386936 (-0.529368) |\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) | 0.009206 / 0.011353 (-0.002146) | 0.005084 / 0.011008 (-0.005925) | 0.081402 / 0.038508 (0.042894) | 0.088028 / 0.023109 (0.064919) | 0.539509 / 0.275898 (0.263611) | 0.590759 / 0.323480 (0.267280) | 0.006527 / 0.007986 (-0.001459) | 0.004375 / 0.004328 (0.000047) | 0.082327 / 0.004250 (0.078076) | 0.065442 / 0.037052 (0.028390) | 0.548254 / 0.258489 (0.289765) | 0.598388 / 0.293841 (0.304547) | 0.049409 / 0.128546 (-0.079137) | 0.014366 / 0.075646 (-0.061280) | 0.094568 / 0.419271 (-0.324703) | 0.063685 / 0.043533 (0.020152) | 0.545359 / 0.255139 (0.290220) | 0.573358 / 0.283200 (0.290159) | 0.036864 / 0.141683 (-0.104819) | 1.817985 / 1.452155 (0.365830) | 1.925188 / 1.492716 (0.432472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303205 / 0.018006 (0.285199) | 0.620981 / 0.000490 (0.620491) | 0.004910 / 0.000200 (0.004710) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033791 / 0.037411 (-0.003620) | 0.114974 / 0.014526 (0.100448) | 0.117682 / 0.176557 (-0.058875) | 0.177188 / 0.737135 (-0.559947) | 0.126425 / 0.296338 (-0.169914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636932 / 0.215209 (0.421723) | 6.289054 / 2.077655 (4.211399) | 2.920772 / 1.504120 (1.416652) | 2.672080 / 1.541195 (1.130885) | 2.712271 / 1.468490 (1.243781) | 0.889305 / 4.584777 (-3.695472) | 5.536018 / 3.745712 (1.790306) | 4.687564 / 5.269862 (-0.582298) | 3.204239 / 4.565676 (-1.361437) | 0.095546 / 0.424275 (-0.328729) | 0.008838 / 0.007607 (0.001231) | 0.714584 / 0.226044 (0.488540) | 7.482663 / 2.268929 (5.213735) | 3.621392 / 55.444624 (-51.823232) | 2.987777 / 6.876477 (-3.888700) | 3.312636 / 2.142072 (1.170564) | 1.033721 / 4.805227 (-3.771506) | 0.206292 / 6.500664 (-6.294372) | 0.079423 / 0.075469 (0.003953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798645 / 1.841788 (-0.043143) | 25.544329 / 8.074308 (17.470021) | 23.041318 / 10.191392 (12.849926) | 0.259067 / 0.680424 (-0.421357) | 0.029839 / 0.534201 (-0.504362) | 0.495583 / 0.579283 (-0.083700) | 0.598755 / 0.434364 (0.164391) | 0.574864 / 0.540337 (0.034527) | 0.831160 / 1.386936 (-0.555776) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n" ]

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[ "Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset(...)\r\nformat = dataset.format\r\ndataset = dataset.with_format(\"arrow\")\r\ndataset = dataset.map(embed_table_storage, batched=True)\r\ndataset = dataset.with_format(**format)\r\n# push_to_hub(...)\r\n```\r\n\r\n(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this \"embed\" step)", "Hi, thanks, this solution saves some time.\r\nBut can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?\r\n\r\nEdit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint.", "I've updated the `ruff`'s pre-commit version as part of https://github.com/huggingface/datasets/pull/6434, so feel free to close this PR." ]

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