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https://api.github.com/repos/huggingface/datasets/issues/6528
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6528
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6528). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004875 / 0.011353 (-0.006478) | 0.003501 / 0.011008 (-0.007507) | 0.062604 / 0.038508 (0.024096) | 0.031916 / 0.023109 (0.008806) | 0.256138 / 0.275898 (-0.019760) | 0.278514 / 0.323480 (-0.044966) | 0.002917 / 0.007986 (-0.005069) | 0.002636 / 0.004328 (-0.001693) | 0.049154 / 0.004250 (0.044904) | 0.041985 / 0.037052 (0.004933) | 0.256857 / 0.258489 (-0.001632) | 0.282628 / 0.293841 (-0.011213) | 0.027506 / 0.128546 (-0.101041) | 0.010736 / 0.075646 (-0.064910) | 0.207268 / 0.419271 (-0.212003) | 0.035312 / 0.043533 (-0.008221) | 0.259274 / 0.255139 (0.004135) | 0.281463 / 0.283200 (-0.001737) | 0.019905 / 0.141683 (-0.121778) | 1.108719 / 1.452155 (-0.343435) | 1.177871 / 1.492716 (-0.314845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004435 / 0.018006 (-0.013571) | 0.310643 / 0.000490 (0.310153) | 0.000243 / 0.000200 (0.000043) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018013 / 0.037411 (-0.019398) | 0.060702 / 0.014526 (0.046176) | 0.073243 / 0.176557 (-0.103314) | 0.119523 / 0.737135 (-0.617613) | 0.074204 / 0.296338 (-0.222134) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281075 / 0.215209 (0.065866) | 2.722154 / 2.077655 (0.644499) | 1.441052 / 1.504120 (-0.063068) | 1.305940 / 1.541195 (-0.235255) | 1.356752 / 1.468490 (-0.111738) | 0.570399 / 4.584777 (-4.014378) | 2.329158 / 3.745712 (-1.416554) | 2.749093 / 5.269862 (-2.520768) | 1.717752 / 4.565676 (-2.847925) | 0.063228 / 0.424275 (-0.361047) | 0.004981 / 0.007607 (-0.002626) | 0.330601 / 0.226044 (0.104557) | 3.300987 / 2.268929 (1.032059) | 1.778673 / 55.444624 (-53.665951) | 1.507841 / 6.876477 (-5.368636) | 1.520454 / 2.142072 (-0.621619) | 0.650816 / 4.805227 (-4.154412) | 0.118606 / 6.500664 (-6.382058) | 0.042199 / 0.075469 (-0.033271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.919668 / 1.841788 (-0.922119) | 11.293437 / 8.074308 (3.219129) | 9.928525 / 10.191392 (-0.262867) | 0.127142 / 0.680424 (-0.553282) | 0.013470 / 0.534201 (-0.520731) | 0.284648 / 0.579283 (-0.294636) | 0.264942 / 0.434364 (-0.169422) | 0.321866 / 0.540337 (-0.218471) | 0.414513 / 1.386936 (-0.972423) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005052 / 0.011353 (-0.006301) | 0.003204 / 0.011008 (-0.007804) | 0.051102 / 0.038508 (0.012594) | 0.032105 / 0.023109 (0.008996) | 0.273923 / 0.275898 (-0.001976) | 0.297031 / 0.323480 (-0.026449) | 0.004002 / 0.007986 (-0.003984) | 0.002636 / 0.004328 (-0.001693) | 0.047696 / 0.004250 (0.043445) | 0.044086 / 0.037052 (0.007034) | 0.277779 / 0.258489 (0.019289) | 0.306678 / 0.293841 (0.012837) | 0.028557 / 0.128546 (-0.099989) | 0.010631 / 0.075646 (-0.065015) | 0.056419 / 0.419271 (-0.362852) | 0.054285 / 0.043533 (0.010752) | 0.276506 / 0.255139 (0.021367) | 0.296315 / 0.283200 (0.013116) | 0.018642 / 0.141683 (-0.123040) | 1.146926 / 1.452155 (-0.305229) | 1.257625 / 1.492716 (-0.235092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094231 / 0.018006 (0.076225) | 0.302805 / 0.000490 (0.302315) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022510 / 0.037411 (-0.014901) | 0.076092 / 0.014526 (0.061566) | 0.090642 / 0.176557 (-0.085915) | 0.127016 / 0.737135 (-0.610120) | 0.089169 / 0.296338 (-0.207169) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290812 / 0.215209 (0.075603) | 2.858528 / 2.077655 (0.780873) | 1.577555 / 1.504120 (0.073436) | 1.447810 / 1.541195 (-0.093384) | 1.447546 / 1.468490 (-0.020944) | 0.559118 / 4.584777 (-4.025659) | 2.408930 / 3.745712 (-1.336782) | 2.733761 / 5.269862 (-2.536101) | 1.700107 / 4.565676 (-2.865570) | 0.062447 / 0.424275 (-0.361828) | 0.004999 / 0.007607 (-0.002608) | 0.340207 / 0.226044 (0.114162) | 3.344131 / 2.268929 (1.075203) | 1.902289 / 55.444624 (-53.542335) | 1.628226 / 6.876477 (-5.248251) | 1.629435 / 2.142072 (-0.512637) | 0.625011 / 4.805227 (-4.180216) | 0.119929 / 6.500664 (-6.380735) | 0.041097 / 0.075469 (-0.034372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977461 / 1.841788 (-0.864327) | 12.303189 / 8.074308 (4.228881) | 11.008743 / 10.191392 (0.817351) | 0.128578 / 0.680424 (-0.551845) | 0.015305 / 0.534201 (-0.518896) | 0.286468 / 0.579283 (-0.292816) | 0.275824 / 0.434364 (-0.158540) | 0.321487 / 0.540337 (-0.218851) | 0.420591 / 1.386936 (-0.966345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54 \"CML watermark\")\n" ]
2023-12-22T14:23:18
2023-12-22T14:31:42
2023-12-22 14:25:34+00:00
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6527). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004870 / 0.011353 (-0.006483) | 0.003606 / 0.011008 (-0.007402) | 0.062719 / 0.038508 (0.024211) | 0.031785 / 0.023109 (0.008676) | 0.238809 / 0.275898 (-0.037089) | 0.263000 / 0.323480 (-0.060480) | 0.002844 / 0.007986 (-0.005142) | 0.002698 / 0.004328 (-0.001631) | 0.048070 / 0.004250 (0.043819) | 0.042333 / 0.037052 (0.005280) | 0.243032 / 0.258489 (-0.015457) | 0.273197 / 0.293841 (-0.020644) | 0.027498 / 0.128546 (-0.101048) | 0.010592 / 0.075646 (-0.065055) | 0.204770 / 0.419271 (-0.214502) | 0.034837 / 0.043533 (-0.008696) | 0.242518 / 0.255139 (-0.012621) | 0.267461 / 0.283200 (-0.015739) | 0.018479 / 0.141683 (-0.123204) | 1.105444 / 1.452155 (-0.346710) | 1.163659 / 1.492716 (-0.329057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004717 / 0.018006 (-0.013289) | 0.303338 / 0.000490 (0.302849) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018298 / 0.037411 (-0.019113) | 0.061225 / 0.014526 (0.046699) | 0.073514 / 0.176557 (-0.103043) | 0.120230 / 0.737135 (-0.616905) | 0.076195 / 0.296338 (-0.220144) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284731 / 0.215209 (0.069522) | 2.773463 / 2.077655 (0.695809) | 1.498239 / 1.504120 (-0.005881) | 1.372143 / 1.541195 (-0.169052) | 1.448949 / 1.468490 (-0.019542) | 0.572516 / 4.584777 (-4.012261) | 2.404041 / 3.745712 (-1.341671) | 2.763047 / 5.269862 (-2.506814) | 1.722419 / 4.565676 (-2.843257) | 0.063104 / 0.424275 (-0.361172) | 0.004989 / 0.007607 (-0.002618) | 0.341864 / 0.226044 (0.115820) | 3.391635 / 2.268929 (1.122707) | 1.872694 / 55.444624 (-53.571931) | 1.594490 / 6.876477 (-5.281987) | 1.596940 / 2.142072 (-0.545132) | 0.645265 / 4.805227 (-4.159962) | 0.117408 / 6.500664 (-6.383256) | 0.042405 / 0.075469 (-0.033064) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963207 / 1.841788 (-0.878580) | 11.676551 / 8.074308 (3.602243) | 10.194287 / 10.191392 (0.002895) | 0.130329 / 0.680424 (-0.550094) | 0.015381 / 0.534201 (-0.518820) | 0.288848 / 0.579283 (-0.290435) | 0.264781 / 0.434364 (-0.169583) | 0.321212 / 0.540337 (-0.219126) | 0.418308 / 1.386936 (-0.968628) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005533 / 0.011353 (-0.005819) | 0.003733 / 0.011008 (-0.007276) | 0.048877 / 0.038508 (0.010369) | 0.030263 / 0.023109 (0.007154) | 0.281161 / 0.275898 (0.005263) | 0.302971 / 0.323480 (-0.020509) | 0.003943 / 0.007986 (-0.004043) | 0.002717 / 0.004328 (-0.001612) | 0.047845 / 0.004250 (0.043594) | 0.045809 / 0.037052 (0.008757) | 0.283337 / 0.258489 (0.024848) | 0.312914 / 0.293841 (0.019073) | 0.029074 / 0.128546 (-0.099472) | 0.010775 / 0.075646 (-0.064871) | 0.057461 / 0.419271 (-0.361810) | 0.053756 / 0.043533 (0.010223) | 0.281809 / 0.255139 (0.026670) | 0.298339 / 0.283200 (0.015139) | 0.019270 / 0.141683 (-0.122413) | 1.117575 / 1.452155 (-0.334580) | 1.191703 / 1.492716 (-0.301013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093513 / 0.018006 (0.075507) | 0.301267 / 0.000490 (0.300777) | 0.000211 / 0.000200 (0.000012) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022278 / 0.037411 (-0.015133) | 0.076805 / 0.014526 (0.062279) | 0.088820 / 0.176557 (-0.087736) | 0.127903 / 0.737135 (-0.609233) | 0.092988 / 0.296338 (-0.203350) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297787 / 0.215209 (0.082578) | 2.899652 / 2.077655 (0.821997) | 1.598830 / 1.504120 (0.094710) | 1.469398 / 1.541195 (-0.071797) | 1.511099 / 1.468490 (0.042609) | 0.559785 / 4.584777 (-4.024992) | 2.426448 / 3.745712 (-1.319264) | 2.798811 / 5.269862 (-2.471051) | 1.737790 / 4.565676 (-2.827887) | 0.062219 / 0.424275 (-0.362056) | 0.005120 / 0.007607 (-0.002487) | 0.351051 / 0.226044 (0.125007) | 3.492063 / 2.268929 (1.223134) | 1.965674 / 55.444624 (-53.478950) | 1.672874 / 6.876477 (-5.203603) | 1.709700 / 2.142072 (-0.432373) | 0.639347 / 4.805227 (-4.165880) | 0.126383 / 6.500664 (-6.374281) | 0.042731 / 0.075469 (-0.032738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968619 / 1.841788 (-0.873168) | 12.671030 / 8.074308 (4.596722) | 11.125347 / 10.191392 (0.933955) | 0.142983 / 0.680424 (-0.537441) | 0.015726 / 0.534201 (-0.518475) | 0.288610 / 0.579283 (-0.290673) | 0.276473 / 0.434364 (-0.157891) | 0.326590 / 0.540337 (-0.213748) | 0.423832 / 1.386936 (-0.963104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a85fb52fc8ddb41307e61cbf6a5189f3bba27829 \"CML watermark\")\n" ]
2023-12-22T13:59:56
2023-12-22T14:24:12
2023-12-22 14:17:55+00:00
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6526
Preserve order of configs and splits when using Parquet exports
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6526). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005101 / 0.011353 (-0.006252) | 0.003471 / 0.011008 (-0.007537) | 0.062293 / 0.038508 (0.023785) | 0.032650 / 0.023109 (0.009541) | 0.249241 / 0.275898 (-0.026657) | 0.277079 / 0.323480 (-0.046400) | 0.002971 / 0.007986 (-0.005015) | 0.002637 / 0.004328 (-0.001691) | 0.048415 / 0.004250 (0.044165) | 0.042832 / 0.037052 (0.005779) | 0.247840 / 0.258489 (-0.010649) | 0.283994 / 0.293841 (-0.009847) | 0.027764 / 0.128546 (-0.100782) | 0.010544 / 0.075646 (-0.065102) | 0.208810 / 0.419271 (-0.210462) | 0.035744 / 0.043533 (-0.007789) | 0.252811 / 0.255139 (-0.002328) | 0.276163 / 0.283200 (-0.007036) | 0.018581 / 0.141683 (-0.123102) | 1.130043 / 1.452155 (-0.322112) | 1.194298 / 1.492716 (-0.298418) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004488 / 0.018006 (-0.013518) | 0.302072 / 0.000490 (0.301582) | 0.000211 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017799 / 0.037411 (-0.019613) | 0.061146 / 0.014526 (0.046620) | 0.081796 / 0.176557 (-0.094761) | 0.120407 / 0.737135 (-0.616729) | 0.075211 / 0.296338 (-0.221127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295349 / 0.215209 (0.080140) | 2.953511 / 2.077655 (0.875857) | 1.495332 / 1.504120 (-0.008788) | 1.364144 / 1.541195 (-0.177051) | 1.429562 / 1.468490 (-0.038928) | 0.574325 / 4.584777 (-4.010452) | 2.384352 / 3.745712 (-1.361360) | 2.843625 / 5.269862 (-2.426236) | 1.806802 / 4.565676 (-2.758875) | 0.065076 / 0.424275 (-0.359199) | 0.004970 / 0.007607 (-0.002638) | 0.339935 / 0.226044 (0.113891) | 3.375103 / 2.268929 (1.106175) | 1.822921 / 55.444624 (-53.621703) | 1.546126 / 6.876477 (-5.330350) | 1.573630 / 2.142072 (-0.568442) | 0.655081 / 4.805227 (-4.150146) | 0.122446 / 6.500664 (-6.378218) | 0.042220 / 0.075469 (-0.033249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942127 / 1.841788 (-0.899661) | 11.470401 / 8.074308 (3.396093) | 10.025961 / 10.191392 (-0.165431) | 0.129087 / 0.680424 (-0.551337) | 0.014141 / 0.534201 (-0.520060) | 0.285470 / 0.579283 (-0.293813) | 0.266755 / 0.434364 (-0.167608) | 0.323391 / 0.540337 (-0.216947) | 0.427645 / 1.386936 (-0.959291) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005578 / 0.011353 (-0.005775) | 0.003734 / 0.011008 (-0.007274) | 0.049200 / 0.038508 (0.010692) | 0.030981 / 0.023109 (0.007872) | 0.281195 / 0.275898 (0.005297) | 0.309950 / 0.323480 (-0.013530) | 0.004046 / 0.007986 (-0.003939) | 0.002709 / 0.004328 (-0.001620) | 0.048505 / 0.004250 (0.044254) | 0.046245 / 0.037052 (0.009193) | 0.280130 / 0.258489 (0.021641) | 0.313739 / 0.293841 (0.019898) | 0.029828 / 0.128546 (-0.098718) | 0.011152 / 0.075646 (-0.064495) | 0.057753 / 0.419271 (-0.361518) | 0.055112 / 0.043533 (0.011580) | 0.281861 / 0.255139 (0.026722) | 0.304402 / 0.283200 (0.021203) | 0.019931 / 0.141683 (-0.121752) | 1.150585 / 1.452155 (-0.301570) | 1.217850 / 1.492716 (-0.274866) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091552 / 0.018006 (0.073546) | 0.301772 / 0.000490 (0.301282) | 0.000225 / 0.000200 (0.000025) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023189 / 0.037411 (-0.014223) | 0.078741 / 0.014526 (0.064216) | 0.092320 / 0.176557 (-0.084236) | 0.129636 / 0.737135 (-0.607500) | 0.091673 / 0.296338 (-0.204665) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298542 / 0.215209 (0.083333) | 2.899358 / 2.077655 (0.821703) | 1.673896 / 1.504120 (0.169776) | 1.489518 / 1.541195 (-0.051677) | 1.542853 / 1.468490 (0.074363) | 0.559843 / 4.584777 (-4.024934) | 2.422101 / 3.745712 (-1.323611) | 2.844592 / 5.269862 (-2.425270) | 1.794527 / 4.565676 (-2.771150) | 0.064615 / 0.424275 (-0.359660) | 0.005078 / 0.007607 (-0.002530) | 0.355112 / 0.226044 (0.129068) | 3.462129 / 2.268929 (1.193200) | 1.975393 / 55.444624 (-53.469231) | 1.706513 / 6.876477 (-5.169963) | 1.716954 / 2.142072 (-0.425118) | 0.642094 / 4.805227 (-4.163133) | 0.119215 / 6.500664 (-6.381449) | 0.041941 / 0.075469 (-0.033528) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986774 / 1.841788 (-0.855014) | 12.702049 / 8.074308 (4.627741) | 11.727663 / 10.191392 (1.536271) | 0.135008 / 0.680424 (-0.545416) | 0.016055 / 0.534201 (-0.518146) | 0.293564 / 0.579283 (-0.285719) | 0.284884 / 0.434364 (-0.149480) | 0.332524 / 0.540337 (-0.207814) | 0.425392 / 1.386936 (-0.961544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b5fc585fcaf77b92839e82d0ce2c2fbf0d9ea95 \"CML watermark\")\n" ]
2023-12-22T10:35:56
2023-12-22T11:42:22
2023-12-22 11:36:14+00:00
MEMBER
nan
Preserve order of configs and splits, as defined in dataset infos. Fix #6521.
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "closing in favor of other ideas that would not involve any typing" ]
2023-12-21T22:13:27
2024-01-11T06:34:51
2023-12-21 22:39:27+00:00
MEMBER
nan
see [internal discussion](https://huggingface.slack.com/archives/C02EK7C3SHW/p1703097195609209) Draft to get some feedback on a possible `BBox` feature type that can be used to get object detection bounding boxes data in one format or another. ```python >>> from datasets import load_dataset, BBox >>> ds = load_dataset("svhn", "full_numbers", split="train") >>> ds[0] { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x126409BE0>, 'digits': {'bbox': [[38, 1, 21, 40], [57, 3, 16, 40]], 'label': [4, 6]} } >>> ds = ds.rename_column("digits", "annotations").cast_column("annotations", BBox(format="coco")) >>> ds[0] { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x147730070>, 'annotations': [{'bbox': [38, 1, 21, 40], 'category_id': 4}, {'bbox': [57, 3, 16, 40], 'category_id': 6}] } ``` note that it's a type for a list of bounding boxes, not just one - which would be needed to switch from a format to another using type casting.
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I_kwDODunzps56X3VX
6524
Streaming the Pile: Missing Files
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[ "Hello @FelixLabelle,\r\n\r\nAs you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15\r\n\r\nThe data has been taken down due to reported copyright infringement.\r\n\r\nFeel free to continue the discussion there." ]
2023-12-21T21:25:09
2023-12-22T09:17:05
2023-12-22 09:17:05+00:00
NONE
nan
### Describe the bug The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved. ### Steps to reproduce the bug To reproduce run the following code: ``` from datasets import load_dataset dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True) next(iter(dataset)) ``` I get the following error: `FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst` ### Expected behavior Return the data in a stream. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.3
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fix tests
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6523). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005160 / 0.011353 (-0.006192) | 0.003962 / 0.011008 (-0.007046) | 0.064952 / 0.038508 (0.026444) | 0.053291 / 0.023109 (0.030182) | 0.237182 / 0.275898 (-0.038716) | 0.263855 / 0.323480 (-0.059625) | 0.004157 / 0.007986 (-0.003829) | 0.002901 / 0.004328 (-0.001428) | 0.050679 / 0.004250 (0.046428) | 0.044885 / 0.037052 (0.007832) | 0.243806 / 0.258489 (-0.014683) | 0.273828 / 0.293841 (-0.020013) | 0.028681 / 0.128546 (-0.099866) | 0.011086 / 0.075646 (-0.064560) | 0.211987 / 0.419271 (-0.207285) | 0.035881 / 0.043533 (-0.007652) | 0.249618 / 0.255139 (-0.005521) | 0.262880 / 0.283200 (-0.020319) | 0.017788 / 0.141683 (-0.123895) | 1.209060 / 1.452155 (-0.243094) | 1.272143 / 1.492716 (-0.220574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004594 / 0.018006 (-0.013412) | 0.305188 / 0.000490 (0.304698) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019526 / 0.037411 (-0.017886) | 0.062280 / 0.014526 (0.047754) | 0.074983 / 0.176557 (-0.101573) | 0.123466 / 0.737135 (-0.613670) | 0.076240 / 0.296338 (-0.220099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276001 / 0.215209 (0.060792) | 2.689614 / 2.077655 (0.611959) | 1.441092 / 1.504120 (-0.063028) | 1.319775 / 1.541195 (-0.221419) | 1.386904 / 1.468490 (-0.081587) | 0.561388 / 4.584777 (-4.023389) | 2.386718 / 3.745712 (-1.358994) | 2.813959 / 5.269862 (-2.455903) | 1.727447 / 4.565676 (-2.838230) | 0.061965 / 0.424275 (-0.362310) | 0.004977 / 0.007607 (-0.002630) | 0.335077 / 0.226044 (0.109032) | 3.313860 / 2.268929 (1.044932) | 1.814018 / 55.444624 (-53.630606) | 1.542840 / 6.876477 (-5.333637) | 1.586887 / 2.142072 (-0.555185) | 0.643225 / 4.805227 (-4.162002) | 0.117834 / 6.500664 (-6.382830) | 0.044024 / 0.075469 (-0.031445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952804 / 1.841788 (-0.888984) | 12.447378 / 8.074308 (4.373070) | 11.281734 / 10.191392 (1.090342) | 0.143407 / 0.680424 (-0.537017) | 0.014749 / 0.534201 (-0.519452) | 0.289298 / 0.579283 (-0.289985) | 0.268217 / 0.434364 (-0.166146) | 0.327995 / 0.540337 (-0.212343) | 0.430302 / 1.386936 (-0.956634) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005683 / 0.011353 (-0.005670) | 0.003813 / 0.011008 (-0.007195) | 0.048943 / 0.038508 (0.010435) | 0.060730 / 0.023109 (0.037621) | 0.266925 / 0.275898 (-0.008973) | 0.292553 / 0.323480 (-0.030927) | 0.004236 / 0.007986 (-0.003750) | 0.002790 / 0.004328 (-0.001538) | 0.048962 / 0.004250 (0.044711) | 0.040354 / 0.037052 (0.003302) | 0.266353 / 0.258489 (0.007864) | 0.298397 / 0.293841 (0.004556) | 0.029977 / 0.128546 (-0.098570) | 0.010788 / 0.075646 (-0.064858) | 0.057529 / 0.419271 (-0.361743) | 0.032896 / 0.043533 (-0.010636) | 0.266696 / 0.255139 (0.011557) | 0.283422 / 0.283200 (0.000223) | 0.020939 / 0.141683 (-0.120744) | 1.169867 / 1.452155 (-0.282287) | 1.213586 / 1.492716 (-0.279130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097035 / 0.018006 (0.079029) | 0.306968 / 0.000490 (0.306478) | 0.000234 / 0.000200 (0.000034) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023343 / 0.037411 (-0.014068) | 0.078238 / 0.014526 (0.063712) | 0.091083 / 0.176557 (-0.085474) | 0.131487 / 0.737135 (-0.605649) | 0.092614 / 0.296338 (-0.203724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294454 / 0.215209 (0.079245) | 2.881053 / 2.077655 (0.803398) | 1.623934 / 1.504120 (0.119814) | 1.509001 / 1.541195 (-0.032194) | 1.567541 / 1.468490 (0.099051) | 0.574326 / 4.584777 (-4.010451) | 2.476826 / 3.745712 (-1.268886) | 2.826183 / 5.269862 (-2.443678) | 1.771949 / 4.565676 (-2.793727) | 0.063663 / 0.424275 (-0.360613) | 0.005039 / 0.007607 (-0.002568) | 0.354861 / 0.226044 (0.128816) | 3.397655 / 2.268929 (1.128727) | 1.961958 / 55.444624 (-53.482666) | 1.694795 / 6.876477 (-5.181682) | 1.719459 / 2.142072 (-0.422614) | 0.654512 / 4.805227 (-4.150715) | 0.119285 / 6.500664 (-6.381379) | 0.042146 / 0.075469 (-0.033323) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982187 / 1.841788 (-0.859601) | 12.944627 / 8.074308 (4.870319) | 11.370381 / 10.191392 (1.178989) | 0.142759 / 0.680424 (-0.537665) | 0.016319 / 0.534201 (-0.517882) | 0.291339 / 0.579283 (-0.287944) | 0.276842 / 0.434364 (-0.157522) | 0.324285 / 0.540337 (-0.216052) | 0.426234 / 1.386936 (-0.960702) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e1b82eaa75d2c610e59b463a67d685ec858c0838 \"CML watermark\")\n" ]
2023-12-21T15:36:21
2023-12-21T15:56:54
2023-12-21 15:50:38+00:00
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2052332528
I_kwDODunzps56VBvw
6522
Loading HF Hub Dataset (private org repo) fails to load all features
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2023-12-21T12:26:35
2023-12-21T13:24:31
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### Describe the bug When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default? ### Steps to reproduce the bug Pushing the data. `data_concat` is a `list` of `dict`s. ```python for datum in data_concat: datum_tags = {d["key"]: d["value"] for d in datum["tags"]} split_fraction = # some logic that generates a train/test split number if split_faction < test_fraction: data_test.append(datum) else: data_train.append(datum) dataset = DatasetDict( { "train": Dataset.from_list(data_train), "test": Dataset.from_list(data_test), "full": Dataset.from_list(data_concat), }, ) dataset_shuffled = dataset.shuffle(seed=shuffle_seed) dataset_shuffled.push_to_hub( repo_id=hf_repo_id, private=True, config_name=m, revision=revision, token=hf_token, ) ``` Loading it later: ```python dataset = datasets.load_dataset( path=hf_repo_id, name=name, token=hf_token, ) ``` Produces: ``` DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) }) ``` ### Expected behavior The expected result is below: ``` DatasetDict({ train: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) }) ``` My workaround is as follows: ```python dsinfo = datasets.get_dataset_config_info( path=data_files, config_name=data_config, token=hf_token, ) allfeatures = dsinfo.features.copy() if "tags" not in allfeatures: allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}] dataset = datasets.load_dataset( path=data_files, name=data_config, features=allfeatures, token=hf_token, ) ``` Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`: ``` ValueError: Couldn't cast tags: list<element: struct<key: string, value: string>> child 0, element: struct<key: string, value: string> child 0, key: string child 1, value: string input: <obfuscated> output: <obfuscated> -- schema metadata -- huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532 to {'input': <obfuscated>, 'output': <obfuscated> because column names don't match ``` Traceback for this: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset builder_instance.download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare self._download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info - `datasets` version: 2.15.0 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0
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I_kwDODunzps56Uomi
6521
The order of the splits is not preserved
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[ "After investigation, I think the issue was introduced by the use of the Parquet export:\r\n- #6448\r\n\r\nI am proposing a fix.\r\n\r\nCC: @lhoestq " ]
2023-12-21T11:17:27
2023-12-22T11:36:15
2023-12-22 11:36:15+00:00
MEMBER
nan
We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order. Check: In branch "main" ```python In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA") In [10]: dataset Out[10]: DatasetDict({ test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` Before (2.15.0) it was: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` See issues: - https://huggingface.co/datasets/adversarial_qa/discussions/3 - https://huggingface.co/datasets/beans/discussions/4 This is a regression because it was previously fixed. See: - #6196 - #5728
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6520
Support commit_description parameter in push_to_hub
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6520). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005484 / 0.011353 (-0.005869) | 0.003537 / 0.011008 (-0.007471) | 0.062631 / 0.038508 (0.024123) | 0.048037 / 0.023109 (0.024927) | 0.240342 / 0.275898 (-0.035556) | 0.268103 / 0.323480 (-0.055377) | 0.002927 / 0.007986 (-0.005059) | 0.002609 / 0.004328 (-0.001719) | 0.048112 / 0.004250 (0.043862) | 0.046111 / 0.037052 (0.009058) | 0.249249 / 0.258489 (-0.009240) | 0.277723 / 0.293841 (-0.016118) | 0.028374 / 0.128546 (-0.100172) | 0.010900 / 0.075646 (-0.064746) | 0.206252 / 0.419271 (-0.213019) | 0.035262 / 0.043533 (-0.008271) | 0.247438 / 0.255139 (-0.007701) | 0.270003 / 0.283200 (-0.013197) | 0.019157 / 0.141683 (-0.122526) | 1.116833 / 1.452155 (-0.335322) | 1.174495 / 1.492716 (-0.318221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092490 / 0.018006 (0.074484) | 0.302794 / 0.000490 (0.302304) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018669 / 0.037411 (-0.018743) | 0.061902 / 0.014526 (0.047376) | 0.073612 / 0.176557 (-0.102945) | 0.121196 / 0.737135 (-0.615940) | 0.075960 / 0.296338 (-0.220378) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286983 / 0.215209 (0.071774) | 2.836819 / 2.077655 (0.759165) | 1.506635 / 1.504120 (0.002515) | 1.387134 / 1.541195 (-0.154061) | 1.442310 / 1.468490 (-0.026180) | 0.571281 / 4.584777 (-4.013496) | 2.440220 / 3.745712 (-1.305492) | 2.775306 / 5.269862 (-2.494555) | 1.727047 / 4.565676 (-2.838630) | 0.064955 / 0.424275 (-0.359320) | 0.004982 / 0.007607 (-0.002625) | 0.343153 / 0.226044 (0.117108) | 3.388745 / 2.268929 (1.119817) | 1.878983 / 55.444624 (-53.565641) | 1.592642 / 6.876477 (-5.283835) | 1.601037 / 2.142072 (-0.541035) | 0.636882 / 4.805227 (-4.168345) | 0.117804 / 6.500664 (-6.382861) | 0.042467 / 0.075469 (-0.033002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941534 / 1.841788 (-0.900254) | 12.093230 / 8.074308 (4.018922) | 10.590854 / 10.191392 (0.399462) | 0.136636 / 0.680424 (-0.543788) | 0.015244 / 0.534201 (-0.518957) | 0.300216 / 0.579283 (-0.279067) | 0.267622 / 0.434364 (-0.166742) | 0.337526 / 0.540337 (-0.202811) | 0.426856 / 1.386936 (-0.960080) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005282 / 0.011353 (-0.006071) | 0.003595 / 0.011008 (-0.007413) | 0.049237 / 0.038508 (0.010729) | 0.054057 / 0.023109 (0.030948) | 0.269781 / 0.275898 (-0.006117) | 0.293544 / 0.323480 (-0.029936) | 0.003991 / 0.007986 (-0.003995) | 0.002705 / 0.004328 (-0.001623) | 0.048755 / 0.004250 (0.044505) | 0.040425 / 0.037052 (0.003373) | 0.264753 / 0.258489 (0.006264) | 0.312773 / 0.293841 (0.018932) | 0.030011 / 0.128546 (-0.098535) | 0.010707 / 0.075646 (-0.064939) | 0.058164 / 0.419271 (-0.361107) | 0.033365 / 0.043533 (-0.010168) | 0.268854 / 0.255139 (0.013715) | 0.283618 / 0.283200 (0.000418) | 0.019571 / 0.141683 (-0.122111) | 1.114738 / 1.452155 (-0.337417) | 1.178990 / 1.492716 (-0.313726) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092183 / 0.018006 (0.074177) | 0.303797 / 0.000490 (0.303307) | 0.000218 / 0.000200 (0.000018) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023088 / 0.037411 (-0.014323) | 0.079813 / 0.014526 (0.065287) | 0.089593 / 0.176557 (-0.086964) | 0.128127 / 0.737135 (-0.609008) | 0.091578 / 0.296338 (-0.204761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300153 / 0.215209 (0.084944) | 2.919532 / 2.077655 (0.841877) | 1.587870 / 1.504120 (0.083750) | 1.459031 / 1.541195 (-0.082164) | 1.483305 / 1.468490 (0.014815) | 0.555865 / 4.584777 (-4.028912) | 2.388350 / 3.745712 (-1.357362) | 2.817947 / 5.269862 (-2.451914) | 1.764446 / 4.565676 (-2.801230) | 0.067142 / 0.424275 (-0.357133) | 0.005148 / 0.007607 (-0.002460) | 0.347998 / 0.226044 (0.121953) | 3.431208 / 2.268929 (1.162280) | 1.942175 / 55.444624 (-53.502450) | 1.676606 / 6.876477 (-5.199871) | 1.692431 / 2.142072 (-0.449641) | 0.645974 / 4.805227 (-4.159253) | 0.117729 / 6.500664 (-6.382935) | 0.041670 / 0.075469 (-0.033799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981554 / 1.841788 (-0.860234) | 12.671959 / 8.074308 (4.597650) | 11.230694 / 10.191392 (1.039302) | 0.132694 / 0.680424 (-0.547730) | 0.015694 / 0.534201 (-0.518507) | 0.290271 / 0.579283 (-0.289013) | 0.279358 / 0.434364 (-0.155006) | 0.326515 / 0.540337 (-0.213823) | 0.421755 / 1.386936 (-0.965181) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b2147ac644596b66886f398012351641672ee54 \"CML watermark\")\n" ]
2023-12-21T09:36:11
2023-12-21T14:49:47
2023-12-21 14:43:35+00:00
MEMBER
nan
Support `commit_description` parameter in `push_to_hub`. CC: @Wauplin
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PR_kwDODunzps5ie4MA
6519
Support push_to_hub canonical datasets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6519). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "nice catch @albertvillanova ", "@huggingface/datasets this PR is ready for review.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005306 / 0.011353 (-0.006047) | 0.003454 / 0.011008 (-0.007555) | 0.062157 / 0.038508 (0.023649) | 0.051945 / 0.023109 (0.028835) | 0.241834 / 0.275898 (-0.034064) | 0.265590 / 0.323480 (-0.057890) | 0.003149 / 0.007986 (-0.004837) | 0.002695 / 0.004328 (-0.001633) | 0.049197 / 0.004250 (0.044947) | 0.045576 / 0.037052 (0.008524) | 0.242866 / 0.258489 (-0.015623) | 0.280963 / 0.293841 (-0.012878) | 0.028466 / 0.128546 (-0.100080) | 0.010670 / 0.075646 (-0.064976) | 0.206501 / 0.419271 (-0.212771) | 0.035314 / 0.043533 (-0.008219) | 0.240893 / 0.255139 (-0.014246) | 0.264762 / 0.283200 (-0.018438) | 0.019988 / 0.141683 (-0.121695) | 1.095222 / 1.452155 (-0.356933) | 1.144051 / 1.492716 (-0.348666) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098034 / 0.018006 (0.080028) | 0.308541 / 0.000490 (0.308051) | 0.000261 / 0.000200 (0.000061) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018646 / 0.037411 (-0.018766) | 0.062881 / 0.014526 (0.048355) | 0.074062 / 0.176557 (-0.102494) | 0.120860 / 0.737135 (-0.616276) | 0.075388 / 0.296338 (-0.220951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282974 / 0.215209 (0.067765) | 2.755589 / 2.077655 (0.677934) | 1.459536 / 1.504120 (-0.044584) | 1.364543 / 1.541195 (-0.176652) | 1.429860 / 1.468490 (-0.038630) | 0.573277 / 4.584777 (-4.011500) | 2.422983 / 3.745712 (-1.322730) | 3.257258 / 5.269862 (-2.012603) | 1.930053 / 4.565676 (-2.635623) | 0.067476 / 0.424275 (-0.356799) | 0.005612 / 0.007607 (-0.001995) | 0.351538 / 0.226044 (0.125494) | 3.380356 / 2.268929 (1.111427) | 1.837887 / 55.444624 (-53.606738) | 1.537994 / 6.876477 (-5.338483) | 1.623630 / 2.142072 (-0.518442) | 0.662652 / 4.805227 (-4.142576) | 0.127074 / 6.500664 (-6.373590) | 0.049311 / 0.075469 (-0.026158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.151273 / 1.841788 (-0.690515) | 12.766622 / 8.074308 (4.692314) | 10.967610 / 10.191392 (0.776218) | 0.131305 / 0.680424 (-0.549119) | 0.014227 / 0.534201 (-0.519974) | 0.292054 / 0.579283 (-0.287229) | 0.262737 / 0.434364 (-0.171627) | 0.334360 / 0.540337 (-0.205978) | 0.446711 / 1.386936 (-0.940225) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003508 / 0.011008 (-0.007500) | 0.049287 / 0.038508 (0.010779) | 0.052109 / 0.023109 (0.029000) | 0.271501 / 0.275898 (-0.004397) | 0.290959 / 0.323480 (-0.032521) | 0.004347 / 0.007986 (-0.003638) | 0.002659 / 0.004328 (-0.001669) | 0.048769 / 0.004250 (0.044518) | 0.039388 / 0.037052 (0.002336) | 0.272811 / 0.258489 (0.014322) | 0.305632 / 0.293841 (0.011791) | 0.028419 / 0.128546 (-0.100127) | 0.010617 / 0.075646 (-0.065029) | 0.057433 / 0.419271 (-0.361838) | 0.032383 / 0.043533 (-0.011149) | 0.266566 / 0.255139 (0.011427) | 0.290993 / 0.283200 (0.007794) | 0.019939 / 0.141683 (-0.121743) | 1.157623 / 1.452155 (-0.294532) | 1.183298 / 1.492716 (-0.309419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099074 / 0.018006 (0.081068) | 0.315282 / 0.000490 (0.314792) | 0.000235 / 0.000200 (0.000035) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022692 / 0.037411 (-0.014719) | 0.076455 / 0.014526 (0.061929) | 0.089094 / 0.176557 (-0.087462) | 0.126407 / 0.737135 (-0.610728) | 0.089588 / 0.296338 (-0.206750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.338853 / 0.215209 (0.123644) | 2.809843 / 2.077655 (0.732188) | 1.538262 / 1.504120 (0.034143) | 1.418290 / 1.541195 (-0.122905) | 1.435145 / 1.468490 (-0.033345) | 0.565763 / 4.584777 (-4.019014) | 2.491525 / 3.745712 (-1.254187) | 2.944879 / 5.269862 (-2.324983) | 1.835840 / 4.565676 (-2.729837) | 0.065101 / 0.424275 (-0.359174) | 0.005196 / 0.007607 (-0.002412) | 0.345291 / 0.226044 (0.119247) | 3.399658 / 2.268929 (1.130729) | 1.892321 / 55.444624 (-53.552303) | 1.608293 / 6.876477 (-5.268184) | 1.651188 / 2.142072 (-0.490884) | 0.647806 / 4.805227 (-4.157421) | 0.119318 / 6.500664 (-6.381346) | 0.043058 / 0.075469 (-0.032412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983956 / 1.841788 (-0.857831) | 13.516125 / 8.074308 (5.441817) | 11.712571 / 10.191392 (1.521179) | 0.134253 / 0.680424 (-0.546171) | 0.015844 / 0.534201 (-0.518357) | 0.292444 / 0.579283 (-0.286839) | 0.282182 / 0.434364 (-0.152182) | 0.329327 / 0.540337 (-0.211010) | 0.419960 / 1.386936 (-0.966976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a887ee78835573f5d80f9e414e8443b4caff3541 \"CML watermark\")\n" ]
2023-12-20T15:16:45
2023-12-21T14:48:20
2023-12-21 14:40:57+00:00
MEMBER
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Support `push_to_hub` canonical datasets. This is necessary in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Note that before this PR, the `repo_id` "dataset_name" was transformed to "user/dataset_name". This behavior was introduced by: - #6269
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6518
fix get_metadata_patterns function args error
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6518). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "hello!\r\n@albertvillanova \r\nThank you very much for your recognition。\r\nWhen can this PR be merged?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005205 / 0.011353 (-0.006148) | 0.003730 / 0.011008 (-0.007278) | 0.063195 / 0.038508 (0.024687) | 0.052329 / 0.023109 (0.029219) | 0.247299 / 0.275898 (-0.028599) | 0.269600 / 0.323480 (-0.053880) | 0.004801 / 0.007986 (-0.003185) | 0.002728 / 0.004328 (-0.001600) | 0.049195 / 0.004250 (0.044944) | 0.044859 / 0.037052 (0.007807) | 0.253047 / 0.258489 (-0.005442) | 0.277253 / 0.293841 (-0.016588) | 0.028370 / 0.128546 (-0.100176) | 0.011095 / 0.075646 (-0.064551) | 0.211090 / 0.419271 (-0.208182) | 0.035944 / 0.043533 (-0.007589) | 0.252755 / 0.255139 (-0.002384) | 0.269466 / 0.283200 (-0.013733) | 0.017514 / 0.141683 (-0.124169) | 1.107815 / 1.452155 (-0.344339) | 1.154989 / 1.492716 (-0.337728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093925 / 0.018006 (0.075919) | 0.300923 / 0.000490 (0.300433) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018268 / 0.037411 (-0.019143) | 0.060508 / 0.014526 (0.045983) | 0.074564 / 0.176557 (-0.101992) | 0.121523 / 0.737135 (-0.615612) | 0.077394 / 0.296338 (-0.218945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275859 / 0.215209 (0.060650) | 2.707593 / 2.077655 (0.629938) | 1.419178 / 1.504120 (-0.084942) | 1.286737 / 1.541195 (-0.254458) | 1.350504 / 1.468490 (-0.117986) | 0.570461 / 4.584777 (-4.014316) | 2.400795 / 3.745712 (-1.344917) | 2.840876 / 5.269862 (-2.428986) | 1.724044 / 4.565676 (-2.841633) | 0.063819 / 0.424275 (-0.360456) | 0.004961 / 0.007607 (-0.002647) | 0.342537 / 0.226044 (0.116492) | 3.370942 / 2.268929 (1.102013) | 1.788659 / 55.444624 (-53.655966) | 1.501921 / 6.876477 (-5.374556) | 1.535352 / 2.142072 (-0.606721) | 0.651838 / 4.805227 (-4.153390) | 0.118979 / 6.500664 (-6.381685) | 0.047796 / 0.075469 (-0.027673) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949850 / 1.841788 (-0.891937) | 11.581988 / 8.074308 (3.507680) | 10.462837 / 10.191392 (0.271445) | 0.133298 / 0.680424 (-0.547125) | 0.015008 / 0.534201 (-0.519193) | 0.299265 / 0.579283 (-0.280018) | 0.268864 / 0.434364 (-0.165500) | 0.332888 / 0.540337 (-0.207450) | 0.420423 / 1.386936 (-0.966513) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003628 / 0.011008 (-0.007380) | 0.049545 / 0.038508 (0.011036) | 0.054095 / 0.023109 (0.030985) | 0.270679 / 0.275898 (-0.005219) | 0.295744 / 0.323480 (-0.027736) | 0.004131 / 0.007986 (-0.003855) | 0.002732 / 0.004328 (-0.001596) | 0.048714 / 0.004250 (0.044464) | 0.039916 / 0.037052 (0.002863) | 0.272354 / 0.258489 (0.013865) | 0.310553 / 0.293841 (0.016712) | 0.029525 / 0.128546 (-0.099021) | 0.011322 / 0.075646 (-0.064324) | 0.058007 / 0.419271 (-0.361265) | 0.032883 / 0.043533 (-0.010650) | 0.273609 / 0.255139 (0.018470) | 0.291780 / 0.283200 (0.008581) | 0.020538 / 0.141683 (-0.121145) | 1.118031 / 1.452155 (-0.334123) | 1.160777 / 1.492716 (-0.331940) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092966 / 0.018006 (0.074959) | 0.301432 / 0.000490 (0.300943) | 0.000225 / 0.000200 (0.000025) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022736 / 0.037411 (-0.014676) | 0.077655 / 0.014526 (0.063129) | 0.093386 / 0.176557 (-0.083171) | 0.129694 / 0.737135 (-0.607441) | 0.092790 / 0.296338 (-0.203548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299161 / 0.215209 (0.083952) | 2.923300 / 2.077655 (0.845645) | 1.629661 / 1.504120 (0.125541) | 1.510797 / 1.541195 (-0.030398) | 1.507269 / 1.468490 (0.038778) | 0.574346 / 4.584777 (-4.010431) | 2.454396 / 3.745712 (-1.291316) | 2.843402 / 5.269862 (-2.426460) | 1.774815 / 4.565676 (-2.790861) | 0.063601 / 0.424275 (-0.360674) | 0.004977 / 0.007607 (-0.002630) | 0.347693 / 0.226044 (0.121649) | 3.430054 / 2.268929 (1.161126) | 1.987308 / 55.444624 (-53.457316) | 1.682756 / 6.876477 (-5.193721) | 1.688463 / 2.142072 (-0.453609) | 0.646449 / 4.805227 (-4.158778) | 0.117860 / 6.500664 (-6.382804) | 0.041305 / 0.075469 (-0.034164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987355 / 1.841788 (-0.854433) | 12.398721 / 8.074308 (4.324412) | 11.070442 / 10.191392 (0.879050) | 0.134946 / 0.680424 (-0.545477) | 0.016172 / 0.534201 (-0.518029) | 0.293359 / 0.579283 (-0.285924) | 0.282271 / 0.434364 (-0.152093) | 0.331919 / 0.540337 (-0.208418) | 0.432137 / 1.386936 (-0.954799) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2246d3187222ef939aa8e69cd1aa476cf9526945 \"CML watermark\")\n" ]
2023-12-20T09:06:22
2023-12-21T15:14:17
2023-12-21 15:07:57+00:00
CONTRIBUTOR
nan
Bug get_metadata_patterns arg error https://github.com/huggingface/datasets/issues/6517
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6517
Bug get_metadata_patterns arg error
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2023-12-20T08:56:44
2023-12-22T00:24:23
2023-12-22 00:24:23+00:00
CONTRIBUTOR
nan
https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69 metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)
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PR_kwDODunzps5icYX0
6516
Support huggingface-hub pre-releases
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6516). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003231 / 0.011008 (-0.007777) | 0.062690 / 0.038508 (0.024182) | 0.050811 / 0.023109 (0.027701) | 0.258319 / 0.275898 (-0.017579) | 0.275977 / 0.323480 (-0.047503) | 0.002842 / 0.007986 (-0.005143) | 0.002606 / 0.004328 (-0.001723) | 0.048672 / 0.004250 (0.044421) | 0.038730 / 0.037052 (0.001677) | 0.258531 / 0.258489 (0.000042) | 0.289327 / 0.293841 (-0.004514) | 0.027994 / 0.128546 (-0.100552) | 0.010446 / 0.075646 (-0.065200) | 0.207152 / 0.419271 (-0.212119) | 0.035839 / 0.043533 (-0.007693) | 0.258416 / 0.255139 (0.003277) | 0.274348 / 0.283200 (-0.008851) | 0.019661 / 0.141683 (-0.122022) | 1.103688 / 1.452155 (-0.348466) | 1.207711 / 1.492716 (-0.285006) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090693 / 0.018006 (0.072687) | 0.300648 / 0.000490 (0.300158) | 0.000215 / 0.000200 (0.000015) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018589 / 0.037411 (-0.018822) | 0.061056 / 0.014526 (0.046530) | 0.074512 / 0.176557 (-0.102044) | 0.121260 / 0.737135 (-0.615875) | 0.073111 / 0.296338 (-0.223227) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285811 / 0.215209 (0.070602) | 2.785081 / 2.077655 (0.707426) | 1.469493 / 1.504120 (-0.034627) | 1.346389 / 1.541195 (-0.194806) | 1.391866 / 1.468490 (-0.076624) | 0.567304 / 4.584777 (-4.017473) | 2.407150 / 3.745712 (-1.338562) | 2.809915 / 5.269862 (-2.459946) | 1.741185 / 4.565676 (-2.824491) | 0.063073 / 0.424275 (-0.361202) | 0.004974 / 0.007607 (-0.002633) | 0.336431 / 0.226044 (0.110386) | 3.331371 / 2.268929 (1.062443) | 1.841466 / 55.444624 (-53.603159) | 1.559065 / 6.876477 (-5.317411) | 1.585033 / 2.142072 (-0.557039) | 0.647469 / 4.805227 (-4.157759) | 0.117488 / 6.500664 (-6.383176) | 0.042535 / 0.075469 (-0.032934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936409 / 1.841788 (-0.905379) | 11.301514 / 8.074308 (3.227206) | 10.500465 / 10.191392 (0.309073) | 0.131316 / 0.680424 (-0.549107) | 0.014007 / 0.534201 (-0.520194) | 0.286932 / 0.579283 (-0.292351) | 0.263516 / 0.434364 (-0.170848) | 0.340883 / 0.540337 (-0.199454) | 0.443589 / 1.386936 (-0.943347) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005204 / 0.011353 (-0.006149) | 0.003472 / 0.011008 (-0.007536) | 0.049235 / 0.038508 (0.010727) | 0.050668 / 0.023109 (0.027559) | 0.270198 / 0.275898 (-0.005700) | 0.293942 / 0.323480 (-0.029538) | 0.003964 / 0.007986 (-0.004022) | 0.002596 / 0.004328 (-0.001733) | 0.048654 / 0.004250 (0.044404) | 0.039411 / 0.037052 (0.002358) | 0.271938 / 0.258489 (0.013449) | 0.304308 / 0.293841 (0.010467) | 0.029042 / 0.128546 (-0.099504) | 0.010414 / 0.075646 (-0.065232) | 0.058273 / 0.419271 (-0.360999) | 0.032507 / 0.043533 (-0.011025) | 0.271671 / 0.255139 (0.016532) | 0.289850 / 0.283200 (0.006650) | 0.017292 / 0.141683 (-0.124391) | 1.126160 / 1.452155 (-0.325995) | 1.177365 / 1.492716 (-0.315351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091158 / 0.018006 (0.073152) | 0.299143 / 0.000490 (0.298653) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022558 / 0.037411 (-0.014853) | 0.076139 / 0.014526 (0.061613) | 0.088344 / 0.176557 (-0.088212) | 0.126640 / 0.737135 (-0.610495) | 0.089736 / 0.296338 (-0.206602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295351 / 0.215209 (0.080142) | 2.895779 / 2.077655 (0.818125) | 1.585886 / 1.504120 (0.081766) | 1.458601 / 1.541195 (-0.082594) | 1.468880 / 1.468490 (0.000390) | 0.554686 / 4.584777 (-4.030091) | 2.466276 / 3.745712 (-1.279437) | 2.741938 / 5.269862 (-2.527924) | 1.711793 / 4.565676 (-2.853883) | 0.062928 / 0.424275 (-0.361347) | 0.005177 / 0.007607 (-0.002430) | 0.343908 / 0.226044 (0.117863) | 3.393360 / 2.268929 (1.124431) | 1.928800 / 55.444624 (-53.515824) | 1.652181 / 6.876477 (-5.224296) | 1.643667 / 2.142072 (-0.498405) | 0.632829 / 4.805227 (-4.172398) | 0.114583 / 6.500664 (-6.386081) | 0.041248 / 0.075469 (-0.034221) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986196 / 1.841788 (-0.855592) | 12.006772 / 8.074308 (3.932464) | 10.522661 / 10.191392 (0.331269) | 0.133710 / 0.680424 (-0.546713) | 0.016714 / 0.534201 (-0.517487) | 0.286502 / 0.579283 (-0.292781) | 0.280090 / 0.434364 (-0.154273) | 0.326063 / 0.540337 (-0.214275) | 0.548485 / 1.386936 (-0.838452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f149204a2a5948287adcade5e90707aa5207a92 \"CML watermark\")\n" ]
2023-12-20T07:52:29
2023-12-20T08:51:34
2023-12-20 08:44:44+00:00
MEMBER
nan
Support `huggingface-hub` pre-releases. This way we will have our CI green when testing `huggingface-hub` release candidates. See: https://github.com/huggingface/datasets/tree/ci-test-huggingface-hub-v0.20.0.rc1 Close #6513.
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6515
Why call http_head() when fsspec_head() succeeds
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2023-12-20T02:25:51
2023-12-26T05:35:46
2023-12-26 05:35:46+00:00
CONTRIBUTOR
nan
https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14
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2049600663
PR_kwDODunzps5ia6Os
6514
Cache backward compatibility with 2.15.0
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6514). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "it's hard to tell if this works as expected without a test but i guess it's not trivial to implement such a test.\r\n\r\ni tried to reproduce locally (with this branch merged into the lazy-resolve-and-cache-reload) and it didn't work. \r\nI run:\r\n```\r\n ds = load_dataset(\"polinaeterna/audiofolder_two_configs_in_metadata\", \"v2\", data_files=\"v2/train/*\") \r\n```\r\nand i got this in the cache:\r\n```\r\nv2-374bfde4f55442bc/\r\n└── 0.0.0\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5 # - from this pr\r\n │   ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │   └── dataset_info.json\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5_builder.lock\r\n ├── 5a2339ad2bb7caf6a6daf2f213204e3ac03a13a5.incomplete_info.lock\r\n ├── 7896925d64deea5d # from 2.15.0\r\n │   ├── audiofolder_two_configs_in_metadata-train.arrow\r\n │   └── dataset_info.json\r\n ├── 7896925d64deea5d_builder.lock\r\n └── 7896925d64deea5d.incomplete_info.lock\r\n```\r\nso the first hash (the top-level dir v2-374bfde4f55442bc) matches but the second (after version) doesn't.\r\nmaybe i did something wrong though.\r\n\r\nalso i'm not sure if this is worth too much effort, maybe nobody notices if their datasets will be generated again :D idk", "I just pushed a fix, it should work just fine now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004798 / 0.011353 (-0.006555) | 0.003203 / 0.011008 (-0.007805) | 0.062247 / 0.038508 (0.023738) | 0.029906 / 0.023109 (0.006797) | 0.259370 / 0.275898 (-0.016528) | 0.276084 / 0.323480 (-0.047396) | 0.002910 / 0.007986 (-0.005076) | 0.002364 / 0.004328 (-0.001964) | 0.048080 / 0.004250 (0.043830) | 0.041168 / 0.037052 (0.004116) | 0.259833 / 0.258489 (0.001343) | 0.289882 / 0.293841 (-0.003959) | 0.026790 / 0.128546 (-0.101756) | 0.010336 / 0.075646 (-0.065311) | 0.209628 / 0.419271 (-0.209643) | 0.035080 / 0.043533 (-0.008452) | 0.256278 / 0.255139 (0.001139) | 0.279502 / 0.283200 (-0.003697) | 0.019755 / 0.141683 (-0.121928) | 1.121552 / 1.452155 (-0.330602) | 1.174360 / 1.492716 (-0.318356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093510 / 0.018006 (0.075504) | 0.302065 / 0.000490 (0.301575) | 0.000214 / 0.000200 (0.000014) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017652 / 0.037411 (-0.019759) | 0.060512 / 0.014526 (0.045986) | 0.072441 / 0.176557 (-0.104115) | 0.118058 / 0.737135 (-0.619078) | 0.072657 / 0.296338 (-0.223682) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283949 / 0.215209 (0.068740) | 2.803275 / 2.077655 (0.725620) | 1.527353 / 1.504120 (0.023233) | 1.408176 / 1.541195 (-0.133019) | 1.375335 / 1.468490 (-0.093155) | 0.546426 / 4.584777 (-4.038351) | 2.402210 / 3.745712 (-1.343502) | 2.765879 / 5.269862 (-2.503982) | 1.703722 / 4.565676 (-2.861955) | 0.062669 / 0.424275 (-0.361606) | 0.005006 / 0.007607 (-0.002601) | 0.337941 / 0.226044 (0.111897) | 3.385494 / 2.268929 (1.116566) | 1.817360 / 55.444624 (-53.627264) | 1.548594 / 6.876477 (-5.327883) | 1.548610 / 2.142072 (-0.593463) | 0.630188 / 4.805227 (-4.175040) | 0.117079 / 6.500664 (-6.383585) | 0.042077 / 0.075469 (-0.033392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941606 / 1.841788 (-0.900182) | 11.226277 / 8.074308 (3.151969) | 10.118005 / 10.191392 (-0.073387) | 0.130408 / 0.680424 (-0.550015) | 0.014419 / 0.534201 (-0.519782) | 0.284812 / 0.579283 (-0.294471) | 0.266951 / 0.434364 (-0.167413) | 0.322251 / 0.540337 (-0.218087) | 0.415014 / 1.386936 (-0.971922) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003028 / 0.011008 (-0.007980) | 0.048322 / 0.038508 (0.009814) | 0.030550 / 0.023109 (0.007441) | 0.264360 / 0.275898 (-0.011538) | 0.289544 / 0.323480 (-0.033936) | 0.004053 / 0.007986 (-0.003933) | 0.002480 / 0.004328 (-0.001848) | 0.048215 / 0.004250 (0.043964) | 0.044208 / 0.037052 (0.007156) | 0.263943 / 0.258489 (0.005454) | 0.297648 / 0.293841 (0.003807) | 0.029315 / 0.128546 (-0.099231) | 0.010533 / 0.075646 (-0.065114) | 0.057021 / 0.419271 (-0.362251) | 0.053751 / 0.043533 (0.010218) | 0.265153 / 0.255139 (0.010014) | 0.284988 / 0.283200 (0.001788) | 0.018459 / 0.141683 (-0.123224) | 1.225657 / 1.452155 (-0.226498) | 1.195737 / 1.492716 (-0.296979) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093030 / 0.018006 (0.075024) | 0.301022 / 0.000490 (0.300533) | 0.000228 / 0.000200 (0.000028) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022073 / 0.037411 (-0.015339) | 0.075912 / 0.014526 (0.061386) | 0.087628 / 0.176557 (-0.088929) | 0.125607 / 0.737135 (-0.611529) | 0.088568 / 0.296338 (-0.207770) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303482 / 0.215209 (0.088273) | 2.965987 / 2.077655 (0.888333) | 1.615273 / 1.504120 (0.111153) | 1.482851 / 1.541195 (-0.058344) | 1.562627 / 1.468490 (0.094137) | 0.563626 / 4.584777 (-4.021151) | 2.448741 / 3.745712 (-1.296971) | 2.761006 / 5.269862 (-2.508855) | 1.711242 / 4.565676 (-2.854434) | 0.064593 / 0.424275 (-0.359682) | 0.005044 / 0.007607 (-0.002563) | 0.354131 / 0.226044 (0.128087) | 3.511698 / 2.268929 (1.242770) | 1.951087 / 55.444624 (-53.493538) | 1.682171 / 6.876477 (-5.194305) | 1.666330 / 2.142072 (-0.475742) | 0.654880 / 4.805227 (-4.150347) | 0.118544 / 6.500664 (-6.382120) | 0.040753 / 0.075469 (-0.034717) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967771 / 1.841788 (-0.874017) | 12.017277 / 8.074308 (3.942969) | 10.624947 / 10.191392 (0.433555) | 0.128834 / 0.680424 (-0.551590) | 0.015739 / 0.534201 (-0.518462) | 0.285906 / 0.579283 (-0.293377) | 0.273659 / 0.434364 (-0.160705) | 0.324044 / 0.540337 (-0.216293) | 0.419469 / 1.386936 (-0.967467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2afbf785f8d0551cdd65a81c5c3228e469613724 \"CML watermark\")\n" ]
2023-12-19T23:52:25
2023-12-21T21:14:11
2023-12-21 21:07:55+00:00
MEMBER
nan
...for datasets without scripts It takes into account the changes in cache from - https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema - https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing requires https://github.com/huggingface/datasets/pull/6493 to be merged
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6513
Support huggingface-hub 0.20.0
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2023-12-19T15:15:46
2023-12-20T08:44:45
2023-12-20 08:44:45+00:00
MEMBER
nan
CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1 We need to merge: - #6510 - #6512 - #6516
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6512
Remove deprecated HfFolder
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6512). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005468 / 0.011353 (-0.005885) | 0.003447 / 0.011008 (-0.007561) | 0.062569 / 0.038508 (0.024061) | 0.049427 / 0.023109 (0.026318) | 0.238463 / 0.275898 (-0.037435) | 0.268320 / 0.323480 (-0.055159) | 0.002834 / 0.007986 (-0.005151) | 0.002679 / 0.004328 (-0.001649) | 0.048613 / 0.004250 (0.044363) | 0.038793 / 0.037052 (0.001741) | 0.247710 / 0.258489 (-0.010779) | 0.277557 / 0.293841 (-0.016284) | 0.027134 / 0.128546 (-0.101412) | 0.010346 / 0.075646 (-0.065301) | 0.205782 / 0.419271 (-0.213490) | 0.035549 / 0.043533 (-0.007983) | 0.241667 / 0.255139 (-0.013472) | 0.268358 / 0.283200 (-0.014842) | 0.017119 / 0.141683 (-0.124563) | 1.108487 / 1.452155 (-0.343668) | 1.177519 / 1.492716 (-0.315197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090925 / 0.018006 (0.072919) | 0.310422 / 0.000490 (0.309932) | 0.000212 / 0.000200 (0.000012) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018912 / 0.037411 (-0.018499) | 0.061534 / 0.014526 (0.047008) | 0.073608 / 0.176557 (-0.102949) | 0.119278 / 0.737135 (-0.617858) | 0.074698 / 0.296338 (-0.221640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287224 / 0.215209 (0.072014) | 2.792022 / 2.077655 (0.714367) | 1.474605 / 1.504120 (-0.029515) | 1.348714 / 1.541195 (-0.192481) | 1.381339 / 1.468490 (-0.087151) | 0.553033 / 4.584777 (-4.031744) | 2.360745 / 3.745712 (-1.384967) | 2.779281 / 5.269862 (-2.490580) | 1.743922 / 4.565676 (-2.821754) | 0.063817 / 0.424275 (-0.360458) | 0.004954 / 0.007607 (-0.002653) | 0.340039 / 0.226044 (0.113994) | 3.336771 / 2.268929 (1.067843) | 1.825573 / 55.444624 (-53.619051) | 1.525362 / 6.876477 (-5.351115) | 1.578793 / 2.142072 (-0.563280) | 0.638432 / 4.805227 (-4.166795) | 0.117601 / 6.500664 (-6.383063) | 0.041890 / 0.075469 (-0.033579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936896 / 1.841788 (-0.904892) | 11.426979 / 8.074308 (3.352671) | 10.636043 / 10.191392 (0.444651) | 0.136172 / 0.680424 (-0.544252) | 0.014249 / 0.534201 (-0.519952) | 0.287806 / 0.579283 (-0.291477) | 0.266046 / 0.434364 (-0.168318) | 0.326155 / 0.540337 (-0.214183) | 0.455508 / 1.386936 (-0.931428) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005199 / 0.011353 (-0.006154) | 0.003476 / 0.011008 (-0.007532) | 0.050519 / 0.038508 (0.012011) | 0.050732 / 0.023109 (0.027623) | 0.270140 / 0.275898 (-0.005758) | 0.295539 / 0.323480 (-0.027941) | 0.004057 / 0.007986 (-0.003928) | 0.002771 / 0.004328 (-0.001558) | 0.049157 / 0.004250 (0.044906) | 0.039863 / 0.037052 (0.002811) | 0.275934 / 0.258489 (0.017445) | 0.306971 / 0.293841 (0.013130) | 0.029405 / 0.128546 (-0.099141) | 0.010746 / 0.075646 (-0.064900) | 0.058427 / 0.419271 (-0.360845) | 0.032448 / 0.043533 (-0.011085) | 0.271851 / 0.255139 (0.016712) | 0.290337 / 0.283200 (0.007138) | 0.019145 / 0.141683 (-0.122538) | 1.112232 / 1.452155 (-0.339922) | 1.215153 / 1.492716 (-0.277564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088590 / 0.018006 (0.070584) | 0.299047 / 0.000490 (0.298558) | 0.000219 / 0.000200 (0.000019) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022755 / 0.037411 (-0.014656) | 0.078720 / 0.014526 (0.064194) | 0.089051 / 0.176557 (-0.087505) | 0.129330 / 0.737135 (-0.607805) | 0.090645 / 0.296338 (-0.205693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294083 / 0.215209 (0.078874) | 2.907195 / 2.077655 (0.829540) | 1.607392 / 1.504120 (0.103272) | 1.481931 / 1.541195 (-0.059263) | 1.486934 / 1.468490 (0.018444) | 0.574093 / 4.584777 (-4.010684) | 2.439775 / 3.745712 (-1.305937) | 2.739818 / 5.269862 (-2.530044) | 1.753922 / 4.565676 (-2.811755) | 0.063738 / 0.424275 (-0.360537) | 0.005219 / 0.007607 (-0.002388) | 0.350342 / 0.226044 (0.124297) | 3.463644 / 2.268929 (1.194716) | 1.971598 / 55.444624 (-53.473026) | 1.671752 / 6.876477 (-5.204724) | 1.686504 / 2.142072 (-0.455569) | 0.655870 / 4.805227 (-4.149357) | 0.117580 / 6.500664 (-6.383084) | 0.041210 / 0.075469 (-0.034259) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996305 / 1.841788 (-0.845482) | 12.426361 / 8.074308 (4.352053) | 10.600309 / 10.191392 (0.408917) | 0.129728 / 0.680424 (-0.550695) | 0.015267 / 0.534201 (-0.518934) | 0.285444 / 0.579283 (-0.293839) | 0.272375 / 0.434364 (-0.161989) | 0.323478 / 0.540337 (-0.216860) | 0.547566 / 1.386936 (-0.839370) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a91582de288d98e94bcb5ab634ca1cfeeff544c5 \"CML watermark\")\n" ]
2023-12-19T14:40:49
2023-12-19T20:21:13
2023-12-19 20:14:30+00:00
MEMBER
nan
...and use `huggingface_hub.get_token()` instead
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6511
Implement get dataset default config name
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6511). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, this PR is ready for review.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005343 / 0.011353 (-0.006010) | 0.003521 / 0.011008 (-0.007487) | 0.061835 / 0.038508 (0.023327) | 0.052633 / 0.023109 (0.029524) | 0.243897 / 0.275898 (-0.032001) | 0.272961 / 0.323480 (-0.050519) | 0.003013 / 0.007986 (-0.004973) | 0.002692 / 0.004328 (-0.001636) | 0.050099 / 0.004250 (0.045848) | 0.045381 / 0.037052 (0.008329) | 0.249981 / 0.258489 (-0.008508) | 0.276789 / 0.293841 (-0.017052) | 0.027929 / 0.128546 (-0.100617) | 0.010933 / 0.075646 (-0.064714) | 0.206757 / 0.419271 (-0.212514) | 0.035334 / 0.043533 (-0.008199) | 0.249411 / 0.255139 (-0.005728) | 0.268893 / 0.283200 (-0.014306) | 0.019175 / 0.141683 (-0.122507) | 1.106932 / 1.452155 (-0.345223) | 1.177819 / 1.492716 (-0.314897) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092895 / 0.018006 (0.074889) | 0.303658 / 0.000490 (0.303169) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018978 / 0.037411 (-0.018434) | 0.060459 / 0.014526 (0.045934) | 0.072900 / 0.176557 (-0.103657) | 0.119803 / 0.737135 (-0.617332) | 0.074349 / 0.296338 (-0.221989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283715 / 0.215209 (0.068505) | 2.752394 / 2.077655 (0.674739) | 1.446619 / 1.504120 (-0.057501) | 1.319612 / 1.541195 (-0.221582) | 1.374769 / 1.468490 (-0.093721) | 0.571543 / 4.584777 (-4.013234) | 2.389106 / 3.745712 (-1.356607) | 2.797837 / 5.269862 (-2.472025) | 1.737615 / 4.565676 (-2.828062) | 0.063268 / 0.424275 (-0.361007) | 0.005118 / 0.007607 (-0.002489) | 0.340238 / 0.226044 (0.114193) | 3.366207 / 2.268929 (1.097278) | 1.845934 / 55.444624 (-53.598690) | 1.540640 / 6.876477 (-5.335837) | 1.585489 / 2.142072 (-0.556584) | 0.641178 / 4.805227 (-4.164049) | 0.118701 / 6.500664 (-6.381964) | 0.042719 / 0.075469 (-0.032750) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946706 / 1.841788 (-0.895082) | 11.846230 / 8.074308 (3.771921) | 10.459268 / 10.191392 (0.267876) | 0.130557 / 0.680424 (-0.549867) | 0.014292 / 0.534201 (-0.519909) | 0.287455 / 0.579283 (-0.291828) | 0.265213 / 0.434364 (-0.169151) | 0.325670 / 0.540337 (-0.214667) | 0.422800 / 1.386936 (-0.964136) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005454 / 0.011353 (-0.005899) | 0.003567 / 0.011008 (-0.007441) | 0.048696 / 0.038508 (0.010188) | 0.058844 / 0.023109 (0.035735) | 0.277011 / 0.275898 (0.001113) | 0.302544 / 0.323480 (-0.020936) | 0.004077 / 0.007986 (-0.003908) | 0.002720 / 0.004328 (-0.001609) | 0.058251 / 0.004250 (0.054001) | 0.040946 / 0.037052 (0.003893) | 0.276261 / 0.258489 (0.017772) | 0.352827 / 0.293841 (0.058986) | 0.029915 / 0.128546 (-0.098632) | 0.010562 / 0.075646 (-0.065084) | 0.057836 / 0.419271 (-0.361436) | 0.033129 / 0.043533 (-0.010404) | 0.276053 / 0.255139 (0.020914) | 0.292045 / 0.283200 (0.008846) | 0.020504 / 0.141683 (-0.121179) | 1.129746 / 1.452155 (-0.322409) | 1.190888 / 1.492716 (-0.301829) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095202 / 0.018006 (0.077196) | 0.303956 / 0.000490 (0.303466) | 0.000226 / 0.000200 (0.000026) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021960 / 0.037411 (-0.015451) | 0.076209 / 0.014526 (0.061683) | 0.088813 / 0.176557 (-0.087744) | 0.129061 / 0.737135 (-0.608074) | 0.091202 / 0.296338 (-0.205136) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301394 / 0.215209 (0.086185) | 2.948057 / 2.077655 (0.870403) | 1.591371 / 1.504120 (0.087251) | 1.463515 / 1.541195 (-0.077680) | 1.516477 / 1.468490 (0.047987) | 0.577223 / 4.584777 (-4.007554) | 2.506716 / 3.745712 (-1.238996) | 2.833385 / 5.269862 (-2.436477) | 1.808896 / 4.565676 (-2.756781) | 0.063241 / 0.424275 (-0.361034) | 0.005057 / 0.007607 (-0.002550) | 0.350108 / 0.226044 (0.124063) | 3.470252 / 2.268929 (1.201324) | 1.925689 / 55.444624 (-53.518935) | 1.667521 / 6.876477 (-5.208955) | 1.690909 / 2.142072 (-0.451164) | 0.647070 / 4.805227 (-4.158157) | 0.117596 / 6.500664 (-6.383068) | 0.042431 / 0.075469 (-0.033038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977297 / 1.841788 (-0.864490) | 12.947399 / 8.074308 (4.873091) | 10.964949 / 10.191392 (0.773557) | 0.130905 / 0.680424 (-0.549518) | 0.015207 / 0.534201 (-0.518994) | 0.288151 / 0.579283 (-0.291132) | 0.281817 / 0.434364 (-0.152547) | 0.326398 / 0.540337 (-0.213940) | 0.421354 / 1.386936 (-0.965582) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b04288f0b94c987a278c5bb8459746bc35ba367 \"CML watermark\")\n" ]
2023-12-19T11:26:19
2023-12-21T14:48:57
2023-12-21 14:42:41+00:00
MEMBER
nan
Implement `get_dataset_default_config_name`. Now that we support setting a configuration as default in `push_to_hub` (see #6500), we need a programmatically way to know in advance which is the default configuration. This will be used in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Follow-up of: - #6500 CC: @severo
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6510
Replace `list_files_info` with `list_repo_tree` in `push_to_hub`
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6510). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI errors are unrelated to the changes, so I'm merging.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005161 / 0.011353 (-0.006192) | 0.003494 / 0.011008 (-0.007515) | 0.062601 / 0.038508 (0.024093) | 0.052876 / 0.023109 (0.029767) | 0.255595 / 0.275898 (-0.020303) | 0.283108 / 0.323480 (-0.040371) | 0.003856 / 0.007986 (-0.004130) | 0.002686 / 0.004328 (-0.001642) | 0.048604 / 0.004250 (0.044353) | 0.037886 / 0.037052 (0.000834) | 0.252902 / 0.258489 (-0.005587) | 0.286906 / 0.293841 (-0.006935) | 0.028570 / 0.128546 (-0.099976) | 0.010684 / 0.075646 (-0.064962) | 0.208154 / 0.419271 (-0.211118) | 0.036169 / 0.043533 (-0.007364) | 0.276026 / 0.255139 (0.020887) | 0.272274 / 0.283200 (-0.010925) | 0.017690 / 0.141683 (-0.123993) | 1.202400 / 1.452155 (-0.249755) | 1.231223 / 1.492716 (-0.261494) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095229 / 0.018006 (0.077222) | 0.302205 / 0.000490 (0.301716) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018877 / 0.037411 (-0.018534) | 0.062286 / 0.014526 (0.047760) | 0.075191 / 0.176557 (-0.101366) | 0.121419 / 0.737135 (-0.615716) | 0.075641 / 0.296338 (-0.220697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282914 / 0.215209 (0.067705) | 2.769156 / 2.077655 (0.691501) | 1.480219 / 1.504120 (-0.023901) | 1.355742 / 1.541195 (-0.185453) | 1.399740 / 1.468490 (-0.068750) | 0.556365 / 4.584777 (-4.028412) | 2.399679 / 3.745712 (-1.346033) | 2.850510 / 5.269862 (-2.419351) | 1.781428 / 4.565676 (-2.784249) | 0.063045 / 0.424275 (-0.361230) | 0.004931 / 0.007607 (-0.002676) | 0.343743 / 0.226044 (0.117698) | 3.374907 / 2.268929 (1.105978) | 1.857774 / 55.444624 (-53.586851) | 1.577154 / 6.876477 (-5.299323) | 1.626597 / 2.142072 (-0.515475) | 0.653991 / 4.805227 (-4.151236) | 0.121306 / 6.500664 (-6.379358) | 0.042131 / 0.075469 (-0.033339) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948826 / 1.841788 (-0.892962) | 11.922497 / 8.074308 (3.848188) | 10.592334 / 10.191392 (0.400942) | 0.129145 / 0.680424 (-0.551279) | 0.014652 / 0.534201 (-0.519549) | 0.286074 / 0.579283 (-0.293210) | 0.265338 / 0.434364 (-0.169026) | 0.346872 / 0.540337 (-0.193466) | 0.450480 / 1.386936 (-0.936456) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005305 / 0.011353 (-0.006048) | 0.003583 / 0.011008 (-0.007426) | 0.049855 / 0.038508 (0.011347) | 0.052882 / 0.023109 (0.029773) | 0.268429 / 0.275898 (-0.007469) | 0.293375 / 0.323480 (-0.030105) | 0.004052 / 0.007986 (-0.003934) | 0.002685 / 0.004328 (-0.001644) | 0.049206 / 0.004250 (0.044955) | 0.040187 / 0.037052 (0.003135) | 0.270112 / 0.258489 (0.011623) | 0.306380 / 0.293841 (0.012539) | 0.029161 / 0.128546 (-0.099386) | 0.010948 / 0.075646 (-0.064698) | 0.057721 / 0.419271 (-0.361550) | 0.032628 / 0.043533 (-0.010905) | 0.267458 / 0.255139 (0.012319) | 0.291905 / 0.283200 (0.008705) | 0.018096 / 0.141683 (-0.123587) | 1.112744 / 1.452155 (-0.339410) | 1.161962 / 1.492716 (-0.330754) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097449 / 0.018006 (0.079443) | 0.304270 / 0.000490 (0.303780) | 0.000235 / 0.000200 (0.000035) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023550 / 0.037411 (-0.013861) | 0.078246 / 0.014526 (0.063720) | 0.091229 / 0.176557 (-0.085327) | 0.130624 / 0.737135 (-0.606511) | 0.092767 / 0.296338 (-0.203571) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284962 / 0.215209 (0.069753) | 2.761090 / 2.077655 (0.683435) | 1.545409 / 1.504120 (0.041289) | 1.424573 / 1.541195 (-0.116622) | 1.438869 / 1.468490 (-0.029621) | 0.571281 / 4.584777 (-4.013496) | 2.419493 / 3.745712 (-1.326219) | 2.802611 / 5.269862 (-2.467251) | 1.749880 / 4.565676 (-2.815796) | 0.062566 / 0.424275 (-0.361709) | 0.005243 / 0.007607 (-0.002364) | 0.344653 / 0.226044 (0.118608) | 3.367488 / 2.268929 (1.098559) | 1.925871 / 55.444624 (-53.518754) | 1.624258 / 6.876477 (-5.252219) | 1.663742 / 2.142072 (-0.478330) | 0.634553 / 4.805227 (-4.170675) | 0.116745 / 6.500664 (-6.383919) | 0.041734 / 0.075469 (-0.033735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.006808 / 1.841788 (-0.834980) | 12.499711 / 8.074308 (4.425403) | 10.956260 / 10.191392 (0.764868) | 0.132393 / 0.680424 (-0.548031) | 0.015924 / 0.534201 (-0.518277) | 0.289837 / 0.579283 (-0.289446) | 0.281565 / 0.434364 (-0.152799) | 0.337393 / 0.540337 (-0.202945) | 0.560385 / 1.386936 (-0.826551) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f699ab27ef2c0c23dc3a514b5bb155485ff6913 \"CML watermark\")\n" ]
2023-12-18T15:34:19
2023-12-19T18:05:47
2023-12-19 17:58:34+00:00
COLLABORATOR
nan
Starting from `huggingface_hub` 0.20.0, `list_files_info` will be deprecated in favor of `list_repo_tree` (see https://github.com/huggingface/huggingface_hub/pull/1910)
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PR_kwDODunzps5iREyE
6509
Better cast error when generating dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6509). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I created `DatatasetGenerationCastError` in `exceptions.py` that inherits from `DatasetGenerationError` (for backward compatibility) that inherits from `DatasetsError`.\r\n\r\nI also added a help message at the end of the error:\r\n\r\n```\r\nPlease either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004991 / 0.011353 (-0.006361) | 0.003362 / 0.011008 (-0.007646) | 0.062093 / 0.038508 (0.023585) | 0.051533 / 0.023109 (0.028424) | 0.247508 / 0.275898 (-0.028390) | 0.275593 / 0.323480 (-0.047886) | 0.003828 / 0.007986 (-0.004158) | 0.002573 / 0.004328 (-0.001755) | 0.047727 / 0.004250 (0.043477) | 0.037029 / 0.037052 (-0.000023) | 0.250359 / 0.258489 (-0.008130) | 0.282640 / 0.293841 (-0.011201) | 0.027853 / 0.128546 (-0.100693) | 0.010247 / 0.075646 (-0.065400) | 0.206826 / 0.419271 (-0.212445) | 0.035837 / 0.043533 (-0.007695) | 0.251795 / 0.255139 (-0.003344) | 0.275654 / 0.283200 (-0.007545) | 0.017722 / 0.141683 (-0.123960) | 1.120287 / 1.452155 (-0.331868) | 1.203087 / 1.492716 (-0.289630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092320 / 0.018006 (0.074314) | 0.300079 / 0.000490 (0.299589) | 0.000211 / 0.000200 (0.000011) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018193 / 0.037411 (-0.019218) | 0.061310 / 0.014526 (0.046784) | 0.072433 / 0.176557 (-0.104124) | 0.119092 / 0.737135 (-0.618043) | 0.074044 / 0.296338 (-0.222294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297184 / 0.215209 (0.081975) | 2.805197 / 2.077655 (0.727543) | 1.521326 / 1.504120 (0.017206) | 1.374321 / 1.541195 (-0.166874) | 1.388767 / 1.468490 (-0.079723) | 0.571865 / 4.584777 (-4.012912) | 2.385213 / 3.745712 (-1.360499) | 2.726840 / 5.269862 (-2.543021) | 1.725352 / 4.565676 (-2.840325) | 0.063012 / 0.424275 (-0.361263) | 0.004911 / 0.007607 (-0.002697) | 0.336430 / 0.226044 (0.110385) | 3.390616 / 2.268929 (1.121688) | 1.846398 / 55.444624 (-53.598227) | 1.576797 / 6.876477 (-5.299680) | 1.579445 / 2.142072 (-0.562627) | 0.652515 / 4.805227 (-4.152712) | 0.118393 / 6.500664 (-6.382271) | 0.042155 / 0.075469 (-0.033314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942269 / 1.841788 (-0.899518) | 11.318258 / 8.074308 (3.243950) | 10.299948 / 10.191392 (0.108556) | 0.136088 / 0.680424 (-0.544336) | 0.013682 / 0.534201 (-0.520519) | 0.287549 / 0.579283 (-0.291734) | 0.258346 / 0.434364 (-0.176018) | 0.337146 / 0.540337 (-0.203191) | 0.443922 / 1.386936 (-0.943014) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005302 / 0.011353 (-0.006051) | 0.003234 / 0.011008 (-0.007774) | 0.049159 / 0.038508 (0.010651) | 0.050459 / 0.023109 (0.027350) | 0.273718 / 0.275898 (-0.002180) | 0.296997 / 0.323480 (-0.026483) | 0.003948 / 0.007986 (-0.004038) | 0.002590 / 0.004328 (-0.001739) | 0.048129 / 0.004250 (0.043879) | 0.039369 / 0.037052 (0.002317) | 0.276469 / 0.258489 (0.017980) | 0.306359 / 0.293841 (0.012519) | 0.028864 / 0.128546 (-0.099682) | 0.010253 / 0.075646 (-0.065394) | 0.058264 / 0.419271 (-0.361008) | 0.032451 / 0.043533 (-0.011082) | 0.277336 / 0.255139 (0.022197) | 0.296137 / 0.283200 (0.012937) | 0.018094 / 0.141683 (-0.123589) | 1.119539 / 1.452155 (-0.332615) | 1.163116 / 1.492716 (-0.329600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092578 / 0.018006 (0.074572) | 0.300756 / 0.000490 (0.300267) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022333 / 0.037411 (-0.015078) | 0.076632 / 0.014526 (0.062107) | 0.087829 / 0.176557 (-0.088727) | 0.127686 / 0.737135 (-0.609449) | 0.091314 / 0.296338 (-0.205024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297499 / 0.215209 (0.082290) | 2.889775 / 2.077655 (0.812120) | 1.598976 / 1.504120 (0.094856) | 1.478805 / 1.541195 (-0.062389) | 1.481818 / 1.468490 (0.013328) | 0.557972 / 4.584777 (-4.026804) | 2.453248 / 3.745712 (-1.292464) | 2.771823 / 5.269862 (-2.498039) | 1.721527 / 4.565676 (-2.844150) | 0.062786 / 0.424275 (-0.361489) | 0.005298 / 0.007607 (-0.002309) | 0.346660 / 0.226044 (0.120615) | 3.412262 / 2.268929 (1.143334) | 1.940240 / 55.444624 (-53.504384) | 1.654015 / 6.876477 (-5.222461) | 1.652039 / 2.142072 (-0.490034) | 0.636870 / 4.805227 (-4.168357) | 0.116213 / 6.500664 (-6.384451) | 0.040937 / 0.075469 (-0.034532) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001605 / 1.841788 (-0.840183) | 11.986592 / 8.074308 (3.912284) | 10.231288 / 10.191392 (0.039896) | 0.130242 / 0.680424 (-0.550182) | 0.015764 / 0.534201 (-0.518437) | 0.289257 / 0.579283 (-0.290026) | 0.275996 / 0.434364 (-0.158368) | 0.323089 / 0.540337 (-0.217248) | 0.556383 / 1.386936 (-0.830553) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#773324159ad4afd7931588a710839b76670ddf87 \"CML watermark\")\n" ]
2023-12-18T13:57:24
2023-12-19T09:37:12
2023-12-19 09:31:03+00:00
MEMBER
nan
I want to improve the error message for datasets like https://huggingface.co/datasets/m-a-p/COIG-CQIA Cc @albertvillanova @severo is this new error ok ? Or should I use a dedicated error class ? New: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1920, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2322, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2276, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast instruction: string other: string index: string domain: list<item: string> child 0, item: string output: string task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string task_name_in_eng: string input: string to {'answer_from': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'human_verified': Value(dtype='bool', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'copyright': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 936, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1031, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1791, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1922, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 3 new columns (other, index, task_name_in_eng) and 3 missing columns (answer_from, copyright, human_verified). This happened while the json dataset builder was generating data using hf://datasets/m-a-p/COIG-CQIA/coig_pc/coig_pc_core_sample.json (at revision b7b7ecf290f6515036c7c04bd8537228ac2eb474) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) ``` Previously: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1931, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2295, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2253, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") ValueError: Couldn't cast task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string other: string instruction: string task_name_in_eng: string domain: list<item: string> child 0, item: string index: string output: string input: string to {'human_verified': Value(dtype='bool', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'answer_from': Value(dtype='string', id=None), 'copyright': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 949, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1044, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1804, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1949, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ```
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2045733273
PR_kwDODunzps5iNvAu
6508
Read GeoParquet files using parquet reader
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6508). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nI'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?", "> Geometry columns MUST be stored using the BYTE_ARRAY parquet type. They MUST be encoded as [WKB](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry#Well-known_binary).\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#geometry-columns\r\n\r\nIt has metadata:\r\n\r\n> File metadata indicating things like the version of this specification used\r\n> Column metadata with additional metadata for each geometry column\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#metadata", "The specification is very short by the way:\r\n\r\nhttps://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md", "https://github.com/opengeospatial/geoparquet/blob/main/format-specs/compatible-parquet.md is also worth reading for this PR", "> Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?\r\n\r\nYep, let me do that do that later today!\r\n\r\n> I'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?\r\n\r\nGeoParquet is a Parquet file with a `geometry` column that is encoded in a Binary format (technically WKB as @severo mentioned above). It is not a pyarrow extension type (though that would be cool). Regular `parquet` readers such as `pyarrow` would thus see the column as a binary column, while libraries such as `geopandas` which implement a GeoParquet reader would look at the schema metadata.\r\n\r\nE.g. taking this [file](https://huggingface.co/datasets/weiji14/clay_vector_embeddings/resolve/862b1602f326421adc99375912c08603a9f2cc5c/32VLM_v01.gpq) as an example, this is how the 'geo' schema looks like:\r\n\r\n```python\r\nimport pyarrow.parquet as pq\r\n\r\nschema = pq.read_schema(where=\"32VLM_v01.gpq\")\r\nprint(schema.metadata[b\"geo\"])\r\n```\r\n\r\n```\r\n{\r\n \"primary_column\": \"geometry\",\r\n \"columns\": {\r\n \"geometry\": {\r\n \"encoding\": \"WKB\",\r\n \"crs\": {\r\n \"$schema\": \"https://proj.org/schemas/v0.7/projjson.schema.json\",\r\n \"type\": \"GeographicCRS\",\r\n \"name\": \"WGS 84 (CRS84)\",\r\n \"datum_ensemble\": {\r\n \"name\": \"World Geodetic System 1984 ensemble\",\r\n \"members\": [\r\n {\"name\": \"World Geodetic System 1984 (Transit)\"},\r\n {\"name\": \"World Geodetic System 1984 (G730)\"},\r\n {\"name\": \"World Geodetic System 1984 (G873)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1150)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1674)\"},\r\n {\"name\": \"World Geodetic System 1984 (G1762)\"},\r\n {\"name\": \"World Geodetic System 1984 (G2139)\"},\r\n ],\r\n \"ellipsoid\": {\r\n \"name\": \"WGS 84\",\r\n \"semi_major_axis\": 6378137,\r\n \"inverse_flattening\": 298.257223563,\r\n },\r\n \"accuracy\": \"2.0\",\r\n \"id\": {\"authority\": \"EPSG\", \"code\": 6326},\r\n },\r\n \"coordinate_system\": {\r\n \"subtype\": \"ellipsoidal\",\r\n \"axis\": [\r\n {\r\n \"name\": \"Geodetic longitude\",\r\n \"abbreviation\": \"Lon\",\r\n \"direction\": \"east\",\r\n \"unit\": \"degree\",\r\n },\r\n {\r\n \"name\": \"Geodetic latitude\",\r\n \"abbreviation\": \"Lat\",\r\n \"direction\": \"north\",\r\n \"unit\": \"degree\",\r\n },\r\n ],\r\n },\r\n \"scope\": \"Not known.\",\r\n \"area\": \"World.\",\r\n \"bbox\": {\r\n \"south_latitude\": -90,\r\n \"west_longitude\": -180,\r\n \"north_latitude\": 90,\r\n \"east_longitude\": 180,\r\n },\r\n \"id\": {\"authority\": \"OGC\", \"code\": \"CRS84\"},\r\n },\r\n \"geometry_types\": [\"Polygon\"],\r\n \"bbox\": [\r\n 5.370542846111244,\r\n 59.42344573656881,\r\n 7.368267282586697,\r\n 60.42591328670696,\r\n ],\r\n }\r\n },\r\n \"version\": \"1.0.0\",\r\n \"creator\": {\"library\": \"geopandas\", \"version\": \"0.14.1\"},\r\n}\r\n```\r\n\r\nWe can continue the discussion on how to handle this extra 'geo' schema metadata in #6438. I'd like to keep this PR small by just piggy-backing off the default Parquet reader for now, which would just show the 'geometry' column as a binary column.", "Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n\r\nIt would also enable `push_to_hub` to save geoparquet files", "> Thanks ! Also if you can make sure that doing `ds.to_parquet(\"path/to/output.geoparquet\")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.\r\n> \r\n> It would also enable `push_to_hub` to save geoparquet files\r\n\r\nHmm, it should be possible to let PyArrow save a Parquet file with a geometry WKB column, but saving the GeoParquet schema metadata won't be easy without introducing [`geopandas`](https://github.com/geopandas/geopandas) as a dependency. Does this need to be done in this PR, or can it be a separate one?", "I see, then let's keep it like this for now.\r\nI just checked and it would require to add support for keeping the schema metadata in `Features` anyway.\r\n\r\nFeel free to fix your code formatting using\r\n\r\n```\r\nmake style\r\n```\r\n\r\nand we can merge this PR :)\r\n\r\n", "Cool, linted to remove the extra blank line at 7088f585557807a63673cdc58900d7ce56146cf7. :rocket:", "The previous CI failure at https://github.com/huggingface/datasets/actions/runs/7482863299/job/20668381959#step:6:5299 says `datasets.exceptions.DefunctDatasetError: Dataset 'eli5' is defunct and no longer accessible due to unavailability of the source data` which seems unrelated, might be to do with https://github.com/huggingface/datasets/issues/6605. I've updated the PR branch with changes from `main` again, could someone re-run the tests and merge if ok? Thanks!", "sorry, it took me some time to fix the CI on the `main` branch\r\n\r\nwill merge once it's green :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005467 / 0.011353 (-0.005886) | 0.003696 / 0.011008 (-0.007313) | 0.063298 / 0.038508 (0.024790) | 0.032209 / 0.023109 (0.009100) | 0.246307 / 0.275898 (-0.029591) | 0.276864 / 0.323480 (-0.046616) | 0.003941 / 0.007986 (-0.004044) | 0.002616 / 0.004328 (-0.001713) | 0.049543 / 0.004250 (0.045292) | 0.044886 / 0.037052 (0.007833) | 0.266502 / 0.258489 (0.008013) | 0.288401 / 0.293841 (-0.005440) | 0.027911 / 0.128546 (-0.100635) | 0.011011 / 0.075646 (-0.064636) | 0.207972 / 0.419271 (-0.211299) | 0.036324 / 0.043533 (-0.007209) | 0.259450 / 0.255139 (0.004311) | 0.267317 / 0.283200 (-0.015883) | 0.018857 / 0.141683 (-0.122826) | 1.145350 / 1.452155 (-0.306805) | 1.204204 / 1.492716 (-0.288513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103864 / 0.018006 (0.085858) | 0.306941 / 0.000490 (0.306451) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018391 / 0.037411 (-0.019020) | 0.064600 / 0.014526 (0.050074) | 0.075454 / 0.176557 (-0.101102) | 0.120913 / 0.737135 (-0.616223) | 0.076998 / 0.296338 (-0.219341) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279491 / 0.215209 (0.064282) | 2.742471 / 2.077655 (0.664816) | 1.447980 / 1.504120 (-0.056140) | 1.328202 / 1.541195 (-0.212992) | 1.397291 / 1.468490 (-0.071199) | 0.585726 / 4.584777 (-3.999051) | 2.385132 / 3.745712 (-1.360580) | 2.874888 / 5.269862 (-2.394974) | 1.820177 / 4.565676 (-2.745500) | 0.063876 / 0.424275 (-0.360399) | 0.004946 / 0.007607 (-0.002661) | 0.336445 / 0.226044 (0.110401) | 3.396813 / 2.268929 (1.127885) | 1.832644 / 55.444624 (-53.611981) | 1.581304 / 6.876477 (-5.295172) | 1.607243 / 2.142072 (-0.534829) | 0.662752 / 4.805227 (-4.142476) | 0.119494 / 6.500664 (-6.381170) | 0.042573 / 0.075469 (-0.032896) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936784 / 1.841788 (-0.905003) | 12.154288 / 8.074308 (4.079980) | 10.944835 / 10.191392 (0.753443) | 0.132856 / 0.680424 (-0.547568) | 0.015197 / 0.534201 (-0.519004) | 0.290647 / 0.579283 (-0.288636) | 0.273498 / 0.434364 (-0.160866) | 0.324893 / 0.540337 (-0.215444) | 0.427905 / 1.386936 (-0.959032) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005695 / 0.011353 (-0.005658) | 0.003562 / 0.011008 (-0.007446) | 0.050117 / 0.038508 (0.011608) | 0.033876 / 0.023109 (0.010767) | 0.275514 / 0.275898 (-0.000384) | 0.298460 / 0.323480 (-0.025020) | 0.004240 / 0.007986 (-0.003745) | 0.002738 / 0.004328 (-0.001591) | 0.048518 / 0.004250 (0.044268) | 0.049064 / 0.037052 (0.012012) | 0.287094 / 0.258489 (0.028605) | 0.314281 / 0.293841 (0.020440) | 0.057861 / 0.128546 (-0.070686) | 0.010893 / 0.075646 (-0.064753) | 0.062251 / 0.419271 (-0.357020) | 0.036788 / 0.043533 (-0.006745) | 0.272431 / 0.255139 (0.017292) | 0.292022 / 0.283200 (0.008822) | 0.019874 / 0.141683 (-0.121809) | 1.156939 / 1.452155 (-0.295216) | 1.237966 / 1.492716 (-0.254751) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096156 / 0.018006 (0.078150) | 0.306652 / 0.000490 (0.306162) | 0.000230 / 0.000200 (0.000031) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022965 / 0.037411 (-0.014447) | 0.081349 / 0.014526 (0.066823) | 0.089035 / 0.176557 (-0.087521) | 0.128831 / 0.737135 (-0.608304) | 0.090321 / 0.296338 (-0.206017) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293110 / 0.215209 (0.077901) | 2.884493 / 2.077655 (0.806839) | 1.582522 / 1.504120 (0.078402) | 1.518977 / 1.541195 (-0.022218) | 1.528449 / 1.468490 (0.059959) | 0.577369 / 4.584777 (-4.007408) | 2.473060 / 3.745712 (-1.272652) | 3.104363 / 5.269862 (-2.165499) | 1.916529 / 4.565676 (-2.649147) | 0.064594 / 0.424275 (-0.359682) | 0.005386 / 0.007607 (-0.002221) | 0.353336 / 0.226044 (0.127292) | 3.471914 / 2.268929 (1.202985) | 1.959222 / 55.444624 (-53.485402) | 1.677153 / 6.876477 (-5.199324) | 1.716961 / 2.142072 (-0.425112) | 0.658355 / 4.805227 (-4.146873) | 0.117296 / 6.500664 (-6.383368) | 0.041139 / 0.075469 (-0.034330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025220 / 1.841788 (-0.816567) | 14.510987 / 8.074308 (6.436679) | 11.851428 / 10.191392 (1.660036) | 0.143759 / 0.680424 (-0.536665) | 0.015644 / 0.534201 (-0.518557) | 0.296824 / 0.579283 (-0.282459) | 0.281566 / 0.434364 (-0.152798) | 0.335094 / 0.540337 (-0.205244) | 0.425199 / 1.386936 (-0.961737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fabc2c8cee8822572115893715b76dfdabac1631 \"CML watermark\")\n" ]
2023-12-18T04:50:37
2024-01-26T18:22:35
2024-01-26 16:18:41+00:00
CONTRIBUTOR
nan
Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader. Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75 Addresses https://github.com/huggingface/datasets/issues/6438
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2045152928
I_kwDODunzps555o6g
6507
where is glue_metric.py> @Frankie123421 what was the resolution to this?
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2023-12-17T09:58:25
2023-12-18T11:42:49
2023-12-18 11:42:49+00:00
NONE
nan
> @Frankie123421 what was the resolution to this? use glue_metric.py instead of glue.py in load_metric _Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_
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2044975038
I_kwDODunzps5549e-
6506
Incorrect test set labels for RTE and CoLA datasets via load_dataset
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[ "As this is a specific issue of the \"glue\" dataset, I have transferred it to the dataset Discussion page: https://huggingface.co/datasets/glue/discussions/15\r\n\r\nLet's continue the discussion there!" ]
2023-12-16T22:06:08
2023-12-21T09:57:57
2023-12-21 09:57:57+00:00
NONE
nan
### Describe the bug The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1. Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes? ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset rte_data = load_dataset('glue', 'rte') cola_data = load_dataset('glue', 'cola') print(rte_data['test'][0:30]['label']) print(cola_data['test'][0:30]['label']) Output: [-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] The non-label test data seems to be fine: e.g. rte_data['test'][1] is: {'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.", 'sentence2': 'Authorities in Brazil hold 200 people as hostage.', 'label': -1, 'idx': 1} Training and validation data are also fine: e.g. rte_data['train][0] is: {'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.', 'sentence2': 'Weapons of Mass Destruction Found in Iraq.', 'label': 1, 'idx': 0} ### Expected behavior Expected the labels to be binary 0/1 values; Got all -1s instead ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0
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2044721288
I_kwDODunzps553_iI
6505
Got stuck when I trying to load a dataset
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[ "I ran into the same problem when I used a server cluster (Slurm system managed) that couldn't load any of the huggingface datasets or models, but it worked on my laptop. I suspected some system configuration-related problem, but I had no idea. \r\nMy problems are consistent with [issue #2618](https://github.com/huggingface/datasets/issues/2618). All the huggingface-related libraries I use are the latest versions.\r\n\r\n", "> I ran into the same problem when I used a server cluster (Slurm system managed) that couldn't load any of the huggingface datasets or models, but it worked on my laptop. I suspected some system configuration-related problem, but I had no idea. My problems are consistent with [issue #2618](https://github.com/huggingface/datasets/issues/2618). All the huggingface-related libraries I use are the latest versions.\r\n\r\nhave you solved this issue yet? i met the same problem on server but everything works on laptop. I think maybe the filelock repo is contradictory with file system.", "I am having the same issue on a computing cluster but this works on my laptop as well. I instead have this error:\r\n`/home/.conda/envs/py10/lib/python3.10/site-packages/filelock/_unix.py\", line 43, in _acquire\r\n fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 5] Input/output error`\r\n\r\nthe load_dataset command does not work on server for local or hosted hugging-face datasets, and I have tried for several files", "Same here. Is there any solution?", "In my case, `.cahce` was in a shared folder. Moving it into the user's home folder fixed the problem. #2618 for more details", "> In my case, `.cahce` was in a shared folder. Moving it into the user's home folder fixed the problem. #2618 for more details在我的情况下， `.cahce` 在一个共享文件夹中。将其移动到用户的主文件夹中解决了问题。 #2618 获取更多详细信息。\r\n\r\nCan you be more specific? thank." ]
2023-12-16T11:51:07
2024-05-10T05:01:52
NaT
NONE
nan
### Describe the bug Hello, everyone. I met a problem when I am trying to load a data file using load_dataset method on a Debian 10 system. The data file is not very large, only 1.63MB with 600 records. Here is my code: from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') I waited it for 20 minutes. It still no response. I cannot using Ctrl+C to cancel the command. I have to use Ctrl+Z to kill it. I also try it with a txt file, it still no response in a long time. I can load the same file successfully using my laptop (windows 10, python 3.8.5, datasets==2.14.5). I can also make it on another computer (Ubuntu 20.04.5 LTS, python 3.10.13, datasets 2.14.7). It only takes me 1-2 miniutes. Could you give me some suggestions? Thank you. ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') ### Expected behavior I hope it can load the file successfully. ### Environment info OS: Debian GNU/Linux 10 Python: Python 3.10.13 Pip list: Package Version ------------------------- ------------ accelerate 0.25.0 addict 2.4.0 aiofiles 23.2.1 aiohttp 3.9.1 aiosignal 1.3.1 aliyun-python-sdk-core 2.14.0 aliyun-python-sdk-kms 2.16.2 altair 5.2.0 annotated-types 0.6.0 anyio 3.7.1 async-timeout 4.0.3 attrs 23.1.0 certifi 2023.11.17 cffi 1.16.0 charset-normalizer 3.3.2 click 8.1.7 contourpy 1.2.0 crcmod 1.7 cryptography 41.0.7 cycler 0.12.1 datasets 2.14.7 dill 0.3.7 docstring-parser 0.15 einops 0.7.0 exceptiongroup 1.2.0 fastapi 0.105.0 ffmpy 0.3.1 filelock 3.13.1 fonttools 4.46.0 frozenlist 1.4.1 fsspec 2023.10.0 gast 0.5.4 gradio 3.50.2 gradio_client 0.6.1 h11 0.14.0 httpcore 1.0.2 httpx 0.25.2 huggingface-hub 0.19.4 idna 3.6 importlib-metadata 7.0.0 importlib-resources 6.1.1 jieba 0.42.1 Jinja2 3.1.2 jmespath 0.10.0 joblib 1.3.2 jsonschema 4.20.0 jsonschema-specifications 2023.11.2 kiwisolver 1.4.5 markdown-it-py 3.0.0 MarkupSafe 2.1.3 matplotlib 3.8.2 mdurl 0.1.2 modelscope 1.10.0 mpmath 1.3.0 multidict 6.0.4 multiprocess 0.70.15 networkx 3.2.1 nltk 3.8.1 numpy 1.26.2 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.3.101 nvidia-nvtx-cu12 12.1.105 orjson 3.9.10 oss2 2.18.3 packaging 23.2 pandas 2.1.4 peft 0.7.1 Pillow 10.1.0 pip 23.3.1 platformdirs 4.1.0 protobuf 4.25.1 psutil 5.9.6 pyarrow 14.0.1 pyarrow-hotfix 0.6 pycparser 2.21 pycryptodome 3.19.0 pydantic 2.5.2 pydantic_core 2.14.5 pydub 0.25.1 Pygments 2.17.2 pyparsing 3.1.1 python-dateutil 2.8.2 python-multipart 0.0.6 pytz 2023.3.post1 PyYAML 6.0.1 referencing 0.32.0 regex 2023.10.3 requests 2.31.0 rich 13.7.0 rouge-chinese 1.0.3 rpds-py 0.13.2 safetensors 0.4.1 scipy 1.11.4 semantic-version 2.10.0 sentencepiece 0.1.99 setuptools 68.2.2 shtab 1.6.5 simplejson 3.19.2 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 sse-starlette 1.8.2 starlette 0.27.0 sympy 1.12 tiktoken 0.5.2 tokenizers 0.15.0 tomli 2.0.1 toolz 0.12.0 torch 2.1.2 tqdm 4.66.1 transformers 4.36.1 triton 2.1.0 trl 0.7.4 typing_extensions 4.9.0 tyro 0.6.0 tzdata 2023.3 urllib3 2.1.0 uvicorn 0.24.0.post1 websockets 11.0.3 wheel 0.41.2 xxhash 3.4.1 yapf 0.40.2 yarl 1.9.4 zipp 3.17.0
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2044541154
I_kwDODunzps553Tji
6504
Error Pushing to Hub
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2023-12-16T01:05:22
2023-12-16T06:20:53
2023-12-16 06:20:53+00:00
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### Describe the bug Error when trying to push a dataset in a special format to hub ### Steps to reproduce the bug ``` import datasets from datasets import Dataset dataset_dict = { "filename": ["apple", "banana"], "token": [[[1,2],[3,4]],[[1,2],[3,4]]], "label": [0, 1], } dataset = Dataset.from_dict(dataset_dict) dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16")) dataset.push_to_hub("SequenceModel/imagenet_val_256") ``` Error: ``` ... ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple' in "<unicode string>", line 8, column 16: shape: !!python/tuple ^ ``` ### Expected behavior Dataset being pushed to hub ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0
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2043847591
PR_kwDODunzps5iHgZf
6503
Fix streaming xnli
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6503). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005003 / 0.011353 (-0.006350) | 0.003020 / 0.011008 (-0.007988) | 0.061370 / 0.038508 (0.022862) | 0.050996 / 0.023109 (0.027887) | 0.243434 / 0.275898 (-0.032464) | 0.266317 / 0.323480 (-0.057163) | 0.003888 / 0.007986 (-0.004098) | 0.002607 / 0.004328 (-0.001721) | 0.047541 / 0.004250 (0.043290) | 0.037933 / 0.037052 (0.000881) | 0.259695 / 0.258489 (0.001206) | 0.279374 / 0.293841 (-0.014467) | 0.027258 / 0.128546 (-0.101288) | 0.010184 / 0.075646 (-0.065462) | 0.207412 / 0.419271 (-0.211860) | 0.034978 / 0.043533 (-0.008554) | 0.247871 / 0.255139 (-0.007267) | 0.265273 / 0.283200 (-0.017927) | 0.017886 / 0.141683 (-0.123796) | 1.090451 / 1.452155 (-0.361704) | 1.152034 / 1.492716 (-0.340682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094383 / 0.018006 (0.076377) | 0.301151 / 0.000490 (0.300661) | 0.000211 / 0.000200 (0.000011) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018927 / 0.037411 (-0.018484) | 0.062152 / 0.014526 (0.047626) | 0.072177 / 0.176557 (-0.104380) | 0.119792 / 0.737135 (-0.617343) | 0.073333 / 0.296338 (-0.223005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282671 / 0.215209 (0.067462) | 2.721148 / 2.077655 (0.643494) | 1.472689 / 1.504120 (-0.031431) | 1.355226 / 1.541195 (-0.185969) | 1.375935 / 1.468490 (-0.092556) | 0.562600 / 4.584777 (-4.022177) | 2.364046 / 3.745712 (-1.381666) | 2.714984 / 5.269862 (-2.554878) | 1.738413 / 4.565676 (-2.827263) | 0.062564 / 0.424275 (-0.361711) | 0.004964 / 0.007607 (-0.002643) | 0.341300 / 0.226044 (0.115255) | 3.345187 / 2.268929 (1.076259) | 1.857822 / 55.444624 (-53.586803) | 1.581002 / 6.876477 (-5.295475) | 1.585919 / 2.142072 (-0.556153) | 0.640105 / 4.805227 (-4.165122) | 0.117880 / 6.500664 (-6.382784) | 0.042032 / 0.075469 (-0.033437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962701 / 1.841788 (-0.879086) | 11.309251 / 8.074308 (3.234943) | 10.462520 / 10.191392 (0.271128) | 0.127399 / 0.680424 (-0.553025) | 0.014549 / 0.534201 (-0.519652) | 0.297017 / 0.579283 (-0.282266) | 0.266152 / 0.434364 (-0.168212) | 0.349252 / 0.540337 (-0.191085) | 0.457015 / 1.386936 (-0.929921) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005341 / 0.011353 (-0.006012) | 0.003108 / 0.011008 (-0.007900) | 0.048862 / 0.038508 (0.010353) | 0.053354 / 0.023109 (0.030245) | 0.274499 / 0.275898 (-0.001399) | 0.296698 / 0.323480 (-0.026782) | 0.003974 / 0.007986 (-0.004012) | 0.002631 / 0.004328 (-0.001697) | 0.048013 / 0.004250 (0.043762) | 0.040416 / 0.037052 (0.003363) | 0.276581 / 0.258489 (0.018092) | 0.301296 / 0.293841 (0.007455) | 0.029049 / 0.128546 (-0.099497) | 0.010253 / 0.075646 (-0.065393) | 0.057157 / 0.419271 (-0.362114) | 0.031830 / 0.043533 (-0.011703) | 0.274341 / 0.255139 (0.019202) | 0.292583 / 0.283200 (0.009383) | 0.018449 / 0.141683 (-0.123234) | 1.145099 / 1.452155 (-0.307055) | 1.192958 / 1.492716 (-0.299758) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091596 / 0.018006 (0.073590) | 0.300917 / 0.000490 (0.300427) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021657 / 0.037411 (-0.015754) | 0.068464 / 0.014526 (0.053938) | 0.079869 / 0.176557 (-0.096687) | 0.117523 / 0.737135 (-0.619613) | 0.081257 / 0.296338 (-0.215082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294876 / 0.215209 (0.079667) | 2.879372 / 2.077655 (0.801718) | 1.619887 / 1.504120 (0.115767) | 1.482154 / 1.541195 (-0.059041) | 1.494656 / 1.468490 (0.026166) | 0.558914 / 4.584777 (-4.025862) | 2.420948 / 3.745712 (-1.324765) | 2.728992 / 5.269862 (-2.540869) | 1.722135 / 4.565676 (-2.843542) | 0.062182 / 0.424275 (-0.362093) | 0.004933 / 0.007607 (-0.002674) | 0.342759 / 0.226044 (0.116715) | 3.424083 / 2.268929 (1.155154) | 1.950673 / 55.444624 (-53.493951) | 1.683126 / 6.876477 (-5.193351) | 1.673135 / 2.142072 (-0.468937) | 0.633711 / 4.805227 (-4.171516) | 0.114898 / 6.500664 (-6.385766) | 0.040332 / 0.075469 (-0.035137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975102 / 1.841788 (-0.866685) | 11.975731 / 8.074308 (3.901423) | 10.961103 / 10.191392 (0.769711) | 0.131152 / 0.680424 (-0.549272) | 0.016268 / 0.534201 (-0.517933) | 0.285031 / 0.579283 (-0.294252) | 0.279556 / 0.434364 (-0.154808) | 0.324183 / 0.540337 (-0.216154) | 0.571404 / 1.386936 (-0.815532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f67312956fc15572b6a0ca0dfcc0ceb90fbb794 \"CML watermark\")\n" ]
2023-12-15T14:40:57
2023-12-15T14:51:06
2023-12-15 14:44:47+00:00
MEMBER
nan
This code was failing ```python In [1]: from datasets import load_dataset In [2]: ...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True) ...: ...: sample_data = next(iter(ds))["premise"] # pick up one data ...: input_text = list(sample_data.values()) ``` ``` File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict) 102 return translation_dict 103 elif self.languages and set(translation_dict) - lang_set: --> 104 raise ValueError( 105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).' 106 ) 108 # Convert dictionary into tuples, splitting out cases where there are 109 # multiple translations for a single language. 110 translation_tuples = [] ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es). ``` because in streaming mode we expect features encode methods to be no-ops if the example is already encoded. I fixed `TranslationVariableLanguages` to account for that
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6502
Pickle support for `torch.Generator` objects
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005472 / 0.011353 (-0.005881) | 0.003715 / 0.011008 (-0.007293) | 0.063257 / 0.038508 (0.024749) | 0.060683 / 0.023109 (0.037574) | 0.250885 / 0.275898 (-0.025013) | 0.271685 / 0.323480 (-0.051795) | 0.003051 / 0.007986 (-0.004934) | 0.002799 / 0.004328 (-0.001530) | 0.049113 / 0.004250 (0.044863) | 0.038965 / 0.037052 (0.001912) | 0.252688 / 0.258489 (-0.005801) | 0.282536 / 0.293841 (-0.011305) | 0.028722 / 0.128546 (-0.099824) | 0.010586 / 0.075646 (-0.065060) | 0.205145 / 0.419271 (-0.214127) | 0.036996 / 0.043533 (-0.006537) | 0.248874 / 0.255139 (-0.006265) | 0.266148 / 0.283200 (-0.017051) | 0.018540 / 0.141683 (-0.123143) | 1.120216 / 1.452155 (-0.331938) | 1.191072 / 1.492716 (-0.301644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095721 / 0.018006 (0.077714) | 0.313401 / 0.000490 (0.312911) | 0.000234 / 0.000200 (0.000034) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018604 / 0.037411 (-0.018807) | 0.061571 / 0.014526 (0.047045) | 0.075343 / 0.176557 (-0.101213) | 0.121272 / 0.737135 (-0.615864) | 0.076448 / 0.296338 (-0.219890) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286885 / 0.215209 (0.071676) | 2.809100 / 2.077655 (0.731445) | 1.485365 / 1.504120 (-0.018755) | 1.367672 / 1.541195 (-0.173523) | 1.423570 / 1.468490 (-0.044920) | 0.571063 / 4.584777 (-4.013714) | 2.385248 / 3.745712 (-1.360464) | 2.855251 / 5.269862 (-2.414610) | 1.799371 / 4.565676 (-2.766306) | 0.063491 / 0.424275 (-0.360784) | 0.004942 / 0.007607 (-0.002665) | 0.346181 / 0.226044 (0.120137) | 3.388123 / 2.268929 (1.119195) | 1.819093 / 55.444624 (-53.625532) | 1.552998 / 6.876477 (-5.323479) | 1.627930 / 2.142072 (-0.514143) | 0.653438 / 4.805227 (-4.151789) | 0.123831 / 6.500664 (-6.376833) | 0.043340 / 0.075469 (-0.032129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952167 / 1.841788 (-0.889621) | 12.149515 / 8.074308 (4.075207) | 10.665085 / 10.191392 (0.473693) | 0.127768 / 0.680424 (-0.552656) | 0.014022 / 0.534201 (-0.520179) | 0.285959 / 0.579283 (-0.293324) | 0.269727 / 0.434364 (-0.164637) | 0.336646 / 0.540337 (-0.203692) | 0.442932 / 1.386936 (-0.944005) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005351 / 0.011353 (-0.006002) | 0.003561 / 0.011008 (-0.007448) | 0.048890 / 0.038508 (0.010382) | 0.054093 / 0.023109 (0.030984) | 0.274397 / 0.275898 (-0.001501) | 0.296980 / 0.323480 (-0.026500) | 0.004126 / 0.007986 (-0.003860) | 0.002751 / 0.004328 (-0.001578) | 0.049131 / 0.004250 (0.044880) | 0.040769 / 0.037052 (0.003716) | 0.279147 / 0.258489 (0.020658) | 0.302014 / 0.293841 (0.008173) | 0.029847 / 0.128546 (-0.098699) | 0.010710 / 0.075646 (-0.064936) | 0.057626 / 0.419271 (-0.361645) | 0.032801 / 0.043533 (-0.010732) | 0.272698 / 0.255139 (0.017559) | 0.289238 / 0.283200 (0.006039) | 0.017876 / 0.141683 (-0.123807) | 1.152059 / 1.452155 (-0.300096) | 1.212289 / 1.492716 (-0.280427) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092914 / 0.018006 (0.074908) | 0.303092 / 0.000490 (0.302603) | 0.000214 / 0.000200 (0.000014) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022074 / 0.037411 (-0.015337) | 0.070109 / 0.014526 (0.055583) | 0.083360 / 0.176557 (-0.093196) | 0.122445 / 0.737135 (-0.614690) | 0.083625 / 0.296338 (-0.212714) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282788 / 0.215209 (0.067579) | 2.789229 / 2.077655 (0.711574) | 1.571077 / 1.504120 (0.066957) | 1.452627 / 1.541195 (-0.088567) | 1.493176 / 1.468490 (0.024686) | 0.556892 / 4.584777 (-4.027885) | 2.442771 / 3.745712 (-1.302941) | 2.826316 / 5.269862 (-2.443545) | 1.758276 / 4.565676 (-2.807401) | 0.063039 / 0.424275 (-0.361236) | 0.004928 / 0.007607 (-0.002679) | 0.338247 / 0.226044 (0.112202) | 3.346344 / 2.268929 (1.077416) | 1.952520 / 55.444624 (-53.492104) | 1.664520 / 6.876477 (-5.211956) | 1.701528 / 2.142072 (-0.440544) | 0.634746 / 4.805227 (-4.170481) | 0.116879 / 6.500664 (-6.383786) | 0.040990 / 0.075469 (-0.034479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969521 / 1.841788 (-0.872267) | 12.431395 / 8.074308 (4.357087) | 10.907503 / 10.191392 (0.716111) | 0.131028 / 0.680424 (-0.549396) | 0.015239 / 0.534201 (-0.518962) | 0.290793 / 0.579283 (-0.288490) | 0.275072 / 0.434364 (-0.159292) | 0.331036 / 0.540337 (-0.209301) | 0.567858 / 1.386936 (-0.819078) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#092118fc00f7dd718ab3643739d7b23ff16c9eff \"CML watermark\")\n" ]
2023-12-15T13:55:12
2023-12-15T15:04:33
2023-12-15 14:58:22+00:00
COLLABORATOR
nan
Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616
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OverflowError: value too large to convert to int32_t
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2023-12-15T10:10:21
2023-12-15T10:10:21
NaT
NONE
nan
### Describe the bug ![image](https://github.com/huggingface/datasets/assets/47747764/f58044fb-ddda-48b6-ba68-7bbfef781630) ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done
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6500
Enable setting config as default when push_to_hub
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6500). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This is ready for review @huggingface/datasets. ", "Also what if the config is being overwritten and it was the default config and the user doesn't pass `set_default` ?\r\nI'd expect the config to keep being the default one but lmk what you think", "How can you unset a config as the default one? In the case you mentioned, I would expect the config not being the default one.", "Maybe by passing `set_default=False` ? (set_default can be None by default)", "I think that way we are unnecessarily complicating the logic of `push_to_hub` and as I told you, I would expect the contrary: the result of calling `push_to_hub` with a determined set of arguments should always be the same, independently of previous calls and the current state of the config on the Hub. Push to hub should be somehow stateless in that sense, and IMO the user expects that the push overwrites previous config if already present on the Hub. I find very confusing making it to partially update the config on the Hub.", "That makes sense, having it stateless is simpler and no need to do something too fancy indeed", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005329 / 0.011353 (-0.006024) | 0.002998 / 0.011008 (-0.008010) | 0.063756 / 0.038508 (0.025248) | 0.051713 / 0.023109 (0.028603) | 0.248135 / 0.275898 (-0.027763) | 0.269136 / 0.323480 (-0.054344) | 0.002970 / 0.007986 (-0.005015) | 0.002566 / 0.004328 (-0.001763) | 0.048110 / 0.004250 (0.043859) | 0.038415 / 0.037052 (0.001363) | 0.254012 / 0.258489 (-0.004477) | 0.281915 / 0.293841 (-0.011926) | 0.027503 / 0.128546 (-0.101043) | 0.010370 / 0.075646 (-0.065276) | 0.208965 / 0.419271 (-0.210306) | 0.035508 / 0.043533 (-0.008024) | 0.249116 / 0.255139 (-0.006023) | 0.266350 / 0.283200 (-0.016850) | 0.018440 / 0.141683 (-0.123243) | 1.101089 / 1.452155 (-0.351066) | 1.164870 / 1.492716 (-0.327847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090909 / 0.018006 (0.072903) | 0.298041 / 0.000490 (0.297551) | 0.000211 / 0.000200 (0.000012) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018137 / 0.037411 (-0.019275) | 0.059574 / 0.014526 (0.045048) | 0.071754 / 0.176557 (-0.104803) | 0.117980 / 0.737135 (-0.619155) | 0.072903 / 0.296338 (-0.223435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282844 / 0.215209 (0.067635) | 2.740916 / 2.077655 (0.663261) | 1.444546 / 1.504120 (-0.059574) | 1.321904 / 1.541195 (-0.219291) | 1.356957 / 1.468490 (-0.111533) | 0.568389 / 4.584777 (-4.016388) | 2.354042 / 3.745712 (-1.391671) | 2.719427 / 5.269862 (-2.550435) | 1.719616 / 4.565676 (-2.846061) | 0.062537 / 0.424275 (-0.361738) | 0.004915 / 0.007607 (-0.002692) | 0.334716 / 0.226044 (0.108672) | 3.299499 / 2.268929 (1.030571) | 1.814629 / 55.444624 (-53.629996) | 1.515245 / 6.876477 (-5.361232) | 1.553085 / 2.142072 (-0.588987) | 0.643859 / 4.805227 (-4.161368) | 0.116650 / 6.500664 (-6.384014) | 0.041432 / 0.075469 (-0.034037) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948227 / 1.841788 (-0.893561) | 11.331103 / 8.074308 (3.256795) | 10.209658 / 10.191392 (0.018266) | 0.126721 / 0.680424 (-0.553703) | 0.013638 / 0.534201 (-0.520563) | 0.282540 / 0.579283 (-0.296743) | 0.262635 / 0.434364 (-0.171729) | 0.335357 / 0.540337 (-0.204981) | 0.441798 / 1.386936 (-0.945138) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005200 / 0.011353 (-0.006153) | 0.003012 / 0.011008 (-0.007996) | 0.047571 / 0.038508 (0.009063) | 0.055069 / 0.023109 (0.031959) | 0.271150 / 0.275898 (-0.004748) | 0.294957 / 0.323480 (-0.028523) | 0.003922 / 0.007986 (-0.004064) | 0.002627 / 0.004328 (-0.001702) | 0.047777 / 0.004250 (0.043527) | 0.039507 / 0.037052 (0.002454) | 0.276314 / 0.258489 (0.017825) | 0.300436 / 0.293841 (0.006595) | 0.028951 / 0.128546 (-0.099595) | 0.010583 / 0.075646 (-0.065063) | 0.056535 / 0.419271 (-0.362737) | 0.032654 / 0.043533 (-0.010879) | 0.272945 / 0.255139 (0.017806) | 0.291909 / 0.283200 (0.008709) | 0.017545 / 0.141683 (-0.124138) | 1.195897 / 1.452155 (-0.256258) | 1.171855 / 1.492716 (-0.320861) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091919 / 0.018006 (0.073913) | 0.299297 / 0.000490 (0.298807) | 0.000225 / 0.000200 (0.000025) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022271 / 0.037411 (-0.015140) | 0.068903 / 0.014526 (0.054377) | 0.083767 / 0.176557 (-0.092790) | 0.120239 / 0.737135 (-0.616896) | 0.083448 / 0.296338 (-0.212891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295353 / 0.215209 (0.080144) | 2.911452 / 2.077655 (0.833798) | 1.577941 / 1.504120 (0.073821) | 1.454514 / 1.541195 (-0.086681) | 1.459575 / 1.468490 (-0.008915) | 0.572475 / 4.584777 (-4.012302) | 2.443634 / 3.745712 (-1.302078) | 2.801171 / 5.269862 (-2.468691) | 1.724214 / 4.565676 (-2.841462) | 0.063539 / 0.424275 (-0.360736) | 0.004939 / 0.007607 (-0.002668) | 0.347705 / 0.226044 (0.121660) | 3.489591 / 2.268929 (1.220663) | 1.944952 / 55.444624 (-53.499672) | 1.652810 / 6.876477 (-5.223667) | 1.656361 / 2.142072 (-0.485712) | 0.647052 / 4.805227 (-4.158176) | 0.117286 / 6.500664 (-6.383379) | 0.040979 / 0.075469 (-0.034490) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971761 / 1.841788 (-0.870027) | 11.770547 / 8.074308 (3.696239) | 10.402502 / 10.191392 (0.211110) | 0.128280 / 0.680424 (-0.552144) | 0.015160 / 0.534201 (-0.519041) | 0.286706 / 0.579283 (-0.292578) | 0.274539 / 0.434364 (-0.159825) | 0.324591 / 0.540337 (-0.215747) | 0.573846 / 1.386936 (-0.813090) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3329be80b9abfe83285ef940a590a4e9f68835a3 \"CML watermark\")\n" ]
2023-12-15T09:17:41
2023-12-18T11:56:11
2023-12-18 11:50:03+00:00
MEMBER
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Fix #6497.
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docs: add reference Git over SSH
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6499). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005701 / 0.011353 (-0.005652) | 0.003546 / 0.011008 (-0.007463) | 0.063335 / 0.038508 (0.024827) | 0.051987 / 0.023109 (0.028878) | 0.240429 / 0.275898 (-0.035469) | 0.260659 / 0.323480 (-0.062820) | 0.003866 / 0.007986 (-0.004120) | 0.002617 / 0.004328 (-0.001712) | 0.048653 / 0.004250 (0.044403) | 0.038176 / 0.037052 (0.001124) | 0.245496 / 0.258489 (-0.012993) | 0.277141 / 0.293841 (-0.016700) | 0.027886 / 0.128546 (-0.100660) | 0.010738 / 0.075646 (-0.064908) | 0.211255 / 0.419271 (-0.208016) | 0.045205 / 0.043533 (0.001672) | 0.243062 / 0.255139 (-0.012077) | 0.262877 / 0.283200 (-0.020323) | 0.023426 / 0.141683 (-0.118257) | 1.092247 / 1.452155 (-0.359908) | 1.161074 / 1.492716 (-0.331642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090488 / 0.018006 (0.072482) | 0.300993 / 0.000490 (0.300504) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018543 / 0.037411 (-0.018868) | 0.061418 / 0.014526 (0.046892) | 0.073242 / 0.176557 (-0.103314) | 0.120757 / 0.737135 (-0.616378) | 0.073967 / 0.296338 (-0.222372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282341 / 0.215209 (0.067132) | 2.741106 / 2.077655 (0.663451) | 1.416573 / 1.504120 (-0.087547) | 1.287904 / 1.541195 (-0.253291) | 1.309425 / 1.468490 (-0.159065) | 0.582592 / 4.584777 (-4.002184) | 2.404866 / 3.745712 (-1.340846) | 2.895397 / 5.269862 (-2.374464) | 1.799864 / 4.565676 (-2.765812) | 0.064386 / 0.424275 (-0.359889) | 0.004920 / 0.007607 (-0.002687) | 0.330879 / 0.226044 (0.104835) | 3.287064 / 2.268929 (1.018135) | 1.765169 / 55.444624 (-53.679456) | 1.490442 / 6.876477 (-5.386034) | 1.530960 / 2.142072 (-0.611113) | 0.655939 / 4.805227 (-4.149288) | 0.118529 / 6.500664 (-6.382135) | 0.042350 / 0.075469 (-0.033119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959027 / 1.841788 (-0.882761) | 11.911284 / 8.074308 (3.836976) | 10.576898 / 10.191392 (0.385506) | 0.141038 / 0.680424 (-0.539386) | 0.014184 / 0.534201 (-0.520017) | 0.305335 / 0.579283 (-0.273948) | 0.267531 / 0.434364 (-0.166832) | 0.353362 / 0.540337 (-0.186975) | 0.466258 / 1.386936 (-0.920678) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003561 / 0.011008 (-0.007448) | 0.049181 / 0.038508 (0.010673) | 0.056664 / 0.023109 (0.033555) | 0.267142 / 0.275898 (-0.008756) | 0.291871 / 0.323480 (-0.031609) | 0.003996 / 0.007986 (-0.003990) | 0.003147 / 0.004328 (-0.001181) | 0.048527 / 0.004250 (0.044276) | 0.040239 / 0.037052 (0.003187) | 0.269728 / 0.258489 (0.011239) | 0.295531 / 0.293841 (0.001690) | 0.030316 / 0.128546 (-0.098231) | 0.010666 / 0.075646 (-0.064981) | 0.058176 / 0.419271 (-0.361095) | 0.033218 / 0.043533 (-0.010315) | 0.265383 / 0.255139 (0.010244) | 0.285102 / 0.283200 (0.001902) | 0.018295 / 0.141683 (-0.123388) | 1.117830 / 1.452155 (-0.334325) | 1.196919 / 1.492716 (-0.295798) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088547 / 0.018006 (0.070541) | 0.293220 / 0.000490 (0.292730) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022060 / 0.037411 (-0.015351) | 0.071973 / 0.014526 (0.057448) | 0.081721 / 0.176557 (-0.094836) | 0.119990 / 0.737135 (-0.617145) | 0.081639 / 0.296338 (-0.214700) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293712 / 0.215209 (0.078503) | 2.872986 / 2.077655 (0.795331) | 1.568944 / 1.504120 (0.064824) | 1.434555 / 1.541195 (-0.106639) | 1.457747 / 1.468490 (-0.010743) | 0.559296 / 4.584777 (-4.025481) | 2.471845 / 3.745712 (-1.273867) | 2.840916 / 5.269862 (-2.428946) | 1.754909 / 4.565676 (-2.810768) | 0.064585 / 0.424275 (-0.359690) | 0.004992 / 0.007607 (-0.002615) | 0.349149 / 0.226044 (0.123104) | 3.385906 / 2.268929 (1.116977) | 1.940644 / 55.444624 (-53.503980) | 1.638300 / 6.876477 (-5.238177) | 1.649939 / 2.142072 (-0.492133) | 0.645680 / 4.805227 (-4.159547) | 0.118080 / 6.500664 (-6.382584) | 0.040643 / 0.075469 (-0.034826) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969965 / 1.841788 (-0.871822) | 12.099766 / 8.074308 (4.025457) | 10.550650 / 10.191392 (0.359258) | 0.131736 / 0.680424 (-0.548688) | 0.015483 / 0.534201 (-0.518718) | 0.289231 / 0.579283 (-0.290052) | 0.287505 / 0.434364 (-0.146858) | 0.327326 / 0.540337 (-0.213011) | 0.570364 / 1.386936 (-0.816572) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#533c38cef16111e9e8154eeb76c207f1f4936ddf \"CML watermark\")\n" ]
2023-12-15T08:38:31
2023-12-15T11:48:47
2023-12-15 11:42:38+00:00
CONTRIBUTOR
nan
see https://discuss.huggingface.co/t/update-datasets-getting-started-to-new-git-security/65893
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2042075969
PR_kwDODunzps5iBcFj
6498
Fallback on dataset script if user wants to load default config
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6498). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> I was just thinking: what if the user does not pass a config name and the dataset has only a config with a name different from \"default\"?\r\n\r\nYou mean if there is a DEFAULT_CONFIG_NAME defined in the script but the dataset only has one configuration ? We can't easily get the number of configs without running the python code so I don't think we can support detect this case\r\n", "Most datasets with a script don't define DEFAULT_CONFIG_NAME if there is only one configuration anyway.\r\n\r\nSo there is no issue e.g. for `squad`", "> I was trying to mean the case where DEFAULT_CONFIG_NAME is None but there is only a single config in BUILDER_CONFIGS, with a name different from \"default\".\r\n\r\nIn this case we can detect if \"DEFAULT_CONFIG_NAME\" is not mentioned and use the Parquet export. If it is mentioned (and maybe it is set to None or to the single config) I consider that it may have multiple configs and fall back on using the script", "... but the user does not pass the config name.", "In this case we load the single configuration (this is how a DatasetBuilder works)", "see \r\n\r\nhttps://github.com/huggingface/datasets/blob/2feaa589de86dd85941301fc8c3fa091731a67c0/src/datasets/builder.py#L532-L532", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005122 / 0.011353 (-0.006231) | 0.003565 / 0.011008 (-0.007443) | 0.062706 / 0.038508 (0.024198) | 0.049314 / 0.023109 (0.026205) | 0.247325 / 0.275898 (-0.028573) | 0.269788 / 0.323480 (-0.053692) | 0.003895 / 0.007986 (-0.004090) | 0.002788 / 0.004328 (-0.001540) | 0.048615 / 0.004250 (0.044365) | 0.037591 / 0.037052 (0.000539) | 0.253495 / 0.258489 (-0.004994) | 0.281200 / 0.293841 (-0.012641) | 0.027712 / 0.128546 (-0.100834) | 0.010901 / 0.075646 (-0.064745) | 0.205577 / 0.419271 (-0.213694) | 0.035989 / 0.043533 (-0.007544) | 0.252978 / 0.255139 (-0.002161) | 0.268042 / 0.283200 (-0.015157) | 0.017857 / 0.141683 (-0.123826) | 1.096633 / 1.452155 (-0.355521) | 1.147026 / 1.492716 (-0.345691) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095609 / 0.018006 (0.077603) | 0.311941 / 0.000490 (0.311451) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019042 / 0.037411 (-0.018369) | 0.060549 / 0.014526 (0.046023) | 0.074761 / 0.176557 (-0.101796) | 0.121729 / 0.737135 (-0.615406) | 0.075661 / 0.296338 (-0.220677) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284774 / 0.215209 (0.069565) | 2.764576 / 2.077655 (0.686921) | 1.489926 / 1.504120 (-0.014194) | 1.387276 / 1.541195 (-0.153919) | 1.400931 / 1.468490 (-0.067559) | 0.555623 / 4.584777 (-4.029154) | 2.409488 / 3.745712 (-1.336224) | 2.781053 / 5.269862 (-2.488808) | 1.750472 / 4.565676 (-2.815204) | 0.062232 / 0.424275 (-0.362043) | 0.004974 / 0.007607 (-0.002633) | 0.336324 / 0.226044 (0.110280) | 3.286619 / 2.268929 (1.017691) | 1.825070 / 55.444624 (-53.619554) | 1.537993 / 6.876477 (-5.338484) | 1.586520 / 2.142072 (-0.555553) | 0.640090 / 4.805227 (-4.165138) | 0.117637 / 6.500664 (-6.383027) | 0.042318 / 0.075469 (-0.033151) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964051 / 1.841788 (-0.877736) | 11.706259 / 8.074308 (3.631951) | 10.752311 / 10.191392 (0.560919) | 0.128117 / 0.680424 (-0.552307) | 0.014001 / 0.534201 (-0.520200) | 0.286255 / 0.579283 (-0.293028) | 0.263810 / 0.434364 (-0.170554) | 0.329347 / 0.540337 (-0.210991) | 0.437349 / 1.386936 (-0.949587) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005303 / 0.011353 (-0.006050) | 0.003586 / 0.011008 (-0.007422) | 0.049339 / 0.038508 (0.010831) | 0.051287 / 0.023109 (0.028178) | 0.274397 / 0.275898 (-0.001501) | 0.292977 / 0.323480 (-0.030503) | 0.004029 / 0.007986 (-0.003957) | 0.002727 / 0.004328 (-0.001602) | 0.048779 / 0.004250 (0.044528) | 0.040075 / 0.037052 (0.003022) | 0.277676 / 0.258489 (0.019187) | 0.301963 / 0.293841 (0.008122) | 0.029340 / 0.128546 (-0.099206) | 0.010714 / 0.075646 (-0.064932) | 0.057253 / 0.419271 (-0.362018) | 0.033426 / 0.043533 (-0.010107) | 0.276673 / 0.255139 (0.021534) | 0.291053 / 0.283200 (0.007854) | 0.017660 / 0.141683 (-0.124023) | 1.122354 / 1.452155 (-0.329800) | 1.180381 / 1.492716 (-0.312335) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091903 / 0.018006 (0.073897) | 0.300720 / 0.000490 (0.300231) | 0.000288 / 0.000200 (0.000088) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021521 / 0.037411 (-0.015890) | 0.068233 / 0.014526 (0.053707) | 0.081245 / 0.176557 (-0.095312) | 0.119996 / 0.737135 (-0.617139) | 0.082483 / 0.296338 (-0.213856) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302776 / 0.215209 (0.087567) | 2.950776 / 2.077655 (0.873122) | 1.631032 / 1.504120 (0.126912) | 1.502021 / 1.541195 (-0.039174) | 1.514213 / 1.468490 (0.045723) | 0.578246 / 4.584777 (-4.006531) | 2.443768 / 3.745712 (-1.301944) | 2.827811 / 5.269862 (-2.442051) | 1.771529 / 4.565676 (-2.794148) | 0.064479 / 0.424275 (-0.359797) | 0.005061 / 0.007607 (-0.002546) | 0.350966 / 0.226044 (0.124922) | 3.458616 / 2.268929 (1.189687) | 1.967917 / 55.444624 (-53.476707) | 1.704661 / 6.876477 (-5.171815) | 1.698895 / 2.142072 (-0.443178) | 0.663259 / 4.805227 (-4.141968) | 0.122140 / 6.500664 (-6.378525) | 0.041099 / 0.075469 (-0.034371) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972080 / 1.841788 (-0.869708) | 12.123286 / 8.074308 (4.048978) | 10.819854 / 10.191392 (0.628462) | 0.131486 / 0.680424 (-0.548938) | 0.015785 / 0.534201 (-0.518416) | 0.290048 / 0.579283 (-0.289235) | 0.277822 / 0.434364 (-0.156542) | 0.325949 / 0.540337 (-0.214388) | 0.577681 / 1.386936 (-0.809255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#30f6a2d9af183eba4501f0b8d90e9200bdca6bb1 \"CML watermark\")\n" ]
2023-12-14T16:46:01
2023-12-15T13:16:56
2023-12-15 13:10:48+00:00
MEMBER
nan
Right now this code is failing on `main`: ```python load_dataset("openbookqa") ``` This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one. I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`): ```python load_dataset("openbookqa", trust_remote_code=True) ``` Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default) ```python load_dataset("openbookqa", "main") ```
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2041994274
I_kwDODunzps55tlwi
6497
Support setting a default config name in push_to_hub
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2023-12-14T15:59:03
2023-12-18T11:50:04
2023-12-18 11:50:04+00:00
MEMBER
nan
In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.
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2041589386
I_kwDODunzps55sC6K
6496
Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.
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[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]
2023-12-14T11:24:54
2023-12-14T12:22:21
NaT
NONE
nan
**Describe the bug** Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` **Steps to reproduce the bug** This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` **Expected behavior** Would expect to write to the hub without any problem. **Environment info** ``` datasets==2.15.0 huggingface-hub==0.19.4 ```
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2039684839
I_kwDODunzps55kx7n
6494
Image Data loaded Twice
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2023-12-13T13:11:42
2023-12-13T13:11:42
NaT
NONE
nan
### Describe the bug ![1702472610561](https://github.com/huggingface/datasets/assets/28867010/4b7ef5e7-32c3-4b73-84cb-5de059caa0b6) When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images ### Steps to reproduce the bug from datasets import Dataset, load_dataset dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False) # print(dataset["train"][0]["image"] == dataset["train"][1]["image"]) print(dataset) print(dataset["train"]["image"]) print(len(dataset["train"]["image"])) ### Expected behavior DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 8 }) }) [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>] 8 ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.17 - Huggingface_hub version: 0.19.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3
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2039708529
I_kwDODunzps55k3tx
6495
Newline characters don't behave as expected when calling dataset.info
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2023-12-12T23:07:51
2023-12-13T13:24:22
NaT
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nan
### System Info - `transformers` version: 4.32.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.2 - Accelerate version: not installed - Accelerate config: not found - PyTorch version (GPU?): 2.1.1+cpu (False) - Tensorflow version (GPU?): 2.15.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: no - Using distributed or parallel set-up in script?: no ### Who can help? @marios ### Information - [X] The official example scripts - [ ] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction [Source](https://huggingface.co/docs/datasets/v2.2.1/en/access) ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673) ### Expected behavior ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo( description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 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2038221490
PR_kwDODunzps5h0XJK
6493
Lazy data files resolution and offline cache reload
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Naive question: is there any breaking change when loading?\r\n\r\nNo breaking changes except that the cache folders are different\r\n\r\ne.g. for glue sst2 (has parquet export)\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037\r\nOn main\r\n~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\r\n```\r\n\r\ne.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9\r\nOn main (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13\r\nOn 2.15.0 (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564\r\n```\r\n\r\n\r\ne.g. for lhoestq/demo1 (no metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11\r\nOn main\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\n```", "There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory\r\nedit: fixed", "I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.\r\n\r\nAnd for local file it's a hash that takes into account the resolved files (and their last modified dates)", "I also ran the `transformers` CI on this branch and it's green", "FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday", "Merging this one, and hopefully the cache backward compatibility PR soon too :)\r\n\r\nThen it will be release time", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005444 / 0.011353 (-0.005909) | 0.003562 / 0.011008 (-0.007446) | 0.063183 / 0.038508 (0.024675) | 0.048885 / 0.023109 (0.025776) | 0.248422 / 0.275898 (-0.027476) | 0.277844 / 0.323480 (-0.045636) | 0.003019 / 0.007986 (-0.004966) | 0.002660 / 0.004328 (-0.001669) | 0.048928 / 0.004250 (0.044677) | 0.044850 / 0.037052 (0.007798) | 0.248505 / 0.258489 (-0.009984) | 0.282231 / 0.293841 (-0.011610) | 0.028302 / 0.128546 (-0.100244) | 0.010829 / 0.075646 (-0.064818) | 0.206738 / 0.419271 (-0.212533) | 0.035485 / 0.043533 (-0.008048) | 0.244575 / 0.255139 (-0.010564) | 0.281411 / 0.283200 (-0.001789) | 0.019563 / 0.141683 (-0.122120) | 1.113769 / 1.452155 (-0.338386) | 1.176831 / 1.492716 (-0.315885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004718 / 0.018006 (-0.013288) | 0.304103 / 0.000490 (0.303614) | 0.000214 / 0.000200 (0.000014) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019642 / 0.037411 (-0.017769) | 0.060275 / 0.014526 (0.045749) | 0.073072 / 0.176557 (-0.103484) | 0.119789 / 0.737135 (-0.617346) | 0.074535 / 0.296338 (-0.221804) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278799 / 0.215209 (0.063590) | 2.725320 / 2.077655 (0.647665) | 1.419048 / 1.504120 (-0.085071) | 1.335041 / 1.541195 (-0.206154) | 1.373029 / 1.468490 (-0.095461) | 0.566774 / 4.584777 (-4.018003) | 2.383796 / 3.745712 (-1.361916) | 2.734804 / 5.269862 (-2.535057) | 1.712277 / 4.565676 (-2.853399) | 0.062119 / 0.424275 (-0.362156) | 0.004949 / 0.007607 (-0.002658) | 0.336126 / 0.226044 (0.110082) | 3.298602 / 2.268929 (1.029674) | 1.842815 / 55.444624 (-53.601809) | 1.544028 / 6.876477 (-5.332449) | 1.566717 / 2.142072 (-0.575355) | 0.643006 / 4.805227 (-4.162221) | 0.118241 / 6.500664 (-6.382423) | 0.042453 / 0.075469 (-0.033016) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949015 / 1.841788 (-0.892773) | 11.717958 / 8.074308 (3.643649) | 10.482448 / 10.191392 (0.291056) | 0.128564 / 0.680424 (-0.551860) | 0.014792 / 0.534201 (-0.519408) | 0.288636 / 0.579283 (-0.290647) | 0.263345 / 0.434364 (-0.171019) | 0.325753 / 0.540337 (-0.214584) | 0.421294 / 1.386936 (-0.965642) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005367 / 0.011353 (-0.005985) | 0.003802 / 0.011008 (-0.007206) | 0.049322 / 0.038508 (0.010814) | 0.055201 / 0.023109 (0.032092) | 0.287811 / 0.275898 (0.011913) | 0.305141 / 0.323480 (-0.018339) | 0.004095 / 0.007986 (-0.003890) | 0.002733 / 0.004328 (-0.001595) | 0.049508 / 0.004250 (0.045258) | 0.039199 / 0.037052 (0.002147) | 0.282719 / 0.258489 (0.024230) | 0.311156 / 0.293841 (0.017315) | 0.029469 / 0.128546 (-0.099077) | 0.010709 / 0.075646 (-0.064937) | 0.057646 / 0.419271 (-0.361626) | 0.032696 / 0.043533 (-0.010837) | 0.285087 / 0.255139 (0.029948) | 0.294142 / 0.283200 (0.010942) | 0.019779 / 0.141683 (-0.121904) | 1.176844 / 1.452155 (-0.275310) | 1.190925 / 1.492716 (-0.301792) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092885 / 0.018006 (0.074879) | 0.301129 / 0.000490 (0.300640) | 0.000232 / 0.000200 (0.000032) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023202 / 0.037411 (-0.014210) | 0.076850 / 0.014526 (0.062325) | 0.090058 / 0.176557 (-0.086499) | 0.128091 / 0.737135 (-0.609045) | 0.091098 / 0.296338 (-0.205240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292973 / 0.215209 (0.077764) | 2.876022 / 2.077655 (0.798367) | 1.672115 / 1.504120 (0.167995) | 1.555103 / 1.541195 (0.013909) | 1.559832 / 1.468490 (0.091342) | 0.558017 / 4.584777 (-4.026760) | 2.428448 / 3.745712 (-1.317264) | 2.812024 / 5.269862 (-2.457837) | 1.738470 / 4.565676 (-2.827207) | 0.062669 / 0.424275 (-0.361607) | 0.005071 / 0.007607 (-0.002536) | 0.351804 / 0.226044 (0.125759) | 3.412207 / 2.268929 (1.143279) | 2.023478 / 55.444624 (-53.421147) | 1.761281 / 6.876477 (-5.115195) | 1.770789 / 2.142072 (-0.371283) | 0.643062 / 4.805227 (-4.162165) | 0.116616 / 6.500664 (-6.384048) | 0.041816 / 0.075469 (-0.033653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988430 / 1.841788 (-0.853357) | 12.278636 / 8.074308 (4.204328) | 11.066185 / 10.191392 (0.874793) | 0.141191 / 0.680424 (-0.539233) | 0.015547 / 0.534201 (-0.518654) | 0.288045 / 0.579283 (-0.291238) | 0.279651 / 0.434364 (-0.154713) | 0.329869 / 0.540337 (-0.210469) | 0.420391 / 1.386936 (-0.966545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 \"CML watermark\")\n" ]
2023-12-12T17:15:17
2023-12-21T15:19:20
2023-12-21 15:13:11+00:00
MEMBER
nan
Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459 This PR should be merged instead of the two individually, since they are conflicting ## Offline cache reload it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023). DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` - Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>` - Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one - I instantiate a BuilderConfig even if there is no metadata config with the right config_name - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs) - We can reload this config if offline mode by specifying the right config_name (same as online !) - Consequences of this change: - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash. - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :) Fix https://github.com/huggingface/datasets/issues/3547 ## Lazy data files resolution this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
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6492
Make push_to_hub return CommitInfo
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6492). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This PR is ready to review @huggingface/datasets.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005093 / 0.011353 (-0.006259) | 0.003695 / 0.011008 (-0.007313) | 0.064648 / 0.038508 (0.026140) | 0.054677 / 0.023109 (0.031568) | 0.242007 / 0.275898 (-0.033891) | 0.265216 / 0.323480 (-0.058264) | 0.003847 / 0.007986 (-0.004138) | 0.003773 / 0.004328 (-0.000556) | 0.048595 / 0.004250 (0.044345) | 0.038122 / 0.037052 (0.001070) | 0.245698 / 0.258489 (-0.012791) | 0.278095 / 0.293841 (-0.015746) | 0.027488 / 0.128546 (-0.101058) | 0.011002 / 0.075646 (-0.064644) | 0.211443 / 0.419271 (-0.207829) | 0.035664 / 0.043533 (-0.007869) | 0.244754 / 0.255139 (-0.010385) | 0.261078 / 0.283200 (-0.022121) | 0.017768 / 0.141683 (-0.123915) | 1.130765 / 1.452155 (-0.321390) | 1.189825 / 1.492716 (-0.302891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093027 / 0.018006 (0.075021) | 0.302193 / 0.000490 (0.301703) | 0.000207 / 0.000200 (0.000007) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018413 / 0.037411 (-0.018999) | 0.062715 / 0.014526 (0.048190) | 0.073287 / 0.176557 (-0.103269) | 0.120394 / 0.737135 (-0.616741) | 0.077573 / 0.296338 (-0.218765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284445 / 0.215209 (0.069236) | 2.780718 / 2.077655 (0.703063) | 1.460988 / 1.504120 (-0.043132) | 1.345799 / 1.541195 (-0.195395) | 1.399892 / 1.468490 (-0.068598) | 0.576051 / 4.584777 (-4.008726) | 2.418792 / 3.745712 (-1.326921) | 2.901330 / 5.269862 (-2.368532) | 1.765083 / 4.565676 (-2.800593) | 0.063555 / 0.424275 (-0.360720) | 0.004991 / 0.007607 (-0.002616) | 0.339657 / 0.226044 (0.113613) | 3.372963 / 2.268929 (1.104034) | 1.853667 / 55.444624 (-53.590958) | 1.552022 / 6.876477 (-5.324454) | 1.616452 / 2.142072 (-0.525620) | 0.652309 / 4.805227 (-4.152919) | 0.121125 / 6.500664 (-6.379539) | 0.042420 / 0.075469 (-0.033049) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.954514 / 1.841788 (-0.887274) | 11.853736 / 8.074308 (3.779428) | 10.624571 / 10.191392 (0.433179) | 0.134118 / 0.680424 (-0.546306) | 0.014200 / 0.534201 (-0.520001) | 0.290106 / 0.579283 (-0.289177) | 0.270637 / 0.434364 (-0.163727) | 0.336155 / 0.540337 (-0.204182) | 0.443962 / 1.386936 (-0.942974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005282 / 0.011353 (-0.006071) | 0.003526 / 0.011008 (-0.007482) | 0.048994 / 0.038508 (0.010486) | 0.055345 / 0.023109 (0.032236) | 0.271587 / 0.275898 (-0.004311) | 0.294676 / 0.323480 (-0.028804) | 0.003989 / 0.007986 (-0.003996) | 0.002594 / 0.004328 (-0.001735) | 0.048310 / 0.004250 (0.044059) | 0.039945 / 0.037052 (0.002893) | 0.277304 / 0.258489 (0.018815) | 0.312017 / 0.293841 (0.018176) | 0.028364 / 0.128546 (-0.100182) | 0.010683 / 0.075646 (-0.064963) | 0.057990 / 0.419271 (-0.361281) | 0.032418 / 0.043533 (-0.011115) | 0.273835 / 0.255139 (0.018697) | 0.288585 / 0.283200 (0.005385) | 0.018964 / 0.141683 (-0.122719) | 1.148863 / 1.452155 (-0.303292) | 1.195684 / 1.492716 (-0.297032) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091967 / 0.018006 (0.073960) | 0.303236 / 0.000490 (0.302747) | 0.000214 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021960 / 0.037411 (-0.015452) | 0.068744 / 0.014526 (0.054218) | 0.081167 / 0.176557 (-0.095390) | 0.119623 / 0.737135 (-0.617513) | 0.084965 / 0.296338 (-0.211373) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297740 / 0.215209 (0.082531) | 2.924856 / 2.077655 (0.847201) | 1.602080 / 1.504120 (0.097960) | 1.494083 / 1.541195 (-0.047112) | 1.544662 / 1.468490 (0.076172) | 0.581212 / 4.584777 (-4.003565) | 2.451064 / 3.745712 (-1.294648) | 2.875213 / 5.269862 (-2.394649) | 1.780777 / 4.565676 (-2.784900) | 0.063751 / 0.424275 (-0.360524) | 0.004967 / 0.007607 (-0.002641) | 0.350321 / 0.226044 (0.124276) | 3.449585 / 2.268929 (1.180657) | 1.977666 / 55.444624 (-53.466958) | 1.685125 / 6.876477 (-5.191351) | 1.734466 / 2.142072 (-0.407606) | 0.657477 / 4.805227 (-4.147750) | 0.116767 / 6.500664 (-6.383898) | 0.041400 / 0.075469 (-0.034069) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985751 / 1.841788 (-0.856037) | 12.300065 / 8.074308 (4.225756) | 10.608238 / 10.191392 (0.416846) | 0.139907 / 0.680424 (-0.540517) | 0.015379 / 0.534201 (-0.518822) | 0.283528 / 0.579283 (-0.295755) | 0.278751 / 0.434364 (-0.155613) | 0.328811 / 0.540337 (-0.211527) | 0.584041 / 1.386936 (-0.802895) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef0f986518bd252c5314a7e3a419dedcbb166630 \"CML watermark\")\n" ]
2023-12-12T15:18:16
2023-12-13T14:29:01
2023-12-13 14:22:41+00:00
MEMBER
nan
Make `push_to_hub` return `CommitInfo`. This is useful, for example, if we pass `create_pr=True` and we want to know the created PR ID. CC: @severo for the use case in https://huggingface.co/datasets/jmhessel/newyorker_caption_contest/discussions/4
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https://github.com/huggingface/datasets/pull/6491
2037690643
PR_kwDODunzps5hyiTY
6491
Fix metrics dead link
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005193 / 0.011353 (-0.006160) | 0.003246 / 0.011008 (-0.007762) | 0.063053 / 0.038508 (0.024545) | 0.049636 / 0.023109 (0.026527) | 0.240990 / 0.275898 (-0.034908) | 0.263732 / 0.323480 (-0.059747) | 0.004062 / 0.007986 (-0.003923) | 0.002681 / 0.004328 (-0.001648) | 0.048527 / 0.004250 (0.044277) | 0.044159 / 0.037052 (0.007107) | 0.248031 / 0.258489 (-0.010458) | 0.275705 / 0.293841 (-0.018136) | 0.028210 / 0.128546 (-0.100336) | 0.010314 / 0.075646 (-0.065332) | 0.209887 / 0.419271 (-0.209384) | 0.035649 / 0.043533 (-0.007884) | 0.251321 / 0.255139 (-0.003818) | 0.266672 / 0.283200 (-0.016528) | 0.017382 / 0.141683 (-0.124301) | 1.088937 / 1.452155 (-0.363217) | 1.143692 / 1.492716 (-0.349024) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092558 / 0.018006 (0.074552) | 0.301648 / 0.000490 (0.301159) | 0.000208 / 0.000200 (0.000008) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018305 / 0.037411 (-0.019106) | 0.059836 / 0.014526 (0.045310) | 0.072926 / 0.176557 (-0.103631) | 0.119826 / 0.737135 (-0.617309) | 0.074357 / 0.296338 (-0.221982) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279051 / 0.215209 (0.063842) | 2.711402 / 2.077655 (0.633747) | 1.431782 / 1.504120 (-0.072338) | 1.316592 / 1.541195 (-0.224603) | 1.352062 / 1.468490 (-0.116428) | 0.562553 / 4.584777 (-4.022224) | 2.387719 / 3.745712 (-1.357993) | 2.693330 / 5.269862 (-2.576532) | 1.682040 / 4.565676 (-2.883636) | 0.061832 / 0.424275 (-0.362443) | 0.005066 / 0.007607 (-0.002541) | 0.332730 / 0.226044 (0.106685) | 3.315503 / 2.268929 (1.046575) | 1.787129 / 55.444624 (-53.657496) | 1.508955 / 6.876477 (-5.367522) | 1.512620 / 2.142072 (-0.629453) | 0.637120 / 4.805227 (-4.168107) | 0.116005 / 6.500664 (-6.384660) | 0.041973 / 0.075469 (-0.033496) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936996 / 1.841788 (-0.904792) | 11.485975 / 8.074308 (3.411667) | 10.604481 / 10.191392 (0.413089) | 0.130803 / 0.680424 (-0.549621) | 0.014561 / 0.534201 (-0.519640) | 0.285905 / 0.579283 (-0.293378) | 0.271573 / 0.434364 (-0.162791) | 0.329206 / 0.540337 (-0.211132) | 0.411977 / 1.386936 (-0.974959) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005333 / 0.011353 (-0.006020) | 0.003519 / 0.011008 (-0.007489) | 0.050880 / 0.038508 (0.012372) | 0.053681 / 0.023109 (0.030571) | 0.269359 / 0.275898 (-0.006539) | 0.291498 / 0.323480 (-0.031982) | 0.004006 / 0.007986 (-0.003979) | 0.002676 / 0.004328 (-0.001653) | 0.049652 / 0.004250 (0.045401) | 0.040588 / 0.037052 (0.003536) | 0.271701 / 0.258489 (0.013212) | 0.308384 / 0.293841 (0.014543) | 0.028713 / 0.128546 (-0.099833) | 0.010423 / 0.075646 (-0.065223) | 0.058099 / 0.419271 (-0.361172) | 0.032372 / 0.043533 (-0.011161) | 0.269395 / 0.255139 (0.014256) | 0.292252 / 0.283200 (0.009052) | 0.020038 / 0.141683 (-0.121645) | 1.124761 / 1.452155 (-0.327393) | 1.177609 / 1.492716 (-0.315107) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092187 / 0.018006 (0.074181) | 0.301936 / 0.000490 (0.301446) | 0.000230 / 0.000200 (0.000030) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022932 / 0.037411 (-0.014480) | 0.076552 / 0.014526 (0.062027) | 0.088729 / 0.176557 (-0.087827) | 0.127198 / 0.737135 (-0.609937) | 0.091902 / 0.296338 (-0.204436) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299964 / 0.215209 (0.084755) | 2.929352 / 2.077655 (0.851697) | 1.598715 / 1.504120 (0.094595) | 1.462438 / 1.541195 (-0.078756) | 1.474308 / 1.468490 (0.005818) | 0.567120 / 4.584777 (-4.017657) | 2.481757 / 3.745712 (-1.263955) | 2.795375 / 5.269862 (-2.474487) | 1.740346 / 4.565676 (-2.825331) | 0.064048 / 0.424275 (-0.360227) | 0.004995 / 0.007607 (-0.002612) | 0.349084 / 0.226044 (0.123040) | 3.417679 / 2.268929 (1.148750) | 1.910615 / 55.444624 (-53.534009) | 1.694120 / 6.876477 (-5.182356) | 1.658654 / 2.142072 (-0.483419) | 0.638158 / 4.805227 (-4.167069) | 0.115509 / 6.500664 (-6.385156) | 0.040650 / 0.075469 (-0.034819) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988081 / 1.841788 (-0.853707) | 12.210089 / 8.074308 (4.135781) | 11.090203 / 10.191392 (0.898811) | 0.131861 / 0.680424 (-0.548563) | 0.015461 / 0.534201 (-0.518740) | 0.287737 / 0.579283 (-0.291546) | 0.284170 / 0.434364 (-0.150194) | 0.324949 / 0.540337 (-0.215388) | 0.414912 / 1.386936 (-0.972024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf71653947cecd84050daf0448dc5a73c2c071f3 \"CML watermark\")\n" ]
2023-12-12T12:51:49
2023-12-21T15:15:08
2023-12-21 15:08:53+00:00
MEMBER
nan
None
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2037204892
I_kwDODunzps55bUec
6490
`load_dataset(...,save_infos=True)` not working without loading script
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[ "Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.\r\nThe consequence here is any other dataset won't load because of infos mismatch.\r\nTo reproduce this aspect:\r\n1. Do a `load_datasets(...,save_infos=True)` with one dataset without a loading script\r\n2. Try to load another dataset without a loading script in the same format (e.g. json) but with a different schema " ]
2023-12-12T08:09:18
2023-12-12T08:36:22
NaT
NONE
nan
### Describe the bug It seems that saving a dataset infos back into the card file is not working for datasets without a loading script. After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory. Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). ### Steps to reproduce the bug 1. Have a local dataset without any loading script 2. Make sure there are no dataset infos in the README.md 3. Load with `save_infos=True` 4. No change in the dataset README.md 5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`) ### Expected behavior The dataset README.md should be updated and no file should be created in the python environment. ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.6.0
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2036743777
I_kwDODunzps55Zj5h
6489
load_dataset imageflder for aws s3 path
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2023-12-12T00:08:43
2023-12-12T00:09:27
NaT
NONE
nan
### Feature request I would like to load a dataset from S3 using the imagefolder option something like `dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) ` ### Motivation no need of data_files ### Your contribution no experience with this
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2035899898
I_kwDODunzps55WV36
6488
429 Client Error
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[ "Transferring repos as this is a datasets issue ", "I'm getting a similar issue even though I've already downloaded the dataset 😅 \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight\r\n```" ]
2023-12-11T15:06:01
2024-06-20T05:55:45
NaT
NONE
nan
Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it? Thanks Dataset: https://huggingface.co/datasets/cerebras/SlimPajama-627B Error: `requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`
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2035424254
PR_kwDODunzps5hqyfV
6487
Update builder hash with info
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458" ]
2023-12-11T11:09:16
2024-01-11T06:35:07
2023-12-11 11:41:34+00:00
MEMBER
nan
Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change. This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub) Ideally we should take the resolved files into account as well but this will be for another PR
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2035206206
PR_kwDODunzps5hqCSc
6486
Fix docs phrasing about supported formats when sharing a dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6486). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005042 / 0.011353 (-0.006311) | 0.003452 / 0.011008 (-0.007557) | 0.061845 / 0.038508 (0.023337) | 0.052042 / 0.023109 (0.028933) | 0.241791 / 0.275898 (-0.034107) | 0.264639 / 0.323480 (-0.058841) | 0.003940 / 0.007986 (-0.004045) | 0.002768 / 0.004328 (-0.001560) | 0.047851 / 0.004250 (0.043600) | 0.037599 / 0.037052 (0.000547) | 0.251462 / 0.258489 (-0.007028) | 0.274737 / 0.293841 (-0.019104) | 0.027723 / 0.128546 (-0.100823) | 0.010510 / 0.075646 (-0.065137) | 0.205581 / 0.419271 (-0.213691) | 0.035504 / 0.043533 (-0.008029) | 0.242380 / 0.255139 (-0.012759) | 0.259791 / 0.283200 (-0.023409) | 0.017752 / 0.141683 (-0.123931) | 1.089289 / 1.452155 (-0.362865) | 1.161958 / 1.492716 (-0.330759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094288 / 0.018006 (0.076282) | 0.303253 / 0.000490 (0.302763) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018496 / 0.037411 (-0.018915) | 0.060411 / 0.014526 (0.045885) | 0.074294 / 0.176557 (-0.102262) | 0.122934 / 0.737135 (-0.614201) | 0.074710 / 0.296338 (-0.221629) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286394 / 0.215209 (0.071185) | 2.806145 / 2.077655 (0.728490) | 1.497071 / 1.504120 (-0.007049) | 1.362254 / 1.541195 (-0.178940) | 1.389642 / 1.468490 (-0.078848) | 0.554503 / 4.584777 (-4.030274) | 2.348029 / 3.745712 (-1.397684) | 2.780862 / 5.269862 (-2.489000) | 1.728058 / 4.565676 (-2.837619) | 0.062617 / 0.424275 (-0.361658) | 0.004901 / 0.007607 (-0.002707) | 0.346267 / 0.226044 (0.120223) | 3.363744 / 2.268929 (1.094815) | 1.826994 / 55.444624 (-53.617630) | 1.560656 / 6.876477 (-5.315820) | 1.561083 / 2.142072 (-0.580990) | 0.643395 / 4.805227 (-4.161832) | 0.116206 / 6.500664 (-6.384458) | 0.042008 / 0.075469 (-0.033461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953416 / 1.841788 (-0.888371) | 11.461665 / 8.074308 (3.387357) | 10.623865 / 10.191392 (0.432473) | 0.128071 / 0.680424 (-0.552353) | 0.014277 / 0.534201 (-0.519924) | 0.288810 / 0.579283 (-0.290474) | 0.267575 / 0.434364 (-0.166788) | 0.327422 / 0.540337 (-0.212916) | 0.435151 / 1.386936 (-0.951785) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005242 / 0.011353 (-0.006111) | 0.003515 / 0.011008 (-0.007493) | 0.048483 / 0.038508 (0.009975) | 0.051684 / 0.023109 (0.028575) | 0.276564 / 0.275898 (0.000666) | 0.297582 / 0.323480 (-0.025898) | 0.004117 / 0.007986 (-0.003869) | 0.002610 / 0.004328 (-0.001719) | 0.047811 / 0.004250 (0.043561) | 0.040622 / 0.037052 (0.003569) | 0.280265 / 0.258489 (0.021776) | 0.311719 / 0.293841 (0.017878) | 0.028811 / 0.128546 (-0.099735) | 0.010600 / 0.075646 (-0.065047) | 0.056660 / 0.419271 (-0.362611) | 0.032638 / 0.043533 (-0.010894) | 0.276434 / 0.255139 (0.021295) | 0.299095 / 0.283200 (0.015896) | 0.018483 / 0.141683 (-0.123200) | 1.156382 / 1.452155 (-0.295773) | 1.252205 / 1.492716 (-0.240511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097868 / 0.018006 (0.079862) | 0.309438 / 0.000490 (0.308948) | 0.000229 / 0.000200 (0.000029) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021838 / 0.037411 (-0.015573) | 0.068358 / 0.014526 (0.053832) | 0.080432 / 0.176557 (-0.096125) | 0.119788 / 0.737135 (-0.617348) | 0.081742 / 0.296338 (-0.214597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301239 / 0.215209 (0.086030) | 2.962242 / 2.077655 (0.884587) | 1.693918 / 1.504120 (0.189798) | 1.573663 / 1.541195 (0.032468) | 1.583125 / 1.468490 (0.114635) | 0.557267 / 4.584777 (-4.027510) | 2.440048 / 3.745712 (-1.305664) | 2.727572 / 5.269862 (-2.542290) | 1.713557 / 4.565676 (-2.852120) | 0.062526 / 0.424275 (-0.361749) | 0.004982 / 0.007607 (-0.002625) | 0.353850 / 0.226044 (0.127806) | 3.530887 / 2.268929 (1.261958) | 2.047864 / 55.444624 (-53.396761) | 1.770776 / 6.876477 (-5.105701) | 1.757621 / 2.142072 (-0.384451) | 0.633847 / 4.805227 (-4.171381) | 0.114055 / 6.500664 (-6.386609) | 0.040078 / 0.075469 (-0.035391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983721 / 1.841788 (-0.858066) | 11.896537 / 8.074308 (3.822229) | 10.529883 / 10.191392 (0.338491) | 0.129593 / 0.680424 (-0.550831) | 0.016213 / 0.534201 (-0.517988) | 0.289623 / 0.579283 (-0.289660) | 0.280073 / 0.434364 (-0.154291) | 0.327446 / 0.540337 (-0.212892) | 0.574847 / 1.386936 (-0.812089) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2684a98fe38e0c87bb11e050586004108e32b79d \"CML watermark\")\n" ]
2023-12-11T09:21:22
2023-12-13T14:21:29
2023-12-13 14:15:21+00:00
MEMBER
nan
Fix docs phrasing.
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2035141884
I_kwDODunzps55Tcz8
6485
FileNotFoundError: [Errno 2] No such file or directory: 'nul'
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[ "Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519. " ]
2023-12-11T08:52:13
2023-12-14T08:09:08
2023-12-14 08:09:08+00:00
NONE
nan
### Describe the bug it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets" i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul' ![image](https://github.com/huggingface/datasets/assets/73683903/3973c120-ebb1-42b7-bede-b9de053e861d) ![image](https://github.com/huggingface/datasets/assets/73683903/0496adff-a7a7-4dcb-929e-ec11ede71f04) ### Steps to reproduce the bug 1.import datasets ### Expected behavior i just run a single line code and stuct in this bug ### Environment info OS: Windows10 Datasets==2.15.0 python=3.10
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2032946981
I_kwDODunzps55LE8l
6483
Iterable Dataset: rename column clashes with remove column
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[ "Column \"text\" doesn't exist anymore so you can't remove it", "You can get the expected result by fixing typos in the snippet :)\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset.features) - COLUMNS_TO_KEEP)\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Fixed code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\ndataset_features = dataset.features.keys()\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Whoops 😅 Thanks for the swift reply both! Works like a charm!" ]
2023-12-08T16:11:30
2023-12-08T16:27:16
2023-12-08 16:27:04+00:00
CONTRIBUTOR
nan
### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) # rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2
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2033333294
I_kwDODunzps55MjQu
6484
[Feature Request] Dataset versioning
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[ "Hello @kenfus, this is meant to be possible to do yes. Let me ping @lhoestq or @mariosasko from the `datasets` team (`huggingface_hub` is only the underlying library to download files from the Hub but here it looks more like a `datasets` problem). ", "Hi! https://github.com/huggingface/datasets/pull/6459 will fix this." ]
2023-12-08T16:01:35
2023-12-11T19:13:46
NaT
NONE
nan
**Is your feature request related to a problem? Please describe.** I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! **Describe the solution you'd like** The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. **Describe alternatives you've considered** I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. **Additional context** Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```
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2032675918
PR_kwDODunzps5hhl23
6482
Fix max lock length on unix
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005294 / 0.011353 (-0.006059) | 0.003562 / 0.011008 (-0.007446) | 0.062030 / 0.038508 (0.023522) | 0.053335 / 0.023109 (0.030226) | 0.233303 / 0.275898 (-0.042595) | 0.252029 / 0.323480 (-0.071451) | 0.002835 / 0.007986 (-0.005151) | 0.002732 / 0.004328 (-0.001597) | 0.047973 / 0.004250 (0.043723) | 0.038380 / 0.037052 (0.001328) | 0.235028 / 0.258489 (-0.023461) | 0.265555 / 0.293841 (-0.028286) | 0.027136 / 0.128546 (-0.101410) | 0.010806 / 0.075646 (-0.064840) | 0.205040 / 0.419271 (-0.214231) | 0.035063 / 0.043533 (-0.008470) | 0.236351 / 0.255139 (-0.018788) | 0.254556 / 0.283200 (-0.028643) | 0.019528 / 0.141683 (-0.122155) | 1.099012 / 1.452155 (-0.353142) | 1.156250 / 1.492716 (-0.336466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093952 / 0.018006 (0.075946) | 0.304181 / 0.000490 (0.303692) | 0.000227 / 0.000200 (0.000027) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018568 / 0.037411 (-0.018844) | 0.060323 / 0.014526 (0.045798) | 0.073010 / 0.176557 (-0.103546) | 0.121723 / 0.737135 (-0.615412) | 0.075668 / 0.296338 (-0.220670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288429 / 0.215209 (0.073220) | 2.797834 / 2.077655 (0.720180) | 1.480957 / 1.504120 (-0.023163) | 1.360872 / 1.541195 (-0.180323) | 1.406828 / 1.468490 (-0.061663) | 0.587596 / 4.584777 (-3.997181) | 2.533997 / 3.745712 (-1.211715) | 2.906697 / 5.269862 (-2.363164) | 1.801753 / 4.565676 (-2.763923) | 0.064360 / 0.424275 (-0.359915) | 0.005016 / 0.007607 (-0.002591) | 0.347334 / 0.226044 (0.121290) | 3.426344 / 2.268929 (1.157416) | 1.856014 / 55.444624 (-53.588610) | 1.581774 / 6.876477 (-5.294703) | 1.640036 / 2.142072 (-0.502037) | 0.656096 / 4.805227 (-4.149131) | 0.120212 / 6.500664 (-6.380452) | 0.044003 / 0.075469 (-0.031466) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943933 / 1.841788 (-0.897855) | 11.846572 / 8.074308 (3.772263) | 10.330705 / 10.191392 (0.139313) | 0.129767 / 0.680424 (-0.550657) | 0.013508 / 0.534201 (-0.520693) | 0.289672 / 0.579283 (-0.289611) | 0.266427 / 0.434364 (-0.167937) | 0.342766 / 0.540337 (-0.197571) | 0.452068 / 1.386936 (-0.934868) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005308 / 0.011353 (-0.006045) | 0.003712 / 0.011008 (-0.007296) | 0.048848 / 0.038508 (0.010340) | 0.055156 / 0.023109 (0.032047) | 0.271942 / 0.275898 (-0.003956) | 0.293166 / 0.323480 (-0.030314) | 0.004056 / 0.007986 (-0.003930) | 0.002722 / 0.004328 (-0.001606) | 0.048418 / 0.004250 (0.044167) | 0.039320 / 0.037052 (0.002268) | 0.277184 / 0.258489 (0.018695) | 0.312398 / 0.293841 (0.018557) | 0.029392 / 0.128546 (-0.099155) | 0.011314 / 0.075646 (-0.064332) | 0.057883 / 0.419271 (-0.361389) | 0.032603 / 0.043533 (-0.010930) | 0.273025 / 0.255139 (0.017886) | 0.289265 / 0.283200 (0.006065) | 0.017553 / 0.141683 (-0.124129) | 1.127725 / 1.452155 (-0.324430) | 1.202293 / 1.492716 (-0.290423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097179 / 0.018006 (0.079173) | 0.309712 / 0.000490 (0.309222) | 0.000269 / 0.000200 (0.000069) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024742 / 0.037411 (-0.012670) | 0.070097 / 0.014526 (0.055571) | 0.082273 / 0.176557 (-0.094283) | 0.121696 / 0.737135 (-0.615439) | 0.082983 / 0.296338 (-0.213355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292688 / 0.215209 (0.077479) | 2.853436 / 2.077655 (0.775781) | 1.588999 / 1.504120 (0.084879) | 1.454547 / 1.541195 (-0.086648) | 1.476342 / 1.468490 (0.007852) | 0.559464 / 4.584777 (-4.025313) | 2.564597 / 3.745712 (-1.181115) | 2.900460 / 5.269862 (-2.369402) | 1.782156 / 4.565676 (-2.783520) | 0.061768 / 0.424275 (-0.362507) | 0.005042 / 0.007607 (-0.002565) | 0.345168 / 0.226044 (0.119124) | 3.412273 / 2.268929 (1.143344) | 1.953154 / 55.444624 (-53.491470) | 1.667347 / 6.876477 (-5.209130) | 1.685138 / 2.142072 (-0.456934) | 0.643270 / 4.805227 (-4.161958) | 0.115955 / 6.500664 (-6.384709) | 0.041090 / 0.075469 (-0.034379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976324 / 1.841788 (-0.865464) | 12.252294 / 8.074308 (4.177986) | 10.598062 / 10.191392 (0.406670) | 0.129779 / 0.680424 (-0.550644) | 0.015697 / 0.534201 (-0.518504) | 0.287241 / 0.579283 (-0.292042) | 0.287331 / 0.434364 (-0.147033) | 0.331710 / 0.540337 (-0.208628) | 0.574571 / 1.386936 (-0.812365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n" ]
2023-12-08T13:39:30
2023-12-12T11:53:32
2023-12-12 11:47:27+00:00
MEMBER
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reported in https://github.com/huggingface/datasets/pull/6482
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2032650003
I_kwDODunzps55J8cT
6481
using torchrun, save_to_disk suddenly shows SIGTERM
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2023-12-08T13:22:03
2023-12-08T13:22:03
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nan
### Describe the bug When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages: Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard. WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967. ### Steps to reproduce the bug ds_shard = ds_shard.map(map_fn, *args, **kwargs) ds_shard.save_to_disk(ds_shard_filepaths[rank]) Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s] WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python Traceback (most recent call last): File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module> sys.exit(main()) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main run(args) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run elastic_launch( File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ========================================================== run.py FAILED ---------------------------------------------------------- Failures: <NO_OTHER_FAILURES> ---------------------------------------------------------- Root Cause (first observed failure): [0]: time : 2023-12-08_20:09:04 rank : 0 (local_rank: 0) exitcode : -7 (pid: 2224967) error_file: <N/A> traceback : Signal 7 (SIGBUS) received by PID 2224967 ### Expected behavior I hope it can save successfully without any issues, but it seems there is a problem. ### Environment info `datasets` version: 2.14.6 - Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 14.0.0 - Pandas version: 2.1.2
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2031116653
PR_kwDODunzps5hcS7P
6480
Add IterableDataset `__repr__`
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6480). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005392 / 0.011353 (-0.005960) | 0.003120 / 0.011008 (-0.007888) | 0.062017 / 0.038508 (0.023509) | 0.048824 / 0.023109 (0.025715) | 0.232300 / 0.275898 (-0.043598) | 0.262045 / 0.323480 (-0.061435) | 0.002909 / 0.007986 (-0.005077) | 0.003916 / 0.004328 (-0.000413) | 0.049469 / 0.004250 (0.045218) | 0.038965 / 0.037052 (0.001913) | 0.247841 / 0.258489 (-0.010648) | 0.268259 / 0.293841 (-0.025582) | 0.027588 / 0.128546 (-0.100958) | 0.010334 / 0.075646 (-0.065312) | 0.205811 / 0.419271 (-0.213460) | 0.035456 / 0.043533 (-0.008077) | 0.242774 / 0.255139 (-0.012365) | 0.260377 / 0.283200 (-0.022823) | 0.017469 / 0.141683 (-0.124214) | 1.199665 / 1.452155 (-0.252489) | 1.259316 / 1.492716 (-0.233400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092357 / 0.018006 (0.074350) | 0.303745 / 0.000490 (0.303255) | 0.000212 / 0.000200 (0.000012) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018820 / 0.037411 (-0.018592) | 0.061548 / 0.014526 (0.047022) | 0.072527 / 0.176557 (-0.104030) | 0.119696 / 0.737135 (-0.617440) | 0.074153 / 0.296338 (-0.222185) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283952 / 0.215209 (0.068743) | 2.769844 / 2.077655 (0.692189) | 1.526100 / 1.504120 (0.021980) | 1.417584 / 1.541195 (-0.123611) | 1.440523 / 1.468490 (-0.027967) | 0.556994 / 4.584777 (-4.027783) | 2.400392 / 3.745712 (-1.345320) | 2.727794 / 5.269862 (-2.542068) | 1.724671 / 4.565676 (-2.841006) | 0.062111 / 0.424275 (-0.362164) | 0.004925 / 0.007607 (-0.002682) | 0.342748 / 0.226044 (0.116704) | 3.376790 / 2.268929 (1.107862) | 1.856498 / 55.444624 (-53.588127) | 1.574143 / 6.876477 (-5.302334) | 1.591828 / 2.142072 (-0.550245) | 0.644416 / 4.805227 (-4.160811) | 0.116862 / 6.500664 (-6.383802) | 0.041484 / 0.075469 (-0.033985) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975704 / 1.841788 (-0.866084) | 11.196447 / 8.074308 (3.122139) | 10.567518 / 10.191392 (0.376126) | 0.126786 / 0.680424 (-0.553638) | 0.013768 / 0.534201 (-0.520433) | 0.284531 / 0.579283 (-0.294752) | 0.260855 / 0.434364 (-0.173509) | 0.328888 / 0.540337 (-0.211450) | 0.439911 / 1.386936 (-0.947025) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005108 / 0.011353 (-0.006245) | 0.003006 / 0.011008 (-0.008003) | 0.048673 / 0.038508 (0.010165) | 0.051066 / 0.023109 (0.027957) | 0.279578 / 0.275898 (0.003680) | 0.298356 / 0.323480 (-0.025123) | 0.003965 / 0.007986 (-0.004020) | 0.002662 / 0.004328 (-0.001667) | 0.049037 / 0.004250 (0.044786) | 0.039385 / 0.037052 (0.002333) | 0.284545 / 0.258489 (0.026055) | 0.314240 / 0.293841 (0.020399) | 0.028493 / 0.128546 (-0.100053) | 0.010400 / 0.075646 (-0.065247) | 0.057375 / 0.419271 (-0.361896) | 0.032382 / 0.043533 (-0.011151) | 0.283163 / 0.255139 (0.028024) | 0.298967 / 0.283200 (0.015768) | 0.017564 / 0.141683 (-0.124119) | 1.172425 / 1.452155 (-0.279730) | 1.219975 / 1.492716 (-0.272742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090664 / 0.018006 (0.072658) | 0.298419 / 0.000490 (0.297929) | 0.000211 / 0.000200 (0.000011) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021739 / 0.037411 (-0.015672) | 0.068274 / 0.014526 (0.053748) | 0.080820 / 0.176557 (-0.095736) | 0.119809 / 0.737135 (-0.617326) | 0.081612 / 0.296338 (-0.214727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303346 / 0.215209 (0.088137) | 2.971648 / 2.077655 (0.893993) | 1.634828 / 1.504120 (0.130708) | 1.510851 / 1.541195 (-0.030344) | 1.515236 / 1.468490 (0.046745) | 0.558487 / 4.584777 (-4.026289) | 2.436263 / 3.745712 (-1.309449) | 2.718525 / 5.269862 (-2.551336) | 1.727421 / 4.565676 (-2.838255) | 0.061396 / 0.424275 (-0.362879) | 0.004951 / 0.007607 (-0.002656) | 0.352950 / 0.226044 (0.126906) | 3.473766 / 2.268929 (1.204838) | 1.971299 / 55.444624 (-53.473325) | 1.712173 / 6.876477 (-5.164304) | 1.711334 / 2.142072 (-0.430738) | 0.627291 / 4.805227 (-4.177936) | 0.113779 / 6.500664 (-6.386885) | 0.046561 / 0.075469 (-0.028908) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989507 / 1.841788 (-0.852280) | 11.777883 / 8.074308 (3.703575) | 10.525453 / 10.191392 (0.334061) | 0.129118 / 0.680424 (-0.551306) | 0.014989 / 0.534201 (-0.519212) | 0.282324 / 0.579283 (-0.296959) | 0.280688 / 0.434364 (-0.153676) | 0.322579 / 0.540337 (-0.217758) | 0.554327 / 1.386936 (-0.832609) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79e94fcdf3d4378ddcdf7e130bb1ae23d99c6fce \"CML watermark\")\n" ]
2023-12-07T16:31:50
2023-12-08T13:33:06
2023-12-08 13:26:54+00:00
MEMBER
nan
Example for glue sst2: Dataset ``` DatasetDict({ test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) }) ``` IterableDataset (new) ``` IterableDatasetDict({ test: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) train: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) validation: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) }) ``` IterableDataset (before) ``` {'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>} {'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0} ```
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2029040121
PR_kwDODunzps5hVLom
6479
More robust preupload retry mechanism
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6479). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005669 / 0.011353 (-0.005683) | 0.003684 / 0.011008 (-0.007324) | 0.063477 / 0.038508 (0.024969) | 0.068760 / 0.023109 (0.045651) | 0.252741 / 0.275898 (-0.023157) | 0.286499 / 0.323480 (-0.036981) | 0.003311 / 0.007986 (-0.004674) | 0.003487 / 0.004328 (-0.000842) | 0.049636 / 0.004250 (0.045385) | 0.040983 / 0.037052 (0.003931) | 0.262230 / 0.258489 (0.003740) | 0.292131 / 0.293841 (-0.001710) | 0.028231 / 0.128546 (-0.100315) | 0.010912 / 0.075646 (-0.064734) | 0.211248 / 0.419271 (-0.208023) | 0.036679 / 0.043533 (-0.006854) | 0.258139 / 0.255139 (0.003000) | 0.277568 / 0.283200 (-0.005631) | 0.019576 / 0.141683 (-0.122107) | 1.102588 / 1.452155 (-0.349567) | 1.178587 / 1.492716 (-0.314130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098968 / 0.018006 (0.080962) | 0.298777 / 0.000490 (0.298287) | 0.000220 / 0.000200 (0.000020) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020408 / 0.037411 (-0.017003) | 0.062832 / 0.014526 (0.048306) | 0.076047 / 0.176557 (-0.100509) | 0.125209 / 0.737135 (-0.611926) | 0.079098 / 0.296338 (-0.217240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285603 / 0.215209 (0.070394) | 2.811530 / 2.077655 (0.733875) | 1.481012 / 1.504120 (-0.023108) | 1.362740 / 1.541195 (-0.178455) | 1.448999 / 1.468490 (-0.019491) | 0.557740 / 4.584777 (-4.027037) | 2.391377 / 3.745712 (-1.354335) | 2.973181 / 5.269862 (-2.296681) | 1.837147 / 4.565676 (-2.728530) | 0.064445 / 0.424275 (-0.359831) | 0.004992 / 0.007607 (-0.002615) | 0.339207 / 0.226044 (0.113162) | 3.378508 / 2.268929 (1.109580) | 1.843969 / 55.444624 (-53.600655) | 1.597794 / 6.876477 (-5.278682) | 1.657665 / 2.142072 (-0.484407) | 0.654267 / 4.805227 (-4.150961) | 0.120408 / 6.500664 (-6.380256) | 0.045298 / 0.075469 (-0.030171) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949030 / 1.841788 (-0.892758) | 12.922161 / 8.074308 (4.847852) | 11.115660 / 10.191392 (0.924268) | 0.130556 / 0.680424 (-0.549868) | 0.016278 / 0.534201 (-0.517923) | 0.288137 / 0.579283 (-0.291146) | 0.265978 / 0.434364 (-0.168386) | 0.331491 / 0.540337 (-0.208847) | 0.437782 / 1.386936 (-0.949154) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005342 / 0.011353 (-0.006010) | 0.003636 / 0.011008 (-0.007373) | 0.049527 / 0.038508 (0.011019) | 0.054856 / 0.023109 (0.031746) | 0.271922 / 0.275898 (-0.003976) | 0.295654 / 0.323480 (-0.027826) | 0.004023 / 0.007986 (-0.003963) | 0.002814 / 0.004328 (-0.001515) | 0.048963 / 0.004250 (0.044712) | 0.039936 / 0.037052 (0.002884) | 0.274336 / 0.258489 (0.015847) | 0.310100 / 0.293841 (0.016259) | 0.030006 / 0.128546 (-0.098540) | 0.010750 / 0.075646 (-0.064896) | 0.057989 / 0.419271 (-0.361283) | 0.033692 / 0.043533 (-0.009841) | 0.274084 / 0.255139 (0.018945) | 0.289428 / 0.283200 (0.006229) | 0.018739 / 0.141683 (-0.122944) | 1.126224 / 1.452155 (-0.325931) | 1.171595 / 1.492716 (-0.321121) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093983 / 0.018006 (0.075977) | 0.298516 / 0.000490 (0.298026) | 0.000221 / 0.000200 (0.000022) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022498 / 0.037411 (-0.014914) | 0.071909 / 0.014526 (0.057383) | 0.083940 / 0.176557 (-0.092617) | 0.121059 / 0.737135 (-0.616076) | 0.084141 / 0.296338 (-0.212198) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301792 / 0.215209 (0.086583) | 2.971971 / 2.077655 (0.894317) | 1.618718 / 1.504120 (0.114598) | 1.495816 / 1.541195 (-0.045379) | 1.546709 / 1.468490 (0.078219) | 0.571448 / 4.584777 (-4.013329) | 2.459182 / 3.745712 (-1.286531) | 2.937584 / 5.269862 (-2.332278) | 1.804670 / 4.565676 (-2.761007) | 0.062264 / 0.424275 (-0.362011) | 0.004915 / 0.007607 (-0.002692) | 0.355054 / 0.226044 (0.129009) | 3.490468 / 2.268929 (1.221539) | 1.978948 / 55.444624 (-53.465677) | 1.701020 / 6.876477 (-5.175457) | 1.744684 / 2.142072 (-0.397388) | 0.635880 / 4.805227 (-4.169347) | 0.115933 / 6.500664 (-6.384732) | 0.042646 / 0.075469 (-0.032823) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999486 / 1.841788 (-0.842302) | 13.373854 / 8.074308 (5.299546) | 10.959784 / 10.191392 (0.768392) | 0.131032 / 0.680424 (-0.549392) | 0.015059 / 0.534201 (-0.519142) | 0.289892 / 0.579283 (-0.289391) | 0.279383 / 0.434364 (-0.154981) | 0.337670 / 0.540337 (-0.202668) | 0.597102 / 1.386936 (-0.789834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd9044cdaabc1f9abce02c1b71bdb48fd3525d4e \"CML watermark\")\n" ]
2023-12-06T17:19:38
2023-12-06T19:47:29
2023-12-06 19:41:06+00:00
COLLABORATOR
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2028071596
I_kwDODunzps544eqs
6478
How to load data from lakefs
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[ "You can create a `pandas` DataFrame following [this](https://lakefs.io/data-version-control/dvc-using-python/) tutorial, and then convert this DataFrame to a `Dataset` with `datasets.Dataset.from_pandas`. For larger datasets (to memory map them), you can use `Dataset.from_generator` with a generator function that reads lakeFS files with `s3fs`.", "@mariosasko hello,\r\nThis can achieve and https://huggingface.co/datasets Does the same effect apply to the dataset? For example, downloading while using", "There is a blogspot from lakes on this topic: https://lakefs.io/blog/data-version-control-hugging-face-datasets/" ]
2023-12-06T09:04:11
2024-07-03T19:13:57
2024-07-03 19:13:56+00:00
CONTRIBUTOR
nan
My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references
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2028022374
PR_kwDODunzps5hRq_N
6477
Fix PermissionError on Windows CI
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005383 / 0.011353 (-0.005969) | 0.003644 / 0.011008 (-0.007364) | 0.063375 / 0.038508 (0.024866) | 0.055567 / 0.023109 (0.032457) | 0.261376 / 0.275898 (-0.014522) | 0.283731 / 0.323480 (-0.039749) | 0.004022 / 0.007986 (-0.003964) | 0.002780 / 0.004328 (-0.001549) | 0.049407 / 0.004250 (0.045156) | 0.038208 / 0.037052 (0.001156) | 0.256275 / 0.258489 (-0.002214) | 0.293203 / 0.293841 (-0.000638) | 0.028411 / 0.128546 (-0.100135) | 0.010753 / 0.075646 (-0.064894) | 0.210420 / 0.419271 (-0.208851) | 0.036062 / 0.043533 (-0.007471) | 0.260455 / 0.255139 (0.005317) | 0.294991 / 0.283200 (0.011791) | 0.019020 / 0.141683 (-0.122662) | 1.118334 / 1.452155 (-0.333821) | 1.227391 / 1.492716 (-0.265325) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094700 / 0.018006 (0.076694) | 0.302378 / 0.000490 (0.301888) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018745 / 0.037411 (-0.018667) | 0.061103 / 0.014526 (0.046578) | 0.075369 / 0.176557 (-0.101188) | 0.121573 / 0.737135 (-0.615563) | 0.076898 / 0.296338 (-0.219440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284143 / 0.215209 (0.068934) | 2.774298 / 2.077655 (0.696644) | 1.483557 / 1.504120 (-0.020563) | 1.365091 / 1.541195 (-0.176104) | 1.390170 / 1.468490 (-0.078320) | 0.561179 / 4.584777 (-4.023598) | 2.401654 / 3.745712 (-1.344058) | 2.782628 / 5.269862 (-2.487233) | 1.731497 / 4.565676 (-2.834179) | 0.061798 / 0.424275 (-0.362477) | 0.004998 / 0.007607 (-0.002609) | 0.336920 / 0.226044 (0.110875) | 3.371891 / 2.268929 (1.102963) | 1.832173 / 55.444624 (-53.612452) | 1.573515 / 6.876477 (-5.302962) | 1.595609 / 2.142072 (-0.546463) | 0.647652 / 4.805227 (-4.157575) | 0.118501 / 6.500664 (-6.382164) | 0.042521 / 0.075469 (-0.032948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939310 / 1.841788 (-0.902478) | 11.459855 / 8.074308 (3.385547) | 10.677954 / 10.191392 (0.486562) | 0.141029 / 0.680424 (-0.539395) | 0.014321 / 0.534201 (-0.519880) | 0.306679 / 0.579283 (-0.272604) | 0.262303 / 0.434364 (-0.172061) | 0.327422 / 0.540337 (-0.212915) | 0.436159 / 1.386936 (-0.950777) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005430 / 0.011353 (-0.005923) | 0.003646 / 0.011008 (-0.007362) | 0.049272 / 0.038508 (0.010764) | 0.075367 / 0.023109 (0.052257) | 0.275959 / 0.275898 (0.000061) | 0.296317 / 0.323480 (-0.027163) | 0.004129 / 0.007986 (-0.003857) | 0.002731 / 0.004328 (-0.001597) | 0.048475 / 0.004250 (0.044225) | 0.041571 / 0.037052 (0.004518) | 0.277993 / 0.258489 (0.019504) | 0.298709 / 0.293841 (0.004868) | 0.033117 / 0.128546 (-0.095429) | 0.010914 / 0.075646 (-0.064732) | 0.057599 / 0.419271 (-0.361673) | 0.033354 / 0.043533 (-0.010179) | 0.275669 / 0.255139 (0.020530) | 0.288451 / 0.283200 (0.005251) | 0.019953 / 0.141683 (-0.121729) | 1.148608 / 1.452155 (-0.303547) | 1.184818 / 1.492716 (-0.307898) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099566 / 0.018006 (0.081560) | 0.344935 / 0.000490 (0.344445) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021925 / 0.037411 (-0.015486) | 0.068623 / 0.014526 (0.054097) | 0.081533 / 0.176557 (-0.095024) | 0.120996 / 0.737135 (-0.616139) | 0.082495 / 0.296338 (-0.213844) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294990 / 0.215209 (0.079781) | 2.892344 / 2.077655 (0.814690) | 1.611090 / 1.504120 (0.106970) | 1.496072 / 1.541195 (-0.045123) | 1.486069 / 1.468490 (0.017579) | 0.569769 / 4.584777 (-4.015008) | 2.477623 / 3.745712 (-1.268089) | 2.819576 / 5.269862 (-2.450286) | 1.745717 / 4.565676 (-2.819959) | 0.063763 / 0.424275 (-0.360512) | 0.004970 / 0.007607 (-0.002637) | 0.344879 / 0.226044 (0.118834) | 3.452795 / 2.268929 (1.183867) | 1.964468 / 55.444624 (-53.480156) | 1.674526 / 6.876477 (-5.201951) | 1.679716 / 2.142072 (-0.462356) | 0.650005 / 4.805227 (-4.155222) | 0.117019 / 6.500664 (-6.383646) | 0.048297 / 0.075469 (-0.027172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965422 / 1.841788 (-0.876366) | 11.989414 / 8.074308 (3.915106) | 10.938462 / 10.191392 (0.747070) | 0.140089 / 0.680424 (-0.540334) | 0.015533 / 0.534201 (-0.518668) | 0.292188 / 0.579283 (-0.287095) | 0.277903 / 0.434364 (-0.156461) | 0.326164 / 0.540337 (-0.214173) | 0.565674 / 1.386936 (-0.821262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d78f07091bc42c41bea068bf1b6116e2bde46a6f \"CML watermark\")\n" ]
2023-12-06T08:34:53
2023-12-06T09:24:11
2023-12-06 09:17:52+00:00
MEMBER
nan
Fix #6476.
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6476
CI on windows is broken: PermissionError
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2023-12-06T08:32:53
2023-12-06T09:17:53
2023-12-06 09:17:53+00:00
MEMBER
nan
See: https://github.com/huggingface/datasets/actions/runs/7104781624/job/19340572394 ``` FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow' ```
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6475
laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
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[ "~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~", "This is a real issue and not related to paths.", "Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.", "I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...", "Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n", "Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug." ]
2023-12-06T00:07:34
2023-12-06T23:26:23
NaT
NONE
nan
### Describe the bug I have downloaded laion2B-en, and I'm receiving the following error trying to load it: ``` Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s] Traceback (most recent call last): File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module> count = compute_frequencies() ^^^^^^^^^^^^^^^^^^^^^ File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset datasets = map_nested( ^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested return function(data_struct) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset ds = self._as_dataset( ^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() ^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command. ``` This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux. I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128. ### Steps to reproduce the bug ``` # as a huggingface developer, you may already have laion2B-en somewhere _CACHE_DIR = "." from datasets import load_dataset load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ``` ### Expected behavior This should correctly load as a memory mapped Arrow dataset. ### Environment info - `datasets` version: 2.15.0 - Platform: Windows-10-10.0.20348-SP0 (this is windows 2022) - Python version: 3.11.4 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.10.0
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2027006715
PR_kwDODunzps5hONZc
6474
Deprecate Beam API and download from HF GCS bucket
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005280 / 0.011353 (-0.006073) | 0.003770 / 0.011008 (-0.007238) | 0.064320 / 0.038508 (0.025812) | 0.031250 / 0.023109 (0.008141) | 0.245113 / 0.275898 (-0.030785) | 0.268646 / 0.323480 (-0.054834) | 0.003147 / 0.007986 (-0.004839) | 0.002736 / 0.004328 (-0.001592) | 0.050802 / 0.004250 (0.046551) | 0.044539 / 0.037052 (0.007487) | 0.261702 / 0.258489 (0.003213) | 0.304696 / 0.293841 (0.010855) | 0.028601 / 0.128546 (-0.099945) | 0.010731 / 0.075646 (-0.064915) | 0.208183 / 0.419271 (-0.211089) | 0.036597 / 0.043533 (-0.006936) | 0.245107 / 0.255139 (-0.010032) | 0.265936 / 0.283200 (-0.017264) | 0.019430 / 0.141683 (-0.122253) | 1.184697 / 1.452155 (-0.267458) | 1.206073 / 1.492716 (-0.286643) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.110242 / 0.018006 (0.092236) | 0.304185 / 0.000490 (0.303695) | 0.000220 / 0.000200 (0.000020) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018839 / 0.037411 (-0.018573) | 0.062840 / 0.014526 (0.048314) | 0.075849 / 0.176557 (-0.100708) | 0.122983 / 0.737135 (-0.614153) | 0.075352 / 0.296338 (-0.220987) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283617 / 0.215209 (0.068408) | 2.801903 / 2.077655 (0.724248) | 1.447678 / 1.504120 (-0.056442) | 1.327561 / 1.541195 (-0.213633) | 1.348714 / 1.468490 (-0.119776) | 0.574283 / 4.584777 (-4.010493) | 2.449396 / 3.745712 (-1.296316) | 2.908659 / 5.269862 (-2.361203) | 1.813935 / 4.565676 (-2.751742) | 0.063141 / 0.424275 (-0.361134) | 0.005024 / 0.007607 (-0.002583) | 0.338854 / 0.226044 (0.112810) | 3.346680 / 2.268929 (1.077752) | 1.833792 / 55.444624 (-53.610832) | 1.553680 / 6.876477 (-5.322796) | 1.590403 / 2.142072 (-0.551670) | 0.657442 / 4.805227 (-4.147785) | 0.119961 / 6.500664 (-6.380703) | 0.042602 / 0.075469 (-0.032867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977510 / 1.841788 (-0.864278) | 12.105448 / 8.074308 (4.031140) | 9.635282 / 10.191392 (-0.556110) | 0.132453 / 0.680424 (-0.547971) | 0.014225 / 0.534201 (-0.519976) | 0.292847 / 0.579283 (-0.286436) | 0.271498 / 0.434364 (-0.162866) | 0.329785 / 0.540337 (-0.210552) | 0.435058 / 1.386936 (-0.951879) |\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) | 0.005656 / 0.011353 (-0.005697) | 0.003903 / 0.011008 (-0.007106) | 0.050670 / 0.038508 (0.012162) | 0.032405 / 0.023109 (0.009296) | 0.271443 / 0.275898 (-0.004455) | 0.298989 / 0.323480 (-0.024491) | 0.004397 / 0.007986 (-0.003589) | 0.003027 / 0.004328 (-0.001301) | 0.050227 / 0.004250 (0.045977) | 0.047798 / 0.037052 (0.010745) | 0.287610 / 0.258489 (0.029120) | 0.314084 / 0.293841 (0.020243) | 0.030406 / 0.128546 (-0.098140) | 0.011159 / 0.075646 (-0.064487) | 0.059510 / 0.419271 (-0.359762) | 0.056842 / 0.043533 (0.013309) | 0.274653 / 0.255139 (0.019514) | 0.290175 / 0.283200 (0.006976) | 0.019822 / 0.141683 (-0.121861) | 1.168894 / 1.452155 (-0.283261) | 1.205867 / 1.492716 (-0.286850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094907 / 0.018006 (0.076901) | 0.304920 / 0.000490 (0.304431) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023015 / 0.037411 (-0.014396) | 0.076868 / 0.014526 (0.062342) | 0.089099 / 0.176557 (-0.087458) | 0.128786 / 0.737135 (-0.608350) | 0.090836 / 0.296338 (-0.205503) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284552 / 0.215209 (0.069343) | 2.800870 / 2.077655 (0.723215) | 1.577167 / 1.504120 (0.073047) | 1.448788 / 1.541195 (-0.092406) | 1.475493 / 1.468490 (0.007003) | 0.566639 / 4.584777 (-4.018138) | 2.503671 / 3.745712 (-1.242041) | 2.796769 / 5.269862 (-2.473092) | 1.829344 / 4.565676 (-2.736332) | 0.064200 / 0.424275 (-0.360075) | 0.005066 / 0.007607 (-0.002541) | 0.335390 / 0.226044 (0.109346) | 3.421265 / 2.268929 (1.152336) | 1.942245 / 55.444624 (-53.502379) | 1.641401 / 6.876477 (-5.235076) | 1.824574 / 2.142072 (-0.317499) | 0.661379 / 4.805227 (-4.143849) | 0.117207 / 6.500664 (-6.383457) | 0.041630 / 0.075469 (-0.033839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009636 / 1.841788 (-0.832152) | 12.998379 / 8.074308 (4.924071) | 10.609013 / 10.191392 (0.417621) | 0.142794 / 0.680424 (-0.537630) | 0.015648 / 0.534201 (-0.518553) | 0.292825 / 0.579283 (-0.286458) | 0.280790 / 0.434364 (-0.153574) | 0.329016 / 0.540337 (-0.211322) | 0.442440 / 1.386936 (-0.944496) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f90b65d6ccaceaa532b851a1010ba3dcc77b8b42 \"CML watermark\")\n" ]
2023-12-05T19:51:33
2024-03-12T14:56:25
2024-03-12 14:50:12+00:00
COLLABORATOR
nan
Deprecate the Beam API and download from the HF GCS bucked. TODO: - [x] Convert the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) to an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19)) - [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7)) - [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5)) - [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14))
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6473
Fix CI quality
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005270 / 0.011353 (-0.006083) | 0.003471 / 0.011008 (-0.007537) | 0.061942 / 0.038508 (0.023434) | 0.052671 / 0.023109 (0.029562) | 0.250541 / 0.275898 (-0.025357) | 0.270677 / 0.323480 (-0.052803) | 0.002933 / 0.007986 (-0.005053) | 0.003264 / 0.004328 (-0.001064) | 0.048055 / 0.004250 (0.043804) | 0.037459 / 0.037052 (0.000407) | 0.254926 / 0.258489 (-0.003563) | 0.292547 / 0.293841 (-0.001294) | 0.027959 / 0.128546 (-0.100587) | 0.010762 / 0.075646 (-0.064884) | 0.204961 / 0.419271 (-0.214310) | 0.035488 / 0.043533 (-0.008045) | 0.254102 / 0.255139 (-0.001037) | 0.273654 / 0.283200 (-0.009546) | 0.018126 / 0.141683 (-0.123556) | 1.082330 / 1.452155 (-0.369825) | 1.147179 / 1.492716 (-0.345538) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093223 / 0.018006 (0.075217) | 0.301912 / 0.000490 (0.301422) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018407 / 0.037411 (-0.019004) | 0.060412 / 0.014526 (0.045886) | 0.074063 / 0.176557 (-0.102494) | 0.118743 / 0.737135 (-0.618392) | 0.076484 / 0.296338 (-0.219854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289929 / 0.215209 (0.074720) | 2.825096 / 2.077655 (0.747442) | 1.511444 / 1.504120 (0.007324) | 1.394812 / 1.541195 (-0.146383) | 1.419751 / 1.468490 (-0.048739) | 0.569995 / 4.584777 (-4.014782) | 2.402586 / 3.745712 (-1.343126) | 2.826223 / 5.269862 (-2.443639) | 1.751554 / 4.565676 (-2.814123) | 0.064266 / 0.424275 (-0.360009) | 0.005047 / 0.007607 (-0.002561) | 0.341513 / 0.226044 (0.115469) | 3.372106 / 2.268929 (1.103177) | 1.872693 / 55.444624 (-53.571931) | 1.588200 / 6.876477 (-5.288276) | 1.630800 / 2.142072 (-0.511272) | 0.654266 / 4.805227 (-4.150961) | 0.124292 / 6.500664 (-6.376372) | 0.042876 / 0.075469 (-0.032593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948406 / 1.841788 (-0.893382) | 11.652947 / 8.074308 (3.578639) | 10.218195 / 10.191392 (0.026803) | 0.128447 / 0.680424 (-0.551976) | 0.014092 / 0.534201 (-0.520109) | 0.287631 / 0.579283 (-0.291652) | 0.264843 / 0.434364 (-0.169521) | 0.329997 / 0.540337 (-0.210340) | 0.439597 / 1.386936 (-0.947339) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005418 / 0.011353 (-0.005935) | 0.003589 / 0.011008 (-0.007419) | 0.050074 / 0.038508 (0.011566) | 0.052566 / 0.023109 (0.029456) | 0.293447 / 0.275898 (0.017549) | 0.320518 / 0.323480 (-0.002962) | 0.004094 / 0.007986 (-0.003892) | 0.002690 / 0.004328 (-0.001639) | 0.048200 / 0.004250 (0.043949) | 0.040692 / 0.037052 (0.003640) | 0.297086 / 0.258489 (0.038597) | 0.323827 / 0.293841 (0.029986) | 0.029511 / 0.128546 (-0.099035) | 0.011079 / 0.075646 (-0.064568) | 0.058562 / 0.419271 (-0.360709) | 0.032897 / 0.043533 (-0.010636) | 0.297244 / 0.255139 (0.042105) | 0.316812 / 0.283200 (0.033612) | 0.018468 / 0.141683 (-0.123215) | 1.140948 / 1.452155 (-0.311207) | 1.195453 / 1.492716 (-0.297263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092677 / 0.018006 (0.074671) | 0.300775 / 0.000490 (0.300285) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021617 / 0.037411 (-0.015794) | 0.077135 / 0.014526 (0.062610) | 0.079848 / 0.176557 (-0.096709) | 0.118475 / 0.737135 (-0.618661) | 0.081174 / 0.296338 (-0.215164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294424 / 0.215209 (0.079215) | 2.863989 / 2.077655 (0.786334) | 1.590604 / 1.504120 (0.086484) | 1.474345 / 1.541195 (-0.066849) | 1.482120 / 1.468490 (0.013630) | 0.567829 / 4.584777 (-4.016948) | 2.493782 / 3.745712 (-1.251930) | 2.823460 / 5.269862 (-2.446402) | 1.732677 / 4.565676 (-2.833000) | 0.065518 / 0.424275 (-0.358757) | 0.004923 / 0.007607 (-0.002684) | 0.349313 / 0.226044 (0.123268) | 3.428618 / 2.268929 (1.159689) | 1.970641 / 55.444624 (-53.473983) | 1.655884 / 6.876477 (-5.220593) | 1.657151 / 2.142072 (-0.484921) | 0.661208 / 4.805227 (-4.144019) | 0.119129 / 6.500664 (-6.381535) | 0.040770 / 0.075469 (-0.034699) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964865 / 1.841788 (-0.876923) | 12.050218 / 8.074308 (3.975910) | 10.458749 / 10.191392 (0.267357) | 0.141856 / 0.680424 (-0.538568) | 0.015091 / 0.534201 (-0.519109) | 0.288897 / 0.579283 (-0.290387) | 0.275343 / 0.434364 (-0.159021) | 0.328363 / 0.540337 (-0.211975) | 0.579243 / 1.386936 (-0.807693) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n" ]
2023-12-05T15:36:23
2023-12-05T18:14:50
2023-12-05 18:08:41+00:00
MEMBER
nan
Fix #6472.
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6472
CI quality is broken
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2023-12-05T15:35:34
2023-12-06T08:17:34
2023-12-05 18:08:43+00:00
MEMBER
nan
See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359 ``` Would reformat: src/datasets/features/image.py 1 file would be reformatted, 253 files left unchanged ```
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6471
Remove delete doc CI
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6471). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005573 / 0.011353 (-0.005780) | 0.003449 / 0.011008 (-0.007559) | 0.063323 / 0.038508 (0.024815) | 0.049369 / 0.023109 (0.026260) | 0.254280 / 0.275898 (-0.021618) | 0.267721 / 0.323480 (-0.055759) | 0.002894 / 0.007986 (-0.005092) | 0.002646 / 0.004328 (-0.001683) | 0.049284 / 0.004250 (0.045033) | 0.037947 / 0.037052 (0.000895) | 0.251654 / 0.258489 (-0.006836) | 0.279729 / 0.293841 (-0.014112) | 0.028022 / 0.128546 (-0.100525) | 0.010653 / 0.075646 (-0.064993) | 0.208567 / 0.419271 (-0.210704) | 0.035863 / 0.043533 (-0.007670) | 0.248522 / 0.255139 (-0.006617) | 0.270274 / 0.283200 (-0.012925) | 0.019683 / 0.141683 (-0.122000) | 1.136342 / 1.452155 (-0.315812) | 1.206757 / 1.492716 (-0.285960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094682 / 0.018006 (0.076676) | 0.304092 / 0.000490 (0.303602) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018606 / 0.037411 (-0.018805) | 0.060568 / 0.014526 (0.046042) | 0.074067 / 0.176557 (-0.102490) | 0.118979 / 0.737135 (-0.618156) | 0.075676 / 0.296338 (-0.220663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290452 / 0.215209 (0.075243) | 2.848868 / 2.077655 (0.771213) | 1.534932 / 1.504120 (0.030812) | 1.386717 / 1.541195 (-0.154478) | 1.416645 / 1.468490 (-0.051845) | 0.569020 / 4.584777 (-4.015757) | 2.421168 / 3.745712 (-1.324545) | 2.781358 / 5.269862 (-2.488503) | 1.758495 / 4.565676 (-2.807182) | 0.063851 / 0.424275 (-0.360424) | 0.004968 / 0.007607 (-0.002639) | 0.339198 / 0.226044 (0.113154) | 3.356392 / 2.268929 (1.087464) | 1.858145 / 55.444624 (-53.586479) | 1.589000 / 6.876477 (-5.287477) | 1.569175 / 2.142072 (-0.572897) | 0.650571 / 4.805227 (-4.154657) | 0.120288 / 6.500664 (-6.380376) | 0.042489 / 0.075469 (-0.032980) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939963 / 1.841788 (-0.901824) | 11.493612 / 8.074308 (3.419304) | 10.353780 / 10.191392 (0.162388) | 0.141945 / 0.680424 (-0.538479) | 0.014397 / 0.534201 (-0.519804) | 0.286971 / 0.579283 (-0.292312) | 0.266787 / 0.434364 (-0.167577) | 0.330385 / 0.540337 (-0.209952) | 0.438542 / 1.386936 (-0.948394) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005360 / 0.011353 (-0.005993) | 0.003720 / 0.011008 (-0.007288) | 0.048790 / 0.038508 (0.010282) | 0.050256 / 0.023109 (0.027147) | 0.275445 / 0.275898 (-0.000453) | 0.297725 / 0.323480 (-0.025755) | 0.004077 / 0.007986 (-0.003909) | 0.002759 / 0.004328 (-0.001569) | 0.047653 / 0.004250 (0.043403) | 0.040205 / 0.037052 (0.003153) | 0.281028 / 0.258489 (0.022539) | 0.304682 / 0.293841 (0.010841) | 0.030158 / 0.128546 (-0.098388) | 0.010957 / 0.075646 (-0.064689) | 0.058193 / 0.419271 (-0.361079) | 0.033277 / 0.043533 (-0.010256) | 0.279501 / 0.255139 (0.024362) | 0.295381 / 0.283200 (0.012181) | 0.017889 / 0.141683 (-0.123794) | 1.121354 / 1.452155 (-0.330801) | 1.225702 / 1.492716 (-0.267014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093385 / 0.018006 (0.075378) | 0.304642 / 0.000490 (0.304152) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021456 / 0.037411 (-0.015955) | 0.068536 / 0.014526 (0.054010) | 0.080867 / 0.176557 (-0.095689) | 0.119093 / 0.737135 (-0.618042) | 0.081875 / 0.296338 (-0.214464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304434 / 0.215209 (0.089225) | 2.990303 / 2.077655 (0.912649) | 1.616959 / 1.504120 (0.112839) | 1.493256 / 1.541195 (-0.047939) | 1.542857 / 1.468490 (0.074367) | 0.575517 / 4.584777 (-4.009260) | 2.455165 / 3.745712 (-1.290547) | 2.810089 / 5.269862 (-2.459773) | 1.756502 / 4.565676 (-2.809175) | 0.064801 / 0.424275 (-0.359475) | 0.004969 / 0.007607 (-0.002638) | 0.360227 / 0.226044 (0.134183) | 3.575029 / 2.268929 (1.306100) | 1.989955 / 55.444624 (-53.454669) | 1.705306 / 6.876477 (-5.171171) | 1.688523 / 2.142072 (-0.453550) | 0.663266 / 4.805227 (-4.141962) | 0.121852 / 6.500664 (-6.378812) | 0.041853 / 0.075469 (-0.033616) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983535 / 1.841788 (-0.858252) | 11.827656 / 8.074308 (3.753348) | 10.663265 / 10.191392 (0.471873) | 0.145942 / 0.680424 (-0.534482) | 0.016004 / 0.534201 (-0.518197) | 0.288907 / 0.579283 (-0.290376) | 0.279100 / 0.434364 (-0.155264) | 0.328061 / 0.540337 (-0.212276) | 0.570253 / 1.386936 (-0.816683) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b52cbc18919869460557e15028e7f489eae8afc7 \"CML watermark\")\n" ]
2023-12-05T12:37:50
2023-12-05T12:44:59
2023-12-05 12:38:50+00:00
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2024724319
I_kwDODunzps54rtdf
6470
If an image in a dataset is corrupted, we get unescapable error
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2023-12-04T20:58:49
2023-12-04T20:58:49
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### Describe the bug Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1 ### Steps to reproduce the bug ``` from datasets import load_dataset, VerificationMode dataset = load_dataset( 'sasha/birdsnap', split="train", verification_mode=VerificationMode.ALL_CHECKS, streaming=True # I recommend using streaming=True when reproducing, as this dataset is large ) for idx, row in enumerate(dataset): # Iterating to 9287 took 7 minutes for me # If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287] pass # error at 9287 OSError: image file is truncated (45 bytes not processed) # note that we can't avoid the error using a try/except + continue inside the loop ``` ### Expected behavior Able to escape errors in casting to Image() without killing the whole loop ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0
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2023695839
PR_kwDODunzps5hC6xf
6469
Don't expand_info in HF glob
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004998 / 0.011353 (-0.006355) | 0.003523 / 0.011008 (-0.007486) | 0.064932 / 0.038508 (0.026424) | 0.050107 / 0.023109 (0.026998) | 0.253715 / 0.275898 (-0.022183) | 0.275364 / 0.323480 (-0.048116) | 0.003902 / 0.007986 (-0.004084) | 0.002716 / 0.004328 (-0.001612) | 0.048458 / 0.004250 (0.044208) | 0.037802 / 0.037052 (0.000750) | 0.262328 / 0.258489 (0.003839) | 0.285911 / 0.293841 (-0.007930) | 0.027112 / 0.128546 (-0.101435) | 0.010780 / 0.075646 (-0.064867) | 0.206447 / 0.419271 (-0.212824) | 0.035771 / 0.043533 (-0.007761) | 0.255031 / 0.255139 (-0.000108) | 0.270530 / 0.283200 (-0.012670) | 0.017152 / 0.141683 (-0.124530) | 1.094734 / 1.452155 (-0.357421) | 1.163480 / 1.492716 (-0.329237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092944 / 0.018006 (0.074938) | 0.301042 / 0.000490 (0.300553) | 0.000238 / 0.000200 (0.000038) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019090 / 0.037411 (-0.018321) | 0.061046 / 0.014526 (0.046520) | 0.073330 / 0.176557 (-0.103227) | 0.121124 / 0.737135 (-0.616012) | 0.080544 / 0.296338 (-0.215795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.323866 / 0.215209 (0.108657) | 2.797727 / 2.077655 (0.720072) | 1.502994 / 1.504120 (-0.001126) | 1.376177 / 1.541195 (-0.165018) | 1.422741 / 1.468490 (-0.045749) | 0.562990 / 4.584777 (-4.021786) | 2.431781 / 3.745712 (-1.313931) | 2.783226 / 5.269862 (-2.486635) | 1.788055 / 4.565676 (-2.777621) | 0.064206 / 0.424275 (-0.360069) | 0.004989 / 0.007607 (-0.002618) | 0.338282 / 0.226044 (0.112237) | 3.356226 / 2.268929 (1.087297) | 1.855644 / 55.444624 (-53.588980) | 1.580876 / 6.876477 (-5.295601) | 1.617418 / 2.142072 (-0.524655) | 0.636816 / 4.805227 (-4.168411) | 0.117680 / 6.500664 (-6.382985) | 0.042560 / 0.075469 (-0.032909) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956410 / 1.841788 (-0.885377) | 11.764886 / 8.074308 (3.690578) | 10.535801 / 10.191392 (0.344409) | 0.137797 / 0.680424 (-0.542627) | 0.014368 / 0.534201 (-0.519833) | 0.286213 / 0.579283 (-0.293070) | 0.267093 / 0.434364 (-0.167271) | 0.334802 / 0.540337 (-0.205535) | 0.441866 / 1.386936 (-0.945070) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005348 / 0.011353 (-0.006005) | 0.003551 / 0.011008 (-0.007458) | 0.049226 / 0.038508 (0.010718) | 0.052072 / 0.023109 (0.028963) | 0.268025 / 0.275898 (-0.007873) | 0.289968 / 0.323480 (-0.033512) | 0.004034 / 0.007986 (-0.003952) | 0.002675 / 0.004328 (-0.001653) | 0.048099 / 0.004250 (0.043848) | 0.040141 / 0.037052 (0.003089) | 0.272974 / 0.258489 (0.014485) | 0.296097 / 0.293841 (0.002256) | 0.028972 / 0.128546 (-0.099575) | 0.010689 / 0.075646 (-0.064957) | 0.057853 / 0.419271 (-0.361418) | 0.032488 / 0.043533 (-0.011045) | 0.272018 / 0.255139 (0.016879) | 0.287179 / 0.283200 (0.003980) | 0.018446 / 0.141683 (-0.123237) | 1.140346 / 1.452155 (-0.311809) | 1.247743 / 1.492716 (-0.244974) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091987 / 0.018006 (0.073980) | 0.300527 / 0.000490 (0.300037) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021390 / 0.037411 (-0.016021) | 0.068768 / 0.014526 (0.054242) | 0.080798 / 0.176557 (-0.095759) | 0.119081 / 0.737135 (-0.618054) | 0.082461 / 0.296338 (-0.213878) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286631 / 0.215209 (0.071422) | 2.804633 / 2.077655 (0.726978) | 1.574122 / 1.504120 (0.070002) | 1.459994 / 1.541195 (-0.081201) | 1.499739 / 1.468490 (0.031249) | 0.579595 / 4.584777 (-4.005182) | 2.426407 / 3.745712 (-1.319306) | 2.917994 / 5.269862 (-2.351868) | 1.846439 / 4.565676 (-2.719238) | 0.063274 / 0.424275 (-0.361001) | 0.005028 / 0.007607 (-0.002579) | 0.341114 / 0.226044 (0.115070) | 3.402677 / 2.268929 (1.133748) | 1.940980 / 55.444624 (-53.503645) | 1.651902 / 6.876477 (-5.224575) | 1.677037 / 2.142072 (-0.465036) | 0.651576 / 4.805227 (-4.153651) | 0.116398 / 6.500664 (-6.384266) | 0.041060 / 0.075469 (-0.034409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973278 / 1.841788 (-0.868509) | 12.248332 / 8.074308 (4.174024) | 10.830627 / 10.191392 (0.639235) | 0.143146 / 0.680424 (-0.537278) | 0.016249 / 0.534201 (-0.517952) | 0.298563 / 0.579283 (-0.280720) | 0.278643 / 0.434364 (-0.155721) | 0.338206 / 0.540337 (-0.202132) | 0.589485 / 1.386936 (-0.797451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]
2023-12-04T12:00:37
2023-12-15T13:18:37
2023-12-15 13:12:30+00:00
MEMBER
nan
Finally fix https://github.com/huggingface/datasets/issues/5537
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2023617877
PR_kwDODunzps5hCpbN
6468
Use auth to get parquet export
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n" ]
2023-12-04T11:18:27
2023-12-04T17:21:22
2023-12-04 17:15:11+00:00
MEMBER
nan
added `token` to the `_datasets_server` functions
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2023174233
I_kwDODunzps54lzBZ
6467
New version release request
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[ "We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)", "Thanks!" ]
2023-12-04T07:08:26
2023-12-04T15:42:22
2023-12-04 15:42:22+00:00
CONTRIBUTOR
nan
### Feature request Hi! I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0. To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us? Thanks very much! ### Motivation . ### Your contribution .
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2022601176
I_kwDODunzps54jnHY
6466
Can't align optional features of struct
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[ "Friendly bump, I would be happy to work on this issue once I get the go-ahead from the dev team. ", "Thanks for the PR!\r\n\r\nI'm struggling with this as well and would love to see this PR merged. My case is slightly different, with keys completely missing rather than being `None`:\r\n\r\n```\r\nds = Dataset.from_dict({'speaker': [{'name': 'Ben'}]})\r\nds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': '[email protected]'}]})\r\nprint(concatenate_datasets([ds, ds2]).features)\r\nprint(concatenate_datasets([ds, ds2]).to_dict())\r\n```\r\n\r\nI would expect this to work as well because other Dataset functions already handle this situation well. For example, this works just as expected:\r\n\r\n```\r\nds = Dataset.from_dict({'n': [1,2]})\r\nds_mapped = ds.map(lambda x: {\r\n 'speaker': {'name': 'Ben'} if x['n'] == 1 else {'name': 'Fred', 'email': '[email protected]'}\r\n})\r\nprint(ds_mapped)\r\n```", "@vova-cyberhaven can you check with the new release if it fixes your issue? " ]
2023-12-03T15:57:07
2024-02-15T15:19:33
2024-02-08 14:38:34+00:00
CONTRIBUTOR
nan
### Describe the bug Hello! I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional. I have a column named `speaker`, and this holds some information about a speaker. ```python @dataclass class Speaker: name: str email: Optional[str] ``` If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features` ### Steps to reproduce the bug You can run the following script: ```python ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': '[email protected]'}]}) concatenate_datasets([ds, ds2]) >>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null"). ``` ### Expected behavior I think this should work; if two top-level columns were in the same situation it would properly cast to `string`. ```python ds = Dataset.from_dict({'email': [None, None]}) ds2 = Dataset.from_dict({'email': ['[email protected]', '[email protected]']}) concatenate_datasets([ds, ds2]) >>> # Works! ``` ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35 - Python version: 3.9.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 - `fsspec` version: 2023.6.0 I would be happy to fix this issue.
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2022212468
I_kwDODunzps54iIN0
6465
`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset
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[ "Hi, thanks for reporting! https://github.com/huggingface/datasets/pull/6459 will fix this." ]
2023-12-02T21:35:17
2023-12-04T16:13:10
NaT
NONE
nan
### Describe the bug When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset ### Steps to reproduce the bug Here is a minimal example script to 1. create an initial dataset and upload 2. download it so it is stored in cache 3. change the dataset and re-upload 4. redownload ```python import time from datasets import Dataset, DatasetDict, DownloadMode, load_dataset username = "YOUR_USERNAME_HERE" initial = Dataset.from_dict({"foo": [1, 2, 3]}) print(f"Intial {initial['foo']}") initial_ds = DatasetDict({"train": initial}) initial_ds.push_to_hub("test") time.sleep(1) download = load_dataset(f"{username}/test", split="train") changed = download.map(lambda x: {"foo": x["foo"] + 1}) print(f"Changed {changed['foo']}") changed.push_to_hub("test") time.sleep(1) download_again = load_dataset(f"{username}/test", split="train") print(f"Download Changed {download_again['foo']}") # >>> gives the out-dated [1,2,3] when it should be changed [2,3,4] ``` The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset ```python download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) print(f"Force Download Changed {download_again_force['foo']}") # >>> [2,3,4] ``` ### Expected behavior I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match ### Environment info - `datasets` version: 2.15.0 │ - Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │ - Python version: 3.8.17 │ - `huggingface_hub` version: 0.19.4 │ - PyArrow version: 13.0.0 │ - Pandas version: 2.0.3 │ - `fsspec` version: 2023.6.0
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2020860462
PR_kwDODunzps5g5djo
6464
Add concurrent loading of shards to datasets.load_from_disk
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[ "If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.\r\n\r\nHave you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report", "I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. \r\nOk I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. ", "I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20 \r\n\r\nThe CI fail is unrelated to this PR - I'm trying to fix it on `main` right now", "> Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20\r\n> \r\n> The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now\r\n\r\nThank you! I'll let him know :)", "great work guys! letting you know here too", "<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) | 0.005268 / 0.011353 (-0.006085) | 0.003520 / 0.011008 (-0.007488) | 0.063247 / 0.038508 (0.024739) | 0.032337 / 0.023109 (0.009228) | 0.243251 / 0.275898 (-0.032647) | 0.265816 / 0.323480 (-0.057664) | 0.002960 / 0.007986 (-0.005025) | 0.002733 / 0.004328 (-0.001595) | 0.048965 / 0.004250 (0.044715) | 0.044341 / 0.037052 (0.007289) | 0.260352 / 0.258489 (0.001863) | 0.288546 / 0.293841 (-0.005295) | 0.027903 / 0.128546 (-0.100643) | 0.010897 / 0.075646 (-0.064749) | 0.210852 / 0.419271 (-0.208419) | 0.036302 / 0.043533 (-0.007231) | 0.247440 / 0.255139 (-0.007699) | 0.263024 / 0.283200 (-0.020176) | 0.017732 / 0.141683 (-0.123951) | 1.144206 / 1.452155 (-0.307949) | 1.206135 / 1.492716 (-0.286581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098404 / 0.018006 (0.080398) | 0.310268 / 0.000490 (0.309778) | 0.000231 / 0.000200 (0.000031) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018342 / 0.037411 (-0.019070) | 0.060620 / 0.014526 (0.046094) | 0.074248 / 0.176557 (-0.102308) | 0.121025 / 0.737135 (-0.616110) | 0.075331 / 0.296338 (-0.221008) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293721 / 0.215209 (0.078512) | 2.854259 / 2.077655 (0.776605) | 1.520735 / 1.504120 (0.016615) | 1.393490 / 1.541195 (-0.147705) | 1.494905 / 1.468490 (0.026415) | 0.573812 / 4.584777 (-4.010965) | 2.418383 / 3.745712 (-1.327329) | 2.803916 / 5.269862 (-2.465945) | 1.741646 / 4.565676 (-2.824030) | 0.063341 / 0.424275 (-0.360934) | 0.004950 / 0.007607 (-0.002658) | 0.341758 / 0.226044 (0.115714) | 3.392918 / 2.268929 (1.123989) | 1.867037 / 55.444624 (-53.577587) | 1.571381 / 6.876477 (-5.305096) | 1.582883 / 2.142072 (-0.559190) | 0.663660 / 4.805227 (-4.141567) | 0.119587 / 6.500664 (-6.381077) | 0.042071 / 0.075469 (-0.033398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940976 / 1.841788 (-0.900811) | 11.841958 / 8.074308 (3.767650) | 10.510954 / 10.191392 (0.319562) | 0.131927 / 0.680424 (-0.548497) | 0.015373 / 0.534201 (-0.518828) | 0.294245 / 0.579283 (-0.285038) | 0.269355 / 0.434364 (-0.165009) | 0.330173 / 0.540337 (-0.210165) | 0.436809 / 1.386936 (-0.950127) |\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) | 0.005609 / 0.011353 (-0.005744) | 0.003800 / 0.011008 (-0.007208) | 0.055693 / 0.038508 (0.017185) | 0.032606 / 0.023109 (0.009497) | 0.302372 / 0.275898 (0.026474) | 0.370530 / 0.323480 (0.047050) | 0.004291 / 0.007986 (-0.003694) | 0.002783 / 0.004328 (-0.001546) | 0.049351 / 0.004250 (0.045101) | 0.048186 / 0.037052 (0.011133) | 0.290022 / 0.258489 (0.031533) | 0.323358 / 0.293841 (0.029517) | 0.053929 / 0.128546 (-0.074617) | 0.011251 / 0.075646 (-0.064395) | 0.058885 / 0.419271 (-0.360387) | 0.033833 / 0.043533 (-0.009699) | 0.283546 / 0.255139 (0.028407) | 0.292416 / 0.283200 (0.009216) | 0.017682 / 0.141683 (-0.124001) | 1.141791 / 1.452155 (-0.310364) | 1.202540 / 1.492716 (-0.290177) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101240 / 0.018006 (0.083233) | 0.313274 / 0.000490 (0.312784) | 0.000255 / 0.000200 (0.000055) | 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) | 0.023144 / 0.037411 (-0.014268) | 0.078418 / 0.014526 (0.063892) | 0.089716 / 0.176557 (-0.086840) | 0.129065 / 0.737135 (-0.608070) | 0.090976 / 0.296338 (-0.205362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294585 / 0.215209 (0.079376) | 2.921350 / 2.077655 (0.843695) | 1.600977 / 1.504120 (0.096857) | 1.483218 / 1.541195 (-0.057977) | 1.533599 / 1.468490 (0.065109) | 0.580064 / 4.584777 (-4.004712) | 2.463501 / 3.745712 (-1.282211) | 2.905853 / 5.269862 (-2.364009) | 1.799701 / 4.565676 (-2.765975) | 0.065057 / 0.424275 (-0.359218) | 0.005080 / 0.007607 (-0.002527) | 0.352292 / 0.226044 (0.126248) | 3.429664 / 2.268929 (1.160735) | 1.970752 / 55.444624 (-53.473872) | 1.697151 / 6.876477 (-5.179326) | 1.751678 / 2.142072 (-0.390394) | 0.679264 / 4.805227 (-4.125963) | 0.118197 / 6.500664 (-6.382467) | 0.041834 / 0.075469 (-0.033635) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985756 / 1.841788 (-0.856032) | 13.335160 / 8.074308 (5.260852) | 11.524807 / 10.191392 (1.333415) | 0.134892 / 0.680424 (-0.545532) | 0.016855 / 0.534201 (-0.517346) | 0.294599 / 0.579283 (-0.284685) | 0.285988 / 0.434364 (-0.148376) | 0.331423 / 0.540337 (-0.208914) | 0.418765 / 1.386936 (-0.968171) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65434e449b6bb6c57121d9518d92abe9a97e0bb0 \"CML watermark\")\n" ]
2023-12-01T13:13:53
2024-01-26T15:17:43
2024-01-26 15:10:26+00:00
CONTRIBUTOR
nan
In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads. - Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252). - I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config. - I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`. ### Tests on luster file system (on a shared partial node): Loading 1231 shards of ~2GBs. The files were pre-loaded in another process before the script runs (couldn't get a fresh node). ```python import logging from time import perf_counter import datasets logger = datasets.logging.get_logger(__name__) datasets.logging.set_verbosity_info() logging.basicConfig(level=logging.DEBUG, format="%(message)s") class catchtime: # context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time def __init__(self, debug_print="Time", logger=logger): self.debug_print = debug_print self.logger = logger def __enter__(self): self.start = perf_counter() return self def __exit__(self, type, value, traceback): self.time = perf_counter() - self.start readout = f"{self.debug_print}: {self.time:.3f} seconds" self.logger.info(readout) dataset_path="" # warmup with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=16 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=32 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=32) # num_proc=1 with catchtime("Loading in conseq", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=1) ``` #### Run 1 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s] Loading in parallel: 88.690 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s] Loading in parallel: 109.339 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s] Loading in parallel: 66.931 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s] Loading in conseq: 309.792 seconds ``` #### Run 2 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s] Loading in parallel: 98.831 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s] Loading in parallel: 121.669 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s] Loading in parallel: 68.192 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s] Loading in conseq: 319.759 seconds ``` #### Run 3 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s] Loading in parallel: 96.936 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s] Loading in parallel: 120.761 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s] Loading in parallel: 68.666 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s] Loading in conseq: 335.777 seconds ``` fix #2252
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Disable benchmarks in PRs
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[ "It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression", "<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) | 0.005357 / 0.011353 (-0.005996) | 0.003295 / 0.011008 (-0.007713) | 0.062354 / 0.038508 (0.023846) | 0.054207 / 0.023109 (0.031098) | 0.240030 / 0.275898 (-0.035869) | 0.267863 / 0.323480 (-0.055617) | 0.002925 / 0.007986 (-0.005061) | 0.002634 / 0.004328 (-0.001695) | 0.047952 / 0.004250 (0.043702) | 0.038424 / 0.037052 (0.001372) | 0.248059 / 0.258489 (-0.010430) | 0.271923 / 0.293841 (-0.021918) | 0.027513 / 0.128546 (-0.101034) | 0.010344 / 0.075646 (-0.065302) | 0.210864 / 0.419271 (-0.208407) | 0.035911 / 0.043533 (-0.007622) | 0.245166 / 0.255139 (-0.009973) | 0.260914 / 0.283200 (-0.022285) | 0.016709 / 0.141683 (-0.124974) | 1.098324 / 1.452155 (-0.353830) | 1.162638 / 1.492716 (-0.330079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094419 / 0.018006 (0.076413) | 0.303209 / 0.000490 (0.302719) | 0.000214 / 0.000200 (0.000014) | 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) | 0.018350 / 0.037411 (-0.019061) | 0.060625 / 0.014526 (0.046099) | 0.072545 / 0.176557 (-0.104012) | 0.120905 / 0.737135 (-0.616231) | 0.073858 / 0.296338 (-0.222480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282011 / 0.215209 (0.066802) | 2.758741 / 2.077655 (0.681086) | 1.431691 / 1.504120 (-0.072429) | 1.315883 / 1.541195 (-0.225312) | 1.344235 / 1.468490 (-0.124255) | 0.562117 / 4.584777 (-4.022660) | 2.385641 / 3.745712 (-1.360071) | 2.785402 / 5.269862 (-2.484460) | 1.753912 / 4.565676 (-2.811764) | 0.064054 / 0.424275 (-0.360221) | 0.005050 / 0.007607 (-0.002557) | 0.336452 / 0.226044 (0.110407) | 3.302481 / 2.268929 (1.033553) | 1.794105 / 55.444624 (-53.650519) | 1.519346 / 6.876477 (-5.357131) | 1.514911 / 2.142072 (-0.627161) | 0.655779 / 4.805227 (-4.149449) | 0.117913 / 6.500664 (-6.382751) | 0.042229 / 0.075469 (-0.033240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935196 / 1.841788 (-0.906591) | 11.490113 / 8.074308 (3.415805) | 10.542446 / 10.191392 (0.351054) | 0.129614 / 0.680424 (-0.550810) | 0.014919 / 0.534201 (-0.519282) | 0.288448 / 0.579283 (-0.290835) | 0.266929 / 0.434364 (-0.167435) | 0.328830 / 0.540337 (-0.211507) | 0.475510 / 1.386936 (-0.911426) |\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) | 0.005469 / 0.011353 (-0.005884) | 0.003798 / 0.011008 (-0.007210) | 0.049129 / 0.038508 (0.010621) | 0.055490 / 0.023109 (0.032380) | 0.265828 / 0.275898 (-0.010070) | 0.286031 / 0.323480 (-0.037448) | 0.004075 / 0.007986 (-0.003910) | 0.002668 / 0.004328 (-0.001660) | 0.047823 / 0.004250 (0.043573) | 0.041946 / 0.037052 (0.004894) | 0.270359 / 0.258489 (0.011869) | 0.294287 / 0.293841 (0.000446) | 0.029643 / 0.128546 (-0.098903) | 0.010523 / 0.075646 (-0.065123) | 0.057370 / 0.419271 (-0.361902) | 0.033149 / 0.043533 (-0.010384) | 0.264408 / 0.255139 (0.009269) | 0.280413 / 0.283200 (-0.002787) | 0.018313 / 0.141683 (-0.123370) | 1.105982 / 1.452155 (-0.346173) | 1.182486 / 1.492716 (-0.310230) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092643 / 0.018006 (0.074637) | 0.301320 / 0.000490 (0.300831) | 0.000221 / 0.000200 (0.000021) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021253 / 0.037411 (-0.016158) | 0.068052 / 0.014526 (0.053527) | 0.080821 / 0.176557 (-0.095736) | 0.119320 / 0.737135 (-0.617816) | 0.081952 / 0.296338 (-0.214387) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288536 / 0.215209 (0.073327) | 2.819900 / 2.077655 (0.742245) | 1.545210 / 1.504120 (0.041090) | 1.422047 / 1.541195 (-0.119147) | 1.439158 / 1.468490 (-0.029332) | 0.564910 / 4.584777 (-4.019867) | 2.430474 / 3.745712 (-1.315238) | 2.763979 / 5.269862 (-2.505882) | 1.732203 / 4.565676 (-2.833474) | 0.062692 / 0.424275 (-0.361583) | 0.004936 / 0.007607 (-0.002671) | 0.341626 / 0.226044 (0.115582) | 3.366623 / 2.268929 (1.097694) | 1.917198 / 55.444624 (-53.527426) | 1.637635 / 6.876477 (-5.238842) | 1.625953 / 2.142072 (-0.516119) | 0.634936 / 4.805227 (-4.170291) | 0.115336 / 6.500664 (-6.385328) | 0.040946 / 0.075469 (-0.034524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964865 / 1.841788 (-0.876922) | 12.077233 / 8.074308 (4.002925) | 10.664120 / 10.191392 (0.472728) | 0.132084 / 0.680424 (-0.548340) | 0.015931 / 0.534201 (-0.518270) | 0.289181 / 0.579283 (-0.290102) | 0.276943 / 0.434364 (-0.157420) | 0.324884 / 0.540337 (-0.215453) | 0.552570 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e \"CML watermark\")\n" ]
2023-12-01T11:35:30
2023-12-01T12:09:09
2023-12-01 12:03:04+00:00
MEMBER
nan
In order to keep PR pages less spammy / more readable. Having the benchmarks on commits on `main` is enough imo
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Missing DatasetNotFoundError
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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) | 0.005594 / 0.011353 (-0.005759) | 0.003672 / 0.011008 (-0.007337) | 0.062796 / 0.038508 (0.024288) | 0.059432 / 0.023109 (0.036323) | 0.253976 / 0.275898 (-0.021922) | 0.281155 / 0.323480 (-0.042325) | 0.003023 / 0.007986 (-0.004962) | 0.003320 / 0.004328 (-0.001008) | 0.049059 / 0.004250 (0.044809) | 0.040252 / 0.037052 (0.003200) | 0.259526 / 0.258489 (0.001037) | 0.318798 / 0.293841 (0.024957) | 0.027883 / 0.128546 (-0.100663) | 0.010883 / 0.075646 (-0.064763) | 0.206948 / 0.419271 (-0.212323) | 0.036335 / 0.043533 (-0.007198) | 0.253209 / 0.255139 (-0.001930) | 0.275173 / 0.283200 (-0.008026) | 0.020365 / 0.141683 (-0.121318) | 1.121630 / 1.452155 (-0.330524) | 1.174680 / 1.492716 (-0.318036) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098372 / 0.018006 (0.080366) | 0.309949 / 0.000490 (0.309460) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019495 / 0.037411 (-0.017916) | 0.062321 / 0.014526 (0.047795) | 0.074525 / 0.176557 (-0.102031) | 0.121832 / 0.737135 (-0.615303) | 0.077612 / 0.296338 (-0.218727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288156 / 0.215209 (0.072947) | 2.816411 / 2.077655 (0.738756) | 1.497926 / 1.504120 (-0.006193) | 1.378137 / 1.541195 (-0.163058) | 1.446466 / 1.468490 (-0.022024) | 0.566195 / 4.584777 (-4.018582) | 2.391933 / 3.745712 (-1.353780) | 2.929290 / 5.269862 (-2.340572) | 1.828215 / 4.565676 (-2.737462) | 0.063312 / 0.424275 (-0.360963) | 0.005199 / 0.007607 (-0.002408) | 0.342883 / 0.226044 (0.116838) | 3.378388 / 2.268929 (1.109459) | 1.865710 / 55.444624 (-53.578915) | 1.573442 / 6.876477 (-5.303035) | 1.631228 / 2.142072 (-0.510845) | 0.651614 / 4.805227 (-4.153613) | 0.118177 / 6.500664 (-6.382487) | 0.043303 / 0.075469 (-0.032166) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950694 / 1.841788 (-0.891094) | 12.559851 / 8.074308 (4.485543) | 10.751123 / 10.191392 (0.559731) | 0.143107 / 0.680424 (-0.537317) | 0.014469 / 0.534201 (-0.519732) | 0.289531 / 0.579283 (-0.289752) | 0.267316 / 0.434364 (-0.167047) | 0.327748 / 0.540337 (-0.212590) | 0.437758 / 1.386936 (-0.949178) |\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) | 0.005669 / 0.011353 (-0.005684) | 0.003831 / 0.011008 (-0.007177) | 0.049096 / 0.038508 (0.010588) | 0.061408 / 0.023109 (0.038299) | 0.274571 / 0.275898 (-0.001327) | 0.299978 / 0.323480 (-0.023501) | 0.004216 / 0.007986 (-0.003769) | 0.002848 / 0.004328 (-0.001480) | 0.048755 / 0.004250 (0.044504) | 0.042576 / 0.037052 (0.005524) | 0.276781 / 0.258489 (0.018292) | 0.300903 / 0.293841 (0.007062) | 0.030243 / 0.128546 (-0.098303) | 0.010967 / 0.075646 (-0.064679) | 0.057879 / 0.419271 (-0.361392) | 0.033206 / 0.043533 (-0.010327) | 0.277620 / 0.255139 (0.022481) | 0.296263 / 0.283200 (0.013064) | 0.019022 / 0.141683 (-0.122660) | 1.125615 / 1.452155 (-0.326539) | 1.278016 / 1.492716 (-0.214700) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096836 / 0.018006 (0.078830) | 0.307491 / 0.000490 (0.307001) | 0.000230 / 0.000200 (0.000030) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021552 / 0.037411 (-0.015859) | 0.071099 / 0.014526 (0.056573) | 0.082432 / 0.176557 (-0.094124) | 0.121826 / 0.737135 (-0.615310) | 0.084902 / 0.296338 (-0.211437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.328113 / 0.215209 (0.112904) | 2.989613 / 2.077655 (0.911959) | 1.604904 / 1.504120 (0.100784) | 1.485459 / 1.541195 (-0.055735) | 1.524829 / 1.468490 (0.056339) | 0.580589 / 4.584777 (-4.004188) | 2.440087 / 3.745712 (-1.305625) | 2.944697 / 5.269862 (-2.325164) | 1.832728 / 4.565676 (-2.732949) | 0.064423 / 0.424275 (-0.359852) | 0.004991 / 0.007607 (-0.002616) | 0.357878 / 0.226044 (0.131834) | 3.515415 / 2.268929 (1.246487) | 1.964492 / 55.444624 (-53.480132) | 1.684058 / 6.876477 (-5.192418) | 1.730294 / 2.142072 (-0.411778) | 0.661228 / 4.805227 (-4.143999) | 0.122894 / 6.500664 (-6.377770) | 0.041776 / 0.075469 (-0.033693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969849 / 1.841788 (-0.871939) | 12.897067 / 8.074308 (4.822758) | 10.908200 / 10.191392 (0.716808) | 0.141139 / 0.680424 (-0.539285) | 0.015377 / 0.534201 (-0.518824) | 0.288625 / 0.579283 (-0.290658) | 0.279020 / 0.434364 (-0.155344) | 0.328386 / 0.540337 (-0.211951) | 0.590833 / 1.386936 (-0.796103) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39ea60eaabb05d8ee38c072f375816cf87fce1a9 \"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) | 0.004986 / 0.011353 (-0.006367) | 0.003070 / 0.011008 (-0.007938) | 0.062433 / 0.038508 (0.023925) | 0.050639 / 0.023109 (0.027530) | 0.241807 / 0.275898 (-0.034091) | 0.262517 / 0.323480 (-0.060963) | 0.003826 / 0.007986 (-0.004160) | 0.002602 / 0.004328 (-0.001727) | 0.048508 / 0.004250 (0.044257) | 0.037276 / 0.037052 (0.000224) | 0.245757 / 0.258489 (-0.012732) | 0.272969 / 0.293841 (-0.020871) | 0.027139 / 0.128546 (-0.101407) | 0.010265 / 0.075646 (-0.065381) | 0.207279 / 0.419271 (-0.211992) | 0.035312 / 0.043533 (-0.008221) | 0.247535 / 0.255139 (-0.007604) | 0.260668 / 0.283200 (-0.022532) | 0.016496 / 0.141683 (-0.125187) | 1.137510 / 1.452155 (-0.314645) | 1.167870 / 1.492716 (-0.324847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091743 / 0.018006 (0.073736) | 0.298649 / 0.000490 (0.298159) | 0.000208 / 0.000200 (0.000009) | 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) | 0.019053 / 0.037411 (-0.018359) | 0.060300 / 0.014526 (0.045774) | 0.072154 / 0.176557 (-0.104402) | 0.120293 / 0.737135 (-0.616842) | 0.073923 / 0.296338 (-0.222415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283058 / 0.215209 (0.067849) | 2.769503 / 2.077655 (0.691849) | 1.457016 / 1.504120 (-0.047104) | 1.335753 / 1.541195 (-0.205441) | 1.325986 / 1.468490 (-0.142504) | 0.562553 / 4.584777 (-4.022224) | 2.406144 / 3.745712 (-1.339568) | 2.778063 / 5.269862 (-2.491799) | 1.782199 / 4.565676 (-2.783477) | 0.062490 / 0.424275 (-0.361785) | 0.004912 / 0.007607 (-0.002695) | 0.338500 / 0.226044 (0.112456) | 3.309746 / 2.268929 (1.040818) | 1.819693 / 55.444624 (-53.624931) | 1.510295 / 6.876477 (-5.366182) | 1.578402 / 2.142072 (-0.563671) | 0.637517 / 4.805227 (-4.167710) | 0.117018 / 6.500664 (-6.383647) | 0.048149 / 0.075469 (-0.027320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939424 / 1.841788 (-0.902364) | 11.494891 / 8.074308 (3.420583) | 10.115194 / 10.191392 (-0.076198) | 0.126751 / 0.680424 (-0.553673) | 0.013567 / 0.534201 (-0.520634) | 0.282501 / 0.579283 (-0.296782) | 0.260594 / 0.434364 (-0.173770) | 0.325940 / 0.540337 (-0.214397) | 0.426186 / 1.386936 (-0.960750) |\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) | 0.005405 / 0.011353 (-0.005948) | 0.003557 / 0.011008 (-0.007451) | 0.051139 / 0.038508 (0.012631) | 0.053446 / 0.023109 (0.030337) | 0.268051 / 0.275898 (-0.007847) | 0.292343 / 0.323480 (-0.031136) | 0.004716 / 0.007986 (-0.003269) | 0.002677 / 0.004328 (-0.001651) | 0.047634 / 0.004250 (0.043384) | 0.041062 / 0.037052 (0.004009) | 0.269225 / 0.258489 (0.010736) | 0.297462 / 0.293841 (0.003621) | 0.029292 / 0.128546 (-0.099254) | 0.010947 / 0.075646 (-0.064699) | 0.057845 / 0.419271 (-0.361426) | 0.032793 / 0.043533 (-0.010740) | 0.265308 / 0.255139 (0.010169) | 0.288242 / 0.283200 (0.005043) | 0.018311 / 0.141683 (-0.123372) | 1.140957 / 1.452155 (-0.311197) | 1.204883 / 1.492716 (-0.287833) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091375 / 0.018006 (0.073368) | 0.285922 / 0.000490 (0.285432) | 0.000238 / 0.000200 (0.000038) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021277 / 0.037411 (-0.016134) | 0.068853 / 0.014526 (0.054328) | 0.081002 / 0.176557 (-0.095555) | 0.120998 / 0.737135 (-0.616138) | 0.082741 / 0.296338 (-0.213598) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299398 / 0.215209 (0.084189) | 2.909622 / 2.077655 (0.831967) | 1.624381 / 1.504120 (0.120261) | 1.501683 / 1.541195 (-0.039512) | 1.523045 / 1.468490 (0.054555) | 0.548960 / 4.584777 (-4.035817) | 2.413297 / 3.745712 (-1.332415) | 2.817852 / 5.269862 (-2.452010) | 1.754407 / 4.565676 (-2.811270) | 0.061912 / 0.424275 (-0.362363) | 0.004880 / 0.007607 (-0.002727) | 0.353989 / 0.226044 (0.127944) | 3.496147 / 2.268929 (1.227219) | 2.003026 / 55.444624 (-53.441598) | 1.702013 / 6.876477 (-5.174463) | 1.680935 / 2.142072 (-0.461137) | 0.630183 / 4.805227 (-4.175044) | 0.113786 / 6.500664 (-6.386878) | 0.040061 / 0.075469 (-0.035408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957218 / 1.841788 (-0.884569) | 11.914469 / 8.074308 (3.840160) | 10.488896 / 10.191392 (0.297504) | 0.129292 / 0.680424 (-0.551132) | 0.016603 / 0.534201 (-0.517598) | 0.287367 / 0.579283 (-0.291916) | 0.271332 / 0.434364 (-0.163032) | 0.325577 / 0.540337 (-0.214761) | 0.560553 / 1.386936 (-0.826383) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d31e434bbeafdf6a70cb80539342d8fe5f5fd27 \"CML watermark\")\n" ]
2023-11-30T18:09:43
2023-11-30T18:36:40
2023-11-30 18:30:30+00:00
MEMBER
nan
continuation of https://github.com/huggingface/datasets/pull/6431 this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too
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https://api.github.com/repos/huggingface/datasets/issues/6461
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2018850731
PR_kwDODunzps5gykvO
6461
Fix shard retry mechanism in `push_to_hub`
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[]
None
[ "@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872", "We could but I'm not sure to have witness a 504 on S3 before. The issue reported in https://github.com/huggingface/datasets/issues/3872 is a 504 on the `/upload` endpoint on the Hub and this is not an endpoint that is retried on [this line](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300).", "<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) | 0.005110 / 0.011353 (-0.006243) | 0.003307 / 0.011008 (-0.007701) | 0.062601 / 0.038508 (0.024093) | 0.049644 / 0.023109 (0.026534) | 0.243195 / 0.275898 (-0.032703) | 0.273543 / 0.323480 (-0.049936) | 0.003862 / 0.007986 (-0.004123) | 0.002624 / 0.004328 (-0.001705) | 0.048273 / 0.004250 (0.044023) | 0.037820 / 0.037052 (0.000768) | 0.249134 / 0.258489 (-0.009355) | 0.319359 / 0.293841 (0.025518) | 0.027816 / 0.128546 (-0.100730) | 0.010422 / 0.075646 (-0.065225) | 0.206607 / 0.419271 (-0.212665) | 0.035719 / 0.043533 (-0.007814) | 0.250300 / 0.255139 (-0.004839) | 0.290377 / 0.283200 (0.007177) | 0.018459 / 0.141683 (-0.123224) | 1.114664 / 1.452155 (-0.337490) | 1.171429 / 1.492716 (-0.321288) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091483 / 0.018006 (0.073477) | 0.302770 / 0.000490 (0.302281) | 0.000203 / 0.000200 (0.000003) | 0.000047 / 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) | 0.018870 / 0.037411 (-0.018541) | 0.062692 / 0.014526 (0.048166) | 0.075381 / 0.176557 (-0.101176) | 0.122338 / 0.737135 (-0.614797) | 0.075608 / 0.296338 (-0.220730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288115 / 0.215209 (0.072906) | 2.816183 / 2.077655 (0.738528) | 1.535601 / 1.504120 (0.031481) | 1.409546 / 1.541195 (-0.131648) | 1.438569 / 1.468490 (-0.029921) | 0.561797 / 4.584777 (-4.022980) | 2.373921 / 3.745712 (-1.371791) | 2.739437 / 5.269862 (-2.530424) | 1.750921 / 4.565676 (-2.814755) | 0.062114 / 0.424275 (-0.362161) | 0.004965 / 0.007607 (-0.002642) | 0.348614 / 0.226044 (0.122569) | 3.519631 / 2.268929 (1.250703) | 1.910797 / 55.444624 (-53.533827) | 1.610541 / 6.876477 (-5.265936) | 1.617972 / 2.142072 (-0.524100) | 0.639421 / 4.805227 (-4.165806) | 0.117371 / 6.500664 (-6.383293) | 0.041851 / 0.075469 (-0.033618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945563 / 1.841788 (-0.896224) | 11.362399 / 8.074308 (3.288090) | 10.468468 / 10.191392 (0.277075) | 0.128925 / 0.680424 (-0.551499) | 0.013892 / 0.534201 (-0.520309) | 0.285487 / 0.579283 (-0.293796) | 0.269295 / 0.434364 (-0.165069) | 0.324843 / 0.540337 (-0.215495) | 0.438452 / 1.386936 (-0.948484) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005303 / 0.011353 (-0.006050) | 0.003162 / 0.011008 (-0.007846) | 0.048177 / 0.038508 (0.009669) | 0.048708 / 0.023109 (0.025599) | 0.271663 / 0.275898 (-0.004235) | 0.289948 / 0.323480 (-0.033532) | 0.003955 / 0.007986 (-0.004030) | 0.002616 / 0.004328 (-0.001713) | 0.047510 / 0.004250 (0.043260) | 0.039938 / 0.037052 (0.002886) | 0.277449 / 0.258489 (0.018960) | 0.300315 / 0.293841 (0.006474) | 0.029263 / 0.128546 (-0.099283) | 0.010403 / 0.075646 (-0.065244) | 0.056682 / 0.419271 (-0.362590) | 0.032757 / 0.043533 (-0.010776) | 0.273291 / 0.255139 (0.018152) | 0.289023 / 0.283200 (0.005824) | 0.017843 / 0.141683 (-0.123840) | 1.124762 / 1.452155 (-0.327393) | 1.176646 / 1.492716 (-0.316070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004568 / 0.018006 (-0.013438) | 0.300715 / 0.000490 (0.300225) | 0.000212 / 0.000200 (0.000012) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021528 / 0.037411 (-0.015883) | 0.068317 / 0.014526 (0.053792) | 0.081358 / 0.176557 (-0.095199) | 0.119297 / 0.737135 (-0.617838) | 0.082445 / 0.296338 (-0.213893) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289681 / 0.215209 (0.074472) | 2.843862 / 2.077655 (0.766208) | 1.574257 / 1.504120 (0.070137) | 1.454026 / 1.541195 (-0.087169) | 1.478379 / 1.468490 (0.009889) | 0.558259 / 4.584777 (-4.026518) | 2.513261 / 3.745712 (-1.232451) | 2.759751 / 5.269862 (-2.510111) | 1.730335 / 4.565676 (-2.835341) | 0.063805 / 0.424275 (-0.360470) | 0.004991 / 0.007607 (-0.002616) | 0.346586 / 0.226044 (0.120542) | 3.369163 / 2.268929 (1.100234) | 1.934734 / 55.444624 (-53.509890) | 1.658864 / 6.876477 (-5.217613) | 1.645621 / 2.142072 (-0.496452) | 0.636633 / 4.805227 (-4.168594) | 0.116839 / 6.500664 (-6.383825) | 0.040863 / 0.075469 (-0.034606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960925 / 1.841788 (-0.880863) | 11.769189 / 8.074308 (3.694881) | 10.713662 / 10.191392 (0.522270) | 0.140510 / 0.680424 (-0.539914) | 0.015424 / 0.534201 (-0.518777) | 0.288039 / 0.579283 (-0.291244) | 0.277623 / 0.434364 (-0.156741) | 0.322622 / 0.540337 (-0.217716) | 0.539805 / 1.386936 (-0.847131) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07ad81c15bd3b954defe779fc37ba5f432f5ff2a \"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) | 0.005501 / 0.011353 (-0.005852) | 0.003754 / 0.011008 (-0.007254) | 0.062628 / 0.038508 (0.024120) | 0.059951 / 0.023109 (0.036842) | 0.254851 / 0.275898 (-0.021047) | 0.272133 / 0.323480 (-0.051347) | 0.003962 / 0.007986 (-0.004024) | 0.002759 / 0.004328 (-0.001569) | 0.048412 / 0.004250 (0.044161) | 0.039349 / 0.037052 (0.002297) | 0.253093 / 0.258489 (-0.005397) | 0.287048 / 0.293841 (-0.006793) | 0.027197 / 0.128546 (-0.101349) | 0.010828 / 0.075646 (-0.064819) | 0.206371 / 0.419271 (-0.212901) | 0.035881 / 0.043533 (-0.007652) | 0.254905 / 0.255139 (-0.000234) | 0.273819 / 0.283200 (-0.009381) | 0.018041 / 0.141683 (-0.123642) | 1.103970 / 1.452155 (-0.348185) | 1.166340 / 1.492716 (-0.326377) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093196 / 0.018006 (0.075190) | 0.302690 / 0.000490 (0.302200) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019552 / 0.037411 (-0.017860) | 0.062337 / 0.014526 (0.047811) | 0.074070 / 0.176557 (-0.102486) | 0.120998 / 0.737135 (-0.616137) | 0.076265 / 0.296338 (-0.220074) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.272637 / 0.215209 (0.057427) | 2.693350 / 2.077655 (0.615696) | 1.398020 / 1.504120 (-0.106100) | 1.285706 / 1.541195 (-0.255488) | 1.342810 / 1.468490 (-0.125680) | 0.565378 / 4.584777 (-4.019399) | 2.390131 / 3.745712 (-1.355581) | 2.892137 / 5.269862 (-2.377725) | 1.819840 / 4.565676 (-2.745836) | 0.062789 / 0.424275 (-0.361486) | 0.004920 / 0.007607 (-0.002687) | 0.329281 / 0.226044 (0.103237) | 3.261664 / 2.268929 (0.992735) | 1.775102 / 55.444624 (-53.669523) | 1.514341 / 6.876477 (-5.362136) | 1.530805 / 2.142072 (-0.611267) | 0.641009 / 4.805227 (-4.164218) | 0.118626 / 6.500664 (-6.382038) | 0.042732 / 0.075469 (-0.032737) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933179 / 1.841788 (-0.908609) | 12.085247 / 8.074308 (4.010939) | 10.541596 / 10.191392 (0.350204) | 0.140141 / 0.680424 (-0.540283) | 0.014646 / 0.534201 (-0.519555) | 0.289640 / 0.579283 (-0.289643) | 0.281042 / 0.434364 (-0.153322) | 0.326462 / 0.540337 (-0.213876) | 0.441981 / 1.386936 (-0.944955) |\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) | 0.005259 / 0.011353 (-0.006094) | 0.003766 / 0.011008 (-0.007242) | 0.048782 / 0.038508 (0.010273) | 0.064946 / 0.023109 (0.041836) | 0.264529 / 0.275898 (-0.011369) | 0.289675 / 0.323480 (-0.033805) | 0.004057 / 0.007986 (-0.003928) | 0.002805 / 0.004328 (-0.001523) | 0.047709 / 0.004250 (0.043459) | 0.041149 / 0.037052 (0.004096) | 0.271254 / 0.258489 (0.012765) | 0.296685 / 0.293841 (0.002844) | 0.029486 / 0.128546 (-0.099060) | 0.010608 / 0.075646 (-0.065038) | 0.056392 / 0.419271 (-0.362879) | 0.033181 / 0.043533 (-0.010352) | 0.267029 / 0.255139 (0.011890) | 0.284987 / 0.283200 (0.001787) | 0.018045 / 0.141683 (-0.123637) | 1.137358 / 1.452155 (-0.314796) | 1.184007 / 1.492716 (-0.308709) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004603 / 0.018006 (-0.013403) | 0.303901 / 0.000490 (0.303411) | 0.000225 / 0.000200 (0.000025) | 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) | 0.021957 / 0.037411 (-0.015454) | 0.069427 / 0.014526 (0.054901) | 0.082394 / 0.176557 (-0.094163) | 0.120745 / 0.737135 (-0.616390) | 0.084571 / 0.296338 (-0.211767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292832 / 0.215209 (0.077623) | 2.824295 / 2.077655 (0.746640) | 1.563273 / 1.504120 (0.059153) | 1.440202 / 1.541195 (-0.100992) | 1.489810 / 1.468490 (0.021320) | 0.561120 / 4.584777 (-4.023657) | 2.439045 / 3.745712 (-1.306667) | 2.867139 / 5.269862 (-2.402722) | 1.793812 / 4.565676 (-2.771865) | 0.062797 / 0.424275 (-0.361478) | 0.005033 / 0.007607 (-0.002574) | 0.343648 / 0.226044 (0.117604) | 3.432285 / 2.268929 (1.163357) | 1.918175 / 55.444624 (-53.526449) | 1.637245 / 6.876477 (-5.239232) | 1.709246 / 2.142072 (-0.432826) | 0.634744 / 4.805227 (-4.170483) | 0.115782 / 6.500664 (-6.384882) | 0.041228 / 0.075469 (-0.034241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962369 / 1.841788 (-0.879418) | 12.750819 / 8.074308 (4.676511) | 10.927356 / 10.191392 (0.735964) | 0.143454 / 0.680424 (-0.536970) | 0.015348 / 0.534201 (-0.518853) | 0.291207 / 0.579283 (-0.288076) | 0.276924 / 0.434364 (-0.157440) | 0.327287 / 0.540337 (-0.213050) | 0.577439 / 1.386936 (-0.809497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#544ad95f6b6da7fee44a2bc838e15a5e0156c946 \"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) | 0.005070 / 0.011353 (-0.006283) | 0.003475 / 0.011008 (-0.007533) | 0.061985 / 0.038508 (0.023477) | 0.048539 / 0.023109 (0.025430) | 0.229935 / 0.275898 (-0.045963) | 0.255247 / 0.323480 (-0.068233) | 0.003919 / 0.007986 (-0.004066) | 0.002664 / 0.004328 (-0.001664) | 0.048892 / 0.004250 (0.044642) | 0.037381 / 0.037052 (0.000328) | 0.238517 / 0.258489 (-0.019972) | 0.284069 / 0.293841 (-0.009772) | 0.027513 / 0.128546 (-0.101033) | 0.010778 / 0.075646 (-0.064868) | 0.205004 / 0.419271 (-0.214268) | 0.035553 / 0.043533 (-0.007980) | 0.230117 / 0.255139 (-0.025022) | 0.251150 / 0.283200 (-0.032050) | 0.017951 / 0.141683 (-0.123732) | 1.145548 / 1.452155 (-0.306607) | 1.191659 / 1.492716 (-0.301057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092335 / 0.018006 (0.074329) | 0.300264 / 0.000490 (0.299774) | 0.000206 / 0.000200 (0.000006) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018608 / 0.037411 (-0.018804) | 0.060376 / 0.014526 (0.045850) | 0.073551 / 0.176557 (-0.103006) | 0.118840 / 0.737135 (-0.618295) | 0.074447 / 0.296338 (-0.221892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287033 / 0.215209 (0.071824) | 2.770958 / 2.077655 (0.693303) | 1.443986 / 1.504120 (-0.060134) | 1.314627 / 1.541195 (-0.226567) | 1.342287 / 1.468490 (-0.126203) | 0.559607 / 4.584777 (-4.025170) | 2.409678 / 3.745712 (-1.336034) | 2.772566 / 5.269862 (-2.497295) | 1.743511 / 4.565676 (-2.822165) | 0.062277 / 0.424275 (-0.361998) | 0.004952 / 0.007607 (-0.002655) | 0.330581 / 0.226044 (0.104537) | 3.280385 / 2.268929 (1.011456) | 1.809599 / 55.444624 (-53.635025) | 1.532186 / 6.876477 (-5.344290) | 1.529689 / 2.142072 (-0.612383) | 0.645213 / 4.805227 (-4.160014) | 0.117564 / 6.500664 (-6.383100) | 0.041657 / 0.075469 (-0.033812) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943912 / 1.841788 (-0.897876) | 11.414317 / 8.074308 (3.340009) | 10.394915 / 10.191392 (0.203523) | 0.129271 / 0.680424 (-0.551153) | 0.013934 / 0.534201 (-0.520267) | 0.288217 / 0.579283 (-0.291066) | 0.267171 / 0.434364 (-0.167193) | 0.327112 / 0.540337 (-0.213225) | 0.446680 / 1.386936 (-0.940256) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005200 / 0.011353 (-0.006152) | 0.003453 / 0.011008 (-0.007555) | 0.048736 / 0.038508 (0.010228) | 0.051073 / 0.023109 (0.027964) | 0.276591 / 0.275898 (0.000693) | 0.294495 / 0.323480 (-0.028985) | 0.004069 / 0.007986 (-0.003917) | 0.002945 / 0.004328 (-0.001383) | 0.047090 / 0.004250 (0.042839) | 0.040445 / 0.037052 (0.003393) | 0.278464 / 0.258489 (0.019975) | 0.304020 / 0.293841 (0.010179) | 0.028811 / 0.128546 (-0.099736) | 0.010388 / 0.075646 (-0.065259) | 0.057214 / 0.419271 (-0.362057) | 0.032588 / 0.043533 (-0.010945) | 0.277694 / 0.255139 (0.022555) | 0.294979 / 0.283200 (0.011779) | 0.018384 / 0.141683 (-0.123299) | 1.162332 / 1.452155 (-0.289822) | 1.188355 / 1.492716 (-0.304361) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090501 / 0.018006 (0.072495) | 0.303122 / 0.000490 (0.302632) | 0.000222 / 0.000200 (0.000022) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022536 / 0.037411 (-0.014876) | 0.068452 / 0.014526 (0.053926) | 0.080932 / 0.176557 (-0.095625) | 0.119185 / 0.737135 (-0.617950) | 0.081513 / 0.296338 (-0.214825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291522 / 0.215209 (0.076313) | 2.849467 / 2.077655 (0.771812) | 1.597395 / 1.504120 (0.093275) | 1.512872 / 1.541195 (-0.028323) | 1.488144 / 1.468490 (0.019654) | 0.572436 / 4.584777 (-4.012341) | 2.440129 / 3.745712 (-1.305583) | 2.788045 / 5.269862 (-2.481817) | 1.754246 / 4.565676 (-2.811430) | 0.066706 / 0.424275 (-0.357569) | 0.005035 / 0.007607 (-0.002573) | 0.336621 / 0.226044 (0.110576) | 3.322820 / 2.268929 (1.053891) | 1.940494 / 55.444624 (-53.504130) | 1.670022 / 6.876477 (-5.206454) | 1.666353 / 2.142072 (-0.475720) | 0.646180 / 4.805227 (-4.159047) | 0.116676 / 6.500664 (-6.383988) | 0.040559 / 0.075469 (-0.034910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971396 / 1.841788 (-0.870392) | 11.782426 / 8.074308 (3.708118) | 10.672034 / 10.191392 (0.480642) | 0.137658 / 0.680424 (-0.542766) | 0.016210 / 0.534201 (-0.517991) | 0.288302 / 0.579283 (-0.290981) | 0.280775 / 0.434364 (-0.153589) | 0.326962 / 0.540337 (-0.213375) | 0.558511 / 1.386936 (-0.828425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76020180407d7ea9a0b535758d8d1b241fd19d8c \"CML watermark\")\n" ]
2023-11-30T14:57:14
2023-12-01T17:57:39
2023-12-01 17:51:33+00:00
COLLABORATOR
nan
When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that. Fix https://github.com/huggingface/datasets/issues/6392
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6460
jsonlines files don't load with `load_dataset`
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[ "Hi @serenalotreck,\r\n\r\nWe use Apache Arrow `pyarrow` to read jsonlines and it throws an error when trying to load your data files:\r\n```python\r\nIn [1]: import pyarrow as pa\r\n\r\nIn [2]: data = pa.json.read_json(\"train.jsonl\")\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-14-e9b104832528> in <module>\r\n----> 1 data = pa.json.read_json(\"train.jsonl\")\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0\r\n```\r\n\r\nI think it has to do with the data structure of the fields \"ner\" (and also \"relations\"):\r\n```json\r\n\"ner\": [\r\n [\r\n [0, 4, \"Biochemical_process\"], \r\n [15, 16, \"Protein\"]\r\n ], \r\n```\r\nArrow interprets this data structure as an array, an arrays contain just a single data type: \r\n- when reading sequentially, it finds first the `0` and infers that the data is of type `number`;\r\n- when it finds the string `\"Biochemical_process\"`, it cannot cast it to number and throws the `ArrowInvalid` error\r\n\r\nOne solution could be to change the data structure of your data files. Any other ideas, @huggingface/datasets ?", "Hi @albertvillanova, \r\n\r\nThanks for the explanation! To the best of my knowledge, arrays in a json [can contain multiple data types](https://docs.actian.com/ingres/11.2/index.html#page/SQLRef/Data_Types.htm), and I'm able to read these files with the `jsonlines` package. Is the requirement for arrays to only have one data type specific to PyArrow?\r\n\r\nI'd prefer to keep the data structure as is, since it's a specific input requirement for the models this data was generated for. Any thoughts on how to enable the use of `load_dataset` with this dataset would be great!", "Hi again @serenalotreck,\r\n\r\nYes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n\r\nAs this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n\r\nLet's continue the discussion there! :hugs: ", "> Hi again @serenalotreck,\r\n> \r\n> Yes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n> \r\n> As this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n> \r\n> Let's continue the discussion there! 🤗\r\n\r\nThis is really terrible. My JSONL format data is very simple, but I still report this error\r\n![image](https://github.com/huggingface/datasets/assets/58240629/e3fed922-ced4-406c-b5bc-90a4b891c4ee)\r\nThe error message is as follows：\r\n File \"pyarrow/_json.pyx\", line 290, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column(/inputs) changed from string to number in row 208\r\n" ]
2023-11-29T21:20:11
2023-12-29T02:58:29
2023-12-05 13:30:53+00:00
NONE
nan
### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare **download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 8.0.0 - Pandas version: 1.3.5
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6459
Retrieve cached datasets that were pushed to hub when offline
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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) | 0.005292 / 0.011353 (-0.006061) | 0.003811 / 0.011008 (-0.007197) | 0.064912 / 0.038508 (0.026404) | 0.061199 / 0.023109 (0.038090) | 0.242953 / 0.275898 (-0.032945) | 0.271789 / 0.323480 (-0.051691) | 0.003994 / 0.007986 (-0.003991) | 0.002723 / 0.004328 (-0.001606) | 0.049952 / 0.004250 (0.045701) | 0.039489 / 0.037052 (0.002437) | 0.261143 / 0.258489 (0.002654) | 0.288800 / 0.293841 (-0.005041) | 0.028130 / 0.128546 (-0.100416) | 0.010724 / 0.075646 (-0.064922) | 0.208218 / 0.419271 (-0.211054) | 0.036224 / 0.043533 (-0.007309) | 0.247189 / 0.255139 (-0.007950) | 0.274702 / 0.283200 (-0.008498) | 0.019714 / 0.141683 (-0.121969) | 1.134853 / 1.452155 (-0.317301) | 1.192655 / 1.492716 (-0.300062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096391 / 0.018006 (0.078385) | 0.303802 / 0.000490 (0.303312) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019530 / 0.037411 (-0.017881) | 0.061588 / 0.014526 (0.047062) | 0.075122 / 0.176557 (-0.101434) | 0.120980 / 0.737135 (-0.616155) | 0.075807 / 0.296338 (-0.220532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281672 / 0.215209 (0.066463) | 2.779884 / 2.077655 (0.702229) | 1.502026 / 1.504120 (-0.002094) | 1.369474 / 1.541195 (-0.171721) | 1.402694 / 1.468490 (-0.065796) | 0.559120 / 4.584777 (-4.025657) | 2.355320 / 3.745712 (-1.390393) | 2.823987 / 5.269862 (-2.445875) | 1.763888 / 4.565676 (-2.801788) | 0.061715 / 0.424275 (-0.362560) | 0.005015 / 0.007607 (-0.002592) | 0.342669 / 0.226044 (0.116625) | 3.360651 / 2.268929 (1.091722) | 1.887277 / 55.444624 (-53.557348) | 1.555613 / 6.876477 (-5.320864) | 1.614126 / 2.142072 (-0.527946) | 0.643797 / 4.805227 (-4.161430) | 0.118365 / 6.500664 (-6.382299) | 0.042596 / 0.075469 (-0.032873) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951383 / 1.841788 (-0.890405) | 13.169812 / 8.074308 (5.095504) | 10.772460 / 10.191392 (0.581068) | 0.133248 / 0.680424 (-0.547176) | 0.014597 / 0.534201 (-0.519604) | 0.289758 / 0.579283 (-0.289525) | 0.266324 / 0.434364 (-0.168040) | 0.334811 / 0.540337 (-0.205526) | 0.445566 / 1.386936 (-0.941370) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005668 / 0.011353 (-0.005684) | 0.003583 / 0.011008 (-0.007425) | 0.050681 / 0.038508 (0.012173) | 0.063244 / 0.023109 (0.040135) | 0.279624 / 0.275898 (0.003726) | 0.308030 / 0.323480 (-0.015450) | 0.004160 / 0.007986 (-0.003826) | 0.002633 / 0.004328 (-0.001696) | 0.048475 / 0.004250 (0.044225) | 0.043106 / 0.037052 (0.006054) | 0.283678 / 0.258489 (0.025189) | 0.309730 / 0.293841 (0.015889) | 0.030290 / 0.128546 (-0.098256) | 0.011112 / 0.075646 (-0.064534) | 0.058234 / 0.419271 (-0.361038) | 0.033553 / 0.043533 (-0.009979) | 0.279902 / 0.255139 (0.024763) | 0.298041 / 0.283200 (0.014841) | 0.019367 / 0.141683 (-0.122316) | 1.142438 / 1.452155 (-0.309717) | 1.197305 / 1.492716 (-0.295411) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090875 / 0.018006 (0.072869) | 0.301174 / 0.000490 (0.300685) | 0.000216 / 0.000200 (0.000016) | 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) | 0.021544 / 0.037411 (-0.015867) | 0.071371 / 0.014526 (0.056846) | 0.080821 / 0.176557 (-0.095736) | 0.120054 / 0.737135 (-0.617082) | 0.082611 / 0.296338 (-0.213728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293787 / 0.215209 (0.078578) | 2.862610 / 2.077655 (0.784955) | 1.597282 / 1.504120 (0.093162) | 1.485094 / 1.541195 (-0.056101) | 1.507384 / 1.468490 (0.038893) | 0.558470 / 4.584777 (-4.026307) | 2.414137 / 3.745712 (-1.331575) | 2.863342 / 5.269862 (-2.406520) | 1.776973 / 4.565676 (-2.788704) | 0.062296 / 0.424275 (-0.361979) | 0.004954 / 0.007607 (-0.002653) | 0.346037 / 0.226044 (0.119993) | 3.441864 / 2.268929 (1.172935) | 1.969842 / 55.444624 (-53.474783) | 1.714878 / 6.876477 (-5.161599) | 1.738141 / 2.142072 (-0.403931) | 0.645929 / 4.805227 (-4.159298) | 0.117332 / 6.500664 (-6.383332) | 0.041963 / 0.075469 (-0.033507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983229 / 1.841788 (-0.858559) | 13.186932 / 8.074308 (5.112624) | 11.220549 / 10.191392 (1.029157) | 0.142105 / 0.680424 (-0.538319) | 0.015210 / 0.534201 (-0.518991) | 0.290055 / 0.579283 (-0.289228) | 0.274513 / 0.434364 (-0.159851) | 0.346834 / 0.540337 (-0.193504) | 0.575897 / 1.386936 (-0.811039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). 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) | 0.005308 / 0.011353 (-0.006045) | 0.003135 / 0.011008 (-0.007873) | 0.061820 / 0.038508 (0.023312) | 0.052005 / 0.023109 (0.028895) | 0.233507 / 0.275898 (-0.042391) | 0.257790 / 0.323480 (-0.065690) | 0.002848 / 0.007986 (-0.005138) | 0.002645 / 0.004328 (-0.001683) | 0.048379 / 0.004250 (0.044128) | 0.038320 / 0.037052 (0.001268) | 0.245470 / 0.258489 (-0.013019) | 0.274854 / 0.293841 (-0.018987) | 0.027335 / 0.128546 (-0.101211) | 0.010349 / 0.075646 (-0.065297) | 0.205872 / 0.419271 (-0.213400) | 0.035896 / 0.043533 (-0.007637) | 0.241645 / 0.255139 (-0.013494) | 0.260033 / 0.283200 (-0.023167) | 0.020325 / 0.141683 (-0.121358) | 1.116768 / 1.452155 (-0.335387) | 1.188067 / 1.492716 (-0.304649) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092622 / 0.018006 (0.074616) | 0.302663 / 0.000490 (0.302173) | 0.000227 / 0.000200 (0.000027) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018633 / 0.037411 (-0.018778) | 0.060117 / 0.014526 (0.045592) | 0.072713 / 0.176557 (-0.103844) | 0.119955 / 0.737135 (-0.617180) | 0.074698 / 0.296338 (-0.221640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277157 / 0.215209 (0.061948) | 2.699650 / 2.077655 (0.621995) | 1.413625 / 1.504120 (-0.090494) | 1.295900 / 1.541195 (-0.245295) | 1.306280 / 1.468490 (-0.162210) | 0.555354 / 4.584777 (-4.029423) | 2.386866 / 3.745712 (-1.358847) | 2.794069 / 5.269862 (-2.475793) | 1.736275 / 4.565676 (-2.829401) | 0.061812 / 0.424275 (-0.362464) | 0.004957 / 0.007607 (-0.002650) | 0.334533 / 0.226044 (0.108488) | 3.251096 / 2.268929 (0.982168) | 1.768193 / 55.444624 (-53.676431) | 1.473752 / 6.876477 (-5.402724) | 1.476320 / 2.142072 (-0.665753) | 0.642485 / 4.805227 (-4.162742) | 0.116986 / 6.500664 (-6.383678) | 0.042083 / 0.075469 (-0.033386) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941364 / 1.841788 (-0.900424) | 11.587408 / 8.074308 (3.513100) | 10.500198 / 10.191392 (0.308806) | 0.129126 / 0.680424 (-0.551298) | 0.015206 / 0.534201 (-0.518995) | 0.286580 / 0.579283 (-0.292703) | 0.263566 / 0.434364 (-0.170798) | 0.331662 / 0.540337 (-0.208676) | 0.431423 / 1.386936 (-0.955513) |\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) | 0.005151 / 0.011353 (-0.006202) | 0.003425 / 0.011008 (-0.007583) | 0.049301 / 0.038508 (0.010793) | 0.052005 / 0.023109 (0.028895) | 0.289594 / 0.275898 (0.013696) | 0.312630 / 0.323480 (-0.010849) | 0.003988 / 0.007986 (-0.003998) | 0.002705 / 0.004328 (-0.001624) | 0.048529 / 0.004250 (0.044279) | 0.039645 / 0.037052 (0.002592) | 0.293430 / 0.258489 (0.034941) | 0.311697 / 0.293841 (0.017856) | 0.029044 / 0.128546 (-0.099502) | 0.010282 / 0.075646 (-0.065364) | 0.057641 / 0.419271 (-0.361630) | 0.032733 / 0.043533 (-0.010800) | 0.293553 / 0.255139 (0.038414) | 0.308850 / 0.283200 (0.025651) | 0.018452 / 0.141683 (-0.123231) | 1.147931 / 1.452155 (-0.304224) | 1.173093 / 1.492716 (-0.319623) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100862 / 0.018006 (0.082856) | 0.309286 / 0.000490 (0.308796) | 0.000223 / 0.000200 (0.000023) | 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) | 0.021365 / 0.037411 (-0.016046) | 0.068987 / 0.014526 (0.054461) | 0.081092 / 0.176557 (-0.095465) | 0.119852 / 0.737135 (-0.617283) | 0.082850 / 0.296338 (-0.213489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288477 / 0.215209 (0.073268) | 2.833766 / 2.077655 (0.756111) | 1.576670 / 1.504120 (0.072550) | 1.431643 / 1.541195 (-0.109552) | 1.442132 / 1.468490 (-0.026358) | 0.556079 / 4.584777 (-4.028698) | 2.465042 / 3.745712 (-1.280670) | 2.786329 / 5.269862 (-2.483532) | 1.779428 / 4.565676 (-2.786249) | 0.062278 / 0.424275 (-0.361997) | 0.004867 / 0.007607 (-0.002740) | 0.348444 / 0.226044 (0.122399) | 3.389824 / 2.268929 (1.120896) | 1.919141 / 55.444624 (-53.525484) | 1.635411 / 6.876477 (-5.241066) | 1.654869 / 2.142072 (-0.487204) | 0.634467 / 4.805227 (-4.170761) | 0.114330 / 6.500664 (-6.386334) | 0.039900 / 0.075469 (-0.035569) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970851 / 1.841788 (-0.870937) | 11.951660 / 8.074308 (3.877352) | 10.571115 / 10.191392 (0.379723) | 0.131040 / 0.680424 (-0.549384) | 0.015299 / 0.534201 (-0.518902) | 0.287851 / 0.579283 (-0.291432) | 0.278366 / 0.434364 (-0.155998) | 0.326468 / 0.540337 (-0.213870) | 0.552288 / 1.386936 (-0.834648) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n" ]
2023-11-29T16:56:15
2024-03-25T13:55:42
2024-03-25 13:55:42+00:00
MEMBER
nan
I drafted the logic to retrieve a no-script dataset in the cache. For example it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/*/*/0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp. DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` fix https://github.com/huggingface/datasets/issues/3547 ## Implementation details (EDITED) I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there TODO: - [x] tests - [ ] compatible with https://github.com/huggingface/datasets/pull/6458
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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) | 0.005097 / 0.011353 (-0.006256) | 0.003523 / 0.011008 (-0.007485) | 0.062827 / 0.038508 (0.024319) | 0.051677 / 0.023109 (0.028568) | 0.248919 / 0.275898 (-0.026980) | 0.275892 / 0.323480 (-0.047588) | 0.003908 / 0.007986 (-0.004077) | 0.002622 / 0.004328 (-0.001706) | 0.048634 / 0.004250 (0.044383) | 0.037903 / 0.037052 (0.000850) | 0.255754 / 0.258489 (-0.002735) | 0.283343 / 0.293841 (-0.010498) | 0.027886 / 0.128546 (-0.100660) | 0.010849 / 0.075646 (-0.064797) | 0.208255 / 0.419271 (-0.211017) | 0.035664 / 0.043533 (-0.007869) | 0.254661 / 0.255139 (-0.000478) | 0.274366 / 0.283200 (-0.008834) | 0.017240 / 0.141683 (-0.124443) | 1.092952 / 1.452155 (-0.359203) | 1.148373 / 1.492716 (-0.344344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091592 / 0.018006 (0.073586) | 0.301926 / 0.000490 (0.301436) | 0.000207 / 0.000200 (0.000007) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018525 / 0.037411 (-0.018887) | 0.060539 / 0.014526 (0.046014) | 0.073812 / 0.176557 (-0.102745) | 0.120655 / 0.737135 (-0.616480) | 0.076931 / 0.296338 (-0.219407) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282797 / 0.215209 (0.067588) | 2.746573 / 2.077655 (0.668918) | 1.477652 / 1.504120 (-0.026468) | 1.349922 / 1.541195 (-0.191273) | 1.374347 / 1.468490 (-0.094143) | 0.574096 / 4.584777 (-4.010681) | 2.383317 / 3.745712 (-1.362395) | 2.809320 / 5.269862 (-2.460541) | 1.758947 / 4.565676 (-2.806729) | 0.064029 / 0.424275 (-0.360246) | 0.004936 / 0.007607 (-0.002672) | 0.331403 / 0.226044 (0.105358) | 3.260908 / 2.268929 (0.991980) | 1.817670 / 55.444624 (-53.626954) | 1.525863 / 6.876477 (-5.350613) | 1.542017 / 2.142072 (-0.600055) | 0.638900 / 4.805227 (-4.166327) | 0.119485 / 6.500664 (-6.381179) | 0.042588 / 0.075469 (-0.032881) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951583 / 1.841788 (-0.890205) | 11.621917 / 8.074308 (3.547609) | 10.511062 / 10.191392 (0.319670) | 0.130137 / 0.680424 (-0.550287) | 0.014048 / 0.534201 (-0.520153) | 0.290621 / 0.579283 (-0.288662) | 0.271665 / 0.434364 (-0.162699) | 0.331260 / 0.540337 (-0.209077) | 0.441621 / 1.386936 (-0.945316) |\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) | 0.005272 / 0.011353 (-0.006081) | 0.003656 / 0.011008 (-0.007352) | 0.049245 / 0.038508 (0.010737) | 0.054130 / 0.023109 (0.031021) | 0.274775 / 0.275898 (-0.001123) | 0.296664 / 0.323480 (-0.026816) | 0.004870 / 0.007986 (-0.003115) | 0.002728 / 0.004328 (-0.001601) | 0.048087 / 0.004250 (0.043837) | 0.041448 / 0.037052 (0.004396) | 0.279110 / 0.258489 (0.020621) | 0.303660 / 0.293841 (0.009819) | 0.029767 / 0.128546 (-0.098779) | 0.010799 / 0.075646 (-0.064848) | 0.058650 / 0.419271 (-0.360622) | 0.033088 / 0.043533 (-0.010445) | 0.274456 / 0.255139 (0.019317) | 0.290206 / 0.283200 (0.007007) | 0.017259 / 0.141683 (-0.124424) | 1.176501 / 1.452155 (-0.275654) | 1.197552 / 1.492716 (-0.295165) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092865 / 0.018006 (0.074859) | 0.302437 / 0.000490 (0.301947) | 0.000209 / 0.000200 (0.000009) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021211 / 0.037411 (-0.016200) | 0.068858 / 0.014526 (0.054332) | 0.081783 / 0.176557 (-0.094773) | 0.120472 / 0.737135 (-0.616663) | 0.083900 / 0.296338 (-0.212438) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295157 / 0.215209 (0.079948) | 2.910979 / 2.077655 (0.833324) | 1.575772 / 1.504120 (0.071652) | 1.456955 / 1.541195 (-0.084239) | 1.468982 / 1.468490 (0.000492) | 0.560309 / 4.584777 (-4.024468) | 2.460171 / 3.745712 (-1.285541) | 2.805713 / 5.269862 (-2.464149) | 1.754074 / 4.565676 (-2.811603) | 0.063333 / 0.424275 (-0.360942) | 0.004940 / 0.007607 (-0.002667) | 0.346141 / 0.226044 (0.120097) | 3.463431 / 2.268929 (1.194502) | 1.929135 / 55.444624 (-53.515490) | 1.660191 / 6.876477 (-5.216286) | 1.668327 / 2.142072 (-0.473746) | 0.644183 / 4.805227 (-4.161044) | 0.115738 / 6.500664 (-6.384926) | 0.041347 / 0.075469 (-0.034122) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961565 / 1.841788 (-0.880222) | 12.232589 / 8.074308 (4.158281) | 10.778774 / 10.191392 (0.587382) | 0.132709 / 0.680424 (-0.547715) | 0.015964 / 0.534201 (-0.518237) | 0.286944 / 0.579283 (-0.292340) | 0.279740 / 0.434364 (-0.154624) | 0.333024 / 0.540337 (-0.207314) | 0.438819 / 1.386936 (-0.948117) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51002cb0325772adaf46d6f3ce01d41c01b51079 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6458). 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) | 0.005317 / 0.011353 (-0.006036) | 0.003936 / 0.011008 (-0.007072) | 0.063122 / 0.038508 (0.024614) | 0.061274 / 0.023109 (0.038165) | 0.251764 / 0.275898 (-0.024134) | 0.274849 / 0.323480 (-0.048631) | 0.004059 / 0.007986 (-0.003927) | 0.002874 / 0.004328 (-0.001455) | 0.048716 / 0.004250 (0.044465) | 0.038281 / 0.037052 (0.001228) | 0.265224 / 0.258489 (0.006735) | 0.285962 / 0.293841 (-0.007878) | 0.028522 / 0.128546 (-0.100024) | 0.011150 / 0.075646 (-0.064496) | 0.208362 / 0.419271 (-0.210910) | 0.038900 / 0.043533 (-0.004633) | 0.254113 / 0.255139 (-0.001026) | 0.276721 / 0.283200 (-0.006478) | 0.018372 / 0.141683 (-0.123311) | 1.121336 / 1.452155 (-0.330818) | 1.189548 / 1.492716 (-0.303168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097633 / 0.018006 (0.079627) | 0.304443 / 0.000490 (0.303953) | 0.000218 / 0.000200 (0.000018) | 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) | 0.021757 / 0.037411 (-0.015654) | 0.061978 / 0.014526 (0.047453) | 0.076296 / 0.176557 (-0.100260) | 0.122320 / 0.737135 (-0.614816) | 0.076738 / 0.296338 (-0.219601) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284328 / 0.215209 (0.069119) | 2.793071 / 2.077655 (0.715417) | 1.504768 / 1.504120 (0.000648) | 1.386083 / 1.541195 (-0.155111) | 1.457593 / 1.468490 (-0.010897) | 0.575887 / 4.584777 (-4.008890) | 2.419396 / 3.745712 (-1.326316) | 2.931305 / 5.269862 (-2.338556) | 1.840759 / 4.565676 (-2.724917) | 0.063801 / 0.424275 (-0.360474) | 0.004966 / 0.007607 (-0.002641) | 0.341612 / 0.226044 (0.115568) | 3.402842 / 2.268929 (1.133913) | 1.860521 / 55.444624 (-53.584103) | 1.603156 / 6.876477 (-5.273321) | 1.665835 / 2.142072 (-0.476237) | 0.655299 / 4.805227 (-4.149929) | 0.124527 / 6.500664 (-6.376137) | 0.044021 / 0.075469 (-0.031449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972068 / 1.841788 (-0.869720) | 12.393202 / 8.074308 (4.318894) | 10.420876 / 10.191392 (0.229484) | 0.140684 / 0.680424 (-0.539740) | 0.014442 / 0.534201 (-0.519759) | 0.288182 / 0.579283 (-0.291101) | 0.265029 / 0.434364 (-0.169334) | 0.327133 / 0.540337 (-0.213204) | 0.443403 / 1.386936 (-0.943533) |\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) | 0.005559 / 0.011353 (-0.005794) | 0.004046 / 0.011008 (-0.006962) | 0.048991 / 0.038508 (0.010483) | 0.059576 / 0.023109 (0.036467) | 0.273596 / 0.275898 (-0.002302) | 0.296658 / 0.323480 (-0.026822) | 0.004089 / 0.007986 (-0.003897) | 0.002777 / 0.004328 (-0.001551) | 0.048216 / 0.004250 (0.043966) | 0.043200 / 0.037052 (0.006148) | 0.276815 / 0.258489 (0.018326) | 0.300570 / 0.293841 (0.006729) | 0.030250 / 0.128546 (-0.098296) | 0.011322 / 0.075646 (-0.064324) | 0.057843 / 0.419271 (-0.361429) | 0.033366 / 0.043533 (-0.010167) | 0.275636 / 0.255139 (0.020497) | 0.293750 / 0.283200 (0.010550) | 0.018551 / 0.141683 (-0.123132) | 1.160919 / 1.452155 (-0.291236) | 1.214519 / 1.492716 (-0.278197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100074 / 0.018006 (0.082068) | 0.308434 / 0.000490 (0.307944) | 0.000232 / 0.000200 (0.000032) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022600 / 0.037411 (-0.014811) | 0.070506 / 0.014526 (0.055980) | 0.081185 / 0.176557 (-0.095371) | 0.120688 / 0.737135 (-0.616448) | 0.082897 / 0.296338 (-0.213441) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.306661 / 0.215209 (0.091452) | 2.989656 / 2.077655 (0.912001) | 1.618868 / 1.504120 (0.114749) | 1.485045 / 1.541195 (-0.056149) | 1.549359 / 1.468490 (0.080869) | 0.593596 / 4.584777 (-3.991181) | 2.466215 / 3.745712 (-1.279497) | 2.956570 / 5.269862 (-2.313292) | 1.823160 / 4.565676 (-2.742516) | 0.063442 / 0.424275 (-0.360833) | 0.004928 / 0.007607 (-0.002679) | 0.358464 / 0.226044 (0.132419) | 3.566345 / 2.268929 (1.297417) | 2.006784 / 55.444624 (-53.437840) | 1.687091 / 6.876477 (-5.189386) | 1.729464 / 2.142072 (-0.412609) | 0.655656 / 4.805227 (-4.149572) | 0.119044 / 6.500664 (-6.381620) | 0.042782 / 0.075469 (-0.032687) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974937 / 1.841788 (-0.866850) | 12.992888 / 8.074308 (4.918580) | 10.893713 / 10.191392 (0.702321) | 0.133853 / 0.680424 (-0.546570) | 0.016055 / 0.534201 (-0.518145) | 0.289342 / 0.579283 (-0.289941) | 0.286094 / 0.434364 (-0.148270) | 0.328670 / 0.540337 (-0.211667) | 0.444605 / 1.386936 (-0.942331) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a5bb38bcc71ea21f2d7304aab374fdb81ded463 \"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) | 0.005705 / 0.011353 (-0.005648) | 0.003519 / 0.011008 (-0.007489) | 0.062009 / 0.038508 (0.023501) | 0.053481 / 0.023109 (0.030372) | 0.262669 / 0.275898 (-0.013229) | 0.280290 / 0.323480 (-0.043189) | 0.002957 / 0.007986 (-0.005029) | 0.002587 / 0.004328 (-0.001741) | 0.047876 / 0.004250 (0.043626) | 0.038868 / 0.037052 (0.001815) | 0.267854 / 0.258489 (0.009365) | 0.290430 / 0.293841 (-0.003411) | 0.028120 / 0.128546 (-0.100427) | 0.011042 / 0.075646 (-0.064605) | 0.206113 / 0.419271 (-0.213158) | 0.036039 / 0.043533 (-0.007494) | 0.257715 / 0.255139 (0.002576) | 0.281279 / 0.283200 (-0.001921) | 0.019790 / 0.141683 (-0.121893) | 1.114472 / 1.452155 (-0.337683) | 1.192219 / 1.492716 (-0.300497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091049 / 0.018006 (0.073043) | 0.300846 / 0.000490 (0.300356) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018569 / 0.037411 (-0.018843) | 0.060075 / 0.014526 (0.045549) | 0.073877 / 0.176557 (-0.102680) | 0.120337 / 0.737135 (-0.616799) | 0.075454 / 0.296338 (-0.220884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290084 / 0.215209 (0.074875) | 2.805712 / 2.077655 (0.728057) | 1.459393 / 1.504120 (-0.044727) | 1.327356 / 1.541195 (-0.213838) | 1.384734 / 1.468490 (-0.083756) | 0.574532 / 4.584777 (-4.010245) | 2.419696 / 3.745712 (-1.326016) | 2.805449 / 5.269862 (-2.464412) | 1.764127 / 4.565676 (-2.801549) | 0.063256 / 0.424275 (-0.361020) | 0.004954 / 0.007607 (-0.002653) | 0.344246 / 0.226044 (0.118202) | 3.396050 / 2.268929 (1.127121) | 1.807621 / 55.444624 (-53.637004) | 1.536627 / 6.876477 (-5.339850) | 1.552450 / 2.142072 (-0.589623) | 0.651156 / 4.805227 (-4.154071) | 0.119358 / 6.500664 (-6.381306) | 0.042810 / 0.075469 (-0.032660) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930646 / 1.841788 (-0.911142) | 11.830454 / 8.074308 (3.756146) | 10.615315 / 10.191392 (0.423923) | 0.130617 / 0.680424 (-0.549807) | 0.014081 / 0.534201 (-0.520120) | 0.285027 / 0.579283 (-0.294256) | 0.267296 / 0.434364 (-0.167068) | 0.331478 / 0.540337 (-0.208859) | 0.442676 / 1.386936 (-0.944260) |\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) | 0.005340 / 0.011353 (-0.006013) | 0.003745 / 0.011008 (-0.007264) | 0.049011 / 0.038508 (0.010503) | 0.051342 / 0.023109 (0.028233) | 0.272482 / 0.275898 (-0.003416) | 0.292816 / 0.323480 (-0.030663) | 0.003977 / 0.007986 (-0.004008) | 0.002642 / 0.004328 (-0.001687) | 0.048213 / 0.004250 (0.043963) | 0.040341 / 0.037052 (0.003289) | 0.275176 / 0.258489 (0.016687) | 0.301098 / 0.293841 (0.007257) | 0.029052 / 0.128546 (-0.099495) | 0.010796 / 0.075646 (-0.064850) | 0.057654 / 0.419271 (-0.361618) | 0.032914 / 0.043533 (-0.010619) | 0.271235 / 0.255139 (0.016096) | 0.289883 / 0.283200 (0.006684) | 0.018548 / 0.141683 (-0.123135) | 1.134072 / 1.452155 (-0.318083) | 1.208228 / 1.492716 (-0.284488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094524 / 0.018006 (0.076518) | 0.310162 / 0.000490 (0.309672) | 0.000237 / 0.000200 (0.000037) | 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) | 0.021090 / 0.037411 (-0.016321) | 0.068351 / 0.014526 (0.053825) | 0.082370 / 0.176557 (-0.094186) | 0.121648 / 0.737135 (-0.615487) | 0.083433 / 0.296338 (-0.212906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294616 / 0.215209 (0.079407) | 2.894194 / 2.077655 (0.816539) | 1.619739 / 1.504120 (0.115619) | 1.492466 / 1.541195 (-0.048729) | 1.511662 / 1.468490 (0.043172) | 0.557179 / 4.584777 (-4.027597) | 2.400669 / 3.745712 (-1.345043) | 2.781363 / 5.269862 (-2.488499) | 1.769144 / 4.565676 (-2.796533) | 0.063996 / 0.424275 (-0.360279) | 0.004922 / 0.007607 (-0.002685) | 0.354483 / 0.226044 (0.128438) | 3.474795 / 2.268929 (1.205867) | 1.985743 / 55.444624 (-53.458881) | 1.693173 / 6.876477 (-5.183303) | 1.695857 / 2.142072 (-0.446216) | 0.654800 / 4.805227 (-4.150427) | 0.117316 / 6.500664 (-6.383348) | 0.040708 / 0.075469 (-0.034761) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977678 / 1.841788 (-0.864109) | 12.214098 / 8.074308 (4.139790) | 10.741857 / 10.191392 (0.550465) | 0.130308 / 0.680424 (-0.550116) | 0.015053 / 0.534201 (-0.519148) | 0.295496 / 0.579283 (-0.283787) | 0.276348 / 0.434364 (-0.158015) | 0.326568 / 0.540337 (-0.213769) | 0.441902 / 1.386936 (-0.945034) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#214a3e6dcb66e9c1a8ff586553e8eee0f1c70710 \"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) | 0.005218 / 0.011353 (-0.006135) | 0.003270 / 0.011008 (-0.007738) | 0.062380 / 0.038508 (0.023872) | 0.052896 / 0.023109 (0.029787) | 0.233060 / 0.275898 (-0.042838) | 0.259194 / 0.323480 (-0.064286) | 0.002880 / 0.007986 (-0.005106) | 0.002643 / 0.004328 (-0.001686) | 0.048084 / 0.004250 (0.043833) | 0.038807 / 0.037052 (0.001755) | 0.244925 / 0.258489 (-0.013564) | 0.269619 / 0.293841 (-0.024222) | 0.026901 / 0.128546 (-0.101646) | 0.010150 / 0.075646 (-0.065497) | 0.206854 / 0.419271 (-0.212417) | 0.035618 / 0.043533 (-0.007915) | 0.239577 / 0.255139 (-0.015562) | 0.259684 / 0.283200 (-0.023516) | 0.019823 / 0.141683 (-0.121860) | 1.074472 / 1.452155 (-0.377682) | 1.142911 / 1.492716 (-0.349805) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092616 / 0.018006 (0.074610) | 0.301974 / 0.000490 (0.301485) | 0.000201 / 0.000200 (0.000002) | 0.000048 / 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) | 0.018864 / 0.037411 (-0.018548) | 0.061007 / 0.014526 (0.046481) | 0.073228 / 0.176557 (-0.103328) | 0.120719 / 0.737135 (-0.616416) | 0.075686 / 0.296338 (-0.220653) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281404 / 0.215209 (0.066195) | 2.777671 / 2.077655 (0.700017) | 1.464689 / 1.504120 (-0.039431) | 1.345357 / 1.541195 (-0.195838) | 1.384273 / 1.468490 (-0.084217) | 0.560298 / 4.584777 (-4.024479) | 2.389877 / 3.745712 (-1.355835) | 2.755564 / 5.269862 (-2.514297) | 1.737754 / 4.565676 (-2.827922) | 0.063025 / 0.424275 (-0.361251) | 0.004975 / 0.007607 (-0.002632) | 0.346741 / 0.226044 (0.120697) | 3.321918 / 2.268929 (1.052989) | 1.815700 / 55.444624 (-53.628924) | 1.547333 / 6.876477 (-5.329144) | 1.564809 / 2.142072 (-0.577263) | 0.638645 / 4.805227 (-4.166582) | 0.118157 / 6.500664 (-6.382507) | 0.041605 / 0.075469 (-0.033864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942515 / 1.841788 (-0.899273) | 11.400386 / 8.074308 (3.326078) | 10.208763 / 10.191392 (0.017370) | 0.138144 / 0.680424 (-0.542280) | 0.014354 / 0.534201 (-0.519847) | 0.288289 / 0.579283 (-0.290994) | 0.265973 / 0.434364 (-0.168391) | 0.327703 / 0.540337 (-0.212634) | 0.435474 / 1.386936 (-0.951462) |\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) | 0.005163 / 0.011353 (-0.006190) | 0.003307 / 0.011008 (-0.007701) | 0.048885 / 0.038508 (0.010377) | 0.049044 / 0.023109 (0.025935) | 0.261408 / 0.275898 (-0.014490) | 0.284625 / 0.323480 (-0.038855) | 0.003970 / 0.007986 (-0.004015) | 0.002754 / 0.004328 (-0.001575) | 0.048271 / 0.004250 (0.044021) | 0.039849 / 0.037052 (0.002797) | 0.266898 / 0.258489 (0.008409) | 0.291445 / 0.293841 (-0.002396) | 0.028477 / 0.128546 (-0.100069) | 0.010656 / 0.075646 (-0.064990) | 0.057732 / 0.419271 (-0.361539) | 0.033298 / 0.043533 (-0.010235) | 0.297773 / 0.255139 (0.042634) | 0.281894 / 0.283200 (-0.001305) | 0.018595 / 0.141683 (-0.123088) | 1.168849 / 1.452155 (-0.283306) | 1.183493 / 1.492716 (-0.309224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092683 / 0.018006 (0.074677) | 0.300387 / 0.000490 (0.299897) | 0.000221 / 0.000200 (0.000021) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021356 / 0.037411 (-0.016055) | 0.068095 / 0.014526 (0.053569) | 0.079806 / 0.176557 (-0.096750) | 0.118965 / 0.737135 (-0.618170) | 0.082066 / 0.296338 (-0.214273) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293105 / 0.215209 (0.077896) | 2.842800 / 2.077655 (0.765146) | 1.572052 / 1.504120 (0.067932) | 1.450156 / 1.541195 (-0.091038) | 1.464227 / 1.468490 (-0.004263) | 0.561215 / 4.584777 (-4.023562) | 2.456117 / 3.745712 (-1.289596) | 2.739766 / 5.269862 (-2.530095) | 1.730354 / 4.565676 (-2.835323) | 0.062636 / 0.424275 (-0.361639) | 0.004933 / 0.007607 (-0.002674) | 0.345800 / 0.226044 (0.119756) | 3.415858 / 2.268929 (1.146929) | 1.937288 / 55.444624 (-53.507336) | 1.661975 / 6.876477 (-5.214502) | 1.660347 / 2.142072 (-0.481726) | 0.642780 / 4.805227 (-4.162448) | 0.116643 / 6.500664 (-6.384021) | 0.041282 / 0.075469 (-0.034187) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976629 / 1.841788 (-0.865159) | 11.900319 / 8.074308 (3.826011) | 10.574198 / 10.191392 (0.382806) | 0.129689 / 0.680424 (-0.550735) | 0.015390 / 0.534201 (-0.518811) | 0.286543 / 0.579283 (-0.292741) | 0.277676 / 0.434364 (-0.156688) | 0.325053 / 0.540337 (-0.215284) | 0.439663 / 1.386936 (-0.947274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a9674e17156ff10124632ba705125288de7442 \"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) | 0.005382 / 0.011353 (-0.005971) | 0.003606 / 0.011008 (-0.007402) | 0.063234 / 0.038508 (0.024726) | 0.053738 / 0.023109 (0.030629) | 0.250405 / 0.275898 (-0.025493) | 0.272244 / 0.323480 (-0.051236) | 0.002896 / 0.007986 (-0.005090) | 0.002684 / 0.004328 (-0.001644) | 0.048394 / 0.004250 (0.044143) | 0.039017 / 0.037052 (0.001964) | 0.259554 / 0.258489 (0.001065) | 0.287215 / 0.293841 (-0.006626) | 0.028290 / 0.128546 (-0.100257) | 0.011482 / 0.075646 (-0.064164) | 0.214264 / 0.419271 (-0.205007) | 0.036257 / 0.043533 (-0.007276) | 0.252873 / 0.255139 (-0.002266) | 0.271269 / 0.283200 (-0.011931) | 0.017173 / 0.141683 (-0.124510) | 1.137474 / 1.452155 (-0.314681) | 1.161499 / 1.492716 (-0.331217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092424 / 0.018006 (0.074418) | 0.283703 / 0.000490 (0.283213) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018307 / 0.037411 (-0.019105) | 0.060780 / 0.014526 (0.046254) | 0.073984 / 0.176557 (-0.102573) | 0.120824 / 0.737135 (-0.616311) | 0.074724 / 0.296338 (-0.221615) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297682 / 0.215209 (0.082473) | 2.853267 / 2.077655 (0.775612) | 1.567643 / 1.504120 (0.063523) | 1.437218 / 1.541195 (-0.103976) | 1.467187 / 1.468490 (-0.001304) | 0.560552 / 4.584777 (-4.024225) | 2.387848 / 3.745712 (-1.357864) | 2.718946 / 5.269862 (-2.550916) | 1.724107 / 4.565676 (-2.841570) | 0.061923 / 0.424275 (-0.362352) | 0.004828 / 0.007607 (-0.002779) | 0.353916 / 0.226044 (0.127871) | 3.404477 / 2.268929 (1.135548) | 1.906078 / 55.444624 (-53.538546) | 1.629686 / 6.876477 (-5.246791) | 1.640839 / 2.142072 (-0.501233) | 0.641082 / 4.805227 (-4.164145) | 0.118078 / 6.500664 (-6.382586) | 0.041881 / 0.075469 (-0.033588) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936062 / 1.841788 (-0.905726) | 11.397678 / 8.074308 (3.323370) | 10.385159 / 10.191392 (0.193766) | 0.127337 / 0.680424 (-0.553087) | 0.013562 / 0.534201 (-0.520639) | 0.290817 / 0.579283 (-0.288466) | 0.259377 / 0.434364 (-0.174987) | 0.324829 / 0.540337 (-0.215508) | 0.434344 / 1.386936 (-0.952592) |\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) | 0.005134 / 0.011353 (-0.006219) | 0.003404 / 0.011008 (-0.007604) | 0.048281 / 0.038508 (0.009772) | 0.050952 / 0.023109 (0.027842) | 0.277553 / 0.275898 (0.001655) | 0.298855 / 0.323480 (-0.024625) | 0.003928 / 0.007986 (-0.004058) | 0.002642 / 0.004328 (-0.001687) | 0.047374 / 0.004250 (0.043123) | 0.039883 / 0.037052 (0.002831) | 0.279808 / 0.258489 (0.021318) | 0.301604 / 0.293841 (0.007763) | 0.028708 / 0.128546 (-0.099838) | 0.010949 / 0.075646 (-0.064697) | 0.057090 / 0.419271 (-0.362181) | 0.032438 / 0.043533 (-0.011095) | 0.274690 / 0.255139 (0.019551) | 0.290912 / 0.283200 (0.007712) | 0.017556 / 0.141683 (-0.124127) | 1.111091 / 1.452155 (-0.341064) | 1.166063 / 1.492716 (-0.326653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090557 / 0.018006 (0.072551) | 0.298661 / 0.000490 (0.298171) | 0.000228 / 0.000200 (0.000028) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021712 / 0.037411 (-0.015699) | 0.068682 / 0.014526 (0.054156) | 0.080108 / 0.176557 (-0.096449) | 0.119480 / 0.737135 (-0.617655) | 0.082703 / 0.296338 (-0.213636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294095 / 0.215209 (0.078886) | 2.884758 / 2.077655 (0.807103) | 1.598312 / 1.504120 (0.094192) | 1.480050 / 1.541195 (-0.061145) | 1.488611 / 1.468490 (0.020121) | 0.556052 / 4.584777 (-4.028724) | 2.435484 / 3.745712 (-1.310228) | 2.741592 / 5.269862 (-2.528270) | 1.706223 / 4.565676 (-2.859454) | 0.062214 / 0.424275 (-0.362061) | 0.004901 / 0.007607 (-0.002706) | 0.346301 / 0.226044 (0.120257) | 3.474516 / 2.268929 (1.205587) | 1.995205 / 55.444624 (-53.449419) | 1.726349 / 6.876477 (-5.150128) | 1.659600 / 2.142072 (-0.482472) | 0.643560 / 4.805227 (-4.161667) | 0.115222 / 6.500664 (-6.385442) | 0.041137 / 0.075469 (-0.034332) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974566 / 1.841788 (-0.867221) | 11.872479 / 8.074308 (3.798171) | 10.496919 / 10.191392 (0.305527) | 0.129087 / 0.680424 (-0.551337) | 0.014627 / 0.534201 (-0.519574) | 0.289070 / 0.579283 (-0.290213) | 0.269609 / 0.434364 (-0.164755) | 0.327785 / 0.540337 (-0.212553) | 0.444634 / 1.386936 (-0.942302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e0960ea165a9481b1ff6eed31771475120cb38 \"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) | 0.005080 / 0.011353 (-0.006273) | 0.003782 / 0.011008 (-0.007226) | 0.062816 / 0.038508 (0.024308) | 0.056338 / 0.023109 (0.033229) | 0.251317 / 0.275898 (-0.024581) | 0.269414 / 0.323480 (-0.054066) | 0.003984 / 0.007986 (-0.004001) | 0.002749 / 0.004328 (-0.001580) | 0.048126 / 0.004250 (0.043876) | 0.038516 / 0.037052 (0.001464) | 0.253809 / 0.258489 (-0.004680) | 0.283309 / 0.293841 (-0.010532) | 0.027015 / 0.128546 (-0.101531) | 0.010610 / 0.075646 (-0.065037) | 0.213024 / 0.419271 (-0.206247) | 0.035734 / 0.043533 (-0.007799) | 0.247909 / 0.255139 (-0.007230) | 0.263539 / 0.283200 (-0.019660) | 0.018408 / 0.141683 (-0.123275) | 1.104366 / 1.452155 (-0.347789) | 1.169668 / 1.492716 (-0.323048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.114366 / 0.018006 (0.096360) | 0.317674 / 0.000490 (0.317184) | 0.000227 / 0.000200 (0.000027) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018955 / 0.037411 (-0.018457) | 0.060716 / 0.014526 (0.046190) | 0.072963 / 0.176557 (-0.103593) | 0.121671 / 0.737135 (-0.615464) | 0.073785 / 0.296338 (-0.222554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292349 / 0.215209 (0.077140) | 2.832049 / 2.077655 (0.754394) | 1.504488 / 1.504120 (0.000368) | 1.403418 / 1.541195 (-0.137777) | 1.449223 / 1.468490 (-0.019267) | 0.563846 / 4.584777 (-4.020931) | 2.376726 / 3.745712 (-1.368986) | 2.823304 / 5.269862 (-2.446558) | 1.774858 / 4.565676 (-2.790818) | 0.063229 / 0.424275 (-0.361046) | 0.004923 / 0.007607 (-0.002684) | 0.347240 / 0.226044 (0.121195) | 3.486563 / 2.268929 (1.217634) | 1.890516 / 55.444624 (-53.554109) | 1.570620 / 6.876477 (-5.305857) | 1.600842 / 2.142072 (-0.541231) | 0.644287 / 4.805227 (-4.160940) | 0.116931 / 6.500664 (-6.383733) | 0.042068 / 0.075469 (-0.033401) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935662 / 1.841788 (-0.906126) | 11.950247 / 8.074308 (3.875939) | 10.636225 / 10.191392 (0.444833) | 0.139137 / 0.680424 (-0.541287) | 0.014473 / 0.534201 (-0.519728) | 0.294213 / 0.579283 (-0.285070) | 0.273413 / 0.434364 (-0.160951) | 0.325930 / 0.540337 (-0.214407) | 0.444265 / 1.386936 (-0.942671) |\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) | 0.005448 / 0.011353 (-0.005904) | 0.003155 / 0.011008 (-0.007853) | 0.048626 / 0.038508 (0.010117) | 0.057427 / 0.023109 (0.034318) | 0.270412 / 0.275898 (-0.005486) | 0.290816 / 0.323480 (-0.032664) | 0.004744 / 0.007986 (-0.003241) | 0.002776 / 0.004328 (-0.001552) | 0.047953 / 0.004250 (0.043703) | 0.041126 / 0.037052 (0.004073) | 0.276046 / 0.258489 (0.017557) | 0.297548 / 0.293841 (0.003707) | 0.029308 / 0.128546 (-0.099238) | 0.010516 / 0.075646 (-0.065131) | 0.056982 / 0.419271 (-0.362290) | 0.032922 / 0.043533 (-0.010611) | 0.271342 / 0.255139 (0.016203) | 0.288963 / 0.283200 (0.005763) | 0.019048 / 0.141683 (-0.122635) | 1.130453 / 1.452155 (-0.321702) | 1.206462 / 1.492716 (-0.286254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099249 / 0.018006 (0.081242) | 0.312409 / 0.000490 (0.311919) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021992 / 0.037411 (-0.015419) | 0.068377 / 0.014526 (0.053851) | 0.080749 / 0.176557 (-0.095807) | 0.120534 / 0.737135 (-0.616602) | 0.082549 / 0.296338 (-0.213790) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299634 / 0.215209 (0.084425) | 2.943496 / 2.077655 (0.865841) | 1.602842 / 1.504120 (0.098722) | 1.462140 / 1.541195 (-0.079055) | 1.511082 / 1.468490 (0.042592) | 0.574148 / 4.584777 (-4.010629) | 2.492158 / 3.745712 (-1.253554) | 2.921695 / 5.269862 (-2.348166) | 1.812416 / 4.565676 (-2.753260) | 0.064145 / 0.424275 (-0.360130) | 0.005133 / 0.007607 (-0.002475) | 0.357935 / 0.226044 (0.131891) | 3.543728 / 2.268929 (1.274800) | 1.948676 / 55.444624 (-53.495948) | 1.664960 / 6.876477 (-5.211517) | 1.678703 / 2.142072 (-0.463370) | 0.645867 / 4.805227 (-4.159360) | 0.117671 / 6.500664 (-6.382993) | 0.040887 / 0.075469 (-0.034582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979127 / 1.841788 (-0.862661) | 12.363904 / 8.074308 (4.289596) | 10.673725 / 10.191392 (0.482333) | 0.143358 / 0.680424 (-0.537066) | 0.015375 / 0.534201 (-0.518825) | 0.287590 / 0.579283 (-0.291694) | 0.284742 / 0.434364 (-0.149622) | 0.326901 / 0.540337 (-0.213437) | 0.443962 / 1.386936 (-0.942974) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68099ca55294bfc12a34781835dd73c533a764bd \"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) | 0.004994 / 0.011353 (-0.006359) | 0.003368 / 0.011008 (-0.007640) | 0.062803 / 0.038508 (0.024295) | 0.050778 / 0.023109 (0.027669) | 0.255955 / 0.275898 (-0.019943) | 0.278215 / 0.323480 (-0.045265) | 0.003801 / 0.007986 (-0.004184) | 0.002703 / 0.004328 (-0.001626) | 0.048369 / 0.004250 (0.044119) | 0.037795 / 0.037052 (0.000743) | 0.255634 / 0.258489 (-0.002855) | 0.284226 / 0.293841 (-0.009615) | 0.027252 / 0.128546 (-0.101294) | 0.010686 / 0.075646 (-0.064961) | 0.206139 / 0.419271 (-0.213133) | 0.035543 / 0.043533 (-0.007990) | 0.257167 / 0.255139 (0.002028) | 0.277784 / 0.283200 (-0.005416) | 0.016938 / 0.141683 (-0.124745) | 1.108595 / 1.452155 (-0.343560) | 1.188542 / 1.492716 (-0.304175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090938 / 0.018006 (0.072932) | 0.298463 / 0.000490 (0.297973) | 0.000203 / 0.000200 (0.000003) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027762 / 0.037411 (-0.009649) | 0.060539 / 0.014526 (0.046014) | 0.075986 / 0.176557 (-0.100570) | 0.133851 / 0.737135 (-0.603285) | 0.074669 / 0.296338 (-0.221670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285614 / 0.215209 (0.070405) | 2.810529 / 2.077655 (0.732874) | 1.537092 / 1.504120 (0.032973) | 1.412211 / 1.541195 (-0.128983) | 1.446395 / 1.468490 (-0.022095) | 0.559008 / 4.584777 (-4.025769) | 2.343445 / 3.745712 (-1.402267) | 2.748113 / 5.269862 (-2.521748) | 1.733593 / 4.565676 (-2.832083) | 0.061720 / 0.424275 (-0.362555) | 0.004930 / 0.007607 (-0.002677) | 0.330646 / 0.226044 (0.104602) | 3.314999 / 2.268929 (1.046071) | 1.854527 / 55.444624 (-53.590098) | 1.605819 / 6.876477 (-5.270657) | 1.591406 / 2.142072 (-0.550667) | 0.624239 / 4.805227 (-4.180988) | 0.115352 / 6.500664 (-6.385312) | 0.041600 / 0.075469 (-0.033869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933179 / 1.841788 (-0.908608) | 11.456372 / 8.074308 (3.382064) | 10.578042 / 10.191392 (0.386650) | 0.128045 / 0.680424 (-0.552379) | 0.014212 / 0.534201 (-0.519989) | 0.284795 / 0.579283 (-0.294488) | 0.266210 / 0.434364 (-0.168153) | 0.344468 / 0.540337 (-0.195869) | 0.434414 / 1.386936 (-0.952522) |\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) | 0.005142 / 0.011353 (-0.006211) | 0.003607 / 0.011008 (-0.007401) | 0.048770 / 0.038508 (0.010262) | 0.051147 / 0.023109 (0.028038) | 0.277329 / 0.275898 (0.001430) | 0.300863 / 0.323480 (-0.022617) | 0.004005 / 0.007986 (-0.003980) | 0.002624 / 0.004328 (-0.001705) | 0.047740 / 0.004250 (0.043489) | 0.040811 / 0.037052 (0.003759) | 0.280020 / 0.258489 (0.021531) | 0.303758 / 0.293841 (0.009918) | 0.028273 / 0.128546 (-0.100274) | 0.010379 / 0.075646 (-0.065267) | 0.057503 / 0.419271 (-0.361768) | 0.032717 / 0.043533 (-0.010816) | 0.277560 / 0.255139 (0.022421) | 0.300622 / 0.283200 (0.017422) | 0.018142 / 0.141683 (-0.123541) | 1.121890 / 1.452155 (-0.330265) | 1.251481 / 1.492716 (-0.241235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091523 / 0.018006 (0.073517) | 0.300173 / 0.000490 (0.299683) | 0.000216 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011025) | 0.078710 / 0.014526 (0.064184) | 0.090594 / 0.176557 (-0.085962) | 0.130623 / 0.737135 (-0.606512) | 0.092637 / 0.296338 (-0.203701) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299427 / 0.215209 (0.084218) | 2.929463 / 2.077655 (0.851808) | 1.608905 / 1.504120 (0.104785) | 1.490863 / 1.541195 (-0.050331) | 1.484286 / 1.468490 (0.015796) | 0.568208 / 4.584777 (-4.016569) | 2.447081 / 3.745712 (-1.298632) | 2.801287 / 5.269862 (-2.468574) | 1.744449 / 4.565676 (-2.821227) | 0.064222 / 0.424275 (-0.360053) | 0.004959 / 0.007607 (-0.002648) | 0.350207 / 0.226044 (0.124162) | 3.471944 / 2.268929 (1.203016) | 1.951715 / 55.444624 (-53.492909) | 1.668764 / 6.876477 (-5.207713) | 1.675322 / 2.142072 (-0.466751) | 0.642217 / 4.805227 (-4.163011) | 0.116776 / 6.500664 (-6.383888) | 0.040812 / 0.075469 (-0.034658) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996478 / 1.841788 (-0.845310) | 12.090647 / 8.074308 (4.016339) | 10.723688 / 10.191392 (0.532296) | 0.141770 / 0.680424 (-0.538653) | 0.015578 / 0.534201 (-0.518623) | 0.288236 / 0.579283 (-0.291047) | 0.278542 / 0.434364 (-0.155822) | 0.327411 / 0.540337 (-0.212927) | 0.450309 / 1.386936 (-0.936627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dd4698f483d37afe243db0ffae774cbd34a4af4 \"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) | 0.004967 / 0.011353 (-0.006385) | 0.003382 / 0.011008 (-0.007627) | 0.063436 / 0.038508 (0.024928) | 0.050769 / 0.023109 (0.027659) | 0.254214 / 0.275898 (-0.021684) | 0.272076 / 0.323480 (-0.051404) | 0.003815 / 0.007986 (-0.004170) | 0.002618 / 0.004328 (-0.001711) | 0.049021 / 0.004250 (0.044771) | 0.037329 / 0.037052 (0.000277) | 0.261112 / 0.258489 (0.002623) | 0.284133 / 0.293841 (-0.009708) | 0.026828 / 0.128546 (-0.101719) | 0.010757 / 0.075646 (-0.064889) | 0.208047 / 0.419271 (-0.211225) | 0.035061 / 0.043533 (-0.008472) | 0.250896 / 0.255139 (-0.004243) | 0.273038 / 0.283200 (-0.010162) | 0.016559 / 0.141683 (-0.125124) | 1.128899 / 1.452155 (-0.323255) | 1.188857 / 1.492716 (-0.303860) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100121 / 0.018006 (0.082114) | 0.298427 / 0.000490 (0.297937) | 0.000218 / 0.000200 (0.000018) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018369 / 0.037411 (-0.019042) | 0.060425 / 0.014526 (0.045899) | 0.073501 / 0.176557 (-0.103055) | 0.120254 / 0.737135 (-0.616881) | 0.074889 / 0.296338 (-0.221450) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287153 / 0.215209 (0.071944) | 2.797036 / 2.077655 (0.719382) | 1.446216 / 1.504120 (-0.057904) | 1.336015 / 1.541195 (-0.205179) | 1.369841 / 1.468490 (-0.098650) | 0.559424 / 4.584777 (-4.025353) | 2.361344 / 3.745712 (-1.384368) | 2.766619 / 5.269862 (-2.503243) | 1.747235 / 4.565676 (-2.818441) | 0.066243 / 0.424275 (-0.358032) | 0.004974 / 0.007607 (-0.002633) | 0.333565 / 0.226044 (0.107520) | 3.319877 / 2.268929 (1.050948) | 1.798024 / 55.444624 (-53.646601) | 1.495896 / 6.876477 (-5.380580) | 1.529243 / 2.142072 (-0.612830) | 0.636609 / 4.805227 (-4.168618) | 0.116151 / 6.500664 (-6.384514) | 0.041779 / 0.075469 (-0.033690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.952176 / 1.841788 (-0.889611) | 11.559160 / 8.074308 (3.484852) | 10.556771 / 10.191392 (0.365379) | 0.127118 / 0.680424 (-0.553306) | 0.014142 / 0.534201 (-0.520059) | 0.286585 / 0.579283 (-0.292698) | 0.260233 / 0.434364 (-0.174131) | 0.324012 / 0.540337 (-0.216326) | 0.435131 / 1.386936 (-0.951805) |\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) | 0.005171 / 0.011353 (-0.006182) | 0.003402 / 0.011008 (-0.007607) | 0.048826 / 0.038508 (0.010318) | 0.050455 / 0.023109 (0.027346) | 0.272120 / 0.275898 (-0.003778) | 0.290404 / 0.323480 (-0.033076) | 0.003986 / 0.007986 (-0.003999) | 0.002569 / 0.004328 (-0.001760) | 0.047845 / 0.004250 (0.043595) | 0.040203 / 0.037052 (0.003150) | 0.278263 / 0.258489 (0.019774) | 0.299255 / 0.293841 (0.005414) | 0.028643 / 0.128546 (-0.099903) | 0.010584 / 0.075646 (-0.065062) | 0.056921 / 0.419271 (-0.362351) | 0.032362 / 0.043533 (-0.011171) | 0.274010 / 0.255139 (0.018871) | 0.288601 / 0.283200 (0.005401) | 0.017856 / 0.141683 (-0.123827) | 1.154112 / 1.452155 (-0.298043) | 1.216288 / 1.492716 (-0.276428) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091399 / 0.018006 (0.073392) | 0.299966 / 0.000490 (0.299477) | 0.000218 / 0.000200 (0.000018) | 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) | 0.021728 / 0.037411 (-0.015683) | 0.068285 / 0.014526 (0.053759) | 0.081767 / 0.176557 (-0.094789) | 0.120000 / 0.737135 (-0.617135) | 0.082149 / 0.296338 (-0.214189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289625 / 0.215209 (0.074416) | 2.835114 / 2.077655 (0.757460) | 1.583207 / 1.504120 (0.079087) | 1.465251 / 1.541195 (-0.075944) | 1.480691 / 1.468490 (0.012200) | 0.569103 / 4.584777 (-4.015674) | 2.416981 / 3.745712 (-1.328731) | 2.761746 / 5.269862 (-2.508115) | 1.720055 / 4.565676 (-2.845621) | 0.063349 / 0.424275 (-0.360926) | 0.004931 / 0.007607 (-0.002676) | 0.343658 / 0.226044 (0.117614) | 3.362996 / 2.268929 (1.094068) | 1.948088 / 55.444624 (-53.496536) | 1.659504 / 6.876477 (-5.216973) | 1.660359 / 2.142072 (-0.481713) | 0.647871 / 4.805227 (-4.157356) | 0.117395 / 6.500664 (-6.383269) | 0.041049 / 0.075469 (-0.034420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953971 / 1.841788 (-0.887817) | 12.076998 / 8.074308 (4.002690) | 10.549021 / 10.191392 (0.357629) | 0.130026 / 0.680424 (-0.550398) | 0.015697 / 0.534201 (-0.518504) | 0.287125 / 0.579283 (-0.292158) | 0.298402 / 0.434364 (-0.135962) | 0.326005 / 0.540337 (-0.214332) | 0.444065 / 1.386936 (-0.942871) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf86d48792f585bf802bb2ff70e0d9c3a4de4bcf \"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) | 0.005053 / 0.011353 (-0.006300) | 0.003537 / 0.011008 (-0.007472) | 0.062923 / 0.038508 (0.024415) | 0.053796 / 0.023109 (0.030687) | 0.242523 / 0.275898 (-0.033375) | 0.264014 / 0.323480 (-0.059466) | 0.002879 / 0.007986 (-0.005106) | 0.003273 / 0.004328 (-0.001055) | 0.048735 / 0.004250 (0.044484) | 0.037541 / 0.037052 (0.000488) | 0.248587 / 0.258489 (-0.009902) | 0.275531 / 0.293841 (-0.018310) | 0.027215 / 0.128546 (-0.101331) | 0.010466 / 0.075646 (-0.065180) | 0.206508 / 0.419271 (-0.212763) | 0.035606 / 0.043533 (-0.007927) | 0.251044 / 0.255139 (-0.004095) | 0.267183 / 0.283200 (-0.016016) | 0.018357 / 0.141683 (-0.123326) | 1.083513 / 1.452155 (-0.368642) | 1.152988 / 1.492716 (-0.339728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091749 / 0.018006 (0.073742) | 0.299946 / 0.000490 (0.299456) | 0.000212 / 0.000200 (0.000013) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018300 / 0.037411 (-0.019111) | 0.060691 / 0.014526 (0.046166) | 0.072998 / 0.176557 (-0.103559) | 0.120581 / 0.737135 (-0.616554) | 0.073912 / 0.296338 (-0.222427) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277602 / 0.215209 (0.062393) | 2.719181 / 2.077655 (0.641526) | 1.450894 / 1.504120 (-0.053226) | 1.314344 / 1.541195 (-0.226851) | 1.351996 / 1.468490 (-0.116494) | 0.586231 / 4.584777 (-3.998546) | 2.349746 / 3.745712 (-1.395967) | 2.810060 / 5.269862 (-2.459802) | 1.761362 / 4.565676 (-2.804314) | 0.062535 / 0.424275 (-0.361740) | 0.004918 / 0.007607 (-0.002689) | 0.336091 / 0.226044 (0.110047) | 3.238139 / 2.268929 (0.969211) | 1.769734 / 55.444624 (-53.674890) | 1.505332 / 6.876477 (-5.371145) | 1.527875 / 2.142072 (-0.614198) | 0.640194 / 4.805227 (-4.165033) | 0.116567 / 6.500664 (-6.384097) | 0.042464 / 0.075469 (-0.033005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930919 / 1.841788 (-0.910869) | 11.462498 / 8.074308 (3.388190) | 10.575359 / 10.191392 (0.383967) | 0.130567 / 0.680424 (-0.549857) | 0.014203 / 0.534201 (-0.519998) | 0.286944 / 0.579283 (-0.292339) | 0.264706 / 0.434364 (-0.169658) | 0.324820 / 0.540337 (-0.215517) | 0.434579 / 1.386936 (-0.952357) |\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) | 0.005164 / 0.011353 (-0.006189) | 0.003442 / 0.011008 (-0.007567) | 0.050146 / 0.038508 (0.011638) | 0.050800 / 0.023109 (0.027691) | 0.263405 / 0.275898 (-0.012493) | 0.284876 / 0.323480 (-0.038604) | 0.004011 / 0.007986 (-0.003975) | 0.002602 / 0.004328 (-0.001726) | 0.046742 / 0.004250 (0.042491) | 0.040393 / 0.037052 (0.003341) | 0.265052 / 0.258489 (0.006563) | 0.294217 / 0.293841 (0.000377) | 0.028429 / 0.128546 (-0.100118) | 0.010418 / 0.075646 (-0.065228) | 0.057285 / 0.419271 (-0.361987) | 0.032137 / 0.043533 (-0.011396) | 0.265867 / 0.255139 (0.010728) | 0.284764 / 0.283200 (0.001564) | 0.017448 / 0.141683 (-0.124235) | 1.172830 / 1.452155 (-0.279325) | 1.223982 / 1.492716 (-0.268735) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091859 / 0.018006 (0.073853) | 0.285421 / 0.000490 (0.284931) | 0.000220 / 0.000200 (0.000020) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021620 / 0.037411 (-0.015792) | 0.069058 / 0.014526 (0.054532) | 0.082560 / 0.176557 (-0.093997) | 0.119511 / 0.737135 (-0.617624) | 0.082318 / 0.296338 (-0.214021) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291499 / 0.215209 (0.076290) | 2.863352 / 2.077655 (0.785698) | 1.557242 / 1.504120 (0.053122) | 1.430170 / 1.541195 (-0.111024) | 1.432850 / 1.468490 (-0.035640) | 0.559716 / 4.584777 (-4.025061) | 2.385405 / 3.745712 (-1.360307) | 2.748938 / 5.269862 (-2.520924) | 1.740802 / 4.565676 (-2.824874) | 0.061811 / 0.424275 (-0.362465) | 0.005174 / 0.007607 (-0.002433) | 0.348687 / 0.226044 (0.122642) | 3.420120 / 2.268929 (1.151191) | 1.918278 / 55.444624 (-53.526346) | 1.631559 / 6.876477 (-5.244918) | 1.635850 / 2.142072 (-0.506222) | 0.644144 / 4.805227 (-4.161083) | 0.115823 / 6.500664 (-6.384841) | 0.041255 / 0.075469 (-0.034214) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960066 / 1.841788 (-0.881722) | 12.011372 / 8.074308 (3.937064) | 10.580532 / 10.191392 (0.389140) | 0.134763 / 0.680424 (-0.545661) | 0.017027 / 0.534201 (-0.517174) | 0.290484 / 0.579283 (-0.288799) | 0.285171 / 0.434364 (-0.149193) | 0.322453 / 0.540337 (-0.217884) | 0.438088 / 1.386936 (-0.948848) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3fc42882a2d84d7482c27063f1e19539e99b9d3 \"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) | 0.005212 / 0.011353 (-0.006141) | 0.003440 / 0.011008 (-0.007568) | 0.063612 / 0.038508 (0.025104) | 0.049070 / 0.023109 (0.025961) | 0.269748 / 0.275898 (-0.006150) | 0.283270 / 0.323480 (-0.040210) | 0.002892 / 0.007986 (-0.005094) | 0.002693 / 0.004328 (-0.001635) | 0.049710 / 0.004250 (0.045459) | 0.036707 / 0.037052 (-0.000345) | 0.299035 / 0.258489 (0.040546) | 0.296443 / 0.293841 (0.002602) | 0.028095 / 0.128546 (-0.100451) | 0.010682 / 0.075646 (-0.064964) | 0.213914 / 0.419271 (-0.205358) | 0.036210 / 0.043533 (-0.007323) | 0.235720 / 0.255139 (-0.019419) | 0.252687 / 0.283200 (-0.030512) | 0.016985 / 0.141683 (-0.124698) | 1.099024 / 1.452155 (-0.353130) | 1.162970 / 1.492716 (-0.329746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093114 / 0.018006 (0.075108) | 0.305168 / 0.000490 (0.304678) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018370 / 0.037411 (-0.019041) | 0.060534 / 0.014526 (0.046008) | 0.073960 / 0.176557 (-0.102596) | 0.120325 / 0.737135 (-0.616810) | 0.073754 / 0.296338 (-0.222585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284244 / 0.215209 (0.069035) | 2.756854 / 2.077655 (0.679199) | 1.477304 / 1.504120 (-0.026816) | 1.374635 / 1.541195 (-0.166560) | 1.383284 / 1.468490 (-0.085206) | 0.564656 / 4.584777 (-4.020121) | 2.361719 / 3.745712 (-1.383993) | 2.794822 / 5.269862 (-2.475039) | 1.742981 / 4.565676 (-2.822696) | 0.063443 / 0.424275 (-0.360832) | 0.004952 / 0.007607 (-0.002655) | 0.342058 / 0.226044 (0.116014) | 3.351093 / 2.268929 (1.082164) | 1.857375 / 55.444624 (-53.587250) | 1.541680 / 6.876477 (-5.334797) | 1.580147 / 2.142072 (-0.561926) | 0.645216 / 4.805227 (-4.160012) | 0.118768 / 6.500664 (-6.381896) | 0.042115 / 0.075469 (-0.033354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.925845 / 1.841788 (-0.915943) | 11.444147 / 8.074308 (3.369839) | 10.291297 / 10.191392 (0.099905) | 0.128129 / 0.680424 (-0.552295) | 0.013774 / 0.534201 (-0.520427) | 0.289278 / 0.579283 (-0.290005) | 0.262353 / 0.434364 (-0.172011) | 0.328517 / 0.540337 (-0.211820) | 0.436050 / 1.386936 (-0.950886) |\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) | 0.005666 / 0.011353 (-0.005687) | 0.003691 / 0.011008 (-0.007318) | 0.049361 / 0.038508 (0.010853) | 0.054245 / 0.023109 (0.031136) | 0.274433 / 0.275898 (-0.001465) | 0.285648 / 0.323480 (-0.037832) | 0.004080 / 0.007986 (-0.003906) | 0.002666 / 0.004328 (-0.001663) | 0.047539 / 0.004250 (0.043288) | 0.041001 / 0.037052 (0.003948) | 0.296018 / 0.258489 (0.037529) | 0.294542 / 0.293841 (0.000701) | 0.030546 / 0.128546 (-0.098001) | 0.010556 / 0.075646 (-0.065090) | 0.058146 / 0.419271 (-0.361126) | 0.033407 / 0.043533 (-0.010126) | 0.263977 / 0.255139 (0.008838) | 0.286228 / 0.283200 (0.003028) | 0.018088 / 0.141683 (-0.123595) | 1.121295 / 1.452155 (-0.330860) | 1.182183 / 1.492716 (-0.310533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.104540 / 0.018006 (0.086534) | 0.303494 / 0.000490 (0.303004) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021274 / 0.037411 (-0.016137) | 0.070146 / 0.014526 (0.055621) | 0.080343 / 0.176557 (-0.096213) | 0.120017 / 0.737135 (-0.617119) | 0.081303 / 0.296338 (-0.215036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294390 / 0.215209 (0.079181) | 2.883366 / 2.077655 (0.805711) | 1.564629 / 1.504120 (0.060509) | 1.432633 / 1.541195 (-0.108562) | 1.438786 / 1.468490 (-0.029704) | 0.569663 / 4.584777 (-4.015114) | 2.448691 / 3.745712 (-1.297021) | 2.817010 / 5.269862 (-2.452851) | 1.757274 / 4.565676 (-2.808402) | 0.064147 / 0.424275 (-0.360129) | 0.004910 / 0.007607 (-0.002697) | 0.344062 / 0.226044 (0.118018) | 3.394223 / 2.268929 (1.125294) | 1.927139 / 55.444624 (-53.517485) | 1.624983 / 6.876477 (-5.251494) | 1.629076 / 2.142072 (-0.512996) | 0.654239 / 4.805227 (-4.150988) | 0.117309 / 6.500664 (-6.383355) | 0.041067 / 0.075469 (-0.034402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993184 / 1.841788 (-0.848604) | 11.969985 / 8.074308 (3.895677) | 10.363356 / 10.191392 (0.171964) | 0.130708 / 0.680424 (-0.549716) | 0.015577 / 0.534201 (-0.518624) | 0.289579 / 0.579283 (-0.289704) | 0.274875 / 0.434364 (-0.159488) | 0.326736 / 0.540337 (-0.213601) | 0.442770 / 1.386936 (-0.944166) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#796a47e388a5c5711a95bd649648608c18219ac5 \"CML watermark\")\n", "Getting the same windows error as in my other PR. I couldn't reproduce on my windows machine though 🧐 ", "`DataFilesList` is a list so we expect to be able to get its length with zero cost, which wouldn't be the case if we make it lazy no ? ", "But we don't call `len` on it, do we? And I couldn't find an instance of `DataFilesList` being used in GitHub's public repos.", "`DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns", "Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)", "> `DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns\r\n\r\nWould be interesting to know how often these scripts call `len` or do random access on `DataFilesList`.\r\n\r\nStill, I think we should opt for a solution that makes more sense for us. To avoid the breaking change, we can define a `BuilderConfig.data_files` property that resolves this iterable. \r\n\r\n> Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)\r\n\r\nThe `BuilderConfig.data_files` property suggested above should address this, no? \r\n\r\nI think we should be more careful not to make our API needlessly complex because of the YAML README feature. And if this can't be avoided, we should probably refactor the builder API.", "> The BuilderConfig.data_files property suggested above should address this, no?\r\n\r\nThat works indeed ! let me try something", "Implementing lazy DataFilesList and .data_files brings more complexity (less readable, more bad side effects) so I think the current solution is the best one", "I opened https://github.com/huggingface/datasets/pull/6493 to continue this and fix conflicts with https://github.com/huggingface/datasets/pull/6459" ]
2023-11-29T13:18:44
2024-02-08T14:41:35
2024-02-08 14:41:35+00:00
MEMBER
nan
Related to discussion at https://github.com/huggingface/datasets/pull/6255 this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
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6457
`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'
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[ "Updating `fsspec>=2023.10.0` did solve the issue.", "May be it should be pinned somewhere?", "> Maybe this should go in datasets directly... anyways you can easily fix this error by updating datasets>=2.15.1.dev0.\r\n\r\n@lhoestq @mariosasko for what I understand this is a bug fixed in `datasets` already, right? No need to do anything in `huggingface_hub`?", "I've opened a PR with a fix in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/1875", "Thanks! PR is merged and will be shipped in next release of `huggingface_hub`." ]
2023-11-29T01:57:36
2023-11-29T15:39:03
2023-11-29 02:02:38+00:00
NONE
nan
### Describe the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Steps to reproduce the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Expected behavior Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Environment info Please see https://github.com/huggingface/huggingface_hub/issues/1872
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https://api.github.com/repos/huggingface/datasets/issues/6456/events
https://github.com/huggingface/datasets/pull/6456
2015186090
PR_kwDODunzps5gmDJY
6456
Don't require trust_remote_code in inspect_dataset
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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) | 0.005705 / 0.011353 (-0.005648) | 0.003536 / 0.011008 (-0.007473) | 0.062852 / 0.038508 (0.024343) | 0.053902 / 0.023109 (0.030793) | 0.239465 / 0.275898 (-0.036433) | 0.270829 / 0.323480 (-0.052651) | 0.004052 / 0.007986 (-0.003934) | 0.002775 / 0.004328 (-0.001554) | 0.048475 / 0.004250 (0.044225) | 0.039430 / 0.037052 (0.002377) | 0.244318 / 0.258489 (-0.014171) | 0.277539 / 0.293841 (-0.016302) | 0.027637 / 0.128546 (-0.100909) | 0.010875 / 0.075646 (-0.064771) | 0.208839 / 0.419271 (-0.210432) | 0.036984 / 0.043533 (-0.006549) | 0.246355 / 0.255139 (-0.008784) | 0.271200 / 0.283200 (-0.011999) | 0.020636 / 0.141683 (-0.121047) | 1.078472 / 1.452155 (-0.373683) | 1.155701 / 1.492716 (-0.337015) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100971 / 0.018006 (0.082965) | 0.310996 / 0.000490 (0.310507) | 0.000218 / 0.000200 (0.000018) | 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) | 0.019300 / 0.037411 (-0.018111) | 0.060625 / 0.014526 (0.046099) | 0.073778 / 0.176557 (-0.102778) | 0.120280 / 0.737135 (-0.616855) | 0.075288 / 0.296338 (-0.221051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289838 / 0.215209 (0.074629) | 2.859492 / 2.077655 (0.781837) | 1.528478 / 1.504120 (0.024358) | 1.417911 / 1.541195 (-0.123283) | 1.444227 / 1.468490 (-0.024263) | 0.566799 / 4.584777 (-4.017978) | 2.402526 / 3.745712 (-1.343186) | 2.805241 / 5.269862 (-2.464620) | 1.798572 / 4.565676 (-2.767104) | 0.062920 / 0.424275 (-0.361355) | 0.004995 / 0.007607 (-0.002612) | 0.340688 / 0.226044 (0.114644) | 3.347967 / 2.268929 (1.079039) | 1.898464 / 55.444624 (-53.546160) | 1.604784 / 6.876477 (-5.271693) | 1.648864 / 2.142072 (-0.493209) | 0.642242 / 4.805227 (-4.162985) | 0.117567 / 6.500664 (-6.383097) | 0.041911 / 0.075469 (-0.033558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949099 / 1.841788 (-0.892689) | 12.367323 / 8.074308 (4.293015) | 10.694238 / 10.191392 (0.502846) | 0.143424 / 0.680424 (-0.537000) | 0.014569 / 0.534201 (-0.519632) | 0.289127 / 0.579283 (-0.290156) | 0.270490 / 0.434364 (-0.163874) | 0.326470 / 0.540337 (-0.213867) | 0.432223 / 1.386936 (-0.954713) |\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) | 0.005380 / 0.011353 (-0.005973) | 0.003582 / 0.011008 (-0.007426) | 0.049341 / 0.038508 (0.010833) | 0.053274 / 0.023109 (0.030165) | 0.284319 / 0.275898 (0.008421) | 0.334248 / 0.323480 (0.010768) | 0.004032 / 0.007986 (-0.003953) | 0.002682 / 0.004328 (-0.001646) | 0.048317 / 0.004250 (0.044067) | 0.040157 / 0.037052 (0.003105) | 0.284594 / 0.258489 (0.026105) | 0.341567 / 0.293841 (0.047726) | 0.029639 / 0.128546 (-0.098908) | 0.010780 / 0.075646 (-0.064867) | 0.057990 / 0.419271 (-0.361282) | 0.032730 / 0.043533 (-0.010803) | 0.290328 / 0.255139 (0.035189) | 0.298563 / 0.283200 (0.015363) | 0.018546 / 0.141683 (-0.123137) | 1.143157 / 1.452155 (-0.308998) | 1.191391 / 1.492716 (-0.301326) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093802 / 0.018006 (0.075796) | 0.312771 / 0.000490 (0.312282) | 0.000221 / 0.000200 (0.000021) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021867 / 0.037411 (-0.015544) | 0.069064 / 0.014526 (0.054538) | 0.082270 / 0.176557 (-0.094287) | 0.120222 / 0.737135 (-0.616913) | 0.084628 / 0.296338 (-0.211710) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295505 / 0.215209 (0.080296) | 2.891105 / 2.077655 (0.813450) | 1.619480 / 1.504120 (0.115360) | 1.498290 / 1.541195 (-0.042905) | 1.547896 / 1.468490 (0.079406) | 0.575188 / 4.584777 (-4.009589) | 2.434426 / 3.745712 (-1.311286) | 2.899286 / 5.269862 (-2.370576) | 1.806085 / 4.565676 (-2.759591) | 0.063660 / 0.424275 (-0.360616) | 0.004933 / 0.007607 (-0.002674) | 0.348274 / 0.226044 (0.122229) | 3.447900 / 2.268929 (1.178971) | 1.956237 / 55.444624 (-53.488387) | 1.680416 / 6.876477 (-5.196061) | 1.732307 / 2.142072 (-0.409766) | 0.668428 / 4.805227 (-4.136799) | 0.119161 / 6.500664 (-6.381503) | 0.041694 / 0.075469 (-0.033775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973730 / 1.841788 (-0.868058) | 12.082452 / 8.074308 (4.008144) | 10.624836 / 10.191392 (0.433444) | 0.144027 / 0.680424 (-0.536397) | 0.014830 / 0.534201 (-0.519370) | 0.289946 / 0.579283 (-0.289337) | 0.281939 / 0.434364 (-0.152424) | 0.325639 / 0.540337 (-0.214699) | 0.551690 / 1.386936 (-0.835246) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e1cf8526c9216b08b5431695d9f8e0eec64cc5f \"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) | 0.005279 / 0.011353 (-0.006074) | 0.003506 / 0.011008 (-0.007502) | 0.062579 / 0.038508 (0.024071) | 0.052809 / 0.023109 (0.029700) | 0.274693 / 0.275898 (-0.001205) | 0.283917 / 0.323480 (-0.039563) | 0.003950 / 0.007986 (-0.004036) | 0.002772 / 0.004328 (-0.001557) | 0.048127 / 0.004250 (0.043877) | 0.037771 / 0.037052 (0.000719) | 0.280595 / 0.258489 (0.022106) | 0.292310 / 0.293841 (-0.001531) | 0.027890 / 0.128546 (-0.100656) | 0.010771 / 0.075646 (-0.064875) | 0.207285 / 0.419271 (-0.211987) | 0.036179 / 0.043533 (-0.007354) | 0.253617 / 0.255139 (-0.001522) | 0.276107 / 0.283200 (-0.007093) | 0.018253 / 0.141683 (-0.123430) | 1.112219 / 1.452155 (-0.339936) | 1.166756 / 1.492716 (-0.325960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095159 / 0.018006 (0.077152) | 0.306097 / 0.000490 (0.305608) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019056 / 0.037411 (-0.018355) | 0.060445 / 0.014526 (0.045919) | 0.073553 / 0.176557 (-0.103004) | 0.120306 / 0.737135 (-0.616829) | 0.075613 / 0.296338 (-0.220725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277839 / 0.215209 (0.062630) | 2.761037 / 2.077655 (0.683382) | 1.508524 / 1.504120 (0.004404) | 1.368994 / 1.541195 (-0.172201) | 1.415961 / 1.468490 (-0.052529) | 0.570490 / 4.584777 (-4.014287) | 2.356355 / 3.745712 (-1.389357) | 2.806626 / 5.269862 (-2.463235) | 1.757849 / 4.565676 (-2.807827) | 0.063504 / 0.424275 (-0.360771) | 0.005021 / 0.007607 (-0.002586) | 0.338880 / 0.226044 (0.112836) | 3.290947 / 2.268929 (1.022018) | 1.818238 / 55.444624 (-53.626386) | 1.529970 / 6.876477 (-5.346507) | 1.557085 / 2.142072 (-0.584987) | 0.645352 / 4.805227 (-4.159876) | 0.123066 / 6.500664 (-6.377598) | 0.043387 / 0.075469 (-0.032082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974512 / 1.841788 (-0.867276) | 11.976411 / 8.074308 (3.902103) | 10.361084 / 10.191392 (0.169692) | 0.127171 / 0.680424 (-0.553253) | 0.014091 / 0.534201 (-0.520110) | 0.288608 / 0.579283 (-0.290675) | 0.261886 / 0.434364 (-0.172478) | 0.331632 / 0.540337 (-0.208705) | 0.437002 / 1.386936 (-0.949934) |\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) | 0.005129 / 0.011353 (-0.006224) | 0.003490 / 0.011008 (-0.007518) | 0.049005 / 0.038508 (0.010497) | 0.054077 / 0.023109 (0.030968) | 0.276653 / 0.275898 (0.000755) | 0.298752 / 0.323480 (-0.024728) | 0.003979 / 0.007986 (-0.004007) | 0.002625 / 0.004328 (-0.001703) | 0.047951 / 0.004250 (0.043701) | 0.040969 / 0.037052 (0.003916) | 0.279879 / 0.258489 (0.021390) | 0.306244 / 0.293841 (0.012403) | 0.029025 / 0.128546 (-0.099522) | 0.010450 / 0.075646 (-0.065197) | 0.056846 / 0.419271 (-0.362426) | 0.033476 / 0.043533 (-0.010057) | 0.273340 / 0.255139 (0.018201) | 0.294783 / 0.283200 (0.011584) | 0.019105 / 0.141683 (-0.122578) | 1.126389 / 1.452155 (-0.325766) | 1.183369 / 1.492716 (-0.309348) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094995 / 0.018006 (0.076989) | 0.306984 / 0.000490 (0.306495) | 0.000224 / 0.000200 (0.000024) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021880 / 0.037411 (-0.015532) | 0.069674 / 0.014526 (0.055148) | 0.082191 / 0.176557 (-0.094366) | 0.120956 / 0.737135 (-0.616179) | 0.083843 / 0.296338 (-0.212495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295139 / 0.215209 (0.079929) | 2.860520 / 2.077655 (0.782865) | 1.578892 / 1.504120 (0.074772) | 1.451003 / 1.541195 (-0.090192) | 1.483099 / 1.468490 (0.014609) | 0.550491 / 4.584777 (-4.034286) | 2.430352 / 3.745712 (-1.315360) | 2.874468 / 5.269862 (-2.395393) | 1.741474 / 4.565676 (-2.824202) | 0.062563 / 0.424275 (-0.361712) | 0.004962 / 0.007607 (-0.002645) | 0.343747 / 0.226044 (0.117703) | 3.419046 / 2.268929 (1.150118) | 1.943774 / 55.444624 (-53.500851) | 1.650989 / 6.876477 (-5.225488) | 1.704083 / 2.142072 (-0.437990) | 0.645447 / 4.805227 (-4.159780) | 0.125105 / 6.500664 (-6.375559) | 0.041319 / 0.075469 (-0.034150) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959708 / 1.841788 (-0.882079) | 12.235906 / 8.074308 (4.161598) | 10.575402 / 10.191392 (0.384010) | 0.143619 / 0.680424 (-0.536805) | 0.015517 / 0.534201 (-0.518684) | 0.285231 / 0.579283 (-0.294052) | 0.281549 / 0.434364 (-0.152815) | 0.326649 / 0.540337 (-0.213689) | 0.565706 / 1.386936 (-0.821230) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb6985bc33277a3ece7f28c74ca742ba84655b0c \"CML watermark\")\n" ]
2023-11-28T19:47:07
2023-11-30T10:40:23
2023-11-30 10:34:12+00:00
MEMBER
nan
don't require `trust_remote_code` in (deprecated) `inspect_dataset` (it defeats its purpose) (not super important but we might as well keep it until the next major release) this is needed to fix the tests in https://github.com/huggingface/datasets/pull/6448
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PR_kwDODunzps5gej3H
6454
Refactor `dill` logic
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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) | 0.005490 / 0.011353 (-0.005863) | 0.003554 / 0.011008 (-0.007454) | 0.062183 / 0.038508 (0.023675) | 0.053093 / 0.023109 (0.029984) | 0.245370 / 0.275898 (-0.030528) | 0.271637 / 0.323480 (-0.051842) | 0.002997 / 0.007986 (-0.004989) | 0.002811 / 0.004328 (-0.001517) | 0.047874 / 0.004250 (0.043623) | 0.039673 / 0.037052 (0.002620) | 0.253219 / 0.258489 (-0.005271) | 0.280438 / 0.293841 (-0.013403) | 0.028393 / 0.128546 (-0.100153) | 0.010914 / 0.075646 (-0.064732) | 0.207491 / 0.419271 (-0.211781) | 0.037565 / 0.043533 (-0.005968) | 0.252382 / 0.255139 (-0.002757) | 0.272204 / 0.283200 (-0.010995) | 0.019007 / 0.141683 (-0.122676) | 1.099767 / 1.452155 (-0.352388) | 1.173220 / 1.492716 (-0.319496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098777 / 0.018006 (0.080771) | 0.325912 / 0.000490 (0.325422) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018815 / 0.037411 (-0.018596) | 0.070031 / 0.014526 (0.055506) | 0.075395 / 0.176557 (-0.101162) | 0.122633 / 0.737135 (-0.614502) | 0.077621 / 0.296338 (-0.218718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290830 / 0.215209 (0.075621) | 2.869214 / 2.077655 (0.791559) | 1.507337 / 1.504120 (0.003217) | 1.351391 / 1.541195 (-0.189804) | 1.386642 / 1.468490 (-0.081848) | 0.570318 / 4.584777 (-4.014459) | 2.423442 / 3.745712 (-1.322270) | 2.897812 / 5.269862 (-2.372050) | 1.796458 / 4.565676 (-2.769219) | 0.063649 / 0.424275 (-0.360626) | 0.005038 / 0.007607 (-0.002570) | 0.357819 / 0.226044 (0.131774) | 3.535478 / 2.268929 (1.266549) | 1.831764 / 55.444624 (-53.612861) | 1.545035 / 6.876477 (-5.331442) | 1.585919 / 2.142072 (-0.556154) | 0.643333 / 4.805227 (-4.161894) | 0.120319 / 6.500664 (-6.380345) | 0.043031 / 0.075469 (-0.032438) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981155 / 1.841788 (-0.860633) | 12.136069 / 8.074308 (4.061760) | 10.579923 / 10.191392 (0.388531) | 0.152963 / 0.680424 (-0.527461) | 0.014783 / 0.534201 (-0.519418) | 0.289177 / 0.579283 (-0.290106) | 0.271784 / 0.434364 (-0.162580) | 0.322381 / 0.540337 (-0.217956) | 0.420034 / 1.386936 (-0.966902) |\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) | 0.005315 / 0.011353 (-0.006038) | 0.003584 / 0.011008 (-0.007424) | 0.048596 / 0.038508 (0.010088) | 0.055940 / 0.023109 (0.032830) | 0.277687 / 0.275898 (0.001789) | 0.301545 / 0.323480 (-0.021935) | 0.004150 / 0.007986 (-0.003836) | 0.002699 / 0.004328 (-0.001629) | 0.047661 / 0.004250 (0.043410) | 0.040618 / 0.037052 (0.003565) | 0.279173 / 0.258489 (0.020684) | 0.306105 / 0.293841 (0.012264) | 0.030099 / 0.128546 (-0.098447) | 0.010784 / 0.075646 (-0.064862) | 0.057418 / 0.419271 (-0.361853) | 0.032632 / 0.043533 (-0.010901) | 0.276064 / 0.255139 (0.020925) | 0.307194 / 0.283200 (0.023995) | 0.017416 / 0.141683 (-0.124267) | 1.107749 / 1.452155 (-0.344406) | 1.161104 / 1.492716 (-0.331612) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102395 / 0.018006 (0.084389) | 0.316933 / 0.000490 (0.316443) | 0.000246 / 0.000200 (0.000046) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022833 / 0.037411 (-0.014579) | 0.069372 / 0.014526 (0.054846) | 0.082139 / 0.176557 (-0.094418) | 0.121666 / 0.737135 (-0.615469) | 0.084039 / 0.296338 (-0.212300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298775 / 0.215209 (0.083566) | 2.973898 / 2.077655 (0.896244) | 1.614436 / 1.504120 (0.110316) | 1.476112 / 1.541195 (-0.065083) | 1.502031 / 1.468490 (0.033541) | 0.580626 / 4.584777 (-4.004151) | 2.493428 / 3.745712 (-1.252285) | 2.931050 / 5.269862 (-2.338811) | 1.823603 / 4.565676 (-2.742073) | 0.064736 / 0.424275 (-0.359539) | 0.004963 / 0.007607 (-0.002644) | 0.355096 / 0.226044 (0.129052) | 3.522801 / 2.268929 (1.253872) | 1.968690 / 55.444624 (-53.475935) | 1.698624 / 6.876477 (-5.177853) | 1.714166 / 2.142072 (-0.427906) | 0.681734 / 4.805227 (-4.123493) | 0.118940 / 6.500664 (-6.381724) | 0.041960 / 0.075469 (-0.033509) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985311 / 1.841788 (-0.856476) | 12.785393 / 8.074308 (4.711085) | 11.289459 / 10.191392 (1.098067) | 0.145297 / 0.680424 (-0.535127) | 0.016125 / 0.534201 (-0.518076) | 0.289445 / 0.579283 (-0.289838) | 0.278974 / 0.434364 (-0.155390) | 0.322456 / 0.540337 (-0.217881) | 0.418218 / 1.386936 (-0.968718) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#66cef090c55d3561412468d94cb545b47fb000fb \"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) | 0.005142 / 0.011353 (-0.006211) | 0.004180 / 0.011008 (-0.006829) | 0.062647 / 0.038508 (0.024139) | 0.055072 / 0.023109 (0.031962) | 0.254681 / 0.275898 (-0.021217) | 0.282650 / 0.323480 (-0.040830) | 0.003950 / 0.007986 (-0.004035) | 0.002862 / 0.004328 (-0.001466) | 0.048420 / 0.004250 (0.044170) | 0.038447 / 0.037052 (0.001394) | 0.258160 / 0.258489 (-0.000329) | 0.288596 / 0.293841 (-0.005245) | 0.027898 / 0.128546 (-0.100648) | 0.011165 / 0.075646 (-0.064482) | 0.206844 / 0.419271 (-0.212427) | 0.036312 / 0.043533 (-0.007221) | 0.257957 / 0.255139 (0.002819) | 0.277387 / 0.283200 (-0.005812) | 0.018205 / 0.141683 (-0.123478) | 1.109870 / 1.452155 (-0.342284) | 1.175005 / 1.492716 (-0.317712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096692 / 0.018006 (0.078686) | 0.307463 / 0.000490 (0.306973) | 0.000218 / 0.000200 (0.000018) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018602 / 0.037411 (-0.018809) | 0.061489 / 0.014526 (0.046964) | 0.072936 / 0.176557 (-0.103620) | 0.119863 / 0.737135 (-0.617272) | 0.073983 / 0.296338 (-0.222355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291444 / 0.215209 (0.076235) | 2.849024 / 2.077655 (0.771369) | 1.533121 / 1.504120 (0.029001) | 1.402148 / 1.541195 (-0.139046) | 1.406397 / 1.468490 (-0.062094) | 0.564241 / 4.584777 (-4.020536) | 2.402052 / 3.745712 (-1.343660) | 2.772639 / 5.269862 (-2.497223) | 1.732342 / 4.565676 (-2.833334) | 0.062361 / 0.424275 (-0.361914) | 0.004945 / 0.007607 (-0.002662) | 0.355841 / 0.226044 (0.129797) | 3.426931 / 2.268929 (1.158003) | 1.865412 / 55.444624 (-53.579212) | 1.592628 / 6.876477 (-5.283849) | 1.662364 / 2.142072 (-0.479708) | 0.653278 / 4.805227 (-4.151949) | 0.118626 / 6.500664 (-6.382038) | 0.042961 / 0.075469 (-0.032508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956279 / 1.841788 (-0.885509) | 11.635540 / 8.074308 (3.561232) | 10.719590 / 10.191392 (0.528198) | 0.130015 / 0.680424 (-0.550409) | 0.014424 / 0.534201 (-0.519777) | 0.288135 / 0.579283 (-0.291148) | 0.270819 / 0.434364 (-0.163545) | 0.320238 / 0.540337 (-0.220099) | 0.421044 / 1.386936 (-0.965892) |\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) | 0.005201 / 0.011353 (-0.006152) | 0.003467 / 0.011008 (-0.007541) | 0.048939 / 0.038508 (0.010431) | 0.051841 / 0.023109 (0.028732) | 0.273708 / 0.275898 (-0.002190) | 0.293491 / 0.323480 (-0.029988) | 0.004830 / 0.007986 (-0.003156) | 0.002696 / 0.004328 (-0.001632) | 0.047727 / 0.004250 (0.043476) | 0.041319 / 0.037052 (0.004266) | 0.273837 / 0.258489 (0.015348) | 0.309860 / 0.293841 (0.016019) | 0.029054 / 0.128546 (-0.099492) | 0.010410 / 0.075646 (-0.065237) | 0.058139 / 0.419271 (-0.361133) | 0.032682 / 0.043533 (-0.010850) | 0.273244 / 0.255139 (0.018105) | 0.291579 / 0.283200 (0.008380) | 0.018262 / 0.141683 (-0.123421) | 1.144590 / 1.452155 (-0.307565) | 1.202474 / 1.492716 (-0.290243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097110 / 0.018006 (0.079104) | 0.307344 / 0.000490 (0.306854) | 0.000229 / 0.000200 (0.000029) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022263 / 0.037411 (-0.015148) | 0.070140 / 0.014526 (0.055614) | 0.081251 / 0.176557 (-0.095306) | 0.120839 / 0.737135 (-0.616297) | 0.083312 / 0.296338 (-0.213026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297381 / 0.215209 (0.082172) | 2.895530 / 2.077655 (0.817875) | 1.608442 / 1.504120 (0.104322) | 1.476237 / 1.541195 (-0.064958) | 1.491306 / 1.468490 (0.022816) | 0.567272 / 4.584777 (-4.017505) | 2.463543 / 3.745712 (-1.282170) | 2.814764 / 5.269862 (-2.455098) | 1.725845 / 4.565676 (-2.839831) | 0.064149 / 0.424275 (-0.360126) | 0.004953 / 0.007607 (-0.002654) | 0.359629 / 0.226044 (0.133585) | 3.482414 / 2.268929 (1.213486) | 1.949897 / 55.444624 (-53.494727) | 1.677383 / 6.876477 (-5.199094) | 1.683655 / 2.142072 (-0.458418) | 0.645671 / 4.805227 (-4.159557) | 0.115612 / 6.500664 (-6.385053) | 0.041013 / 0.075469 (-0.034456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967843 / 1.841788 (-0.873945) | 12.376877 / 8.074308 (4.302569) | 10.988174 / 10.191392 (0.796782) | 0.134660 / 0.680424 (-0.545764) | 0.015801 / 0.534201 (-0.518400) | 0.288699 / 0.579283 (-0.290584) | 0.284887 / 0.434364 (-0.149477) | 0.322000 / 0.540337 (-0.218337) | 0.412360 / 1.386936 (-0.974576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#148454d48b7c36507a283217c7c0e3bcc0539f75 \"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) | 0.005407 / 0.011353 (-0.005946) | 0.003496 / 0.011008 (-0.007512) | 0.062730 / 0.038508 (0.024222) | 0.051882 / 0.023109 (0.028773) | 0.244766 / 0.275898 (-0.031132) | 0.257963 / 0.323480 (-0.065516) | 0.002894 / 0.007986 (-0.005092) | 0.002567 / 0.004328 (-0.001761) | 0.048756 / 0.004250 (0.044506) | 0.039024 / 0.037052 (0.001971) | 0.247303 / 0.258489 (-0.011186) | 0.278341 / 0.293841 (-0.015500) | 0.026725 / 0.128546 (-0.101821) | 0.010577 / 0.075646 (-0.065069) | 0.210483 / 0.419271 (-0.208789) | 0.035230 / 0.043533 (-0.008303) | 0.246125 / 0.255139 (-0.009014) | 0.264039 / 0.283200 (-0.019160) | 0.019881 / 0.141683 (-0.121802) | 1.113475 / 1.452155 (-0.338679) | 1.149606 / 1.492716 (-0.343110) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092946 / 0.018006 (0.074940) | 0.299985 / 0.000490 (0.299495) | 0.000215 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018421 / 0.037411 (-0.018991) | 0.060531 / 0.014526 (0.046005) | 0.074459 / 0.176557 (-0.102098) | 0.120369 / 0.737135 (-0.616766) | 0.075505 / 0.296338 (-0.220833) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289497 / 0.215209 (0.074288) | 2.783139 / 2.077655 (0.705485) | 1.482533 / 1.504120 (-0.021587) | 1.371013 / 1.541195 (-0.170182) | 1.379114 / 1.468490 (-0.089376) | 0.563953 / 4.584777 (-4.020824) | 2.389996 / 3.745712 (-1.355716) | 2.788067 / 5.269862 (-2.481795) | 1.751772 / 4.565676 (-2.813904) | 0.062680 / 0.424275 (-0.361595) | 0.004901 / 0.007607 (-0.002706) | 0.365193 / 0.226044 (0.139149) | 3.389181 / 2.268929 (1.120252) | 1.861659 / 55.444624 (-53.582965) | 1.558899 / 6.876477 (-5.317577) | 1.591079 / 2.142072 (-0.550993) | 0.648300 / 4.805227 (-4.156927) | 0.117486 / 6.500664 (-6.383178) | 0.041961 / 0.075469 (-0.033508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944391 / 1.841788 (-0.897396) | 11.500823 / 8.074308 (3.426515) | 10.580430 / 10.191392 (0.389038) | 0.142845 / 0.680424 (-0.537579) | 0.014305 / 0.534201 (-0.519896) | 0.290723 / 0.579283 (-0.288560) | 0.266206 / 0.434364 (-0.168158) | 0.325482 / 0.540337 (-0.214856) | 0.416224 / 1.386936 (-0.970712) |\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) | 0.005363 / 0.011353 (-0.005990) | 0.003548 / 0.011008 (-0.007460) | 0.048704 / 0.038508 (0.010196) | 0.051025 / 0.023109 (0.027916) | 0.273037 / 0.275898 (-0.002861) | 0.297148 / 0.323480 (-0.026332) | 0.003985 / 0.007986 (-0.004001) | 0.002739 / 0.004328 (-0.001590) | 0.048108 / 0.004250 (0.043857) | 0.040244 / 0.037052 (0.003191) | 0.277825 / 0.258489 (0.019336) | 0.303704 / 0.293841 (0.009863) | 0.029460 / 0.128546 (-0.099086) | 0.010428 / 0.075646 (-0.065218) | 0.057022 / 0.419271 (-0.362249) | 0.032711 / 0.043533 (-0.010822) | 0.274462 / 0.255139 (0.019323) | 0.293499 / 0.283200 (0.010299) | 0.018266 / 0.141683 (-0.123417) | 1.158049 / 1.452155 (-0.294106) | 1.170097 / 1.492716 (-0.322620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093412 / 0.018006 (0.075406) | 0.301538 / 0.000490 (0.301049) | 0.000222 / 0.000200 (0.000022) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021698 / 0.037411 (-0.015713) | 0.068735 / 0.014526 (0.054209) | 0.083010 / 0.176557 (-0.093546) | 0.127491 / 0.737135 (-0.609644) | 0.083005 / 0.296338 (-0.213333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298299 / 0.215209 (0.083090) | 2.894209 / 2.077655 (0.816554) | 1.597455 / 1.504120 (0.093335) | 1.472953 / 1.541195 (-0.068241) | 1.491553 / 1.468490 (0.023063) | 0.556566 / 4.584777 (-4.028211) | 2.419429 / 3.745712 (-1.326283) | 2.788706 / 5.269862 (-2.481156) | 1.759888 / 4.565676 (-2.805789) | 0.062535 / 0.424275 (-0.361740) | 0.004959 / 0.007607 (-0.002648) | 0.345226 / 0.226044 (0.119182) | 3.438539 / 2.268929 (1.169611) | 1.943842 / 55.444624 (-53.500782) | 1.661080 / 6.876477 (-5.215397) | 1.687632 / 2.142072 (-0.454440) | 0.639971 / 4.805227 (-4.165256) | 0.116012 / 6.500664 (-6.384652) | 0.041723 / 0.075469 (-0.033746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965143 / 1.841788 (-0.876645) | 12.086547 / 8.074308 (4.012238) | 10.708787 / 10.191392 (0.517395) | 0.129506 / 0.680424 (-0.550918) | 0.015254 / 0.534201 (-0.518947) | 0.288326 / 0.579283 (-0.290957) | 0.271976 / 0.434364 (-0.162388) | 0.328402 / 0.540337 (-0.211936) | 0.418102 / 1.386936 (-0.968834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b6f13ede3dccedf335bb2d8ff04db306dc710a \"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) | 0.005375 / 0.011353 (-0.005978) | 0.003530 / 0.011008 (-0.007478) | 0.062521 / 0.038508 (0.024013) | 0.051514 / 0.023109 (0.028405) | 0.241623 / 0.275898 (-0.034275) | 0.269054 / 0.323480 (-0.054426) | 0.002877 / 0.007986 (-0.005109) | 0.002724 / 0.004328 (-0.001605) | 0.049045 / 0.004250 (0.044794) | 0.038560 / 0.037052 (0.001507) | 0.248437 / 0.258489 (-0.010052) | 0.276762 / 0.293841 (-0.017079) | 0.027522 / 0.128546 (-0.101024) | 0.010817 / 0.075646 (-0.064829) | 0.208686 / 0.419271 (-0.210585) | 0.035818 / 0.043533 (-0.007715) | 0.249398 / 0.255139 (-0.005741) | 0.268288 / 0.283200 (-0.014911) | 0.019039 / 0.141683 (-0.122644) | 1.135115 / 1.452155 (-0.317040) | 1.195531 / 1.492716 (-0.297185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093126 / 0.018006 (0.075120) | 0.301028 / 0.000490 (0.300539) | 0.000222 / 0.000200 (0.000023) | 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) | 0.018385 / 0.037411 (-0.019027) | 0.060902 / 0.014526 (0.046376) | 0.073168 / 0.176557 (-0.103389) | 0.119216 / 0.737135 (-0.617919) | 0.074225 / 0.296338 (-0.222114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283749 / 0.215209 (0.068540) | 2.741609 / 2.077655 (0.663954) | 1.483439 / 1.504120 (-0.020681) | 1.352896 / 1.541195 (-0.188299) | 1.378824 / 1.468490 (-0.089667) | 0.548731 / 4.584777 (-4.036046) | 2.342717 / 3.745712 (-1.402995) | 2.791592 / 5.269862 (-2.478269) | 1.740605 / 4.565676 (-2.825071) | 0.062059 / 0.424275 (-0.362216) | 0.005028 / 0.007607 (-0.002579) | 0.339205 / 0.226044 (0.113161) | 3.353386 / 2.268929 (1.084458) | 1.785717 / 55.444624 (-53.658907) | 1.523390 / 6.876477 (-5.353086) | 1.556999 / 2.142072 (-0.585073) | 0.636745 / 4.805227 (-4.168483) | 0.115821 / 6.500664 (-6.384843) | 0.042200 / 0.075469 (-0.033269) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948678 / 1.841788 (-0.893110) | 11.588670 / 8.074308 (3.514362) | 10.897130 / 10.191392 (0.705738) | 0.140068 / 0.680424 (-0.540356) | 0.014565 / 0.534201 (-0.519636) | 0.286336 / 0.579283 (-0.292947) | 0.265292 / 0.434364 (-0.169072) | 0.324146 / 0.540337 (-0.216192) | 0.413463 / 1.386936 (-0.973473) |\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) | 0.005187 / 0.011353 (-0.006165) | 0.003471 / 0.011008 (-0.007537) | 0.048968 / 0.038508 (0.010460) | 0.051285 / 0.023109 (0.028176) | 0.283286 / 0.275898 (0.007388) | 0.307046 / 0.323480 (-0.016434) | 0.004017 / 0.007986 (-0.003969) | 0.002655 / 0.004328 (-0.001673) | 0.047762 / 0.004250 (0.043512) | 0.039855 / 0.037052 (0.002803) | 0.283101 / 0.258489 (0.024612) | 0.312905 / 0.293841 (0.019064) | 0.028188 / 0.128546 (-0.100358) | 0.010849 / 0.075646 (-0.064797) | 0.058112 / 0.419271 (-0.361159) | 0.032163 / 0.043533 (-0.011369) | 0.280825 / 0.255139 (0.025686) | 0.300946 / 0.283200 (0.017747) | 0.017409 / 0.141683 (-0.124274) | 1.127360 / 1.452155 (-0.324795) | 1.180409 / 1.492716 (-0.312307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093186 / 0.018006 (0.075180) | 0.300827 / 0.000490 (0.300338) | 0.000220 / 0.000200 (0.000020) | 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) | 0.021560 / 0.037411 (-0.015851) | 0.069158 / 0.014526 (0.054632) | 0.080953 / 0.176557 (-0.095603) | 0.119071 / 0.737135 (-0.618064) | 0.082817 / 0.296338 (-0.213521) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.307259 / 0.215209 (0.092050) | 2.996058 / 2.077655 (0.918404) | 1.627406 / 1.504120 (0.123286) | 1.500715 / 1.541195 (-0.040480) | 1.524278 / 1.468490 (0.055788) | 0.569711 / 4.584777 (-4.015066) | 2.436132 / 3.745712 (-1.309580) | 2.796995 / 5.269862 (-2.472866) | 1.760701 / 4.565676 (-2.804975) | 0.063521 / 0.424275 (-0.360754) | 0.004909 / 0.007607 (-0.002698) | 0.359129 / 0.226044 (0.133085) | 3.567278 / 2.268929 (1.298349) | 2.013821 / 55.444624 (-53.430804) | 1.708021 / 6.876477 (-5.168456) | 1.738959 / 2.142072 (-0.403114) | 0.648620 / 4.805227 (-4.156607) | 0.122016 / 6.500664 (-6.378648) | 0.041802 / 0.075469 (-0.033667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985208 / 1.841788 (-0.856579) | 12.307785 / 8.074308 (4.233477) | 10.587262 / 10.191392 (0.395870) | 0.130468 / 0.680424 (-0.549956) | 0.014912 / 0.534201 (-0.519289) | 0.293822 / 0.579283 (-0.285461) | 0.283021 / 0.434364 (-0.151343) | 0.329560 / 0.540337 (-0.210777) | 0.424741 / 1.386936 (-0.962195) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04426d9c8e0aa5c97af2826064287f8cab6bece0 \"CML watermark\")\n" ]
2023-11-27T20:01:25
2023-11-28T16:29:58
2023-11-28 16:29:31+00:00
COLLABORATOR
nan
Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way) It makes the following improvements to the serialization API: * consistent order of a `dict`'s keys * support for hashing `torch.compile`-ed modules and functions * deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do) ~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed... Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839
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2011907787
PR_kwDODunzps5ga0rv
6453
Update hub-docs reference
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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) | 0.005119 / 0.011353 (-0.006234) | 0.003469 / 0.011008 (-0.007540) | 0.061791 / 0.038508 (0.023283) | 0.051655 / 0.023109 (0.028545) | 0.241157 / 0.275898 (-0.034741) | 0.265930 / 0.323480 (-0.057549) | 0.003851 / 0.007986 (-0.004134) | 0.002412 / 0.004328 (-0.001916) | 0.047498 / 0.004250 (0.043247) | 0.037328 / 0.037052 (0.000276) | 0.250418 / 0.258489 (-0.008071) | 0.277842 / 0.293841 (-0.015999) | 0.027626 / 0.128546 (-0.100920) | 0.009947 / 0.075646 (-0.065699) | 0.204549 / 0.419271 (-0.214722) | 0.037546 / 0.043533 (-0.005987) | 0.245383 / 0.255139 (-0.009756) | 0.263486 / 0.283200 (-0.019713) | 0.017792 / 0.141683 (-0.123891) | 1.158900 / 1.452155 (-0.293255) | 1.194060 / 1.492716 (-0.298657) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090607 / 0.018006 (0.072601) | 0.299909 / 0.000490 (0.299419) | 0.000206 / 0.000200 (0.000006) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018814 / 0.037411 (-0.018597) | 0.062068 / 0.014526 (0.047542) | 0.087221 / 0.176557 (-0.089336) | 0.119594 / 0.737135 (-0.617541) | 0.075485 / 0.296338 (-0.220853) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286093 / 0.215209 (0.070884) | 2.767396 / 2.077655 (0.689741) | 1.500472 / 1.504120 (-0.003648) | 1.389514 / 1.541195 (-0.151680) | 1.438933 / 1.468490 (-0.029557) | 0.562545 / 4.584777 (-4.022232) | 2.383330 / 3.745712 (-1.362382) | 2.799215 / 5.269862 (-2.470647) | 1.732618 / 4.565676 (-2.833058) | 0.061282 / 0.424275 (-0.362993) | 0.005007 / 0.007607 (-0.002601) | 0.339769 / 0.226044 (0.113725) | 3.337146 / 2.268929 (1.068218) | 1.890789 / 55.444624 (-53.553836) | 1.593555 / 6.876477 (-5.282922) | 1.660016 / 2.142072 (-0.482057) | 0.632452 / 4.805227 (-4.172775) | 0.115503 / 6.500664 (-6.385161) | 0.041590 / 0.075469 (-0.033880) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941966 / 1.841788 (-0.899822) | 11.470271 / 8.074308 (3.395963) | 10.579454 / 10.191392 (0.388062) | 0.140970 / 0.680424 (-0.539454) | 0.014057 / 0.534201 (-0.520144) | 0.289326 / 0.579283 (-0.289957) | 0.265366 / 0.434364 (-0.168998) | 0.324612 / 0.540337 (-0.215726) | 0.415832 / 1.386936 (-0.971104) |\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) | 0.005208 / 0.011353 (-0.006145) | 0.003199 / 0.011008 (-0.007809) | 0.048299 / 0.038508 (0.009791) | 0.050727 / 0.023109 (0.027618) | 0.274897 / 0.275898 (-0.001001) | 0.298328 / 0.323480 (-0.025152) | 0.003989 / 0.007986 (-0.003997) | 0.002439 / 0.004328 (-0.001890) | 0.047308 / 0.004250 (0.043058) | 0.039726 / 0.037052 (0.002673) | 0.276279 / 0.258489 (0.017790) | 0.303679 / 0.293841 (0.009838) | 0.028943 / 0.128546 (-0.099603) | 0.010223 / 0.075646 (-0.065423) | 0.056694 / 0.419271 (-0.362577) | 0.032283 / 0.043533 (-0.011250) | 0.275344 / 0.255139 (0.020205) | 0.296358 / 0.283200 (0.013158) | 0.017481 / 0.141683 (-0.124201) | 1.131063 / 1.452155 (-0.321092) | 1.181146 / 1.492716 (-0.311570) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092259 / 0.018006 (0.074253) | 0.299381 / 0.000490 (0.298891) | 0.000216 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021693 / 0.037411 (-0.015718) | 0.070441 / 0.014526 (0.055916) | 0.080648 / 0.176557 (-0.095908) | 0.119002 / 0.737135 (-0.618133) | 0.081412 / 0.296338 (-0.214926) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296475 / 0.215209 (0.081266) | 2.905098 / 2.077655 (0.827443) | 1.596321 / 1.504120 (0.092201) | 1.472640 / 1.541195 (-0.068555) | 1.484453 / 1.468490 (0.015963) | 0.565229 / 4.584777 (-4.019548) | 2.390631 / 3.745712 (-1.355081) | 2.765125 / 5.269862 (-2.504737) | 1.738993 / 4.565676 (-2.826683) | 0.063034 / 0.424275 (-0.361241) | 0.004891 / 0.007607 (-0.002716) | 0.350678 / 0.226044 (0.124633) | 3.530919 / 2.268929 (1.261990) | 1.943758 / 55.444624 (-53.500867) | 1.665553 / 6.876477 (-5.210924) | 1.656990 / 2.142072 (-0.485083) | 0.647027 / 4.805227 (-4.158201) | 0.116771 / 6.500664 (-6.383893) | 0.041012 / 0.075469 (-0.034457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.034226 / 1.841788 (-0.807561) | 12.036726 / 8.074308 (3.962418) | 10.934239 / 10.191392 (0.742847) | 0.130142 / 0.680424 (-0.550281) | 0.015537 / 0.534201 (-0.518664) | 0.286020 / 0.579283 (-0.293263) | 0.276739 / 0.434364 (-0.157625) | 0.326284 / 0.540337 (-0.214054) | 0.413392 / 1.386936 (-0.973544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4787c0022c8b59c15256021478b444a6c51fa984 \"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) | 0.005400 / 0.011353 (-0.005953) | 0.003415 / 0.011008 (-0.007593) | 0.062416 / 0.038508 (0.023908) | 0.055962 / 0.023109 (0.032853) | 0.234725 / 0.275898 (-0.041173) | 0.261775 / 0.323480 (-0.061705) | 0.002868 / 0.007986 (-0.005118) | 0.002426 / 0.004328 (-0.001902) | 0.047989 / 0.004250 (0.043738) | 0.039214 / 0.037052 (0.002162) | 0.246068 / 0.258489 (-0.012421) | 0.270245 / 0.293841 (-0.023596) | 0.027558 / 0.128546 (-0.100988) | 0.010256 / 0.075646 (-0.065390) | 0.210988 / 0.419271 (-0.208283) | 0.035684 / 0.043533 (-0.007849) | 0.245254 / 0.255139 (-0.009885) | 0.255476 / 0.283200 (-0.027724) | 0.018495 / 0.141683 (-0.123188) | 1.115458 / 1.452155 (-0.336697) | 1.166149 / 1.492716 (-0.326567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092736 / 0.018006 (0.074730) | 0.301040 / 0.000490 (0.300550) | 0.000213 / 0.000200 (0.000013) | 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) | 0.018607 / 0.037411 (-0.018805) | 0.062189 / 0.014526 (0.047664) | 0.073782 / 0.176557 (-0.102775) | 0.119895 / 0.737135 (-0.617240) | 0.074907 / 0.296338 (-0.221431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283986 / 0.215209 (0.068777) | 2.824498 / 2.077655 (0.746844) | 1.505848 / 1.504120 (0.001728) | 1.358879 / 1.541195 (-0.182316) | 1.357087 / 1.468490 (-0.111403) | 0.574307 / 4.584777 (-4.010470) | 2.416478 / 3.745712 (-1.329234) | 2.772909 / 5.269862 (-2.496953) | 1.750395 / 4.565676 (-2.815282) | 0.062465 / 0.424275 (-0.361810) | 0.004983 / 0.007607 (-0.002624) | 0.344490 / 0.226044 (0.118445) | 3.405062 / 2.268929 (1.136134) | 1.854972 / 55.444624 (-53.589653) | 1.572789 / 6.876477 (-5.303687) | 1.586109 / 2.142072 (-0.555963) | 0.647431 / 4.805227 (-4.157797) | 0.123079 / 6.500664 (-6.377585) | 0.042766 / 0.075469 (-0.032703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950493 / 1.841788 (-0.891295) | 11.814821 / 8.074308 (3.740513) | 10.494768 / 10.191392 (0.303376) | 0.131322 / 0.680424 (-0.549102) | 0.015253 / 0.534201 (-0.518948) | 0.287405 / 0.579283 (-0.291878) | 0.269664 / 0.434364 (-0.164699) | 0.322700 / 0.540337 (-0.217637) | 0.424103 / 1.386936 (-0.962833) |\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) | 0.005264 / 0.011353 (-0.006088) | 0.003304 / 0.011008 (-0.007704) | 0.048531 / 0.038508 (0.010023) | 0.052752 / 0.023109 (0.029643) | 0.274435 / 0.275898 (-0.001463) | 0.297500 / 0.323480 (-0.025980) | 0.003977 / 0.007986 (-0.004009) | 0.002444 / 0.004328 (-0.001884) | 0.048464 / 0.004250 (0.044214) | 0.040192 / 0.037052 (0.003139) | 0.278256 / 0.258489 (0.019767) | 0.303627 / 0.293841 (0.009786) | 0.028709 / 0.128546 (-0.099837) | 0.010530 / 0.075646 (-0.065117) | 0.057427 / 0.419271 (-0.361844) | 0.032539 / 0.043533 (-0.010994) | 0.272237 / 0.255139 (0.017098) | 0.295288 / 0.283200 (0.012088) | 0.018820 / 0.141683 (-0.122862) | 1.116100 / 1.452155 (-0.336055) | 1.180124 / 1.492716 (-0.312592) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092651 / 0.018006 (0.074644) | 0.301481 / 0.000490 (0.300991) | 0.000217 / 0.000200 (0.000017) | 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) | 0.022461 / 0.037411 (-0.014951) | 0.070623 / 0.014526 (0.056097) | 0.082642 / 0.176557 (-0.093915) | 0.120021 / 0.737135 (-0.617114) | 0.083387 / 0.296338 (-0.212952) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291451 / 0.215209 (0.076242) | 2.865602 / 2.077655 (0.787947) | 1.592051 / 1.504120 (0.087931) | 1.463521 / 1.541195 (-0.077673) | 1.498899 / 1.468490 (0.030409) | 0.570854 / 4.584777 (-4.013923) | 2.410002 / 3.745712 (-1.335710) | 2.768028 / 5.269862 (-2.501834) | 1.740463 / 4.565676 (-2.825214) | 0.063801 / 0.424275 (-0.360474) | 0.005019 / 0.007607 (-0.002588) | 0.348353 / 0.226044 (0.122309) | 3.425793 / 2.268929 (1.156864) | 1.957294 / 55.444624 (-53.487331) | 1.696121 / 6.876477 (-5.180355) | 1.691544 / 2.142072 (-0.450528) | 0.645528 / 4.805227 (-4.159700) | 0.118876 / 6.500664 (-6.381788) | 0.041001 / 0.075469 (-0.034469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983805 / 1.841788 (-0.857983) | 12.085909 / 8.074308 (4.011600) | 10.835395 / 10.191392 (0.644003) | 0.141971 / 0.680424 (-0.538453) | 0.015534 / 0.534201 (-0.518667) | 0.289289 / 0.579283 (-0.289994) | 0.276316 / 0.434364 (-0.158048) | 0.354577 / 0.540337 (-0.185761) | 0.421824 / 1.386936 (-0.965112) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#27d1fe52857c6a25a29cac63a296405136b2797c \"CML watermark\")\n" ]
2023-11-27T09:57:20
2023-11-27T10:23:44
2023-11-27 10:17:34+00:00
NONE
nan
Follow up to huggingface/huggingface.js#296
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6452
Praveen_repo_pull_req
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2023-11-27T07:07:50
2023-11-27T09:28:00
2023-11-27 09:28:00+00:00
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6451
Unable to read "marsyas/gtzan" data
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[ "Hi! We've merged a [PR](https://huggingface.co/datasets/marsyas/gtzan/discussions/1) that fixes the script's path logic on Windows.", "I have transferred the discussion to the corresponding dataset: https://huggingface.co/datasets/marsyas/gtzan/discussions/2\r\n\r\nLet's continue there.", "@mariosasko @albertvillanova \r\n\r\nThank you both very much for the speedy resolution :)" ]
2023-11-25T15:13:17
2023-12-01T12:53:46
2023-11-27 09:36:25+00:00
NONE
nan
Hi, this is my code and the error: ``` from datasets import load_dataset gtzan = load_dataset("marsyas/gtzan", "all") ``` [error_trace.txt](https://github.com/huggingface/datasets/files/13464397/error_trace.txt) [audio_yml.txt](https://github.com/huggingface/datasets/files/13464410/audio_yml.txt) Python 3.11.5 Jupyter Notebook 6.5.4 Windows 10 I'm able to download and work with other datasets, but not this one. For example, both these below work fine: ``` from datasets import load_dataset dataset = load_dataset("facebook/voxpopuli", "pl", split="train", streaming=True) minds = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` Thanks for your help https://huggingface.co/datasets/marsyas/gtzan/tree/main
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2009491386
I_kwDODunzps53xme6
6450
Support multiple image/audio columns in ImageFolder/AudioFolder
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[ "A duplicate of https://github.com/huggingface/datasets/issues/5760" ]
2023-11-24T10:34:09
2023-11-28T11:07:17
2023-11-24 17:24:38+00:00
CONTRIBUTOR
nan
### Feature request Have a metadata.csv file with multiple columns that point to relative image or audio files. ### Motivation Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files. But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column. ### Your contribution no specific contribution
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2008617992
PR_kwDODunzps5gQCVZ
6449
Fix metadata file resolution when inferred pattern is `**`
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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) | 0.005551 / 0.011353 (-0.005802) | 0.003297 / 0.011008 (-0.007711) | 0.062524 / 0.038508 (0.024016) | 0.058467 / 0.023109 (0.035358) | 0.255703 / 0.275898 (-0.020195) | 0.281420 / 0.323480 (-0.042060) | 0.003857 / 0.007986 (-0.004129) | 0.002460 / 0.004328 (-0.001868) | 0.047762 / 0.004250 (0.043512) | 0.038757 / 0.037052 (0.001705) | 0.259937 / 0.258489 (0.001448) | 0.290050 / 0.293841 (-0.003791) | 0.028433 / 0.128546 (-0.100113) | 0.010422 / 0.075646 (-0.065224) | 0.207135 / 0.419271 (-0.212136) | 0.036004 / 0.043533 (-0.007529) | 0.268137 / 0.255139 (0.012998) | 0.275020 / 0.283200 (-0.008179) | 0.018301 / 0.141683 (-0.123382) | 1.095479 / 1.452155 (-0.356676) | 1.145452 / 1.492716 (-0.347265) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092046 / 0.018006 (0.074040) | 0.299784 / 0.000490 (0.299294) | 0.000214 / 0.000200 (0.000014) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019071 / 0.037411 (-0.018340) | 0.072836 / 0.014526 (0.058310) | 0.073974 / 0.176557 (-0.102583) | 0.120903 / 0.737135 (-0.616232) | 0.075740 / 0.296338 (-0.220599) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276365 / 0.215209 (0.061156) | 2.671217 / 2.077655 (0.593563) | 1.438862 / 1.504120 (-0.065258) | 1.327348 / 1.541195 (-0.213847) | 1.349514 / 1.468490 (-0.118976) | 0.548793 / 4.584777 (-4.035984) | 2.364458 / 3.745712 (-1.381255) | 2.716205 / 5.269862 (-2.553657) | 1.735714 / 4.565676 (-2.829963) | 0.061140 / 0.424275 (-0.363135) | 0.004926 / 0.007607 (-0.002681) | 0.330449 / 0.226044 (0.104404) | 3.255243 / 2.268929 (0.986315) | 1.824254 / 55.444624 (-53.620371) | 1.540262 / 6.876477 (-5.336215) | 1.535632 / 2.142072 (-0.606441) | 0.635224 / 4.805227 (-4.170003) | 0.116230 / 6.500664 (-6.384435) | 0.042706 / 0.075469 (-0.032763) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948796 / 1.841788 (-0.892992) | 11.448403 / 8.074308 (3.374095) | 10.523862 / 10.191392 (0.332470) | 0.129694 / 0.680424 (-0.550730) | 0.014146 / 0.534201 (-0.520055) | 0.285706 / 0.579283 (-0.293577) | 0.262572 / 0.434364 (-0.171792) | 0.321251 / 0.540337 (-0.219087) | 0.417130 / 1.386936 (-0.969806) |\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) | 0.005266 / 0.011353 (-0.006086) | 0.003339 / 0.011008 (-0.007670) | 0.048411 / 0.038508 (0.009903) | 0.053951 / 0.023109 (0.030842) | 0.271228 / 0.275898 (-0.004670) | 0.290066 / 0.323480 (-0.033414) | 0.004087 / 0.007986 (-0.003898) | 0.002446 / 0.004328 (-0.001882) | 0.047049 / 0.004250 (0.042798) | 0.040866 / 0.037052 (0.003813) | 0.273711 / 0.258489 (0.015222) | 0.298192 / 0.293841 (0.004351) | 0.029025 / 0.128546 (-0.099521) | 0.010479 / 0.075646 (-0.065167) | 0.056941 / 0.419271 (-0.362330) | 0.032914 / 0.043533 (-0.010619) | 0.270432 / 0.255139 (0.015293) | 0.291274 / 0.283200 (0.008074) | 0.018602 / 0.141683 (-0.123081) | 1.136707 / 1.452155 (-0.315447) | 1.184704 / 1.492716 (-0.308012) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090041 / 0.018006 (0.072035) | 0.300185 / 0.000490 (0.299696) | 0.000221 / 0.000200 (0.000022) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022074 / 0.037411 (-0.015337) | 0.070763 / 0.014526 (0.056237) | 0.082141 / 0.176557 (-0.094415) | 0.120286 / 0.737135 (-0.616850) | 0.082680 / 0.296338 (-0.213659) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292223 / 0.215209 (0.077014) | 2.856711 / 2.077655 (0.779056) | 1.581194 / 1.504120 (0.077075) | 1.496567 / 1.541195 (-0.044628) | 1.485256 / 1.468490 (0.016766) | 0.550633 / 4.584777 (-4.034144) | 2.420281 / 3.745712 (-1.325431) | 2.764373 / 5.269862 (-2.505489) | 1.735958 / 4.565676 (-2.829719) | 0.062562 / 0.424275 (-0.361714) | 0.004918 / 0.007607 (-0.002689) | 0.346038 / 0.226044 (0.119994) | 3.443478 / 2.268929 (1.174550) | 1.949366 / 55.444624 (-53.495259) | 1.686140 / 6.876477 (-5.190337) | 1.683038 / 2.142072 (-0.459034) | 0.629270 / 4.805227 (-4.175958) | 0.114947 / 6.500664 (-6.385717) | 0.040635 / 0.075469 (-0.034834) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969746 / 1.841788 (-0.872041) | 11.922662 / 8.074308 (3.848354) | 10.441432 / 10.191392 (0.250040) | 0.128950 / 0.680424 (-0.551473) | 0.015964 / 0.534201 (-0.518237) | 0.289176 / 0.579283 (-0.290107) | 0.279203 / 0.434364 (-0.155161) | 0.323833 / 0.540337 (-0.216505) | 0.540297 / 1.386936 (-0.846639) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ed759d0f5aea6d166caa0532aa17c209bb3af79 \"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) | 0.005288 / 0.011353 (-0.006065) | 0.003383 / 0.011008 (-0.007625) | 0.061926 / 0.038508 (0.023418) | 0.049080 / 0.023109 (0.025971) | 0.244852 / 0.275898 (-0.031046) | 0.263957 / 0.323480 (-0.059523) | 0.002810 / 0.007986 (-0.005175) | 0.002384 / 0.004328 (-0.001945) | 0.047807 / 0.004250 (0.043556) | 0.038374 / 0.037052 (0.001321) | 0.244414 / 0.258489 (-0.014075) | 0.272257 / 0.293841 (-0.021584) | 0.027356 / 0.128546 (-0.101190) | 0.010235 / 0.075646 (-0.065411) | 0.214896 / 0.419271 (-0.204375) | 0.035604 / 0.043533 (-0.007929) | 0.246584 / 0.255139 (-0.008555) | 0.263281 / 0.283200 (-0.019918) | 0.019689 / 0.141683 (-0.121994) | 1.114100 / 1.452155 (-0.338054) | 1.177644 / 1.492716 (-0.315073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088892 / 0.018006 (0.070886) | 0.298128 / 0.000490 (0.297639) | 0.000199 / 0.000200 (-0.000001) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019337 / 0.037411 (-0.018075) | 0.062096 / 0.014526 (0.047570) | 0.073019 / 0.176557 (-0.103537) | 0.118801 / 0.737135 (-0.618334) | 0.074779 / 0.296338 (-0.221559) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289892 / 0.215209 (0.074683) | 2.824131 / 2.077655 (0.746476) | 1.466351 / 1.504120 (-0.037768) | 1.339528 / 1.541195 (-0.201667) | 1.369257 / 1.468490 (-0.099233) | 0.561175 / 4.584777 (-4.023602) | 2.394174 / 3.745712 (-1.351538) | 2.749668 / 5.269862 (-2.520193) | 1.747146 / 4.565676 (-2.818530) | 0.063054 / 0.424275 (-0.361221) | 0.004970 / 0.007607 (-0.002637) | 0.342985 / 0.226044 (0.116941) | 3.334894 / 2.268929 (1.065966) | 1.838459 / 55.444624 (-53.606165) | 1.579755 / 6.876477 (-5.296722) | 1.560200 / 2.142072 (-0.581872) | 0.642643 / 4.805227 (-4.162585) | 0.117741 / 6.500664 (-6.382923) | 0.042440 / 0.075469 (-0.033029) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937476 / 1.841788 (-0.904312) | 11.403556 / 8.074308 (3.329248) | 10.317207 / 10.191392 (0.125815) | 0.145277 / 0.680424 (-0.535147) | 0.015297 / 0.534201 (-0.518904) | 0.287511 / 0.579283 (-0.291772) | 0.263516 / 0.434364 (-0.170848) | 0.320803 / 0.540337 (-0.219534) | 0.415580 / 1.386936 (-0.971356) |\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) | 0.005239 / 0.011353 (-0.006114) | 0.003506 / 0.011008 (-0.007502) | 0.048635 / 0.038508 (0.010127) | 0.052067 / 0.023109 (0.028957) | 0.277526 / 0.275898 (0.001628) | 0.300536 / 0.323480 (-0.022944) | 0.003982 / 0.007986 (-0.004004) | 0.002413 / 0.004328 (-0.001915) | 0.046523 / 0.004250 (0.042273) | 0.039383 / 0.037052 (0.002331) | 0.281208 / 0.258489 (0.022719) | 0.306199 / 0.293841 (0.012359) | 0.028646 / 0.128546 (-0.099900) | 0.010664 / 0.075646 (-0.064982) | 0.057393 / 0.419271 (-0.361879) | 0.032171 / 0.043533 (-0.011362) | 0.277576 / 0.255139 (0.022437) | 0.296039 / 0.283200 (0.012840) | 0.017519 / 0.141683 (-0.124164) | 1.153172 / 1.452155 (-0.298982) | 1.180274 / 1.492716 (-0.312442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088287 / 0.018006 (0.070280) | 0.297922 / 0.000490 (0.297433) | 0.000216 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021936 / 0.037411 (-0.015475) | 0.070181 / 0.014526 (0.055655) | 0.082068 / 0.176557 (-0.094488) | 0.119327 / 0.737135 (-0.617808) | 0.083642 / 0.296338 (-0.212697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299449 / 0.215209 (0.084240) | 2.914362 / 2.077655 (0.836707) | 1.611906 / 1.504120 (0.107786) | 1.488805 / 1.541195 (-0.052390) | 1.536010 / 1.468490 (0.067520) | 0.566772 / 4.584777 (-4.018004) | 2.397897 / 3.745712 (-1.347815) | 2.786048 / 5.269862 (-2.483814) | 1.745153 / 4.565676 (-2.820523) | 0.063870 / 0.424275 (-0.360405) | 0.004968 / 0.007607 (-0.002640) | 0.344455 / 0.226044 (0.118410) | 3.465772 / 2.268929 (1.196844) | 1.965761 / 55.444624 (-53.478863) | 1.687960 / 6.876477 (-5.188516) | 1.713987 / 2.142072 (-0.428085) | 0.643760 / 4.805227 (-4.161467) | 0.117623 / 6.500664 (-6.383042) | 0.041086 / 0.075469 (-0.034383) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985129 / 1.841788 (-0.856659) | 11.986676 / 8.074308 (3.912368) | 10.493440 / 10.191392 (0.302048) | 0.130070 / 0.680424 (-0.550353) | 0.015293 / 0.534201 (-0.518908) | 0.285683 / 0.579283 (-0.293600) | 0.275656 / 0.434364 (-0.158708) | 0.328704 / 0.540337 (-0.211633) | 0.537249 / 1.386936 (-0.849687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7ee58f322082d3af5f11863d1f809444910827a \"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) | 0.005170 / 0.011353 (-0.006183) | 0.003267 / 0.011008 (-0.007741) | 0.061992 / 0.038508 (0.023484) | 0.053414 / 0.023109 (0.030305) | 0.245678 / 0.275898 (-0.030220) | 0.261320 / 0.323480 (-0.062160) | 0.003887 / 0.007986 (-0.004099) | 0.002543 / 0.004328 (-0.001786) | 0.048496 / 0.004250 (0.044246) | 0.037392 / 0.037052 (0.000340) | 0.243728 / 0.258489 (-0.014761) | 0.272524 / 0.293841 (-0.021317) | 0.027578 / 0.128546 (-0.100968) | 0.010530 / 0.075646 (-0.065116) | 0.206014 / 0.419271 (-0.213257) | 0.035987 / 0.043533 (-0.007546) | 0.243544 / 0.255139 (-0.011595) | 0.263872 / 0.283200 (-0.019327) | 0.017867 / 0.141683 (-0.123816) | 1.105159 / 1.452155 (-0.346996) | 1.186640 / 1.492716 (-0.306076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092888 / 0.018006 (0.074882) | 0.302024 / 0.000490 (0.301534) | 0.000220 / 0.000200 (0.000020) | 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) | 0.019329 / 0.037411 (-0.018083) | 0.062135 / 0.014526 (0.047609) | 0.075125 / 0.176557 (-0.101431) | 0.120743 / 0.737135 (-0.616393) | 0.078687 / 0.296338 (-0.217652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279449 / 0.215209 (0.064240) | 2.727310 / 2.077655 (0.649656) | 1.442710 / 1.504120 (-0.061410) | 1.315271 / 1.541195 (-0.225923) | 1.360435 / 1.468490 (-0.108055) | 0.567720 / 4.584777 (-4.017057) | 2.397049 / 3.745712 (-1.348663) | 2.891180 / 5.269862 (-2.378682) | 1.774179 / 4.565676 (-2.791497) | 0.063155 / 0.424275 (-0.361120) | 0.004963 / 0.007607 (-0.002644) | 0.337526 / 0.226044 (0.111482) | 3.266016 / 2.268929 (0.997088) | 1.808819 / 55.444624 (-53.635806) | 1.525326 / 6.876477 (-5.351151) | 1.566937 / 2.142072 (-0.575135) | 0.654226 / 4.805227 (-4.151001) | 0.118968 / 6.500664 (-6.381696) | 0.042666 / 0.075469 (-0.032803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940792 / 1.841788 (-0.900996) | 11.736380 / 8.074308 (3.662072) | 10.709538 / 10.191392 (0.518146) | 0.141390 / 0.680424 (-0.539034) | 0.014204 / 0.534201 (-0.519996) | 0.284842 / 0.579283 (-0.294441) | 0.266315 / 0.434364 (-0.168049) | 0.331619 / 0.540337 (-0.208718) | 0.416446 / 1.386936 (-0.970491) |\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) | 0.005298 / 0.011353 (-0.006055) | 0.003507 / 0.011008 (-0.007501) | 0.048315 / 0.038508 (0.009807) | 0.054855 / 0.023109 (0.031746) | 0.271558 / 0.275898 (-0.004340) | 0.316851 / 0.323480 (-0.006628) | 0.004054 / 0.007986 (-0.003932) | 0.002433 / 0.004328 (-0.001896) | 0.046442 / 0.004250 (0.042191) | 0.040853 / 0.037052 (0.003801) | 0.272537 / 0.258489 (0.014048) | 0.293736 / 0.293841 (-0.000105) | 0.029112 / 0.128546 (-0.099434) | 0.010573 / 0.075646 (-0.065074) | 0.056501 / 0.419271 (-0.362771) | 0.032541 / 0.043533 (-0.010992) | 0.271004 / 0.255139 (0.015865) | 0.289276 / 0.283200 (0.006076) | 0.018618 / 0.141683 (-0.123065) | 1.149435 / 1.452155 (-0.302719) | 1.205113 / 1.492716 (-0.287604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094726 / 0.018006 (0.076720) | 0.304347 / 0.000490 (0.303857) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021374 / 0.037411 (-0.016037) | 0.070574 / 0.014526 (0.056049) | 0.081749 / 0.176557 (-0.094807) | 0.119829 / 0.737135 (-0.617306) | 0.082602 / 0.296338 (-0.213737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293378 / 0.215209 (0.078169) | 2.893607 / 2.077655 (0.815952) | 1.577734 / 1.504120 (0.073614) | 1.453670 / 1.541195 (-0.087525) | 1.467354 / 1.468490 (-0.001136) | 0.563415 / 4.584777 (-4.021362) | 2.438330 / 3.745712 (-1.307382) | 2.761822 / 5.269862 (-2.508040) | 1.730944 / 4.565676 (-2.834732) | 0.062251 / 0.424275 (-0.362024) | 0.004969 / 0.007607 (-0.002638) | 0.371238 / 0.226044 (0.145194) | 3.399831 / 2.268929 (1.130903) | 1.936156 / 55.444624 (-53.508469) | 1.649716 / 6.876477 (-5.226761) | 1.669107 / 2.142072 (-0.472965) | 0.633696 / 4.805227 (-4.171531) | 0.115857 / 6.500664 (-6.384807) | 0.041012 / 0.075469 (-0.034457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964777 / 1.841788 (-0.877010) | 12.037613 / 8.074308 (3.963305) | 10.579241 / 10.191392 (0.387849) | 0.130932 / 0.680424 (-0.549492) | 0.015621 / 0.534201 (-0.518580) | 0.286898 / 0.579283 (-0.292385) | 0.281139 / 0.434364 (-0.153225) | 0.325240 / 0.540337 (-0.215097) | 0.554302 / 1.386936 (-0.832635) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48d2378944a47987f96562ee856167aef1e78522 \"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) | 0.005258 / 0.011353 (-0.006095) | 0.003863 / 0.011008 (-0.007145) | 0.064585 / 0.038508 (0.026077) | 0.058013 / 0.023109 (0.034904) | 0.249042 / 0.275898 (-0.026856) | 0.273434 / 0.323480 (-0.050046) | 0.004779 / 0.007986 (-0.003207) | 0.002550 / 0.004328 (-0.001778) | 0.048290 / 0.004250 (0.044040) | 0.038777 / 0.037052 (0.001725) | 0.253039 / 0.258489 (-0.005450) | 0.285365 / 0.293841 (-0.008476) | 0.028053 / 0.128546 (-0.100494) | 0.010521 / 0.075646 (-0.065125) | 0.210954 / 0.419271 (-0.208317) | 0.035720 / 0.043533 (-0.007813) | 0.252540 / 0.255139 (-0.002599) | 0.264786 / 0.283200 (-0.018414) | 0.018692 / 0.141683 (-0.122990) | 1.108971 / 1.452155 (-0.343183) | 1.201004 / 1.492716 (-0.291712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095936 / 0.018006 (0.077930) | 0.302979 / 0.000490 (0.302489) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018859 / 0.037411 (-0.018552) | 0.062559 / 0.014526 (0.048034) | 0.073545 / 0.176557 (-0.103012) | 0.120780 / 0.737135 (-0.616355) | 0.074998 / 0.296338 (-0.221340) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276728 / 0.215209 (0.061519) | 2.715310 / 2.077655 (0.637655) | 1.444927 / 1.504120 (-0.059193) | 1.323867 / 1.541195 (-0.217328) | 1.364962 / 1.468490 (-0.103528) | 0.556792 / 4.584777 (-4.027985) | 2.409151 / 3.745712 (-1.336561) | 2.811836 / 5.269862 (-2.458026) | 1.777369 / 4.565676 (-2.788308) | 0.061398 / 0.424275 (-0.362877) | 0.004924 / 0.007607 (-0.002683) | 0.341228 / 0.226044 (0.115183) | 3.369570 / 2.268929 (1.100641) | 1.858151 / 55.444624 (-53.586474) | 1.587352 / 6.876477 (-5.289125) | 1.625004 / 2.142072 (-0.517068) | 0.635317 / 4.805227 (-4.169910) | 0.117197 / 6.500664 (-6.383467) | 0.042672 / 0.075469 (-0.032797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940419 / 1.841788 (-0.901368) | 12.156882 / 8.074308 (4.082574) | 10.646780 / 10.191392 (0.455388) | 0.129279 / 0.680424 (-0.551144) | 0.013967 / 0.534201 (-0.520234) | 0.287956 / 0.579283 (-0.291327) | 0.265250 / 0.434364 (-0.169114) | 0.323357 / 0.540337 (-0.216980) | 0.412045 / 1.386936 (-0.974891) |\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) | 0.005264 / 0.011353 (-0.006089) | 0.003575 / 0.011008 (-0.007433) | 0.049249 / 0.038508 (0.010741) | 0.057069 / 0.023109 (0.033959) | 0.327547 / 0.275898 (0.051649) | 0.299027 / 0.323480 (-0.024453) | 0.004768 / 0.007986 (-0.003217) | 0.002522 / 0.004328 (-0.001807) | 0.048020 / 0.004250 (0.043770) | 0.041328 / 0.037052 (0.004275) | 0.281385 / 0.258489 (0.022895) | 0.304957 / 0.293841 (0.011116) | 0.031371 / 0.128546 (-0.097175) | 0.010523 / 0.075646 (-0.065124) | 0.057073 / 0.419271 (-0.362198) | 0.032913 / 0.043533 (-0.010620) | 0.284963 / 0.255139 (0.029824) | 0.291997 / 0.283200 (0.008798) | 0.018325 / 0.141683 (-0.123357) | 1.126681 / 1.452155 (-0.325473) | 1.183011 / 1.492716 (-0.309705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092544 / 0.018006 (0.074538) | 0.299841 / 0.000490 (0.299351) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022279 / 0.037411 (-0.015133) | 0.072515 / 0.014526 (0.057989) | 0.083068 / 0.176557 (-0.093488) | 0.120600 / 0.737135 (-0.616536) | 0.083574 / 0.296338 (-0.212765) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293393 / 0.215209 (0.078184) | 2.865420 / 2.077655 (0.787765) | 1.562419 / 1.504120 (0.058299) | 1.440846 / 1.541195 (-0.100349) | 1.471993 / 1.468490 (0.003503) | 0.572510 / 4.584777 (-4.012267) | 2.427417 / 3.745712 (-1.318295) | 2.895347 / 5.269862 (-2.374515) | 1.790578 / 4.565676 (-2.775098) | 0.064489 / 0.424275 (-0.359786) | 0.005044 / 0.007607 (-0.002564) | 0.340774 / 0.226044 (0.114730) | 3.391414 / 2.268929 (1.122486) | 1.939980 / 55.444624 (-53.504644) | 1.658514 / 6.876477 (-5.217963) | 1.741406 / 2.142072 (-0.400667) | 0.649033 / 4.805227 (-4.156194) | 0.117587 / 6.500664 (-6.383077) | 0.042042 / 0.075469 (-0.033427) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980490 / 1.841788 (-0.861298) | 12.664045 / 8.074308 (4.589737) | 10.944437 / 10.191392 (0.753045) | 0.142059 / 0.680424 (-0.538365) | 0.015914 / 0.534201 (-0.518287) | 0.288826 / 0.579283 (-0.290457) | 0.282351 / 0.434364 (-0.152013) | 0.325302 / 0.540337 (-0.215035) | 0.416900 / 1.386936 (-0.970036) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"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) | 0.005591 / 0.011353 (-0.005762) | 0.003445 / 0.011008 (-0.007563) | 0.064290 / 0.038508 (0.025782) | 0.053046 / 0.023109 (0.029936) | 0.229101 / 0.275898 (-0.046797) | 0.255515 / 0.323480 (-0.067964) | 0.002912 / 0.007986 (-0.005073) | 0.002466 / 0.004328 (-0.001863) | 0.049348 / 0.004250 (0.045098) | 0.039492 / 0.037052 (0.002440) | 0.236301 / 0.258489 (-0.022188) | 0.270109 / 0.293841 (-0.023732) | 0.027506 / 0.128546 (-0.101040) | 0.010381 / 0.075646 (-0.065265) | 0.209999 / 0.419271 (-0.209273) | 0.035827 / 0.043533 (-0.007705) | 0.237231 / 0.255139 (-0.017908) | 0.254345 / 0.283200 (-0.028854) | 0.019689 / 0.141683 (-0.121994) | 1.096103 / 1.452155 (-0.356052) | 1.172393 / 1.492716 (-0.320323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101749 / 0.018006 (0.083743) | 0.310913 / 0.000490 (0.310424) | 0.000217 / 0.000200 (0.000017) | 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) | 0.018743 / 0.037411 (-0.018669) | 0.064190 / 0.014526 (0.049664) | 0.074575 / 0.176557 (-0.101982) | 0.124143 / 0.737135 (-0.612993) | 0.077415 / 0.296338 (-0.218924) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286175 / 0.215209 (0.070965) | 2.781169 / 2.077655 (0.703515) | 1.495130 / 1.504120 (-0.008990) | 1.379136 / 1.541195 (-0.162059) | 1.397548 / 1.468490 (-0.070942) | 0.564467 / 4.584777 (-4.020310) | 2.408896 / 3.745712 (-1.336816) | 2.857771 / 5.269862 (-2.412091) | 1.776531 / 4.565676 (-2.789145) | 0.062700 / 0.424275 (-0.361575) | 0.004965 / 0.007607 (-0.002642) | 0.344026 / 0.226044 (0.117982) | 3.390829 / 2.268929 (1.121900) | 1.875258 / 55.444624 (-53.569366) | 1.602435 / 6.876477 (-5.274042) | 1.613619 / 2.142072 (-0.528454) | 0.639421 / 4.805227 (-4.165806) | 0.117697 / 6.500664 (-6.382967) | 0.042878 / 0.075469 (-0.032591) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957694 / 1.841788 (-0.884094) | 11.888917 / 8.074308 (3.814609) | 10.643389 / 10.191392 (0.451997) | 0.143358 / 0.680424 (-0.537066) | 0.014382 / 0.534201 (-0.519819) | 0.288731 / 0.579283 (-0.290552) | 0.270040 / 0.434364 (-0.164324) | 0.323586 / 0.540337 (-0.216751) | 0.415743 / 1.386936 (-0.971193) |\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) | 0.005228 / 0.011353 (-0.006125) | 0.003445 / 0.011008 (-0.007563) | 0.051072 / 0.038508 (0.012563) | 0.053087 / 0.023109 (0.029978) | 0.273116 / 0.275898 (-0.002782) | 0.298633 / 0.323480 (-0.024847) | 0.004067 / 0.007986 (-0.003919) | 0.002537 / 0.004328 (-0.001791) | 0.049326 / 0.004250 (0.045075) | 0.041011 / 0.037052 (0.003959) | 0.277748 / 0.258489 (0.019258) | 0.304152 / 0.293841 (0.010311) | 0.029012 / 0.128546 (-0.099534) | 0.010589 / 0.075646 (-0.065057) | 0.057564 / 0.419271 (-0.361707) | 0.032785 / 0.043533 (-0.010747) | 0.272508 / 0.255139 (0.017369) | 0.294127 / 0.283200 (0.010927) | 0.018466 / 0.141683 (-0.123217) | 1.129341 / 1.452155 (-0.322814) | 1.194631 / 1.492716 (-0.298086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098558 / 0.018006 (0.080552) | 0.312353 / 0.000490 (0.311863) | 0.000269 / 0.000200 (0.000069) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022148 / 0.037411 (-0.015263) | 0.070601 / 0.014526 (0.056075) | 0.081780 / 0.176557 (-0.094777) | 0.121993 / 0.737135 (-0.615142) | 0.084263 / 0.296338 (-0.212076) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300501 / 0.215209 (0.085292) | 2.927534 / 2.077655 (0.849879) | 1.595527 / 1.504120 (0.091407) | 1.475607 / 1.541195 (-0.065587) | 1.496707 / 1.468490 (0.028217) | 0.559051 / 4.584777 (-4.025726) | 2.427126 / 3.745712 (-1.318586) | 2.820908 / 5.269862 (-2.448953) | 1.757492 / 4.565676 (-2.808185) | 0.062391 / 0.424275 (-0.361884) | 0.004950 / 0.007607 (-0.002657) | 0.351204 / 0.226044 (0.125160) | 3.485068 / 2.268929 (1.216139) | 1.976418 / 55.444624 (-53.468207) | 1.682715 / 6.876477 (-5.193761) | 1.703457 / 2.142072 (-0.438616) | 0.643476 / 4.805227 (-4.161751) | 0.116321 / 6.500664 (-6.384343) | 0.040776 / 0.075469 (-0.034694) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974152 / 1.841788 (-0.867635) | 12.390170 / 8.074308 (4.315862) | 10.866283 / 10.191392 (0.674891) | 0.145049 / 0.680424 (-0.535375) | 0.016404 / 0.534201 (-0.517797) | 0.288799 / 0.579283 (-0.290484) | 0.285917 / 0.434364 (-0.148447) | 0.328455 / 0.540337 (-0.211883) | 0.417286 / 1.386936 (-0.969650) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n" ]
2023-11-23T17:35:02
2023-11-27T10:02:56
2023-11-24 17:13:02+00:00
COLLABORATOR
nan
Refetch metadata files in case they were dropped by `filter_extensions` in the previous step. Fix #6442
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6448
Use parquet export if possible
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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) | 0.005177 / 0.011353 (-0.006176) | 0.003002 / 0.011008 (-0.008006) | 0.061915 / 0.038508 (0.023407) | 0.052065 / 0.023109 (0.028956) | 0.246114 / 0.275898 (-0.029784) | 0.273974 / 0.323480 (-0.049506) | 0.002983 / 0.007986 (-0.005003) | 0.002444 / 0.004328 (-0.001885) | 0.048424 / 0.004250 (0.044174) | 0.039609 / 0.037052 (0.002557) | 0.257771 / 0.258489 (-0.000718) | 0.286228 / 0.293841 (-0.007613) | 0.023925 / 0.128546 (-0.104621) | 0.007248 / 0.075646 (-0.068398) | 0.202205 / 0.419271 (-0.217067) | 0.037124 / 0.043533 (-0.006409) | 0.254872 / 0.255139 (-0.000267) | 0.275252 / 0.283200 (-0.007947) | 0.019251 / 0.141683 (-0.122432) | 1.074921 / 1.452155 (-0.377234) | 1.146515 / 1.492716 (-0.346202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091998 / 0.018006 (0.073992) | 0.299146 / 0.000490 (0.298656) | 0.000240 / 0.000200 (0.000040) | 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) | 0.019266 / 0.037411 (-0.018145) | 0.062560 / 0.014526 (0.048034) | 0.075012 / 0.176557 (-0.101544) | 0.120077 / 0.737135 (-0.617058) | 0.077851 / 0.296338 (-0.218488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290629 / 0.215209 (0.075420) | 2.823847 / 2.077655 (0.746192) | 1.516966 / 1.504120 (0.012846) | 1.393383 / 1.541195 (-0.147812) | 1.427688 / 1.468490 (-0.040802) | 0.407456 / 4.584777 (-4.177321) | 2.378280 / 3.745712 (-1.367433) | 2.689800 / 5.269862 (-2.580061) | 1.588037 / 4.565676 (-2.977640) | 0.045837 / 0.424275 (-0.378438) | 0.004884 / 0.007607 (-0.002724) | 0.340464 / 0.226044 (0.114420) | 3.377158 / 2.268929 (1.108230) | 1.897854 / 55.444624 (-53.546771) | 1.588285 / 6.876477 (-5.288191) | 1.651708 / 2.142072 (-0.490364) | 0.482018 / 4.805227 (-4.323209) | 0.101583 / 6.500664 (-6.399081) | 0.042306 / 0.075469 (-0.033163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948659 / 1.841788 (-0.893128) | 11.809778 / 8.074308 (3.735470) | 10.481896 / 10.191392 (0.290504) | 0.143538 / 0.680424 (-0.536885) | 0.014105 / 0.534201 (-0.520096) | 0.272278 / 0.579283 (-0.307005) | 0.264241 / 0.434364 (-0.170123) | 0.307187 / 0.540337 (-0.233150) | 0.401270 / 1.386936 (-0.985666) |\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) | 0.004831 / 0.011353 (-0.006521) | 0.002896 / 0.011008 (-0.008112) | 0.047479 / 0.038508 (0.008971) | 0.050665 / 0.023109 (0.027555) | 0.275243 / 0.275898 (-0.000655) | 0.296547 / 0.323480 (-0.026933) | 0.004022 / 0.007986 (-0.003963) | 0.002425 / 0.004328 (-0.001904) | 0.047086 / 0.004250 (0.042836) | 0.039611 / 0.037052 (0.002558) | 0.275272 / 0.258489 (0.016783) | 0.302429 / 0.293841 (0.008588) | 0.024308 / 0.128546 (-0.104238) | 0.007167 / 0.075646 (-0.068479) | 0.052825 / 0.419271 (-0.366446) | 0.032319 / 0.043533 (-0.011213) | 0.273334 / 0.255139 (0.018195) | 0.291161 / 0.283200 (0.007961) | 0.017918 / 0.141683 (-0.123764) | 1.110005 / 1.452155 (-0.342150) | 1.176616 / 1.492716 (-0.316100) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092478 / 0.018006 (0.074471) | 0.311431 / 0.000490 (0.310942) | 0.000237 / 0.000200 (0.000037) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021979 / 0.037411 (-0.015432) | 0.080617 / 0.014526 (0.066091) | 0.081534 / 0.176557 (-0.095023) | 0.121073 / 0.737135 (-0.616062) | 0.083235 / 0.296338 (-0.213104) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289527 / 0.215209 (0.074318) | 2.839668 / 2.077655 (0.762013) | 1.601737 / 1.504120 (0.097617) | 1.496028 / 1.541195 (-0.045167) | 1.511933 / 1.468490 (0.043443) | 0.399819 / 4.584777 (-4.184958) | 2.394147 / 3.745712 (-1.351565) | 2.520767 / 5.269862 (-2.749095) | 1.589496 / 4.565676 (-2.976180) | 0.046673 / 0.424275 (-0.377602) | 0.004858 / 0.007607 (-0.002749) | 0.357986 / 0.226044 (0.131941) | 3.376217 / 2.268929 (1.107289) | 1.981853 / 55.444624 (-53.462771) | 1.682240 / 6.876477 (-5.194236) | 1.830643 / 2.142072 (-0.311429) | 0.478286 / 4.805227 (-4.326941) | 0.099589 / 6.500664 (-6.401075) | 0.041173 / 0.075469 (-0.034296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985160 / 1.841788 (-0.856628) | 12.312963 / 8.074308 (4.238655) | 10.577225 / 10.191392 (0.385833) | 0.130167 / 0.680424 (-0.550257) | 0.016657 / 0.534201 (-0.517544) | 0.271330 / 0.579283 (-0.307953) | 0.276979 / 0.434364 (-0.157385) | 0.304904 / 0.540337 (-0.235434) | 0.412090 / 1.386936 (-0.974846) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1adc80151e892122ecb60f4e0b4572b136b2dd47 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6448). All of your documentation changes will be reflected on that endpoint.", "hooray! very excited about this", "<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) | 0.005039 / 0.011353 (-0.006314) | 0.003577 / 0.011008 (-0.007431) | 0.062892 / 0.038508 (0.024384) | 0.056334 / 0.023109 (0.033225) | 0.252281 / 0.275898 (-0.023617) | 0.274945 / 0.323480 (-0.048535) | 0.003906 / 0.007986 (-0.004080) | 0.002483 / 0.004328 (-0.001845) | 0.049006 / 0.004250 (0.044756) | 0.038375 / 0.037052 (0.001323) | 0.257376 / 0.258489 (-0.001113) | 0.292512 / 0.293841 (-0.001328) | 0.027134 / 0.128546 (-0.101412) | 0.010579 / 0.075646 (-0.065068) | 0.212021 / 0.419271 (-0.207250) | 0.035851 / 0.043533 (-0.007682) | 0.258076 / 0.255139 (0.002937) | 0.271758 / 0.283200 (-0.011442) | 0.018222 / 0.141683 (-0.123461) | 1.120481 / 1.452155 (-0.331674) | 1.187007 / 1.492716 (-0.305710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094986 / 0.018006 (0.076980) | 0.302121 / 0.000490 (0.301631) | 0.000211 / 0.000200 (0.000011) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019260 / 0.037411 (-0.018152) | 0.062909 / 0.014526 (0.048383) | 0.075644 / 0.176557 (-0.100912) | 0.120966 / 0.737135 (-0.616170) | 0.076678 / 0.296338 (-0.219661) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286754 / 0.215209 (0.071545) | 2.797467 / 2.077655 (0.719812) | 1.436798 / 1.504120 (-0.067322) | 1.315032 / 1.541195 (-0.226163) | 1.367841 / 1.468490 (-0.100649) | 0.578917 / 4.584777 (-4.005860) | 2.439773 / 3.745712 (-1.305939) | 2.932779 / 5.269862 (-2.337082) | 1.843895 / 4.565676 (-2.721782) | 0.063351 / 0.424275 (-0.360925) | 0.004998 / 0.007607 (-0.002610) | 0.347385 / 0.226044 (0.121340) | 3.449969 / 2.268929 (1.181040) | 1.857734 / 55.444624 (-53.586890) | 1.541341 / 6.876477 (-5.335136) | 1.574915 / 2.142072 (-0.567158) | 0.660178 / 4.805227 (-4.145049) | 0.117686 / 6.500664 (-6.382978) | 0.042602 / 0.075469 (-0.032867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937735 / 1.841788 (-0.904052) | 11.962091 / 8.074308 (3.887783) | 10.401715 / 10.191392 (0.210323) | 0.142200 / 0.680424 (-0.538224) | 0.014137 / 0.534201 (-0.520064) | 0.289853 / 0.579283 (-0.289430) | 0.267100 / 0.434364 (-0.167264) | 0.323401 / 0.540337 (-0.216936) | 0.418665 / 1.386936 (-0.968271) |\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) | 0.005480 / 0.011353 (-0.005873) | 0.003401 / 0.011008 (-0.007607) | 0.049304 / 0.038508 (0.010796) | 0.062043 / 0.023109 (0.038934) | 0.270571 / 0.275898 (-0.005327) | 0.295226 / 0.323480 (-0.028254) | 0.004152 / 0.007986 (-0.003834) | 0.002511 / 0.004328 (-0.001817) | 0.048480 / 0.004250 (0.044229) | 0.043964 / 0.037052 (0.006912) | 0.273545 / 0.258489 (0.015056) | 0.295152 / 0.293841 (0.001311) | 0.029224 / 0.128546 (-0.099322) | 0.010629 / 0.075646 (-0.065018) | 0.057433 / 0.419271 (-0.361839) | 0.033115 / 0.043533 (-0.010418) | 0.269893 / 0.255139 (0.014754) | 0.288658 / 0.283200 (0.005459) | 0.018216 / 0.141683 (-0.123467) | 1.123039 / 1.452155 (-0.329116) | 1.182892 / 1.492716 (-0.309825) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095948 / 0.018006 (0.077942) | 0.305811 / 0.000490 (0.305321) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022996 / 0.037411 (-0.014415) | 0.073836 / 0.014526 (0.059310) | 0.082658 / 0.176557 (-0.093899) | 0.121970 / 0.737135 (-0.615166) | 0.086096 / 0.296338 (-0.210242) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291032 / 0.215209 (0.075823) | 2.864613 / 2.077655 (0.786958) | 1.567530 / 1.504120 (0.063410) | 1.460291 / 1.541195 (-0.080903) | 1.527066 / 1.468490 (0.058576) | 0.571160 / 4.584777 (-4.013617) | 2.465261 / 3.745712 (-1.280451) | 2.915547 / 5.269862 (-2.354314) | 1.835822 / 4.565676 (-2.729855) | 0.064328 / 0.424275 (-0.359947) | 0.005061 / 0.007607 (-0.002546) | 0.357105 / 0.226044 (0.131061) | 3.491363 / 2.268929 (1.222435) | 1.943213 / 55.444624 (-53.501412) | 1.675778 / 6.876477 (-5.200699) | 1.719016 / 2.142072 (-0.423057) | 0.658993 / 4.805227 (-4.146235) | 0.122320 / 6.500664 (-6.378344) | 0.049030 / 0.075469 (-0.026439) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964762 / 1.841788 (-0.877025) | 12.367251 / 8.074308 (4.292943) | 10.886213 / 10.191392 (0.694821) | 0.141533 / 0.680424 (-0.538891) | 0.015646 / 0.534201 (-0.518555) | 0.288583 / 0.579283 (-0.290700) | 0.280353 / 0.434364 (-0.154010) | 0.329095 / 0.540337 (-0.211242) | 0.565118 / 1.386936 (-0.821818) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#493bf695dc3ee6cc81bfd0aae6a38f70547bb752 \"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) | 0.006475 / 0.011353 (-0.004878) | 0.004080 / 0.011008 (-0.006928) | 0.066479 / 0.038508 (0.027971) | 0.073270 / 0.023109 (0.050161) | 0.244412 / 0.275898 (-0.031486) | 0.273778 / 0.323480 (-0.049702) | 0.003186 / 0.007986 (-0.004800) | 0.003419 / 0.004328 (-0.000910) | 0.049743 / 0.004250 (0.045492) | 0.043581 / 0.037052 (0.006529) | 0.248215 / 0.258489 (-0.010274) | 0.280873 / 0.293841 (-0.012967) | 0.029282 / 0.128546 (-0.099264) | 0.011241 / 0.075646 (-0.064405) | 0.215031 / 0.419271 (-0.204241) | 0.038764 / 0.043533 (-0.004769) | 0.259363 / 0.255139 (0.004224) | 0.279253 / 0.283200 (-0.003946) | 0.019524 / 0.141683 (-0.122159) | 1.104735 / 1.452155 (-0.347420) | 1.159823 / 1.492716 (-0.332894) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108383 / 0.018006 (0.090377) | 0.332904 / 0.000490 (0.332415) | 0.000222 / 0.000200 (0.000022) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020693 / 0.037411 (-0.016719) | 0.071764 / 0.014526 (0.057238) | 0.077073 / 0.176557 (-0.099484) | 0.124604 / 0.737135 (-0.612532) | 0.078057 / 0.296338 (-0.218282) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291014 / 0.215209 (0.075805) | 2.865885 / 2.077655 (0.788231) | 1.506141 / 1.504120 (0.002021) | 1.435924 / 1.541195 (-0.105271) | 1.461994 / 1.468490 (-0.006497) | 0.571779 / 4.584777 (-4.012998) | 2.461950 / 3.745712 (-1.283762) | 3.079771 / 5.269862 (-2.190091) | 1.933337 / 4.565676 (-2.632339) | 0.063405 / 0.424275 (-0.360870) | 0.005203 / 0.007607 (-0.002404) | 0.345077 / 0.226044 (0.119032) | 3.487189 / 2.268929 (1.218261) | 1.903733 / 55.444624 (-53.540891) | 1.705596 / 6.876477 (-5.170880) | 1.718849 / 2.142072 (-0.423223) | 0.658745 / 4.805227 (-4.146482) | 0.120847 / 6.500664 (-6.379817) | 0.045670 / 0.075469 (-0.029799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965969 / 1.841788 (-0.875819) | 13.520489 / 8.074308 (5.446181) | 12.322363 / 10.191392 (2.130971) | 0.146605 / 0.680424 (-0.533819) | 0.015061 / 0.534201 (-0.519140) | 0.298125 / 0.579283 (-0.281159) | 0.276864 / 0.434364 (-0.157500) | 0.326787 / 0.540337 (-0.213550) | 0.436897 / 1.386936 (-0.950039) |\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) | 0.005862 / 0.011353 (-0.005491) | 0.003716 / 0.011008 (-0.007292) | 0.052849 / 0.038508 (0.014341) | 0.072114 / 0.023109 (0.049005) | 0.277800 / 0.275898 (0.001902) | 0.325321 / 0.323480 (0.001841) | 0.004428 / 0.007986 (-0.003557) | 0.002527 / 0.004328 (-0.001801) | 0.048847 / 0.004250 (0.044596) | 0.047355 / 0.037052 (0.010303) | 0.279331 / 0.258489 (0.020842) | 0.310477 / 0.293841 (0.016636) | 0.029661 / 0.128546 (-0.098886) | 0.010812 / 0.075646 (-0.064834) | 0.059803 / 0.419271 (-0.359469) | 0.033554 / 0.043533 (-0.009978) | 0.276890 / 0.255139 (0.021751) | 0.308911 / 0.283200 (0.025712) | 0.020752 / 0.141683 (-0.120931) | 1.120896 / 1.452155 (-0.331259) | 1.186428 / 1.492716 (-0.306288) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106551 / 0.018006 (0.088545) | 0.354455 / 0.000490 (0.353966) | 0.000353 / 0.000200 (0.000153) | 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) | 0.023488 / 0.037411 (-0.013923) | 0.080548 / 0.014526 (0.066022) | 0.084431 / 0.176557 (-0.092126) | 0.140698 / 0.737135 (-0.596438) | 0.085692 / 0.296338 (-0.210647) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.314253 / 0.215209 (0.099044) | 2.993236 / 2.077655 (0.915582) | 1.639013 / 1.504120 (0.134893) | 1.543966 / 1.541195 (0.002771) | 1.567732 / 1.468490 (0.099242) | 0.565857 / 4.584777 (-4.018920) | 2.545339 / 3.745712 (-1.200373) | 3.134546 / 5.269862 (-2.135316) | 1.940350 / 4.565676 (-2.625326) | 0.063847 / 0.424275 (-0.360429) | 0.005079 / 0.007607 (-0.002528) | 0.365762 / 0.226044 (0.139718) | 3.610921 / 2.268929 (1.341993) | 2.035151 / 55.444624 (-53.409473) | 1.773409 / 6.876477 (-5.103068) | 1.790332 / 2.142072 (-0.351741) | 0.683019 / 4.805227 (-4.122209) | 0.119566 / 6.500664 (-6.381099) | 0.043578 / 0.075469 (-0.031891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996568 / 1.841788 (-0.845219) | 14.094366 / 8.074308 (6.020058) | 12.433600 / 10.191392 (2.242208) | 0.139835 / 0.680424 (-0.540589) | 0.016454 / 0.534201 (-0.517747) | 0.294073 / 0.579283 (-0.285210) | 0.309032 / 0.434364 (-0.125332) | 0.330699 / 0.540337 (-0.209638) | 0.619392 / 1.386936 (-0.767544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#026fbce1c93a30188b6d0646bb975da8f56e2a2f \"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) | 0.005389 / 0.011353 (-0.005964) | 0.003209 / 0.011008 (-0.007799) | 0.061610 / 0.038508 (0.023102) | 0.049781 / 0.023109 (0.026672) | 0.240208 / 0.275898 (-0.035690) | 0.263307 / 0.323480 (-0.060173) | 0.002908 / 0.007986 (-0.005078) | 0.002375 / 0.004328 (-0.001953) | 0.047462 / 0.004250 (0.043212) | 0.038643 / 0.037052 (0.001591) | 0.246287 / 0.258489 (-0.012202) | 0.278715 / 0.293841 (-0.015126) | 0.027507 / 0.128546 (-0.101039) | 0.010168 / 0.075646 (-0.065479) | 0.204131 / 0.419271 (-0.215140) | 0.035452 / 0.043533 (-0.008081) | 0.251721 / 0.255139 (-0.003418) | 0.266642 / 0.283200 (-0.016558) | 0.017741 / 0.141683 (-0.123942) | 1.094672 / 1.452155 (-0.357482) | 1.162715 / 1.492716 (-0.330002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092154 / 0.018006 (0.074148) | 0.301376 / 0.000490 (0.300886) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018534 / 0.037411 (-0.018877) | 0.061995 / 0.014526 (0.047469) | 0.072654 / 0.176557 (-0.103903) | 0.119501 / 0.737135 (-0.617635) | 0.073756 / 0.296338 (-0.222583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280066 / 0.215209 (0.064857) | 2.744207 / 2.077655 (0.666553) | 1.483367 / 1.504120 (-0.020753) | 1.386173 / 1.541195 (-0.155022) | 1.381833 / 1.468490 (-0.086657) | 0.552780 / 4.584777 (-4.031997) | 2.395541 / 3.745712 (-1.350171) | 2.747507 / 5.269862 (-2.522355) | 1.735074 / 4.565676 (-2.830602) | 0.062096 / 0.424275 (-0.362179) | 0.004905 / 0.007607 (-0.002702) | 0.338327 / 0.226044 (0.112283) | 3.365391 / 2.268929 (1.096462) | 1.839663 / 55.444624 (-53.604961) | 1.577535 / 6.876477 (-5.298942) | 1.558054 / 2.142072 (-0.584018) | 0.636520 / 4.805227 (-4.168708) | 0.116182 / 6.500664 (-6.384482) | 0.042078 / 0.075469 (-0.033391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938512 / 1.841788 (-0.903276) | 11.455749 / 8.074308 (3.381441) | 10.510985 / 10.191392 (0.319593) | 0.140865 / 0.680424 (-0.539559) | 0.014073 / 0.534201 (-0.520128) | 0.294747 / 0.579283 (-0.284536) | 0.266147 / 0.434364 (-0.168217) | 0.325354 / 0.540337 (-0.214984) | 0.422182 / 1.386936 (-0.964754) |\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) | 0.005231 / 0.011353 (-0.006122) | 0.003032 / 0.011008 (-0.007977) | 0.049608 / 0.038508 (0.011099) | 0.051441 / 0.023109 (0.028332) | 0.273812 / 0.275898 (-0.002086) | 0.294318 / 0.323480 (-0.029162) | 0.003958 / 0.007986 (-0.004028) | 0.002384 / 0.004328 (-0.001944) | 0.047942 / 0.004250 (0.043691) | 0.039179 / 0.037052 (0.002127) | 0.277504 / 0.258489 (0.019014) | 0.299713 / 0.293841 (0.005872) | 0.028989 / 0.128546 (-0.099557) | 0.010267 / 0.075646 (-0.065379) | 0.058318 / 0.419271 (-0.360954) | 0.032214 / 0.043533 (-0.011318) | 0.277964 / 0.255139 (0.022825) | 0.293055 / 0.283200 (0.009856) | 0.018532 / 0.141683 (-0.123151) | 1.128620 / 1.452155 (-0.323535) | 1.187365 / 1.492716 (-0.305351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092137 / 0.018006 (0.074130) | 0.299726 / 0.000490 (0.299236) | 0.000222 / 0.000200 (0.000022) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021342 / 0.037411 (-0.016070) | 0.069943 / 0.014526 (0.055417) | 0.079862 / 0.176557 (-0.096694) | 0.118917 / 0.737135 (-0.618218) | 0.081861 / 0.296338 (-0.214477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295883 / 0.215209 (0.080674) | 2.881640 / 2.077655 (0.803986) | 1.597705 / 1.504120 (0.093585) | 1.473220 / 1.541195 (-0.067975) | 1.501006 / 1.468490 (0.032516) | 0.559409 / 4.584777 (-4.025368) | 2.442709 / 3.745712 (-1.303003) | 2.742139 / 5.269862 (-2.527723) | 1.726002 / 4.565676 (-2.839674) | 0.062436 / 0.424275 (-0.361840) | 0.004896 / 0.007607 (-0.002711) | 0.349203 / 0.226044 (0.123159) | 3.435175 / 2.268929 (1.166247) | 1.954888 / 55.444624 (-53.489737) | 1.666233 / 6.876477 (-5.210243) | 1.680852 / 2.142072 (-0.461221) | 0.644271 / 4.805227 (-4.160956) | 0.115160 / 6.500664 (-6.385504) | 0.040681 / 0.075469 (-0.034788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963810 / 1.841788 (-0.877977) | 11.860860 / 8.074308 (3.786552) | 10.541703 / 10.191392 (0.350311) | 0.131532 / 0.680424 (-0.548892) | 0.016790 / 0.534201 (-0.517411) | 0.286695 / 0.579283 (-0.292588) | 0.279628 / 0.434364 (-0.154735) | 0.324622 / 0.540337 (-0.215715) | 0.535507 / 1.386936 (-0.851429) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11217347e4bcfe1aaf794d164a5dd9f085b2f682 \"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) | 0.005672 / 0.011353 (-0.005681) | 0.003411 / 0.011008 (-0.007597) | 0.062528 / 0.038508 (0.024020) | 0.055209 / 0.023109 (0.032100) | 0.248366 / 0.275898 (-0.027532) | 0.279522 / 0.323480 (-0.043957) | 0.002907 / 0.007986 (-0.005079) | 0.002369 / 0.004328 (-0.001959) | 0.047982 / 0.004250 (0.043731) | 0.039009 / 0.037052 (0.001956) | 0.256422 / 0.258489 (-0.002067) | 0.288530 / 0.293841 (-0.005311) | 0.028164 / 0.128546 (-0.100382) | 0.010448 / 0.075646 (-0.065198) | 0.208863 / 0.419271 (-0.210408) | 0.036291 / 0.043533 (-0.007242) | 0.251642 / 0.255139 (-0.003497) | 0.275589 / 0.283200 (-0.007610) | 0.019839 / 0.141683 (-0.121844) | 1.092800 / 1.452155 (-0.359355) | 1.147950 / 1.492716 (-0.344766) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094920 / 0.018006 (0.076914) | 0.303049 / 0.000490 (0.302559) | 0.000199 / 0.000200 (-0.000001) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018820 / 0.037411 (-0.018591) | 0.063319 / 0.014526 (0.048793) | 0.073644 / 0.176557 (-0.102912) | 0.120045 / 0.737135 (-0.617091) | 0.076219 / 0.296338 (-0.220119) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283897 / 0.215209 (0.068688) | 2.822836 / 2.077655 (0.745182) | 1.490505 / 1.504120 (-0.013615) | 1.359777 / 1.541195 (-0.181418) | 1.420536 / 1.468490 (-0.047954) | 0.562308 / 4.584777 (-4.022469) | 2.419249 / 3.745712 (-1.326463) | 2.827620 / 5.269862 (-2.442241) | 1.783171 / 4.565676 (-2.782505) | 0.063206 / 0.424275 (-0.361069) | 0.004966 / 0.007607 (-0.002641) | 0.339647 / 0.226044 (0.113602) | 3.378157 / 2.268929 (1.109229) | 1.873221 / 55.444624 (-53.571403) | 1.606367 / 6.876477 (-5.270109) | 1.624976 / 2.142072 (-0.517096) | 0.652653 / 4.805227 (-4.152574) | 0.117997 / 6.500664 (-6.382667) | 0.041955 / 0.075469 (-0.033514) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961420 / 1.841788 (-0.880368) | 11.807624 / 8.074308 (3.733316) | 10.668249 / 10.191392 (0.476857) | 0.141855 / 0.680424 (-0.538569) | 0.014451 / 0.534201 (-0.519750) | 0.289706 / 0.579283 (-0.289577) | 0.268392 / 0.434364 (-0.165972) | 0.323435 / 0.540337 (-0.216903) | 0.420667 / 1.386936 (-0.966269) |\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) | 0.005382 / 0.011353 (-0.005971) | 0.003361 / 0.011008 (-0.007647) | 0.048420 / 0.038508 (0.009912) | 0.053702 / 0.023109 (0.030593) | 0.286976 / 0.275898 (0.011078) | 0.296708 / 0.323480 (-0.026772) | 0.004013 / 0.007986 (-0.003972) | 0.002444 / 0.004328 (-0.001884) | 0.047797 / 0.004250 (0.043547) | 0.042361 / 0.037052 (0.005309) | 0.277543 / 0.258489 (0.019054) | 0.300736 / 0.293841 (0.006896) | 0.029894 / 0.128546 (-0.098653) | 0.014119 / 0.075646 (-0.061527) | 0.057636 / 0.419271 (-0.361636) | 0.032533 / 0.043533 (-0.010999) | 0.280963 / 0.255139 (0.025824) | 0.291305 / 0.283200 (0.008106) | 0.018391 / 0.141683 (-0.123292) | 1.140042 / 1.452155 (-0.312113) | 1.179485 / 1.492716 (-0.313231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094668 / 0.018006 (0.076661) | 0.301677 / 0.000490 (0.301187) | 0.000245 / 0.000200 (0.000045) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021376 / 0.037411 (-0.016036) | 0.070628 / 0.014526 (0.056102) | 0.082249 / 0.176557 (-0.094308) | 0.120423 / 0.737135 (-0.616712) | 0.083792 / 0.296338 (-0.212546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298884 / 0.215209 (0.083675) | 2.931849 / 2.077655 (0.854194) | 1.591888 / 1.504120 (0.087768) | 1.455781 / 1.541195 (-0.085414) | 1.500312 / 1.468490 (0.031822) | 0.558466 / 4.584777 (-4.026311) | 2.450449 / 3.745712 (-1.295263) | 2.842768 / 5.269862 (-2.427094) | 1.755614 / 4.565676 (-2.810062) | 0.063200 / 0.424275 (-0.361075) | 0.005022 / 0.007607 (-0.002585) | 0.358282 / 0.226044 (0.132238) | 3.575392 / 2.268929 (1.306464) | 1.960258 / 55.444624 (-53.484366) | 1.675518 / 6.876477 (-5.200959) | 1.696630 / 2.142072 (-0.445442) | 0.647185 / 4.805227 (-4.158042) | 0.117038 / 6.500664 (-6.383626) | 0.041622 / 0.075469 (-0.033848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962503 / 1.841788 (-0.879285) | 12.194950 / 8.074308 (4.120642) | 10.662233 / 10.191392 (0.470841) | 0.131618 / 0.680424 (-0.548806) | 0.016000 / 0.534201 (-0.518201) | 0.291546 / 0.579283 (-0.287737) | 0.279537 / 0.434364 (-0.154827) | 0.328716 / 0.540337 (-0.211622) | 0.547565 / 1.386936 (-0.839371) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de8f5f09f60613d47b5d7eb901752321c7b6a49 \"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) | 0.005209 / 0.011353 (-0.006144) | 0.003017 / 0.011008 (-0.007991) | 0.062017 / 0.038508 (0.023509) | 0.048268 / 0.023109 (0.025158) | 0.246384 / 0.275898 (-0.029514) | 0.270441 / 0.323480 (-0.053039) | 0.002763 / 0.007986 (-0.005222) | 0.003140 / 0.004328 (-0.001188) | 0.048720 / 0.004250 (0.044470) | 0.038175 / 0.037052 (0.001123) | 0.254184 / 0.258489 (-0.004306) | 0.275515 / 0.293841 (-0.018326) | 0.027309 / 0.128546 (-0.101238) | 0.010507 / 0.075646 (-0.065140) | 0.210315 / 0.419271 (-0.208956) | 0.035203 / 0.043533 (-0.008329) | 0.253015 / 0.255139 (-0.002124) | 0.271465 / 0.283200 (-0.011734) | 0.019543 / 0.141683 (-0.122140) | 1.119242 / 1.452155 (-0.332913) | 1.149359 / 1.492716 (-0.343357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088935 / 0.018006 (0.070928) | 0.293922 / 0.000490 (0.293432) | 0.000202 / 0.000200 (0.000002) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018174 / 0.037411 (-0.019237) | 0.060215 / 0.014526 (0.045689) | 0.072868 / 0.176557 (-0.103689) | 0.117998 / 0.737135 (-0.619137) | 0.074159 / 0.296338 (-0.222179) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289229 / 0.215209 (0.074020) | 2.840414 / 2.077655 (0.762759) | 1.468357 / 1.504120 (-0.035763) | 1.347714 / 1.541195 (-0.193481) | 1.363704 / 1.468490 (-0.104786) | 0.572059 / 4.584777 (-4.012718) | 2.400631 / 3.745712 (-1.345081) | 2.755779 / 5.269862 (-2.514083) | 1.740937 / 4.565676 (-2.824739) | 0.063473 / 0.424275 (-0.360802) | 0.005012 / 0.007607 (-0.002595) | 0.336057 / 0.226044 (0.110012) | 3.382126 / 2.268929 (1.113197) | 1.807838 / 55.444624 (-53.636786) | 1.534594 / 6.876477 (-5.341883) | 1.529951 / 2.142072 (-0.612121) | 0.636661 / 4.805227 (-4.168566) | 0.117090 / 6.500664 (-6.383574) | 0.042310 / 0.075469 (-0.033160) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.924440 / 1.841788 (-0.917347) | 11.120517 / 8.074308 (3.046209) | 10.177210 / 10.191392 (-0.014182) | 0.139060 / 0.680424 (-0.541364) | 0.013818 / 0.534201 (-0.520383) | 0.285634 / 0.579283 (-0.293649) | 0.268657 / 0.434364 (-0.165706) | 0.325842 / 0.540337 (-0.214496) | 0.439902 / 1.386936 (-0.947034) |\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) | 0.005202 / 0.011353 (-0.006150) | 0.003002 / 0.011008 (-0.008006) | 0.048729 / 0.038508 (0.010221) | 0.048178 / 0.023109 (0.025069) | 0.288573 / 0.275898 (0.012675) | 0.311122 / 0.323480 (-0.012358) | 0.003953 / 0.007986 (-0.004033) | 0.002544 / 0.004328 (-0.001785) | 0.047762 / 0.004250 (0.043511) | 0.039711 / 0.037052 (0.002658) | 0.308389 / 0.258489 (0.049900) | 0.321913 / 0.293841 (0.028072) | 0.029166 / 0.128546 (-0.099380) | 0.010697 / 0.075646 (-0.064950) | 0.057758 / 0.419271 (-0.361514) | 0.032743 / 0.043533 (-0.010789) | 0.290933 / 0.255139 (0.035794) | 0.309404 / 0.283200 (0.026205) | 0.017691 / 0.141683 (-0.123992) | 1.157713 / 1.452155 (-0.294442) | 1.210485 / 1.492716 (-0.282231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088959 / 0.018006 (0.070953) | 0.298531 / 0.000490 (0.298041) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021129 / 0.037411 (-0.016283) | 0.068419 / 0.014526 (0.053893) | 0.079328 / 0.176557 (-0.097228) | 0.118603 / 0.737135 (-0.618532) | 0.080489 / 0.296338 (-0.215850) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292464 / 0.215209 (0.077254) | 2.898221 / 2.077655 (0.820566) | 1.600868 / 1.504120 (0.096748) | 1.485128 / 1.541195 (-0.056067) | 1.493091 / 1.468490 (0.024600) | 0.576117 / 4.584777 (-4.008660) | 2.450440 / 3.745712 (-1.295273) | 2.746026 / 5.269862 (-2.523836) | 1.722555 / 4.565676 (-2.843122) | 0.062869 / 0.424275 (-0.361406) | 0.004918 / 0.007607 (-0.002689) | 0.348470 / 0.226044 (0.122425) | 3.420267 / 2.268929 (1.151339) | 1.942973 / 55.444624 (-53.501651) | 1.667684 / 6.876477 (-5.208793) | 1.669618 / 2.142072 (-0.472454) | 0.630275 / 4.805227 (-4.174952) | 0.115072 / 6.500664 (-6.385592) | 0.040430 / 0.075469 (-0.035039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989827 / 1.841788 (-0.851961) | 11.578068 / 8.074308 (3.503760) | 10.636060 / 10.191392 (0.444668) | 0.131943 / 0.680424 (-0.548481) | 0.015915 / 0.534201 (-0.518286) | 0.287277 / 0.579283 (-0.292006) | 0.279451 / 0.434364 (-0.154913) | 0.325485 / 0.540337 (-0.214852) | 0.544635 / 1.386936 (-0.842301) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22579be6c73867ac1a3c03e925abaf4872f8437 \"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) | 0.005144 / 0.011353 (-0.006209) | 0.003686 / 0.011008 (-0.007322) | 0.064003 / 0.038508 (0.025495) | 0.058962 / 0.023109 (0.035853) | 0.233753 / 0.275898 (-0.042145) | 0.255802 / 0.323480 (-0.067677) | 0.003871 / 0.007986 (-0.004115) | 0.002609 / 0.004328 (-0.001719) | 0.048675 / 0.004250 (0.044425) | 0.037550 / 0.037052 (0.000498) | 0.240658 / 0.258489 (-0.017831) | 0.272303 / 0.293841 (-0.021538) | 0.027455 / 0.128546 (-0.101091) | 0.010706 / 0.075646 (-0.064941) | 0.210878 / 0.419271 (-0.208393) | 0.035763 / 0.043533 (-0.007770) | 0.239937 / 0.255139 (-0.015202) | 0.262520 / 0.283200 (-0.020680) | 0.017676 / 0.141683 (-0.124006) | 1.095036 / 1.452155 (-0.357118) | 1.178318 / 1.492716 (-0.314399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095310 / 0.018006 (0.077304) | 0.307485 / 0.000490 (0.306995) | 0.000212 / 0.000200 (0.000013) | 0.000047 / 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) | 0.018630 / 0.037411 (-0.018781) | 0.060461 / 0.014526 (0.045936) | 0.073117 / 0.176557 (-0.103440) | 0.119737 / 0.737135 (-0.617399) | 0.073909 / 0.296338 (-0.222430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280938 / 0.215209 (0.065729) | 2.755333 / 2.077655 (0.677679) | 1.468153 / 1.504120 (-0.035967) | 1.350247 / 1.541195 (-0.190948) | 1.379834 / 1.468490 (-0.088656) | 0.564027 / 4.584777 (-4.020750) | 2.387794 / 3.745712 (-1.357918) | 2.768529 / 5.269862 (-2.501333) | 1.761994 / 4.565676 (-2.803682) | 0.062079 / 0.424275 (-0.362196) | 0.005018 / 0.007607 (-0.002589) | 0.337576 / 0.226044 (0.111532) | 3.345347 / 2.268929 (1.076418) | 1.821950 / 55.444624 (-53.622674) | 1.545471 / 6.876477 (-5.331006) | 1.534941 / 2.142072 (-0.607131) | 0.626560 / 4.805227 (-4.178668) | 0.116227 / 6.500664 (-6.384437) | 0.041722 / 0.075469 (-0.033747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950480 / 1.841788 (-0.891307) | 11.616355 / 8.074308 (3.542047) | 10.426687 / 10.191392 (0.235295) | 0.129967 / 0.680424 (-0.550457) | 0.013977 / 0.534201 (-0.520224) | 0.287150 / 0.579283 (-0.292133) | 0.264028 / 0.434364 (-0.170336) | 0.325061 / 0.540337 (-0.215277) | 0.441281 / 1.386936 (-0.945655) |\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) | 0.005436 / 0.011353 (-0.005917) | 0.003567 / 0.011008 (-0.007441) | 0.055275 / 0.038508 (0.016767) | 0.053216 / 0.023109 (0.030107) | 0.272826 / 0.275898 (-0.003072) | 0.298399 / 0.323480 (-0.025081) | 0.004803 / 0.007986 (-0.003183) | 0.002681 / 0.004328 (-0.001648) | 0.048704 / 0.004250 (0.044453) | 0.040048 / 0.037052 (0.002996) | 0.278200 / 0.258489 (0.019711) | 0.331167 / 0.293841 (0.037326) | 0.029282 / 0.128546 (-0.099265) | 0.010766 / 0.075646 (-0.064881) | 0.057370 / 0.419271 (-0.361902) | 0.032674 / 0.043533 (-0.010859) | 0.269430 / 0.255139 (0.014291) | 0.288256 / 0.283200 (0.005056) | 0.019340 / 0.141683 (-0.122343) | 1.118058 / 1.452155 (-0.334097) | 1.157811 / 1.492716 (-0.334906) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094091 / 0.018006 (0.076085) | 0.301833 / 0.000490 (0.301343) | 0.000216 / 0.000200 (0.000016) | 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) | 0.021327 / 0.037411 (-0.016085) | 0.068636 / 0.014526 (0.054110) | 0.080246 / 0.176557 (-0.096311) | 0.120524 / 0.737135 (-0.616611) | 0.082226 / 0.296338 (-0.214113) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293579 / 0.215209 (0.078370) | 2.880281 / 2.077655 (0.802626) | 1.594647 / 1.504120 (0.090528) | 1.477152 / 1.541195 (-0.064043) | 1.498122 / 1.468490 (0.029632) | 0.555073 / 4.584777 (-4.029704) | 2.446743 / 3.745712 (-1.298970) | 2.794971 / 5.269862 (-2.474890) | 1.749730 / 4.565676 (-2.815947) | 0.062537 / 0.424275 (-0.361738) | 0.004908 / 0.007607 (-0.002699) | 0.350772 / 0.226044 (0.124727) | 3.486535 / 2.268929 (1.217607) | 1.957414 / 55.444624 (-53.487210) | 1.669169 / 6.876477 (-5.207308) | 1.682396 / 2.142072 (-0.459676) | 0.627379 / 4.805227 (-4.177848) | 0.117218 / 6.500664 (-6.383446) | 0.041000 / 0.075469 (-0.034469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958248 / 1.841788 (-0.883539) | 12.022677 / 8.074308 (3.948369) | 10.331661 / 10.191392 (0.140269) | 0.129765 / 0.680424 (-0.550659) | 0.015073 / 0.534201 (-0.519128) | 0.287212 / 0.579283 (-0.292071) | 0.278310 / 0.434364 (-0.156054) | 0.328155 / 0.540337 (-0.212183) | 0.564990 / 1.386936 (-0.821946) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c16e56371e50adae771288945e3389cb81a31fd \"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) | 0.005576 / 0.011353 (-0.005777) | 0.003430 / 0.011008 (-0.007578) | 0.062714 / 0.038508 (0.024206) | 0.051240 / 0.023109 (0.028131) | 0.236637 / 0.275898 (-0.039261) | 0.262660 / 0.323480 (-0.060820) | 0.002924 / 0.007986 (-0.005061) | 0.002712 / 0.004328 (-0.001616) | 0.048680 / 0.004250 (0.044430) | 0.038997 / 0.037052 (0.001945) | 0.241426 / 0.258489 (-0.017063) | 0.270652 / 0.293841 (-0.023189) | 0.027355 / 0.128546 (-0.101192) | 0.010640 / 0.075646 (-0.065006) | 0.207754 / 0.419271 (-0.211517) | 0.035921 / 0.043533 (-0.007612) | 0.247645 / 0.255139 (-0.007494) | 0.262933 / 0.283200 (-0.020266) | 0.019658 / 0.141683 (-0.122025) | 1.112576 / 1.452155 (-0.339578) | 1.177362 / 1.492716 (-0.315354) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098100 / 0.018006 (0.080093) | 0.310170 / 0.000490 (0.309680) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019626 / 0.037411 (-0.017785) | 0.065468 / 0.014526 (0.050942) | 0.074767 / 0.176557 (-0.101789) | 0.123619 / 0.737135 (-0.613516) | 0.077159 / 0.296338 (-0.219179) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288585 / 0.215209 (0.073376) | 2.771254 / 2.077655 (0.693599) | 1.457091 / 1.504120 (-0.047029) | 1.324341 / 1.541195 (-0.216854) | 1.361960 / 1.468490 (-0.106530) | 0.574197 / 4.584777 (-4.010580) | 2.391440 / 3.745712 (-1.354273) | 2.935060 / 5.269862 (-2.334802) | 1.802792 / 4.565676 (-2.762884) | 0.063530 / 0.424275 (-0.360745) | 0.005129 / 0.007607 (-0.002478) | 0.345977 / 0.226044 (0.119933) | 3.368042 / 2.268929 (1.099113) | 1.789575 / 55.444624 (-53.655050) | 1.509165 / 6.876477 (-5.367312) | 1.579792 / 2.142072 (-0.562280) | 0.652136 / 4.805227 (-4.153091) | 0.117014 / 6.500664 (-6.383650) | 0.042385 / 0.075469 (-0.033084) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963967 / 1.841788 (-0.877821) | 11.847856 / 8.074308 (3.773548) | 10.584088 / 10.191392 (0.392696) | 0.143953 / 0.680424 (-0.536471) | 0.014355 / 0.534201 (-0.519846) | 0.286936 / 0.579283 (-0.292347) | 0.269039 / 0.434364 (-0.165325) | 0.324531 / 0.540337 (-0.215807) | 0.443187 / 1.386936 (-0.943749) |\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) | 0.005448 / 0.011353 (-0.005905) | 0.003742 / 0.011008 (-0.007266) | 0.048808 / 0.038508 (0.010300) | 0.055409 / 0.023109 (0.032300) | 0.271574 / 0.275898 (-0.004324) | 0.295599 / 0.323480 (-0.027881) | 0.004208 / 0.007986 (-0.003778) | 0.002683 / 0.004328 (-0.001645) | 0.048813 / 0.004250 (0.044562) | 0.043672 / 0.037052 (0.006620) | 0.282173 / 0.258489 (0.023684) | 0.295447 / 0.293841 (0.001606) | 0.030461 / 0.128546 (-0.098086) | 0.010988 / 0.075646 (-0.064658) | 0.057050 / 0.419271 (-0.362221) | 0.033329 / 0.043533 (-0.010203) | 0.269700 / 0.255139 (0.014561) | 0.287099 / 0.283200 (0.003899) | 0.018203 / 0.141683 (-0.123480) | 1.142584 / 1.452155 (-0.309571) | 1.181848 / 1.492716 (-0.310869) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096958 / 0.018006 (0.078952) | 0.310563 / 0.000490 (0.310074) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022213 / 0.037411 (-0.015199) | 0.072054 / 0.014526 (0.057528) | 0.086393 / 0.176557 (-0.090163) | 0.122431 / 0.737135 (-0.614704) | 0.085298 / 0.296338 (-0.211041) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290823 / 0.215209 (0.075614) | 2.838026 / 2.077655 (0.760371) | 1.541425 / 1.504120 (0.037305) | 1.431903 / 1.541195 (-0.109292) | 1.476567 / 1.468490 (0.008077) | 0.557856 / 4.584777 (-4.026920) | 2.449101 / 3.745712 (-1.296611) | 2.924633 / 5.269862 (-2.345229) | 1.824420 / 4.565676 (-2.741256) | 0.063735 / 0.424275 (-0.360540) | 0.005025 / 0.007607 (-0.002582) | 0.349458 / 0.226044 (0.123413) | 3.468627 / 2.268929 (1.199699) | 1.925173 / 55.444624 (-53.519451) | 1.655038 / 6.876477 (-5.221439) | 1.698612 / 2.142072 (-0.443460) | 0.643623 / 4.805227 (-4.161604) | 0.116128 / 6.500664 (-6.384536) | 0.042283 / 0.075469 (-0.033186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963029 / 1.841788 (-0.878758) | 13.273985 / 8.074308 (5.199677) | 11.400884 / 10.191392 (1.209492) | 0.152635 / 0.680424 (-0.527788) | 0.016442 / 0.534201 (-0.517759) | 0.289272 / 0.579283 (-0.290012) | 0.285286 / 0.434364 (-0.149078) | 0.330028 / 0.540337 (-0.210310) | 0.596500 / 1.386936 (-0.790436) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c427c4b1dcf84c898ae62dc521bf446bb35e0e7 \"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) | 0.005124 / 0.011353 (-0.006229) | 0.003832 / 0.011008 (-0.007176) | 0.062806 / 0.038508 (0.024298) | 0.053137 / 0.023109 (0.030028) | 0.241155 / 0.275898 (-0.034743) | 0.260521 / 0.323480 (-0.062959) | 0.004005 / 0.007986 (-0.003981) | 0.002754 / 0.004328 (-0.001575) | 0.048934 / 0.004250 (0.044684) | 0.039438 / 0.037052 (0.002385) | 0.242534 / 0.258489 (-0.015955) | 0.275498 / 0.293841 (-0.018343) | 0.027338 / 0.128546 (-0.101208) | 0.010809 / 0.075646 (-0.064837) | 0.206986 / 0.419271 (-0.212285) | 0.035614 / 0.043533 (-0.007919) | 0.245780 / 0.255139 (-0.009359) | 0.259793 / 0.283200 (-0.023407) | 0.018108 / 0.141683 (-0.123575) | 1.103412 / 1.452155 (-0.348742) | 1.162940 / 1.492716 (-0.329776) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092463 / 0.018006 (0.074457) | 0.299516 / 0.000490 (0.299026) | 0.000210 / 0.000200 (0.000010) | 0.000047 / 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) | 0.018261 / 0.037411 (-0.019150) | 0.060178 / 0.014526 (0.045652) | 0.073043 / 0.176557 (-0.103513) | 0.120541 / 0.737135 (-0.616594) | 0.074972 / 0.296338 (-0.221367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287288 / 0.215209 (0.072078) | 2.814915 / 2.077655 (0.737260) | 1.520221 / 1.504120 (0.016101) | 1.396045 / 1.541195 (-0.145149) | 1.419662 / 1.468490 (-0.048828) | 0.589247 / 4.584777 (-3.995530) | 2.411101 / 3.745712 (-1.334611) | 2.777709 / 5.269862 (-2.492153) | 1.750386 / 4.565676 (-2.815291) | 0.063734 / 0.424275 (-0.360541) | 0.005021 / 0.007607 (-0.002586) | 0.338817 / 0.226044 (0.112773) | 3.371218 / 2.268929 (1.102289) | 1.892691 / 55.444624 (-53.551934) | 1.599039 / 6.876477 (-5.277438) | 1.574726 / 2.142072 (-0.567346) | 0.665623 / 4.805227 (-4.139604) | 0.118628 / 6.500664 (-6.382036) | 0.041803 / 0.075469 (-0.033666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948696 / 1.841788 (-0.893092) | 11.502916 / 8.074308 (3.428608) | 10.301174 / 10.191392 (0.109782) | 0.141752 / 0.680424 (-0.538672) | 0.014064 / 0.534201 (-0.520137) | 0.286701 / 0.579283 (-0.292583) | 0.265805 / 0.434364 (-0.168559) | 0.328420 / 0.540337 (-0.211917) | 0.433619 / 1.386936 (-0.953317) |\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) | 0.005262 / 0.011353 (-0.006091) | 0.003361 / 0.011008 (-0.007648) | 0.049525 / 0.038508 (0.011016) | 0.048950 / 0.023109 (0.025841) | 0.273617 / 0.275898 (-0.002281) | 0.296614 / 0.323480 (-0.026866) | 0.004014 / 0.007986 (-0.003971) | 0.002630 / 0.004328 (-0.001698) | 0.048203 / 0.004250 (0.043952) | 0.040912 / 0.037052 (0.003860) | 0.279736 / 0.258489 (0.021247) | 0.301671 / 0.293841 (0.007830) | 0.028546 / 0.128546 (-0.100000) | 0.010440 / 0.075646 (-0.065206) | 0.057869 / 0.419271 (-0.361402) | 0.032876 / 0.043533 (-0.010657) | 0.277649 / 0.255139 (0.022510) | 0.296565 / 0.283200 (0.013365) | 0.017558 / 0.141683 (-0.124125) | 1.155005 / 1.452155 (-0.297149) | 1.204827 / 1.492716 (-0.287889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093248 / 0.018006 (0.075242) | 0.302721 / 0.000490 (0.302231) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021882 / 0.037411 (-0.015530) | 0.068259 / 0.014526 (0.053733) | 0.080982 / 0.176557 (-0.095574) | 0.119386 / 0.737135 (-0.617750) | 0.081745 / 0.296338 (-0.214593) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297812 / 0.215209 (0.082603) | 2.909938 / 2.077655 (0.832283) | 1.603736 / 1.504120 (0.099616) | 1.482989 / 1.541195 (-0.058206) | 1.495107 / 1.468490 (0.026617) | 0.562275 / 4.584777 (-4.022502) | 2.424812 / 3.745712 (-1.320901) | 2.759127 / 5.269862 (-2.510735) | 1.733283 / 4.565676 (-2.832394) | 0.063144 / 0.424275 (-0.361131) | 0.004949 / 0.007607 (-0.002658) | 0.352756 / 0.226044 (0.126711) | 3.496028 / 2.268929 (1.227100) | 1.982804 / 55.444624 (-53.461820) | 1.689787 / 6.876477 (-5.186690) | 1.672699 / 2.142072 (-0.469373) | 0.660169 / 4.805227 (-4.145059) | 0.116535 / 6.500664 (-6.384129) | 0.040616 / 0.075469 (-0.034853) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975055 / 1.841788 (-0.866733) | 11.919295 / 8.074308 (3.844986) | 10.779188 / 10.191392 (0.587796) | 0.143106 / 0.680424 (-0.537318) | 0.015159 / 0.534201 (-0.519041) | 0.289734 / 0.579283 (-0.289549) | 0.278637 / 0.434364 (-0.155727) | 0.328159 / 0.540337 (-0.212178) | 0.570560 / 1.386936 (-0.816376) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#241500208da5fef64ad6ddc1cc5ab2be18f2f76d \"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) | 0.005155 / 0.011353 (-0.006198) | 0.003589 / 0.011008 (-0.007419) | 0.064440 / 0.038508 (0.025932) | 0.051020 / 0.023109 (0.027911) | 0.246099 / 0.275898 (-0.029799) | 0.273383 / 0.323480 (-0.050097) | 0.003984 / 0.007986 (-0.004002) | 0.002791 / 0.004328 (-0.001537) | 0.049076 / 0.004250 (0.044826) | 0.037975 / 0.037052 (0.000922) | 0.253709 / 0.258489 (-0.004780) | 0.281730 / 0.293841 (-0.012111) | 0.028060 / 0.128546 (-0.100486) | 0.010808 / 0.075646 (-0.064838) | 0.206663 / 0.419271 (-0.212609) | 0.035989 / 0.043533 (-0.007544) | 0.252635 / 0.255139 (-0.002504) | 0.280042 / 0.283200 (-0.003158) | 0.016982 / 0.141683 (-0.124700) | 1.098679 / 1.452155 (-0.353475) | 1.157051 / 1.492716 (-0.335666) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098238 / 0.018006 (0.080232) | 0.311990 / 0.000490 (0.311501) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018270 / 0.037411 (-0.019141) | 0.062711 / 0.014526 (0.048186) | 0.074381 / 0.176557 (-0.102175) | 0.119946 / 0.737135 (-0.617189) | 0.075013 / 0.296338 (-0.221325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282106 / 0.215209 (0.066897) | 2.752653 / 2.077655 (0.674999) | 1.488771 / 1.504120 (-0.015349) | 1.372552 / 1.541195 (-0.168643) | 1.390270 / 1.468490 (-0.078220) | 0.558928 / 4.584777 (-4.025849) | 2.411821 / 3.745712 (-1.333891) | 2.771441 / 5.269862 (-2.498421) | 1.747507 / 4.565676 (-2.818169) | 0.061360 / 0.424275 (-0.362915) | 0.004956 / 0.007607 (-0.002652) | 0.332330 / 0.226044 (0.106286) | 3.301405 / 2.268929 (1.032476) | 1.786726 / 55.444624 (-53.657899) | 1.529974 / 6.876477 (-5.346502) | 1.538412 / 2.142072 (-0.603660) | 0.637590 / 4.805227 (-4.167637) | 0.117215 / 6.500664 (-6.383449) | 0.042186 / 0.075469 (-0.033283) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945574 / 1.841788 (-0.896213) | 11.616152 / 8.074308 (3.541844) | 10.365114 / 10.191392 (0.173722) | 0.130358 / 0.680424 (-0.550066) | 0.013587 / 0.534201 (-0.520614) | 0.306024 / 0.579283 (-0.273259) | 0.270577 / 0.434364 (-0.163787) | 0.340768 / 0.540337 (-0.199569) | 0.460841 / 1.386936 (-0.926095) |\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) | 0.005254 / 0.011353 (-0.006099) | 0.003137 / 0.011008 (-0.007871) | 0.048302 / 0.038508 (0.009794) | 0.051952 / 0.023109 (0.028843) | 0.269078 / 0.275898 (-0.006820) | 0.292044 / 0.323480 (-0.031436) | 0.003985 / 0.007986 (-0.004000) | 0.002597 / 0.004328 (-0.001732) | 0.049998 / 0.004250 (0.045747) | 0.040227 / 0.037052 (0.003174) | 0.274714 / 0.258489 (0.016225) | 0.298160 / 0.293841 (0.004319) | 0.028857 / 0.128546 (-0.099690) | 0.010545 / 0.075646 (-0.065101) | 0.057234 / 0.419271 (-0.362038) | 0.032515 / 0.043533 (-0.011018) | 0.271526 / 0.255139 (0.016387) | 0.288556 / 0.283200 (0.005356) | 0.018155 / 0.141683 (-0.123527) | 1.201906 / 1.452155 (-0.250248) | 1.220068 / 1.492716 (-0.272648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100098 / 0.018006 (0.082092) | 0.311081 / 0.000490 (0.310591) | 0.000231 / 0.000200 (0.000032) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022349 / 0.037411 (-0.015062) | 0.069698 / 0.014526 (0.055172) | 0.081334 / 0.176557 (-0.095222) | 0.120847 / 0.737135 (-0.616289) | 0.082091 / 0.296338 (-0.214248) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293810 / 0.215209 (0.078601) | 2.844191 / 2.077655 (0.766536) | 1.594494 / 1.504120 (0.090374) | 1.486531 / 1.541195 (-0.054664) | 1.506307 / 1.468490 (0.037817) | 0.560247 / 4.584777 (-4.024530) | 2.478309 / 3.745712 (-1.267403) | 2.759024 / 5.269862 (-2.510837) | 1.733063 / 4.565676 (-2.832613) | 0.061838 / 0.424275 (-0.362438) | 0.004869 / 0.007607 (-0.002738) | 0.347267 / 0.226044 (0.121222) | 3.407737 / 2.268929 (1.138808) | 1.944420 / 55.444624 (-53.500204) | 1.660060 / 6.876477 (-5.216417) | 1.704219 / 2.142072 (-0.437854) | 0.646969 / 4.805227 (-4.158258) | 0.115750 / 6.500664 (-6.384914) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972537 / 1.841788 (-0.869251) | 12.013530 / 8.074308 (3.939222) | 10.650215 / 10.191392 (0.458823) | 0.132877 / 0.680424 (-0.547547) | 0.016828 / 0.534201 (-0.517372) | 0.288321 / 0.579283 (-0.290962) | 0.284203 / 0.434364 (-0.150161) | 0.324016 / 0.540337 (-0.216321) | 0.575403 / 1.386936 (-0.811533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17ec1a7a610adba3db44f316a930b979872d4ef7 \"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) | 0.005925 / 0.011353 (-0.005427) | 0.005138 / 0.011008 (-0.005870) | 0.069865 / 0.038508 (0.031356) | 0.067181 / 0.023109 (0.044072) | 0.309642 / 0.275898 (0.033743) | 0.302919 / 0.323480 (-0.020561) | 0.003365 / 0.007986 (-0.004620) | 0.003148 / 0.004328 (-0.001180) | 0.054102 / 0.004250 (0.049852) | 0.044196 / 0.037052 (0.007143) | 0.306882 / 0.258489 (0.048393) | 0.315153 / 0.293841 (0.021313) | 0.030458 / 0.128546 (-0.098089) | 0.011773 / 0.075646 (-0.063874) | 0.235075 / 0.419271 (-0.184196) | 0.040840 / 0.043533 (-0.002693) | 0.279897 / 0.255139 (0.024758) | 0.316334 / 0.283200 (0.033135) | 0.020128 / 0.141683 (-0.121555) | 1.237327 / 1.452155 (-0.214828) | 1.290386 / 1.492716 (-0.202331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.118540 / 0.018006 (0.100534) | 0.363282 / 0.000490 (0.362792) | 0.000266 / 0.000200 (0.000066) | 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) | 0.021435 / 0.037411 (-0.015977) | 0.068124 / 0.014526 (0.053598) | 0.082747 / 0.176557 (-0.093809) | 0.137179 / 0.737135 (-0.599956) | 0.084815 / 0.296338 (-0.211523) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.307836 / 0.215209 (0.092626) | 2.983444 / 2.077655 (0.905790) | 1.616430 / 1.504120 (0.112310) | 1.466843 / 1.541195 (-0.074351) | 1.512440 / 1.468490 (0.043950) | 0.652311 / 4.584777 (-3.932466) | 2.676420 / 3.745712 (-1.069292) | 3.265747 / 5.269862 (-2.004115) | 2.028586 / 4.565676 (-2.537090) | 0.071997 / 0.424275 (-0.352278) | 0.007068 / 0.007607 (-0.000539) | 0.367199 / 0.226044 (0.141155) | 3.617970 / 2.268929 (1.349042) | 1.991345 / 55.444624 (-53.453280) | 1.670015 / 6.876477 (-5.206462) | 1.720515 / 2.142072 (-0.421557) | 0.724649 / 4.805227 (-4.080579) | 0.134888 / 6.500664 (-6.365776) | 0.048325 / 0.075469 (-0.027144) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.051058 / 1.841788 (-0.790730) | 13.772809 / 8.074308 (5.698501) | 11.813879 / 10.191392 (1.622487) | 0.160065 / 0.680424 (-0.520359) | 0.016256 / 0.534201 (-0.517945) | 0.320393 / 0.579283 (-0.258890) | 0.314462 / 0.434364 (-0.119901) | 0.371911 / 0.540337 (-0.168427) | 0.506864 / 1.386936 (-0.880072) |\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) | 0.005857 / 0.011353 (-0.005496) | 0.004077 / 0.011008 (-0.006931) | 0.056033 / 0.038508 (0.017525) | 0.067622 / 0.023109 (0.044513) | 0.298956 / 0.275898 (0.023058) | 0.323484 / 0.323480 (0.000004) | 0.004825 / 0.007986 (-0.003160) | 0.003120 / 0.004328 (-0.001208) | 0.055227 / 0.004250 (0.050976) | 0.048439 / 0.037052 (0.011387) | 0.303207 / 0.258489 (0.044718) | 0.329478 / 0.293841 (0.035637) | 0.032516 / 0.128546 (-0.096031) | 0.012260 / 0.075646 (-0.063386) | 0.065037 / 0.419271 (-0.354234) | 0.038799 / 0.043533 (-0.004734) | 0.299102 / 0.255139 (0.043963) | 0.318248 / 0.283200 (0.035048) | 0.020190 / 0.141683 (-0.121493) | 1.263479 / 1.452155 (-0.188676) | 1.329788 / 1.492716 (-0.162928) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.119801 / 0.018006 (0.101794) | 0.359618 / 0.000490 (0.359129) | 0.000260 / 0.000200 (0.000060) | 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) | 0.026876 / 0.037411 (-0.010535) | 0.080637 / 0.014526 (0.066111) | 0.092260 / 0.176557 (-0.084297) | 0.137260 / 0.737135 (-0.599875) | 0.093309 / 0.296338 (-0.203029) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.329327 / 0.215209 (0.114118) | 3.193014 / 2.077655 (1.115359) | 1.755838 / 1.504120 (0.251718) | 1.612279 / 1.541195 (0.071084) | 1.631958 / 1.468490 (0.163468) | 0.630886 / 4.584777 (-3.953891) | 2.739731 / 3.745712 (-1.005981) | 3.186745 / 5.269862 (-2.083117) | 1.987125 / 4.565676 (-2.578552) | 0.070694 / 0.424275 (-0.353581) | 0.006461 / 0.007607 (-0.001146) | 0.386367 / 0.226044 (0.160323) | 3.815837 / 2.268929 (1.546908) | 2.155904 / 55.444624 (-53.288720) | 1.832575 / 6.876477 (-5.043902) | 1.842097 / 2.142072 (-0.299975) | 0.716394 / 4.805227 (-4.088833) | 0.130796 / 6.500664 (-6.369869) | 0.045674 / 0.075469 (-0.029795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.109117 / 1.841788 (-0.732671) | 14.116582 / 8.074308 (6.042274) | 11.926356 / 10.191392 (1.734964) | 0.150543 / 0.680424 (-0.529881) | 0.017426 / 0.534201 (-0.516775) | 0.323058 / 0.579283 (-0.256225) | 0.330228 / 0.434364 (-0.104136) | 0.372533 / 0.540337 (-0.167804) | 0.661348 / 1.386936 (-0.725588) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04ffd22a30ecc7545234559edd9d23c85c6d84d9 \"CML watermark\")\n", "Thanks for the review, I took your comments into account !", "<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) | 0.005477 / 0.011353 (-0.005876) | 0.003509 / 0.011008 (-0.007499) | 0.062884 / 0.038508 (0.024376) | 0.051042 / 0.023109 (0.027933) | 0.285180 / 0.275898 (0.009282) | 0.315353 / 0.323480 (-0.008127) | 0.002943 / 0.007986 (-0.005043) | 0.003286 / 0.004328 (-0.001042) | 0.048885 / 0.004250 (0.044635) | 0.038591 / 0.037052 (0.001539) | 0.288527 / 0.258489 (0.030038) | 0.316102 / 0.293841 (0.022261) | 0.028252 / 0.128546 (-0.100295) | 0.010622 / 0.075646 (-0.065024) | 0.205573 / 0.419271 (-0.213699) | 0.035764 / 0.043533 (-0.007769) | 0.285729 / 0.255139 (0.030590) | 0.304578 / 0.283200 (0.021378) | 0.019862 / 0.141683 (-0.121821) | 1.102866 / 1.452155 (-0.349288) | 1.175161 / 1.492716 (-0.317555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095253 / 0.018006 (0.077246) | 0.302290 / 0.000490 (0.301800) | 0.000243 / 0.000200 (0.000043) | 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) | 0.018680 / 0.037411 (-0.018731) | 0.060375 / 0.014526 (0.045849) | 0.074033 / 0.176557 (-0.102524) | 0.120290 / 0.737135 (-0.616845) | 0.075350 / 0.296338 (-0.220989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277617 / 0.215209 (0.062408) | 2.718201 / 2.077655 (0.640546) | 1.462952 / 1.504120 (-0.041168) | 1.339199 / 1.541195 (-0.201996) | 1.375805 / 1.468490 (-0.092685) | 0.559956 / 4.584777 (-4.024821) | 2.373865 / 3.745712 (-1.371847) | 2.795732 / 5.269862 (-2.474129) | 1.755490 / 4.565676 (-2.810186) | 0.062002 / 0.424275 (-0.362273) | 0.004935 / 0.007607 (-0.002672) | 0.334786 / 0.226044 (0.108741) | 3.237499 / 2.268929 (0.968571) | 1.787561 / 55.444624 (-53.657064) | 1.513300 / 6.876477 (-5.363176) | 1.549797 / 2.142072 (-0.592275) | 0.643587 / 4.805227 (-4.161640) | 0.117275 / 6.500664 (-6.383389) | 0.042184 / 0.075469 (-0.033285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.933366 / 1.841788 (-0.908421) | 11.792282 / 8.074308 (3.717973) | 10.466608 / 10.191392 (0.275216) | 0.142148 / 0.680424 (-0.538275) | 0.014084 / 0.534201 (-0.520117) | 0.287233 / 0.579283 (-0.292050) | 0.266022 / 0.434364 (-0.168342) | 0.326854 / 0.540337 (-0.213483) | 0.451348 / 1.386936 (-0.935588) |\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) | 0.005384 / 0.011353 (-0.005969) | 0.003562 / 0.011008 (-0.007446) | 0.049014 / 0.038508 (0.010506) | 0.057480 / 0.023109 (0.034371) | 0.274456 / 0.275898 (-0.001442) | 0.298387 / 0.323480 (-0.025093) | 0.003909 / 0.007986 (-0.004076) | 0.002646 / 0.004328 (-0.001683) | 0.048374 / 0.004250 (0.044124) | 0.040907 / 0.037052 (0.003854) | 0.278267 / 0.258489 (0.019778) | 0.299862 / 0.293841 (0.006021) | 0.029108 / 0.128546 (-0.099439) | 0.010752 / 0.075646 (-0.064894) | 0.057523 / 0.419271 (-0.361749) | 0.032692 / 0.043533 (-0.010841) | 0.276288 / 0.255139 (0.021149) | 0.291572 / 0.283200 (0.008372) | 0.017818 / 0.141683 (-0.123865) | 1.129517 / 1.452155 (-0.322638) | 1.186630 / 1.492716 (-0.306086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093405 / 0.018006 (0.075399) | 0.301254 / 0.000490 (0.300764) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021793 / 0.037411 (-0.015618) | 0.069033 / 0.014526 (0.054508) | 0.083502 / 0.176557 (-0.093055) | 0.122149 / 0.737135 (-0.614986) | 0.083801 / 0.296338 (-0.212537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299149 / 0.215209 (0.083940) | 2.936550 / 2.077655 (0.858895) | 1.595766 / 1.504120 (0.091647) | 1.487117 / 1.541195 (-0.054078) | 1.494606 / 1.468490 (0.026116) | 0.569346 / 4.584777 (-4.015431) | 2.445642 / 3.745712 (-1.300070) | 2.805696 / 5.269862 (-2.464165) | 1.743796 / 4.565676 (-2.821881) | 0.062695 / 0.424275 (-0.361580) | 0.004885 / 0.007607 (-0.002723) | 0.354186 / 0.226044 (0.128142) | 3.487926 / 2.268929 (1.218997) | 1.965703 / 55.444624 (-53.478922) | 1.682284 / 6.876477 (-5.194193) | 1.705586 / 2.142072 (-0.436487) | 0.655099 / 4.805227 (-4.150128) | 0.116441 / 6.500664 (-6.384223) | 0.040851 / 0.075469 (-0.034618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967361 / 1.841788 (-0.874427) | 12.037718 / 8.074308 (3.963409) | 10.599761 / 10.191392 (0.408369) | 0.143127 / 0.680424 (-0.537297) | 0.015063 / 0.534201 (-0.519138) | 0.286894 / 0.579283 (-0.292389) | 0.301505 / 0.434364 (-0.132859) | 0.324339 / 0.540337 (-0.215999) | 0.591782 / 1.386936 (-0.795154) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96ff08d4aa6dbafc8a10a9d03dfabe236378bcd \"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) | 0.005337 / 0.011353 (-0.006015) | 0.004074 / 0.011008 (-0.006934) | 0.062653 / 0.038508 (0.024145) | 0.054295 / 0.023109 (0.031186) | 0.248284 / 0.275898 (-0.027614) | 0.271604 / 0.323480 (-0.051876) | 0.003931 / 0.007986 (-0.004055) | 0.002907 / 0.004328 (-0.001422) | 0.047991 / 0.004250 (0.043740) | 0.042842 / 0.037052 (0.005790) | 0.253648 / 0.258489 (-0.004841) | 0.282546 / 0.293841 (-0.011295) | 0.028005 / 0.128546 (-0.100541) | 0.010734 / 0.075646 (-0.064912) | 0.210023 / 0.419271 (-0.209248) | 0.035940 / 0.043533 (-0.007592) | 0.250766 / 0.255139 (-0.004373) | 0.267644 / 0.283200 (-0.015556) | 0.020451 / 0.141683 (-0.121232) | 1.114972 / 1.452155 (-0.337183) | 1.159823 / 1.492716 (-0.332893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095527 / 0.018006 (0.077521) | 0.303321 / 0.000490 (0.302831) | 0.000216 / 0.000200 (0.000016) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018725 / 0.037411 (-0.018686) | 0.062537 / 0.014526 (0.048011) | 0.073091 / 0.176557 (-0.103466) | 0.119570 / 0.737135 (-0.617565) | 0.074863 / 0.296338 (-0.221476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284936 / 0.215209 (0.069727) | 2.802498 / 2.077655 (0.724843) | 1.493316 / 1.504120 (-0.010804) | 1.372319 / 1.541195 (-0.168875) | 1.403657 / 1.468490 (-0.064833) | 0.569303 / 4.584777 (-4.015474) | 2.402498 / 3.745712 (-1.343214) | 2.834778 / 5.269862 (-2.435084) | 1.791312 / 4.565676 (-2.774365) | 0.062526 / 0.424275 (-0.361749) | 0.004947 / 0.007607 (-0.002660) | 0.345141 / 0.226044 (0.119097) | 3.371863 / 2.268929 (1.102934) | 1.846023 / 55.444624 (-53.598602) | 1.596368 / 6.876477 (-5.280109) | 1.615902 / 2.142072 (-0.526170) | 0.644333 / 4.805227 (-4.160894) | 0.119460 / 6.500664 (-6.381204) | 0.049122 / 0.075469 (-0.026347) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951839 / 1.841788 (-0.889948) | 11.677074 / 8.074308 (3.602766) | 10.562586 / 10.191392 (0.371194) | 0.143633 / 0.680424 (-0.536791) | 0.014157 / 0.534201 (-0.520044) | 0.289141 / 0.579283 (-0.290142) | 0.264719 / 0.434364 (-0.169645) | 0.327862 / 0.540337 (-0.212476) | 0.451215 / 1.386936 (-0.935721) |\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) | 0.005343 / 0.011353 (-0.006010) | 0.003522 / 0.011008 (-0.007486) | 0.049354 / 0.038508 (0.010846) | 0.051441 / 0.023109 (0.028332) | 0.259350 / 0.275898 (-0.016548) | 0.288946 / 0.323480 (-0.034534) | 0.004052 / 0.007986 (-0.003934) | 0.002690 / 0.004328 (-0.001639) | 0.049996 / 0.004250 (0.045746) | 0.040224 / 0.037052 (0.003171) | 0.264588 / 0.258489 (0.006099) | 0.296474 / 0.293841 (0.002633) | 0.028868 / 0.128546 (-0.099679) | 0.010917 / 0.075646 (-0.064730) | 0.057866 / 0.419271 (-0.361405) | 0.032610 / 0.043533 (-0.010923) | 0.260657 / 0.255139 (0.005518) | 0.276947 / 0.283200 (-0.006253) | 0.018877 / 0.141683 (-0.122806) | 1.126205 / 1.452155 (-0.325949) | 1.206173 / 1.492716 (-0.286543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094464 / 0.018006 (0.076458) | 0.304473 / 0.000490 (0.303984) | 0.000231 / 0.000200 (0.000031) | 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) | 0.021472 / 0.037411 (-0.015939) | 0.070864 / 0.014526 (0.056338) | 0.086607 / 0.176557 (-0.089950) | 0.120679 / 0.737135 (-0.616456) | 0.084271 / 0.296338 (-0.212068) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296448 / 0.215209 (0.081239) | 2.893996 / 2.077655 (0.816341) | 1.573409 / 1.504120 (0.069289) | 1.438799 / 1.541195 (-0.102396) | 1.461241 / 1.468490 (-0.007249) | 0.566737 / 4.584777 (-4.018040) | 2.425709 / 3.745712 (-1.320003) | 2.826764 / 5.269862 (-2.443098) | 1.785330 / 4.565676 (-2.780347) | 0.063721 / 0.424275 (-0.360554) | 0.005158 / 0.007607 (-0.002449) | 0.354961 / 0.226044 (0.128916) | 3.457499 / 2.268929 (1.188570) | 1.931374 / 55.444624 (-53.513251) | 1.646515 / 6.876477 (-5.229962) | 1.629891 / 2.142072 (-0.512182) | 0.648922 / 4.805227 (-4.156305) | 0.114953 / 6.500664 (-6.385711) | 0.040997 / 0.075469 (-0.034472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951049 / 1.841788 (-0.890739) | 12.258298 / 8.074308 (4.183990) | 10.663309 / 10.191392 (0.471917) | 0.142933 / 0.680424 (-0.537491) | 0.015927 / 0.534201 (-0.518273) | 0.286914 / 0.579283 (-0.292369) | 0.286600 / 0.434364 (-0.147764) | 0.324464 / 0.540337 (-0.215874) | 0.575075 / 1.386936 (-0.811861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed47b9d5e9c6aa03a0aa07d8abfd3fa8241da353 \"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) | 0.005298 / 0.011353 (-0.006055) | 0.003645 / 0.011008 (-0.007363) | 0.061629 / 0.038508 (0.023121) | 0.052322 / 0.023109 (0.029212) | 0.242579 / 0.275898 (-0.033319) | 0.263525 / 0.323480 (-0.059955) | 0.002794 / 0.007986 (-0.005192) | 0.002152 / 0.004328 (-0.002177) | 0.048301 / 0.004250 (0.044050) | 0.038177 / 0.037052 (0.001125) | 0.247724 / 0.258489 (-0.010765) | 0.274455 / 0.293841 (-0.019386) | 0.026992 / 0.128546 (-0.101555) | 0.010110 / 0.075646 (-0.065536) | 0.205662 / 0.419271 (-0.213609) | 0.034901 / 0.043533 (-0.008632) | 0.241920 / 0.255139 (-0.013219) | 0.262048 / 0.283200 (-0.021152) | 0.019111 / 0.141683 (-0.122572) | 1.127600 / 1.452155 (-0.324555) | 1.193931 / 1.492716 (-0.298786) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090321 / 0.018006 (0.072315) | 0.299046 / 0.000490 (0.298556) | 0.000197 / 0.000200 (-0.000003) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018278 / 0.037411 (-0.019133) | 0.060114 / 0.014526 (0.045588) | 0.073602 / 0.176557 (-0.102954) | 0.119676 / 0.737135 (-0.617459) | 0.074786 / 0.296338 (-0.221552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280385 / 0.215209 (0.065176) | 2.764259 / 2.077655 (0.686604) | 1.501027 / 1.504120 (-0.003093) | 1.376900 / 1.541195 (-0.164295) | 1.390587 / 1.468490 (-0.077903) | 0.555180 / 4.584777 (-4.029597) | 2.354307 / 3.745712 (-1.391405) | 2.755862 / 5.269862 (-2.514000) | 1.714771 / 4.565676 (-2.850906) | 0.062507 / 0.424275 (-0.361768) | 0.004974 / 0.007607 (-0.002633) | 0.333900 / 0.226044 (0.107856) | 3.266922 / 2.268929 (0.997994) | 1.805401 / 55.444624 (-53.639223) | 1.526970 / 6.876477 (-5.349507) | 1.539425 / 2.142072 (-0.602647) | 0.629364 / 4.805227 (-4.175863) | 0.114929 / 6.500664 (-6.385735) | 0.041258 / 0.075469 (-0.034211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968601 / 1.841788 (-0.873187) | 11.260937 / 8.074308 (3.186629) | 10.393839 / 10.191392 (0.202447) | 0.127988 / 0.680424 (-0.552436) | 0.014564 / 0.534201 (-0.519637) | 0.286560 / 0.579283 (-0.292723) | 0.260493 / 0.434364 (-0.173871) | 0.330949 / 0.540337 (-0.209388) | 0.435798 / 1.386936 (-0.951138) |\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) | 0.005232 / 0.011353 (-0.006121) | 0.003030 / 0.011008 (-0.007978) | 0.048513 / 0.038508 (0.010005) | 0.049501 / 0.023109 (0.026392) | 0.270545 / 0.275898 (-0.005353) | 0.289128 / 0.323480 (-0.034352) | 0.003925 / 0.007986 (-0.004061) | 0.002568 / 0.004328 (-0.001761) | 0.047692 / 0.004250 (0.043442) | 0.039854 / 0.037052 (0.002802) | 0.272654 / 0.258489 (0.014165) | 0.296275 / 0.293841 (0.002434) | 0.029027 / 0.128546 (-0.099519) | 0.010335 / 0.075646 (-0.065311) | 0.056726 / 0.419271 (-0.362546) | 0.033257 / 0.043533 (-0.010275) | 0.272672 / 0.255139 (0.017533) | 0.286298 / 0.283200 (0.003098) | 0.017877 / 0.141683 (-0.123806) | 1.150322 / 1.452155 (-0.301833) | 1.221031 / 1.492716 (-0.271685) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102838 / 0.018006 (0.084832) | 0.298810 / 0.000490 (0.298320) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021232 / 0.037411 (-0.016180) | 0.067949 / 0.014526 (0.053423) | 0.116487 / 0.176557 (-0.060070) | 0.124035 / 0.737135 (-0.613100) | 0.081075 / 0.296338 (-0.215263) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289098 / 0.215209 (0.073889) | 2.844476 / 2.077655 (0.766821) | 1.609576 / 1.504120 (0.105456) | 1.480453 / 1.541195 (-0.060742) | 1.489672 / 1.468490 (0.021182) | 0.589661 / 4.584777 (-3.995116) | 2.453804 / 3.745712 (-1.291908) | 2.722381 / 5.269862 (-2.547480) | 1.720251 / 4.565676 (-2.845425) | 0.066085 / 0.424275 (-0.358190) | 0.004943 / 0.007607 (-0.002664) | 0.355149 / 0.226044 (0.129104) | 3.444323 / 2.268929 (1.175395) | 1.971157 / 55.444624 (-53.473467) | 1.683029 / 6.876477 (-5.193448) | 1.672798 / 2.142072 (-0.469274) | 0.644812 / 4.805227 (-4.160416) | 0.115098 / 6.500664 (-6.385566) | 0.039883 / 0.075469 (-0.035586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960454 / 1.841788 (-0.881334) | 11.604732 / 8.074308 (3.530424) | 10.405481 / 10.191392 (0.214089) | 0.129146 / 0.680424 (-0.551278) | 0.014945 / 0.534201 (-0.519256) | 0.286239 / 0.579283 (-0.293044) | 0.281041 / 0.434364 (-0.153323) | 0.320448 / 0.540337 (-0.219890) | 0.554304 / 1.386936 (-0.832632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2cfb7859b029654829c4dfee230812ddab1f104 \"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) | 0.005510 / 0.011353 (-0.005843) | 0.003575 / 0.011008 (-0.007433) | 0.062232 / 0.038508 (0.023724) | 0.051115 / 0.023109 (0.028006) | 0.250709 / 0.275898 (-0.025189) | 0.274837 / 0.323480 (-0.048642) | 0.002972 / 0.007986 (-0.005014) | 0.002708 / 0.004328 (-0.001621) | 0.048088 / 0.004250 (0.043838) | 0.038588 / 0.037052 (0.001535) | 0.252550 / 0.258489 (-0.005939) | 0.285238 / 0.293841 (-0.008603) | 0.027867 / 0.128546 (-0.100679) | 0.011000 / 0.075646 (-0.064646) | 0.206918 / 0.419271 (-0.212354) | 0.035711 / 0.043533 (-0.007822) | 0.255306 / 0.255139 (0.000167) | 0.298636 / 0.283200 (0.015436) | 0.018222 / 0.141683 (-0.123461) | 1.122276 / 1.452155 (-0.329879) | 1.196471 / 1.492716 (-0.296245) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092072 / 0.018006 (0.074066) | 0.301469 / 0.000490 (0.300979) | 0.000225 / 0.000200 (0.000025) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018672 / 0.037411 (-0.018739) | 0.060235 / 0.014526 (0.045709) | 0.074036 / 0.176557 (-0.102521) | 0.119578 / 0.737135 (-0.617557) | 0.073605 / 0.296338 (-0.222734) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286474 / 0.215209 (0.071264) | 2.779427 / 2.077655 (0.701772) | 1.478746 / 1.504120 (-0.025373) | 1.362692 / 1.541195 (-0.178503) | 1.388194 / 1.468490 (-0.080296) | 0.560707 / 4.584777 (-4.024070) | 2.352846 / 3.745712 (-1.392866) | 2.784400 / 5.269862 (-2.485461) | 1.775642 / 4.565676 (-2.790035) | 0.062324 / 0.424275 (-0.361951) | 0.004938 / 0.007607 (-0.002669) | 0.334149 / 0.226044 (0.108105) | 3.319446 / 2.268929 (1.050517) | 1.810369 / 55.444624 (-53.634255) | 1.559462 / 6.876477 (-5.317014) | 1.611199 / 2.142072 (-0.530873) | 0.655984 / 4.805227 (-4.149244) | 0.118508 / 6.500664 (-6.382156) | 0.043661 / 0.075469 (-0.031808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935046 / 1.841788 (-0.906742) | 11.413501 / 8.074308 (3.339192) | 10.392314 / 10.191392 (0.200922) | 0.131507 / 0.680424 (-0.548917) | 0.014827 / 0.534201 (-0.519374) | 0.289069 / 0.579283 (-0.290214) | 0.268288 / 0.434364 (-0.166076) | 0.326843 / 0.540337 (-0.213495) | 0.441283 / 1.386936 (-0.945653) |\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) | 0.005375 / 0.011353 (-0.005978) | 0.003549 / 0.011008 (-0.007459) | 0.048996 / 0.038508 (0.010488) | 0.051408 / 0.023109 (0.028298) | 0.272265 / 0.275898 (-0.003633) | 0.293228 / 0.323480 (-0.030252) | 0.004147 / 0.007986 (-0.003839) | 0.002673 / 0.004328 (-0.001655) | 0.048116 / 0.004250 (0.043865) | 0.039926 / 0.037052 (0.002874) | 0.276987 / 0.258489 (0.018498) | 0.302955 / 0.293841 (0.009115) | 0.029488 / 0.128546 (-0.099058) | 0.010797 / 0.075646 (-0.064849) | 0.057552 / 0.419271 (-0.361720) | 0.032827 / 0.043533 (-0.010706) | 0.270888 / 0.255139 (0.015749) | 0.289136 / 0.283200 (0.005937) | 0.018815 / 0.141683 (-0.122868) | 1.148624 / 1.452155 (-0.303530) | 1.191184 / 1.492716 (-0.301532) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091712 / 0.018006 (0.073706) | 0.311198 / 0.000490 (0.310708) | 0.000226 / 0.000200 (0.000026) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022097 / 0.037411 (-0.015314) | 0.070641 / 0.014526 (0.056116) | 0.080084 / 0.176557 (-0.096472) | 0.118998 / 0.737135 (-0.618137) | 0.081827 / 0.296338 (-0.214512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298599 / 0.215209 (0.083390) | 2.884759 / 2.077655 (0.807105) | 1.630794 / 1.504120 (0.126674) | 1.454309 / 1.541195 (-0.086886) | 1.466795 / 1.468490 (-0.001695) | 0.565405 / 4.584777 (-4.019372) | 2.460883 / 3.745712 (-1.284829) | 2.764193 / 5.269862 (-2.505668) | 1.734270 / 4.565676 (-2.831407) | 0.063408 / 0.424275 (-0.360867) | 0.004887 / 0.007607 (-0.002720) | 0.347762 / 0.226044 (0.121717) | 3.458385 / 2.268929 (1.189457) | 1.965434 / 55.444624 (-53.479190) | 1.671047 / 6.876477 (-5.205430) | 1.665642 / 2.142072 (-0.476430) | 0.640665 / 4.805227 (-4.164562) | 0.116025 / 6.500664 (-6.384639) | 0.040147 / 0.075469 (-0.035322) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982194 / 1.841788 (-0.859593) | 11.983487 / 8.074308 (3.909179) | 10.660605 / 10.191392 (0.469213) | 0.140647 / 0.680424 (-0.539777) | 0.015870 / 0.534201 (-0.518331) | 0.287032 / 0.579283 (-0.292251) | 0.276629 / 0.434364 (-0.157735) | 0.331171 / 0.540337 (-0.209166) | 0.575346 / 1.386936 (-0.811590) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56433c2f6a42d5fcc5acb46c6275911c29afc371 \"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) | 0.005014 / 0.011353 (-0.006339) | 0.003434 / 0.011008 (-0.007574) | 0.063283 / 0.038508 (0.024775) | 0.048068 / 0.023109 (0.024959) | 0.239521 / 0.275898 (-0.036377) | 0.265294 / 0.323480 (-0.058186) | 0.003790 / 0.007986 (-0.004196) | 0.002577 / 0.004328 (-0.001751) | 0.048618 / 0.004250 (0.044368) | 0.037427 / 0.037052 (0.000375) | 0.245263 / 0.258489 (-0.013226) | 0.276618 / 0.293841 (-0.017223) | 0.026615 / 0.128546 (-0.101931) | 0.010378 / 0.075646 (-0.065268) | 0.205670 / 0.419271 (-0.213601) | 0.035076 / 0.043533 (-0.008457) | 0.245062 / 0.255139 (-0.010077) | 0.264584 / 0.283200 (-0.018616) | 0.017760 / 0.141683 (-0.123922) | 1.148061 / 1.452155 (-0.304094) | 1.192762 / 1.492716 (-0.299955) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090870 / 0.018006 (0.072864) | 0.305458 / 0.000490 (0.304968) | 0.000207 / 0.000200 (0.000007) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018597 / 0.037411 (-0.018814) | 0.060349 / 0.014526 (0.045823) | 0.074854 / 0.176557 (-0.101702) | 0.123243 / 0.737135 (-0.613892) | 0.075843 / 0.296338 (-0.220496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275855 / 0.215209 (0.060645) | 2.723965 / 2.077655 (0.646311) | 1.436010 / 1.504120 (-0.068110) | 1.323495 / 1.541195 (-0.217700) | 1.356234 / 1.468490 (-0.112256) | 0.564388 / 4.584777 (-4.020389) | 2.390180 / 3.745712 (-1.355532) | 2.782863 / 5.269862 (-2.486998) | 1.765048 / 4.565676 (-2.800628) | 0.062680 / 0.424275 (-0.361595) | 0.004929 / 0.007607 (-0.002678) | 0.337578 / 0.226044 (0.111533) | 3.316780 / 2.268929 (1.047851) | 1.803829 / 55.444624 (-53.640795) | 1.524585 / 6.876477 (-5.351891) | 1.549695 / 2.142072 (-0.592377) | 0.638053 / 4.805227 (-4.167174) | 0.116983 / 6.500664 (-6.383681) | 0.042251 / 0.075469 (-0.033218) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946978 / 1.841788 (-0.894810) | 11.809483 / 8.074308 (3.735175) | 10.459974 / 10.191392 (0.268582) | 0.130015 / 0.680424 (-0.550409) | 0.013843 / 0.534201 (-0.520358) | 0.286972 / 0.579283 (-0.292311) | 0.268904 / 0.434364 (-0.165460) | 0.325591 / 0.540337 (-0.214746) | 0.439233 / 1.386936 (-0.947703) |\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) | 0.005804 / 0.011353 (-0.005549) | 0.003431 / 0.011008 (-0.007577) | 0.049041 / 0.038508 (0.010533) | 0.054758 / 0.023109 (0.031649) | 0.262330 / 0.275898 (-0.013568) | 0.288872 / 0.323480 (-0.034608) | 0.004016 / 0.007986 (-0.003970) | 0.002606 / 0.004328 (-0.001722) | 0.047878 / 0.004250 (0.043628) | 0.045066 / 0.037052 (0.008013) | 0.266310 / 0.258489 (0.007820) | 0.290072 / 0.293841 (-0.003768) | 0.028738 / 0.128546 (-0.099809) | 0.010667 / 0.075646 (-0.064979) | 0.057300 / 0.419271 (-0.361972) | 0.032715 / 0.043533 (-0.010818) | 0.264043 / 0.255139 (0.008904) | 0.278652 / 0.283200 (-0.004547) | 0.017873 / 0.141683 (-0.123810) | 1.125981 / 1.452155 (-0.326174) | 1.168548 / 1.492716 (-0.324168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090997 / 0.018006 (0.072991) | 0.300807 / 0.000490 (0.300317) | 0.000223 / 0.000200 (0.000023) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021510 / 0.037411 (-0.015901) | 0.068251 / 0.014526 (0.053725) | 0.082073 / 0.176557 (-0.094484) | 0.120071 / 0.737135 (-0.617064) | 0.082245 / 0.296338 (-0.214093) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290601 / 0.215209 (0.075392) | 2.871855 / 2.077655 (0.794200) | 1.558239 / 1.504120 (0.054119) | 1.447767 / 1.541195 (-0.093427) | 1.446851 / 1.468490 (-0.021639) | 0.573990 / 4.584777 (-4.010787) | 2.439859 / 3.745712 (-1.305853) | 2.795899 / 5.269862 (-2.473963) | 1.746751 / 4.565676 (-2.818926) | 0.062100 / 0.424275 (-0.362175) | 0.004948 / 0.007607 (-0.002659) | 0.344281 / 0.226044 (0.118236) | 3.427499 / 2.268929 (1.158570) | 1.940348 / 55.444624 (-53.504276) | 1.660926 / 6.876477 (-5.215551) | 1.669485 / 2.142072 (-0.472588) | 0.634034 / 4.805227 (-4.171193) | 0.114748 / 6.500664 (-6.385916) | 0.041617 / 0.075469 (-0.033852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966411 / 1.841788 (-0.875376) | 12.040753 / 8.074308 (3.966445) | 10.506542 / 10.191392 (0.315150) | 0.129659 / 0.680424 (-0.550764) | 0.015691 / 0.534201 (-0.518510) | 0.286911 / 0.579283 (-0.292372) | 0.273588 / 0.434364 (-0.160776) | 0.333642 / 0.540337 (-0.206695) | 0.568550 / 1.386936 (-0.818386) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b38ed4705263df92ae06d89baab0932ae10065e0 \"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) | 0.005023 / 0.011353 (-0.006330) | 0.003492 / 0.011008 (-0.007516) | 0.062808 / 0.038508 (0.024300) | 0.051649 / 0.023109 (0.028540) | 0.246871 / 0.275898 (-0.029027) | 0.273430 / 0.323480 (-0.050050) | 0.003851 / 0.007986 (-0.004135) | 0.002643 / 0.004328 (-0.001686) | 0.048499 / 0.004250 (0.044248) | 0.037713 / 0.037052 (0.000661) | 0.256431 / 0.258489 (-0.002058) | 0.306956 / 0.293841 (0.013116) | 0.027116 / 0.128546 (-0.101430) | 0.010769 / 0.075646 (-0.064877) | 0.206218 / 0.419271 (-0.213053) | 0.035592 / 0.043533 (-0.007941) | 0.249629 / 0.255139 (-0.005510) | 0.268438 / 0.283200 (-0.014761) | 0.018557 / 0.141683 (-0.123125) | 1.123988 / 1.452155 (-0.328167) | 1.158196 / 1.492716 (-0.334520) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090221 / 0.018006 (0.072215) | 0.300892 / 0.000490 (0.300402) | 0.000209 / 0.000200 (0.000009) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018305 / 0.037411 (-0.019106) | 0.060294 / 0.014526 (0.045769) | 0.073330 / 0.176557 (-0.103227) | 0.119620 / 0.737135 (-0.617515) | 0.074611 / 0.296338 (-0.221727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285347 / 0.215209 (0.070138) | 2.795144 / 2.077655 (0.717490) | 1.468321 / 1.504120 (-0.035799) | 1.343848 / 1.541195 (-0.197347) | 1.388998 / 1.468490 (-0.079492) | 0.559609 / 4.584777 (-4.025168) | 2.355056 / 3.745712 (-1.390656) | 2.798763 / 5.269862 (-2.471099) | 1.764371 / 4.565676 (-2.801305) | 0.062563 / 0.424275 (-0.361712) | 0.005101 / 0.007607 (-0.002506) | 0.339205 / 0.226044 (0.113161) | 3.336729 / 2.268929 (1.067800) | 1.801987 / 55.444624 (-53.642637) | 1.526720 / 6.876477 (-5.349757) | 1.539324 / 2.142072 (-0.602749) | 0.635805 / 4.805227 (-4.169422) | 0.138762 / 6.500664 (-6.361902) | 0.042092 / 0.075469 (-0.033377) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.928755 / 1.841788 (-0.913032) | 11.468224 / 8.074308 (3.393916) | 10.784568 / 10.191392 (0.593176) | 0.130332 / 0.680424 (-0.550092) | 0.014203 / 0.534201 (-0.519998) | 0.287125 / 0.579283 (-0.292158) | 0.263921 / 0.434364 (-0.170443) | 0.327824 / 0.540337 (-0.212513) | 0.434679 / 1.386936 (-0.952257) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005194 / 0.011353 (-0.006159) | 0.003411 / 0.011008 (-0.007598) | 0.050122 / 0.038508 (0.011614) | 0.049378 / 0.023109 (0.026269) | 0.272980 / 0.275898 (-0.002918) | 0.298047 / 0.323480 (-0.025433) | 0.003945 / 0.007986 (-0.004041) | 0.002633 / 0.004328 (-0.001696) | 0.048935 / 0.004250 (0.044685) | 0.040157 / 0.037052 (0.003104) | 0.277056 / 0.258489 (0.018567) | 0.299824 / 0.293841 (0.005983) | 0.028997 / 0.128546 (-0.099550) | 0.010868 / 0.075646 (-0.064779) | 0.057895 / 0.419271 (-0.361377) | 0.033522 / 0.043533 (-0.010010) | 0.274912 / 0.255139 (0.019773) | 0.288902 / 0.283200 (0.005702) | 0.018016 / 0.141683 (-0.123667) | 1.116669 / 1.452155 (-0.335485) | 1.175007 / 1.492716 (-0.317710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090169 / 0.018006 (0.072163) | 0.310577 / 0.000490 (0.310087) | 0.000215 / 0.000200 (0.000015) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020448 / 0.037411 (-0.016963) | 0.068216 / 0.014526 (0.053690) | 0.081798 / 0.176557 (-0.094759) | 0.119151 / 0.737135 (-0.617985) | 0.085197 / 0.296338 (-0.211142) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294957 / 0.215209 (0.079748) | 2.874065 / 2.077655 (0.796410) | 1.590963 / 1.504120 (0.086843) | 1.459596 / 1.541195 (-0.081599) | 1.467931 / 1.468490 (-0.000559) | 0.562832 / 4.584777 (-4.021944) | 2.426384 / 3.745712 (-1.319328) | 2.767749 / 5.269862 (-2.502112) | 1.746702 / 4.565676 (-2.818975) | 0.063353 / 0.424275 (-0.360922) | 0.005073 / 0.007607 (-0.002534) | 0.348258 / 0.226044 (0.122213) | 3.390351 / 2.268929 (1.121423) | 1.950092 / 55.444624 (-53.494532) | 1.671227 / 6.876477 (-5.205250) | 1.683349 / 2.142072 (-0.458723) | 0.637613 / 4.805227 (-4.167614) | 0.115172 / 6.500664 (-6.385492) | 0.040202 / 0.075469 (-0.035267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963085 / 1.841788 (-0.878702) | 11.895384 / 8.074308 (3.821076) | 10.609906 / 10.191392 (0.418513) | 0.130865 / 0.680424 (-0.549559) | 0.016020 / 0.534201 (-0.518181) | 0.287540 / 0.579283 (-0.291743) | 0.278204 / 0.434364 (-0.156160) | 0.326007 / 0.540337 (-0.214330) | 0.590881 / 1.386936 (-0.796055) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c291e330a7d460ff09d867377de1d4c53fd5394c \"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) | 0.005266 / 0.011353 (-0.006087) | 0.003751 / 0.011008 (-0.007257) | 0.063835 / 0.038508 (0.025327) | 0.052688 / 0.023109 (0.029579) | 0.261957 / 0.275898 (-0.013941) | 0.284264 / 0.323480 (-0.039216) | 0.003958 / 0.007986 (-0.004027) | 0.002696 / 0.004328 (-0.001633) | 0.052791 / 0.004250 (0.048540) | 0.038294 / 0.037052 (0.001242) | 0.259488 / 0.258489 (0.000999) | 0.298368 / 0.293841 (0.004528) | 0.028309 / 0.128546 (-0.100237) | 0.010819 / 0.075646 (-0.064827) | 0.208221 / 0.419271 (-0.211050) | 0.036373 / 0.043533 (-0.007160) | 0.257000 / 0.255139 (0.001861) | 0.273108 / 0.283200 (-0.010092) | 0.019674 / 0.141683 (-0.122009) | 1.119196 / 1.452155 (-0.332958) | 1.161613 / 1.492716 (-0.331104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093408 / 0.018006 (0.075401) | 0.302278 / 0.000490 (0.301788) | 0.000212 / 0.000200 (0.000012) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019417 / 0.037411 (-0.017995) | 0.060847 / 0.014526 (0.046321) | 0.075399 / 0.176557 (-0.101158) | 0.121233 / 0.737135 (-0.615902) | 0.076916 / 0.296338 (-0.219422) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281265 / 0.215209 (0.066056) | 2.651726 / 2.077655 (0.574072) | 1.457726 / 1.504120 (-0.046394) | 1.339250 / 1.541195 (-0.201945) | 1.398529 / 1.468490 (-0.069961) | 0.566574 / 4.584777 (-4.018203) | 2.431576 / 3.745712 (-1.314136) | 2.845884 / 5.269862 (-2.423977) | 1.798051 / 4.565676 (-2.767626) | 0.063619 / 0.424275 (-0.360656) | 0.005286 / 0.007607 (-0.002321) | 0.332834 / 0.226044 (0.106789) | 3.293222 / 2.268929 (1.024293) | 1.837810 / 55.444624 (-53.606815) | 1.568511 / 6.876477 (-5.307966) | 1.627518 / 2.142072 (-0.514555) | 0.643520 / 4.805227 (-4.161708) | 0.118482 / 6.500664 (-6.382182) | 0.049563 / 0.075469 (-0.025906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947767 / 1.841788 (-0.894021) | 11.994999 / 8.074308 (3.920691) | 10.662651 / 10.191392 (0.471259) | 0.142070 / 0.680424 (-0.538354) | 0.014276 / 0.534201 (-0.519925) | 0.288455 / 0.579283 (-0.290828) | 0.266335 / 0.434364 (-0.168029) | 0.328455 / 0.540337 (-0.211883) | 0.440740 / 1.386936 (-0.946196) |\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) | 0.005636 / 0.011353 (-0.005717) | 0.003664 / 0.011008 (-0.007344) | 0.050340 / 0.038508 (0.011832) | 0.062795 / 0.023109 (0.039685) | 0.280874 / 0.275898 (0.004976) | 0.314056 / 0.323480 (-0.009424) | 0.004089 / 0.007986 (-0.003897) | 0.002780 / 0.004328 (-0.001548) | 0.048468 / 0.004250 (0.044218) | 0.042924 / 0.037052 (0.005871) | 0.281381 / 0.258489 (0.022892) | 0.308232 / 0.293841 (0.014391) | 0.030294 / 0.128546 (-0.098252) | 0.011098 / 0.075646 (-0.064548) | 0.057535 / 0.419271 (-0.361736) | 0.034217 / 0.043533 (-0.009316) | 0.283022 / 0.255139 (0.027883) | 0.298425 / 0.283200 (0.015225) | 0.019285 / 0.141683 (-0.122398) | 1.117722 / 1.452155 (-0.334433) | 1.185878 / 1.492716 (-0.306839) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094915 / 0.018006 (0.076909) | 0.311782 / 0.000490 (0.311293) | 0.000217 / 0.000200 (0.000017) | 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) | 0.022652 / 0.037411 (-0.014759) | 0.069766 / 0.014526 (0.055240) | 0.084495 / 0.176557 (-0.092061) | 0.121295 / 0.737135 (-0.615841) | 0.082447 / 0.296338 (-0.213891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294286 / 0.215209 (0.079077) | 2.863694 / 2.077655 (0.786039) | 1.578338 / 1.504120 (0.074219) | 1.478737 / 1.541195 (-0.062458) | 1.528569 / 1.468490 (0.060079) | 0.576944 / 4.584777 (-4.007833) | 2.438730 / 3.745712 (-1.306982) | 2.956138 / 5.269862 (-2.313723) | 1.844484 / 4.565676 (-2.721192) | 0.065980 / 0.424275 (-0.358295) | 0.004998 / 0.007607 (-0.002609) | 0.352063 / 0.226044 (0.126019) | 3.456355 / 2.268929 (1.187426) | 1.971582 / 55.444624 (-53.473042) | 1.684536 / 6.876477 (-5.191940) | 1.726823 / 2.142072 (-0.415250) | 0.660235 / 4.805227 (-4.144992) | 0.119029 / 6.500664 (-6.381635) | 0.042497 / 0.075469 (-0.032972) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971817 / 1.841788 (-0.869970) | 12.900324 / 8.074308 (4.826015) | 10.957495 / 10.191392 (0.766103) | 0.133705 / 0.680424 (-0.546718) | 0.015669 / 0.534201 (-0.518532) | 0.287340 / 0.579283 (-0.291943) | 0.280380 / 0.434364 (-0.153984) | 0.330369 / 0.540337 (-0.209969) | 0.581793 / 1.386936 (-0.805143) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2af5efae1985499d6a0a1b6ab4120337eebf776 \"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) | 0.005038 / 0.011353 (-0.006315) | 0.003737 / 0.011008 (-0.007272) | 0.063118 / 0.038508 (0.024610) | 0.050120 / 0.023109 (0.027011) | 0.240722 / 0.275898 (-0.035176) | 0.263128 / 0.323480 (-0.060352) | 0.003839 / 0.007986 (-0.004147) | 0.002718 / 0.004328 (-0.001610) | 0.047869 / 0.004250 (0.043618) | 0.038092 / 0.037052 (0.001040) | 0.245759 / 0.258489 (-0.012730) | 0.277728 / 0.293841 (-0.016113) | 0.027466 / 0.128546 (-0.101081) | 0.011767 / 0.075646 (-0.063879) | 0.205505 / 0.419271 (-0.213766) | 0.035429 / 0.043533 (-0.008104) | 0.241665 / 0.255139 (-0.013474) | 0.260908 / 0.283200 (-0.022292) | 0.017133 / 0.141683 (-0.124550) | 1.107725 / 1.452155 (-0.344429) | 1.169707 / 1.492716 (-0.323009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094112 / 0.018006 (0.076106) | 0.302596 / 0.000490 (0.302106) | 0.000237 / 0.000200 (0.000037) | 0.000041 / 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) | 0.017923 / 0.037411 (-0.019488) | 0.060356 / 0.014526 (0.045830) | 0.073708 / 0.176557 (-0.102849) | 0.119952 / 0.737135 (-0.617183) | 0.075350 / 0.296338 (-0.220989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289253 / 0.215209 (0.074044) | 2.800772 / 2.077655 (0.723117) | 1.538368 / 1.504120 (0.034248) | 1.401037 / 1.541195 (-0.140158) | 1.427170 / 1.468490 (-0.041320) | 0.560497 / 4.584777 (-4.024280) | 2.417844 / 3.745712 (-1.327868) | 2.798377 / 5.269862 (-2.471484) | 1.756517 / 4.565676 (-2.809160) | 0.063897 / 0.424275 (-0.360378) | 0.005323 / 0.007607 (-0.002284) | 0.339881 / 0.226044 (0.113836) | 3.354858 / 2.268929 (1.085929) | 1.877233 / 55.444624 (-53.567391) | 1.578713 / 6.876477 (-5.297764) | 1.631898 / 2.142072 (-0.510175) | 0.640303 / 4.805227 (-4.164924) | 0.116731 / 6.500664 (-6.383933) | 0.041978 / 0.075469 (-0.033491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963259 / 1.841788 (-0.878529) | 11.983646 / 8.074308 (3.909338) | 10.561596 / 10.191392 (0.370204) | 0.135863 / 0.680424 (-0.544561) | 0.015607 / 0.534201 (-0.518594) | 0.295164 / 0.579283 (-0.284119) | 0.283366 / 0.434364 (-0.150998) | 0.341848 / 0.540337 (-0.198489) | 0.448359 / 1.386936 (-0.938577) |\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) | 0.005366 / 0.011353 (-0.005987) | 0.003621 / 0.011008 (-0.007387) | 0.048615 / 0.038508 (0.010107) | 0.053950 / 0.023109 (0.030841) | 0.273112 / 0.275898 (-0.002786) | 0.295655 / 0.323480 (-0.027825) | 0.004066 / 0.007986 (-0.003920) | 0.002700 / 0.004328 (-0.001628) | 0.047899 / 0.004250 (0.043648) | 0.041633 / 0.037052 (0.004581) | 0.277760 / 0.258489 (0.019271) | 0.302068 / 0.293841 (0.008227) | 0.028879 / 0.128546 (-0.099668) | 0.010756 / 0.075646 (-0.064891) | 0.057190 / 0.419271 (-0.362082) | 0.032555 / 0.043533 (-0.010978) | 0.272045 / 0.255139 (0.016906) | 0.289330 / 0.283200 (0.006130) | 0.018466 / 0.141683 (-0.123216) | 1.180435 / 1.452155 (-0.271720) | 1.192228 / 1.492716 (-0.300488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094871 / 0.018006 (0.076864) | 0.302552 / 0.000490 (0.302062) | 0.000224 / 0.000200 (0.000024) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022008 / 0.037411 (-0.015403) | 0.068528 / 0.014526 (0.054002) | 0.081735 / 0.176557 (-0.094821) | 0.120990 / 0.737135 (-0.616145) | 0.083155 / 0.296338 (-0.213184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305030 / 0.215209 (0.089821) | 3.009812 / 2.077655 (0.932158) | 1.677773 / 1.504120 (0.173654) | 1.552280 / 1.541195 (0.011085) | 1.606248 / 1.468490 (0.137758) | 0.557093 / 4.584777 (-4.027684) | 2.418292 / 3.745712 (-1.327420) | 2.813049 / 5.269862 (-2.456813) | 1.764507 / 4.565676 (-2.801169) | 0.065089 / 0.424275 (-0.359186) | 0.004944 / 0.007607 (-0.002663) | 0.360672 / 0.226044 (0.134628) | 3.525850 / 2.268929 (1.256921) | 2.030091 / 55.444624 (-53.414533) | 1.754669 / 6.876477 (-5.121807) | 1.772673 / 2.142072 (-0.369399) | 0.642904 / 4.805227 (-4.162324) | 0.116018 / 6.500664 (-6.384646) | 0.041308 / 0.075469 (-0.034161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986386 / 1.841788 (-0.855401) | 12.291623 / 8.074308 (4.217315) | 10.655932 / 10.191392 (0.464540) | 0.141736 / 0.680424 (-0.538688) | 0.016669 / 0.534201 (-0.517532) | 0.286875 / 0.579283 (-0.292408) | 0.281898 / 0.434364 (-0.152466) | 0.325206 / 0.540337 (-0.215132) | 0.577607 / 1.386936 (-0.809329) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cf33502493fb9760ea8cc8e51622bf94d0c9e31 \"CML watermark\")\n", "Alright tests are passing (except one on temp dir cleanup windows but I don't think it's related to this PR ?)\r\n\r\n```\r\nFAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmpqy3f2ft_\\\\hf-internal-testing___dataset_with_script\\\\default\\\\0.0.0\\\\c240e2be3370bdbd\\\\dataset_with_script-train.arrow'\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) | 0.005072 / 0.011353 (-0.006281) | 0.003449 / 0.011008 (-0.007559) | 0.062630 / 0.038508 (0.024122) | 0.054276 / 0.023109 (0.031167) | 0.253345 / 0.275898 (-0.022553) | 0.273460 / 0.323480 (-0.050020) | 0.003859 / 0.007986 (-0.004127) | 0.002646 / 0.004328 (-0.001683) | 0.048289 / 0.004250 (0.044038) | 0.037943 / 0.037052 (0.000891) | 0.256569 / 0.258489 (-0.001920) | 0.287809 / 0.293841 (-0.006032) | 0.027675 / 0.128546 (-0.100872) | 0.010554 / 0.075646 (-0.065092) | 0.205157 / 0.419271 (-0.214115) | 0.035464 / 0.043533 (-0.008069) | 0.254300 / 0.255139 (-0.000839) | 0.272907 / 0.283200 (-0.010292) | 0.018146 / 0.141683 (-0.123537) | 1.110528 / 1.452155 (-0.341626) | 1.170156 / 1.492716 (-0.322560) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093151 / 0.018006 (0.075144) | 0.302087 / 0.000490 (0.301598) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018744 / 0.037411 (-0.018667) | 0.059843 / 0.014526 (0.045317) | 0.073165 / 0.176557 (-0.103391) | 0.120464 / 0.737135 (-0.616671) | 0.074992 / 0.296338 (-0.221347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285103 / 0.215209 (0.069894) | 2.820254 / 2.077655 (0.742600) | 1.505336 / 1.504120 (0.001216) | 1.368631 / 1.541195 (-0.172564) | 1.404140 / 1.468490 (-0.064350) | 0.563906 / 4.584777 (-4.020871) | 2.411871 / 3.745712 (-1.333841) | 2.788390 / 5.269862 (-2.481471) | 1.749788 / 4.565676 (-2.815888) | 0.062171 / 0.424275 (-0.362104) | 0.004918 / 0.007607 (-0.002689) | 0.339615 / 0.226044 (0.113571) | 3.337789 / 2.268929 (1.068861) | 1.808445 / 55.444624 (-53.636180) | 1.541015 / 6.876477 (-5.335462) | 1.572389 / 2.142072 (-0.569683) | 0.641739 / 4.805227 (-4.163488) | 0.115844 / 6.500664 (-6.384820) | 0.042504 / 0.075469 (-0.032965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942463 / 1.841788 (-0.899325) | 11.602364 / 8.074308 (3.528056) | 10.628921 / 10.191392 (0.437529) | 0.136154 / 0.680424 (-0.544270) | 0.013842 / 0.534201 (-0.520359) | 0.287085 / 0.579283 (-0.292198) | 0.269860 / 0.434364 (-0.164503) | 0.329525 / 0.540337 (-0.210812) | 0.441287 / 1.386936 (-0.945649) |\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) | 0.005215 / 0.011353 (-0.006138) | 0.003549 / 0.011008 (-0.007460) | 0.049199 / 0.038508 (0.010691) | 0.051655 / 0.023109 (0.028545) | 0.272150 / 0.275898 (-0.003748) | 0.291978 / 0.323480 (-0.031502) | 0.003985 / 0.007986 (-0.004001) | 0.002668 / 0.004328 (-0.001661) | 0.048524 / 0.004250 (0.044274) | 0.039824 / 0.037052 (0.002772) | 0.275566 / 0.258489 (0.017077) | 0.298076 / 0.293841 (0.004235) | 0.029508 / 0.128546 (-0.099038) | 0.010673 / 0.075646 (-0.064973) | 0.057327 / 0.419271 (-0.361944) | 0.032590 / 0.043533 (-0.010943) | 0.273295 / 0.255139 (0.018156) | 0.289127 / 0.283200 (0.005928) | 0.017694 / 0.141683 (-0.123989) | 1.134502 / 1.452155 (-0.317653) | 1.185603 / 1.492716 (-0.307114) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098403 / 0.018006 (0.080396) | 0.302735 / 0.000490 (0.302245) | 0.000228 / 0.000200 (0.000028) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025192 / 0.037411 (-0.012219) | 0.068149 / 0.014526 (0.053623) | 0.082220 / 0.176557 (-0.094336) | 0.119491 / 0.737135 (-0.617645) | 0.082484 / 0.296338 (-0.213855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295339 / 0.215209 (0.080130) | 2.868411 / 2.077655 (0.790757) | 1.590665 / 1.504120 (0.086545) | 1.465995 / 1.541195 (-0.075200) | 1.489205 / 1.468490 (0.020715) | 0.562503 / 4.584777 (-4.022274) | 2.480100 / 3.745712 (-1.265613) | 2.774216 / 5.269862 (-2.495646) | 1.733129 / 4.565676 (-2.832548) | 0.062698 / 0.424275 (-0.361577) | 0.004910 / 0.007607 (-0.002697) | 0.354766 / 0.226044 (0.128722) | 3.435541 / 2.268929 (1.166613) | 1.953357 / 55.444624 (-53.491267) | 1.673584 / 6.876477 (-5.202893) | 1.677749 / 2.142072 (-0.464323) | 0.632601 / 4.805227 (-4.172626) | 0.114875 / 6.500664 (-6.385789) | 0.040577 / 0.075469 (-0.034892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967003 / 1.841788 (-0.874785) | 11.964490 / 8.074308 (3.890181) | 10.493812 / 10.191392 (0.302420) | 0.132177 / 0.680424 (-0.548247) | 0.015149 / 0.534201 (-0.519052) | 0.289011 / 0.579283 (-0.290272) | 0.285479 / 0.434364 (-0.148885) | 0.327090 / 0.540337 (-0.213248) | 0.571747 / 1.386936 (-0.815189) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c9b4cb7ee4720415261216d72051e2a3320fe41 \"CML watermark\")\n" ]
2023-11-23T17:31:57
2023-12-01T17:57:17
2023-12-01 17:50:59+00:00
MEMBER
nan
The idea is to make this code work for datasets with scripts if they have a Parquet export ```python ds = load_dataset("squad", trust_remote_code=False) ``` And more generally, it means we use the Parquet export whenever it's possible (it's safer and faster than dataset scripts). I also added a `config.USE_PARQUET_EXPORT` variable to use in the datasets-server parquet conversion job - [x] Needs https://github.com/huggingface/datasets/pull/6429 to be merged first cc @severo I use the /parquet and /info endpoints from datasets-server
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2008195298
I_kwDODunzps53sqDi
6447
Support one dataset loader per config when using YAML
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2023-11-23T13:03:07
2023-11-23T13:03:07
NaT
CONTRIBUTOR
nan
### Feature request See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1 I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc. ### Motivation It would be more flexible for the users ### Your contribution No specific contribution
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2007092708
I_kwDODunzps53oc3k
6446
Speech Commands v2 dataset doesn't match AST-v2 config
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[ "You can use `.align_labels_with_mapping` on the dataset to align the labels with the model config.\r\n\r\nRegarding the number of labels, only the special `_silence_` label corresponding to noise is missing, which is consistent with the model paper (reports training on 35 labels). You can run a `.filter` to drop it.\r\n\r\nPS: You should create a discussion on a model/dataset repo (on the Hub) for these kinds of questions", "Thanks, will keep that in mind. But I tried running `dataset_aligned = dataset.align_labels_with_mapping(model.config.id2label, 'label')`, and received this error: \r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in align_labels_with_mapping\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in <dictcomp>\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\nAttributeError: 'int' object has no attribute 'lower'\r\n```\r\nMy guess is that the dataset `label` column is purely an int ID, and I'm not sure there's a way to identify which class label the ID belongs to in the dataset easily.", "Replacing `model.config.id2label` with `model.config.label2id` should fix the issue.\r\n\r\nSo, the full code to align the labels with the model config is as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoFeatureExtractor, AutoModelForAudioClassification\r\n\r\n# extractor = AutoFeatureExtractor.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\nmodel = AutoModelForAudioClassification.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\n\r\nds = load_dataset(\"speech_commands\", \"v0.02\")\r\nds = ds.filter(lambda label: label != ds[\"train\"].features[\"label\"].str2int(\"_silence_\"), input_columns=\"label\")\r\nds = ds.align_labels_with_mapping(model.config.label2id, \"label\")\r\n```" ]
2023-11-22T20:46:36
2023-11-28T14:46:08
2023-11-28 14:46:08+00:00
NONE
nan
### Describe the bug [According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`. ### Steps to reproduce the bug ``` >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2") >>> model.config.id2label {0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'} >>> dataset = load_dataset("speech_commands", "v0.02", split="test") >>> torch.unique(torch.Tensor(dataset['label'])) tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.]) ``` If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`. ### Expected behavior The labels should match completely and there should be the same number of label classes between the model config and the dataset itself. ### Environment info datasets = 2.14.6, transformers = 4.33.3
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PR_kwDODunzps5gKg2d
6445
Use `filelock` package for file locking
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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) | 0.005431 / 0.011353 (-0.005922) | 0.003255 / 0.011008 (-0.007753) | 0.062867 / 0.038508 (0.024359) | 0.051917 / 0.023109 (0.028808) | 0.254229 / 0.275898 (-0.021669) | 0.276949 / 0.323480 (-0.046531) | 0.002868 / 0.007986 (-0.005117) | 0.002539 / 0.004328 (-0.001789) | 0.048366 / 0.004250 (0.044115) | 0.038497 / 0.037052 (0.001445) | 0.252158 / 0.258489 (-0.006332) | 0.288868 / 0.293841 (-0.004973) | 0.027956 / 0.128546 (-0.100591) | 0.010500 / 0.075646 (-0.065147) | 0.209263 / 0.419271 (-0.210008) | 0.035415 / 0.043533 (-0.008118) | 0.253104 / 0.255139 (-0.002035) | 0.274646 / 0.283200 (-0.008554) | 0.019923 / 0.141683 (-0.121760) | 1.081870 / 1.452155 (-0.370285) | 1.157159 / 1.492716 (-0.335557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097420 / 0.018006 (0.079414) | 0.315021 / 0.000490 (0.314531) | 0.000218 / 0.000200 (0.000018) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018826 / 0.037411 (-0.018585) | 0.061921 / 0.014526 (0.047395) | 0.086825 / 0.176557 (-0.089731) | 0.120606 / 0.737135 (-0.616529) | 0.074344 / 0.296338 (-0.221994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283238 / 0.215209 (0.068028) | 2.771817 / 2.077655 (0.694162) | 1.500194 / 1.504120 (-0.003926) | 1.379286 / 1.541195 (-0.161908) | 1.447747 / 1.468490 (-0.020743) | 0.587176 / 4.584777 (-3.997601) | 2.411260 / 3.745712 (-1.334452) | 2.897682 / 5.269862 (-2.372180) | 1.821720 / 4.565676 (-2.743957) | 0.063299 / 0.424275 (-0.360976) | 0.004969 / 0.007607 (-0.002639) | 0.346417 / 0.226044 (0.120373) | 3.432936 / 2.268929 (1.164007) | 1.898662 / 55.444624 (-53.545963) | 1.624339 / 6.876477 (-5.252138) | 1.641653 / 2.142072 (-0.500419) | 0.655773 / 4.805227 (-4.149454) | 0.118588 / 6.500664 (-6.382076) | 0.043919 / 0.075469 (-0.031551) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949466 / 1.841788 (-0.892322) | 12.378025 / 8.074308 (4.303717) | 10.750942 / 10.191392 (0.559550) | 0.146575 / 0.680424 (-0.533849) | 0.015453 / 0.534201 (-0.518748) | 0.290608 / 0.579283 (-0.288676) | 0.273000 / 0.434364 (-0.161364) | 0.328019 / 0.540337 (-0.212318) | 0.417396 / 1.386936 (-0.969540) |\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) | 0.005363 / 0.011353 (-0.005990) | 0.003421 / 0.011008 (-0.007587) | 0.049429 / 0.038508 (0.010920) | 0.052774 / 0.023109 (0.029664) | 0.274058 / 0.275898 (-0.001840) | 0.297307 / 0.323480 (-0.026173) | 0.004000 / 0.007986 (-0.003986) | 0.002463 / 0.004328 (-0.001866) | 0.048824 / 0.004250 (0.044574) | 0.041064 / 0.037052 (0.004012) | 0.279066 / 0.258489 (0.020577) | 0.302420 / 0.293841 (0.008579) | 0.029665 / 0.128546 (-0.098881) | 0.010628 / 0.075646 (-0.065018) | 0.057678 / 0.419271 (-0.361594) | 0.032731 / 0.043533 (-0.010802) | 0.274662 / 0.255139 (0.019523) | 0.291878 / 0.283200 (0.008678) | 0.018820 / 0.141683 (-0.122863) | 1.124042 / 1.452155 (-0.328112) | 1.175020 / 1.492716 (-0.317697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099419 / 0.018006 (0.081413) | 0.311511 / 0.000490 (0.311022) | 0.000228 / 0.000200 (0.000028) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022478 / 0.037411 (-0.014933) | 0.071955 / 0.014526 (0.057429) | 0.081423 / 0.176557 (-0.095134) | 0.119574 / 0.737135 (-0.617561) | 0.084724 / 0.296338 (-0.211615) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295537 / 0.215209 (0.080328) | 2.893855 / 2.077655 (0.816201) | 1.602065 / 1.504120 (0.097945) | 1.478193 / 1.541195 (-0.063002) | 1.508250 / 1.468490 (0.039760) | 0.566140 / 4.584777 (-4.018637) | 2.455474 / 3.745712 (-1.290238) | 2.849525 / 5.269862 (-2.420337) | 1.763830 / 4.565676 (-2.801846) | 0.062375 / 0.424275 (-0.361900) | 0.004992 / 0.007607 (-0.002615) | 0.346068 / 0.226044 (0.120023) | 3.452421 / 2.268929 (1.183492) | 1.970346 / 55.444624 (-53.474278) | 1.690865 / 6.876477 (-5.185612) | 1.705358 / 2.142072 (-0.436714) | 0.644261 / 4.805227 (-4.160967) | 0.120596 / 6.500664 (-6.380068) | 0.042699 / 0.075469 (-0.032770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980506 / 1.841788 (-0.861281) | 12.401901 / 8.074308 (4.327593) | 11.169413 / 10.191392 (0.978021) | 0.142540 / 0.680424 (-0.537884) | 0.015730 / 0.534201 (-0.518471) | 0.288871 / 0.579283 (-0.290412) | 0.287487 / 0.434364 (-0.146877) | 0.325133 / 0.540337 (-0.215204) | 0.417979 / 1.386936 (-0.968957) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#965685891db0d06979490aaebab72d5dc628e42b \"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) | 0.005062 / 0.011353 (-0.006291) | 0.003024 / 0.011008 (-0.007984) | 0.061801 / 0.038508 (0.023293) | 0.048934 / 0.023109 (0.025825) | 0.248024 / 0.275898 (-0.027874) | 0.265665 / 0.323480 (-0.057815) | 0.003885 / 0.007986 (-0.004100) | 0.002371 / 0.004328 (-0.001957) | 0.047895 / 0.004250 (0.043644) | 0.039015 / 0.037052 (0.001963) | 0.252320 / 0.258489 (-0.006169) | 0.286533 / 0.293841 (-0.007308) | 0.027694 / 0.128546 (-0.100852) | 0.010254 / 0.075646 (-0.065392) | 0.206586 / 0.419271 (-0.212685) | 0.035681 / 0.043533 (-0.007852) | 0.251645 / 0.255139 (-0.003494) | 0.285462 / 0.283200 (0.002262) | 0.017326 / 0.141683 (-0.124357) | 1.086927 / 1.452155 (-0.365228) | 1.153172 / 1.492716 (-0.339545) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093020 / 0.018006 (0.075014) | 0.300018 / 0.000490 (0.299528) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018828 / 0.037411 (-0.018584) | 0.062569 / 0.014526 (0.048043) | 0.074130 / 0.176557 (-0.102427) | 0.119304 / 0.737135 (-0.617832) | 0.076409 / 0.296338 (-0.219930) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285938 / 0.215209 (0.070729) | 2.780662 / 2.077655 (0.703007) | 1.522401 / 1.504120 (0.018281) | 1.392475 / 1.541195 (-0.148720) | 1.412517 / 1.468490 (-0.055973) | 0.562768 / 4.584777 (-4.022009) | 2.421406 / 3.745712 (-1.324306) | 2.786271 / 5.269862 (-2.483591) | 1.737193 / 4.565676 (-2.828484) | 0.062775 / 0.424275 (-0.361500) | 0.004908 / 0.007607 (-0.002699) | 0.345070 / 0.226044 (0.119026) | 3.383700 / 2.268929 (1.114771) | 1.795974 / 55.444624 (-53.648651) | 1.527656 / 6.876477 (-5.348820) | 1.514035 / 2.142072 (-0.628037) | 0.647652 / 4.805227 (-4.157575) | 0.120121 / 6.500664 (-6.380543) | 0.042259 / 0.075469 (-0.033210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948951 / 1.841788 (-0.892837) | 11.514971 / 8.074308 (3.440663) | 10.722668 / 10.191392 (0.531276) | 0.143034 / 0.680424 (-0.537390) | 0.014800 / 0.534201 (-0.519401) | 0.286189 / 0.579283 (-0.293094) | 0.270735 / 0.434364 (-0.163629) | 0.323907 / 0.540337 (-0.216430) | 0.417569 / 1.386936 (-0.969367) |\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) | 0.005670 / 0.011353 (-0.005683) | 0.003238 / 0.011008 (-0.007770) | 0.048520 / 0.038508 (0.010012) | 0.051341 / 0.023109 (0.028232) | 0.273883 / 0.275898 (-0.002015) | 0.295165 / 0.323480 (-0.028315) | 0.004755 / 0.007986 (-0.003231) | 0.002471 / 0.004328 (-0.001857) | 0.047487 / 0.004250 (0.043237) | 0.040225 / 0.037052 (0.003172) | 0.276758 / 0.258489 (0.018269) | 0.301182 / 0.293841 (0.007341) | 0.029749 / 0.128546 (-0.098797) | 0.010340 / 0.075646 (-0.065306) | 0.057193 / 0.419271 (-0.362079) | 0.033067 / 0.043533 (-0.010466) | 0.272716 / 0.255139 (0.017577) | 0.292301 / 0.283200 (0.009101) | 0.019075 / 0.141683 (-0.122608) | 1.101778 / 1.452155 (-0.350376) | 1.173573 / 1.492716 (-0.319143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091008 / 0.018006 (0.073002) | 0.300749 / 0.000490 (0.300259) | 0.000218 / 0.000200 (0.000018) | 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) | 0.021760 / 0.037411 (-0.015651) | 0.071407 / 0.014526 (0.056881) | 0.081151 / 0.176557 (-0.095406) | 0.120140 / 0.737135 (-0.616995) | 0.082408 / 0.296338 (-0.213931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294828 / 0.215209 (0.079619) | 2.880701 / 2.077655 (0.803047) | 1.604187 / 1.504120 (0.100068) | 1.479236 / 1.541195 (-0.061959) | 1.498875 / 1.468490 (0.030385) | 0.561950 / 4.584777 (-4.022827) | 2.462531 / 3.745712 (-1.283181) | 2.800905 / 5.269862 (-2.468957) | 1.746535 / 4.565676 (-2.819141) | 0.062732 / 0.424275 (-0.361544) | 0.004932 / 0.007607 (-0.002675) | 0.347125 / 0.226044 (0.121081) | 3.431343 / 2.268929 (1.162415) | 1.964999 / 55.444624 (-53.479625) | 1.669709 / 6.876477 (-5.206768) | 1.675148 / 2.142072 (-0.466924) | 0.635436 / 4.805227 (-4.169792) | 0.116598 / 6.500664 (-6.384066) | 0.041447 / 0.075469 (-0.034022) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975751 / 1.841788 (-0.866037) | 12.060246 / 8.074308 (3.985938) | 10.871641 / 10.191392 (0.680249) | 0.142936 / 0.680424 (-0.537488) | 0.015779 / 0.534201 (-0.518422) | 0.287120 / 0.579283 (-0.292163) | 0.283963 / 0.434364 (-0.150401) | 0.341231 / 0.540337 (-0.199107) | 0.419518 / 1.386936 (-0.967418) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0943ff0072dcef473530d8a494f314048f3a3d51 \"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) | 0.005105 / 0.011353 (-0.006248) | 0.002855 / 0.011008 (-0.008153) | 0.062044 / 0.038508 (0.023536) | 0.052948 / 0.023109 (0.029839) | 0.249841 / 0.275898 (-0.026057) | 0.276687 / 0.323480 (-0.046792) | 0.003792 / 0.007986 (-0.004194) | 0.002385 / 0.004328 (-0.001943) | 0.048648 / 0.004250 (0.044398) | 0.038317 / 0.037052 (0.001264) | 0.255235 / 0.258489 (-0.003254) | 0.287870 / 0.293841 (-0.005971) | 0.027429 / 0.128546 (-0.101117) | 0.010182 / 0.075646 (-0.065464) | 0.206980 / 0.419271 (-0.212291) | 0.035444 / 0.043533 (-0.008089) | 0.255073 / 0.255139 (-0.000066) | 0.270636 / 0.283200 (-0.012563) | 0.018003 / 0.141683 (-0.123680) | 1.124691 / 1.452155 (-0.327463) | 1.191872 / 1.492716 (-0.300844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088824 / 0.018006 (0.070818) | 0.302771 / 0.000490 (0.302281) | 0.000210 / 0.000200 (0.000010) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018102 / 0.037411 (-0.019310) | 0.062131 / 0.014526 (0.047605) | 0.073230 / 0.176557 (-0.103327) | 0.119789 / 0.737135 (-0.617346) | 0.074804 / 0.296338 (-0.221534) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293244 / 0.215209 (0.078035) | 2.891401 / 2.077655 (0.813746) | 1.504481 / 1.504120 (0.000361) | 1.381251 / 1.541195 (-0.159944) | 1.387245 / 1.468490 (-0.081245) | 0.552732 / 4.584777 (-4.032045) | 2.386439 / 3.745712 (-1.359273) | 2.718918 / 5.269862 (-2.550944) | 1.725401 / 4.565676 (-2.840275) | 0.061946 / 0.424275 (-0.362329) | 0.004957 / 0.007607 (-0.002650) | 0.342776 / 0.226044 (0.116731) | 3.418911 / 2.268929 (1.149983) | 1.838283 / 55.444624 (-53.606341) | 1.538013 / 6.876477 (-5.338464) | 1.545144 / 2.142072 (-0.596928) | 0.637857 / 4.805227 (-4.167370) | 0.116451 / 6.500664 (-6.384213) | 0.042228 / 0.075469 (-0.033241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943575 / 1.841788 (-0.898212) | 11.492939 / 8.074308 (3.418631) | 10.601605 / 10.191392 (0.410212) | 0.139084 / 0.680424 (-0.541340) | 0.013691 / 0.534201 (-0.520510) | 0.286696 / 0.579283 (-0.292587) | 0.259979 / 0.434364 (-0.174385) | 0.322578 / 0.540337 (-0.217759) | 0.411950 / 1.386936 (-0.974986) |\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) | 0.005168 / 0.011353 (-0.006185) | 0.003238 / 0.011008 (-0.007770) | 0.049028 / 0.038508 (0.010520) | 0.052930 / 0.023109 (0.029821) | 0.274750 / 0.275898 (-0.001148) | 0.294023 / 0.323480 (-0.029457) | 0.003829 / 0.007986 (-0.004157) | 0.002372 / 0.004328 (-0.001956) | 0.048689 / 0.004250 (0.044439) | 0.040056 / 0.037052 (0.003003) | 0.280147 / 0.258489 (0.021658) | 0.304871 / 0.293841 (0.011030) | 0.028734 / 0.128546 (-0.099812) | 0.010624 / 0.075646 (-0.065022) | 0.058705 / 0.419271 (-0.360566) | 0.032140 / 0.043533 (-0.011393) | 0.276702 / 0.255139 (0.021563) | 0.293186 / 0.283200 (0.009987) | 0.018124 / 0.141683 (-0.123559) | 1.139398 / 1.452155 (-0.312757) | 1.174862 / 1.492716 (-0.317855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087627 / 0.018006 (0.069620) | 0.298376 / 0.000490 (0.297886) | 0.000238 / 0.000200 (0.000038) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021344 / 0.037411 (-0.016067) | 0.070208 / 0.014526 (0.055682) | 0.081177 / 0.176557 (-0.095380) | 0.120170 / 0.737135 (-0.616965) | 0.082472 / 0.296338 (-0.213866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293227 / 0.215209 (0.078018) | 2.844619 / 2.077655 (0.766964) | 1.586922 / 1.504120 (0.082803) | 1.460256 / 1.541195 (-0.080938) | 1.475955 / 1.468490 (0.007465) | 0.553226 / 4.584777 (-4.031551) | 2.418869 / 3.745712 (-1.326843) | 2.709256 / 5.269862 (-2.560606) | 1.705935 / 4.565676 (-2.859741) | 0.062391 / 0.424275 (-0.361884) | 0.004929 / 0.007607 (-0.002678) | 0.350358 / 0.226044 (0.124313) | 3.448824 / 2.268929 (1.179896) | 1.929451 / 55.444624 (-53.515174) | 1.669438 / 6.876477 (-5.207038) | 1.660923 / 2.142072 (-0.481150) | 0.633107 / 4.805227 (-4.172120) | 0.114657 / 6.500664 (-6.386007) | 0.041256 / 0.075469 (-0.034214) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968408 / 1.841788 (-0.873380) | 11.749754 / 8.074308 (3.675446) | 10.796670 / 10.191392 (0.605278) | 0.128881 / 0.680424 (-0.551543) | 0.015326 / 0.534201 (-0.518875) | 0.286407 / 0.579283 (-0.292876) | 0.276324 / 0.434364 (-0.158040) | 0.326201 / 0.540337 (-0.214136) | 0.419854 / 1.386936 (-0.967082) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1731d5a8cd103533ef6b438b4429ab51d3a6a0ce \"CML watermark\")\n" ]
2023-11-22T19:04:45
2023-11-23T18:47:30
2023-11-23 18:41:23+00:00
COLLABORATOR
nan
Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂. (Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)
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2006842179
PR_kwDODunzps5gKG_e
6444
Remove `Table.__getstate__` and `Table.__setstate__`
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[ "Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides.", "@mariosasko \r\nCool!\r\nI removed these overrides, and it worked.\r\n\r\nAll modifications are committed. Ready for review!", "_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) | 0.005251 / 0.011353 (-0.006102) | 0.003804 / 0.011008 (-0.007204) | 0.063143 / 0.038508 (0.024635) | 0.059409 / 0.023109 (0.036300) | 0.255319 / 0.275898 (-0.020579) | 0.279194 / 0.323480 (-0.044285) | 0.004643 / 0.007986 (-0.003343) | 0.002560 / 0.004328 (-0.001768) | 0.047490 / 0.004250 (0.043240) | 0.039034 / 0.037052 (0.001982) | 0.257352 / 0.258489 (-0.001137) | 0.293029 / 0.293841 (-0.000812) | 0.027548 / 0.128546 (-0.100998) | 0.011307 / 0.075646 (-0.064339) | 0.210325 / 0.419271 (-0.208946) | 0.035161 / 0.043533 (-0.008372) | 0.253491 / 0.255139 (-0.001648) | 0.272085 / 0.283200 (-0.011115) | 0.018924 / 0.141683 (-0.122759) | 1.111148 / 1.452155 (-0.341007) | 1.178076 / 1.492716 (-0.314641) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092447 / 0.018006 (0.074441) | 0.303680 / 0.000490 (0.303190) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019087 / 0.037411 (-0.018325) | 0.062663 / 0.014526 (0.048137) | 0.074651 / 0.176557 (-0.101905) | 0.121334 / 0.737135 (-0.615802) | 0.076703 / 0.296338 (-0.219636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286505 / 0.215209 (0.071295) | 2.804942 / 2.077655 (0.727287) | 1.481930 / 1.504120 (-0.022190) | 1.369485 / 1.541195 (-0.171710) | 1.424467 / 1.468490 (-0.044023) | 0.556810 / 4.584777 (-4.027967) | 2.416338 / 3.745712 (-1.329374) | 2.901869 / 5.269862 (-2.367992) | 1.827007 / 4.565676 (-2.738669) | 0.062252 / 0.424275 (-0.362024) | 0.005076 / 0.007607 (-0.002531) | 0.343850 / 0.226044 (0.117805) | 3.377611 / 2.268929 (1.108683) | 1.860214 / 55.444624 (-53.584410) | 1.595146 / 6.876477 (-5.281331) | 1.627234 / 2.142072 (-0.514838) | 0.651027 / 4.805227 (-4.154200) | 0.119214 / 6.500664 (-6.381450) | 0.043342 / 0.075469 (-0.032127) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942863 / 1.841788 (-0.898924) | 12.484633 / 8.074308 (4.410324) | 10.560668 / 10.191392 (0.369276) | 0.144647 / 0.680424 (-0.535777) | 0.014734 / 0.534201 (-0.519466) | 0.286575 / 0.579283 (-0.292708) | 0.270913 / 0.434364 (-0.163451) | 0.323792 / 0.540337 (-0.216545) | 0.419186 / 1.386936 (-0.967750) |\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) | 0.005315 / 0.011353 (-0.006038) | 0.003548 / 0.011008 (-0.007460) | 0.049271 / 0.038508 (0.010763) | 0.055198 / 0.023109 (0.032089) | 0.275940 / 0.275898 (0.000042) | 0.307637 / 0.323480 (-0.015843) | 0.003997 / 0.007986 (-0.003988) | 0.002544 / 0.004328 (-0.001785) | 0.050381 / 0.004250 (0.046130) | 0.041158 / 0.037052 (0.004105) | 0.281519 / 0.258489 (0.023030) | 0.308085 / 0.293841 (0.014244) | 0.030464 / 0.128546 (-0.098083) | 0.010690 / 0.075646 (-0.064957) | 0.057458 / 0.419271 (-0.361814) | 0.032814 / 0.043533 (-0.010719) | 0.282435 / 0.255139 (0.027296) | 0.301342 / 0.283200 (0.018142) | 0.017556 / 0.141683 (-0.124127) | 1.159423 / 1.452155 (-0.292732) | 1.177344 / 1.492716 (-0.315372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091086 / 0.018006 (0.073079) | 0.305316 / 0.000490 (0.304826) | 0.000218 / 0.000200 (0.000019) | 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) | 0.021832 / 0.037411 (-0.015579) | 0.071055 / 0.014526 (0.056529) | 0.082982 / 0.176557 (-0.093574) | 0.119966 / 0.737135 (-0.617169) | 0.083539 / 0.296338 (-0.212800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302501 / 0.215209 (0.087292) | 2.936347 / 2.077655 (0.858692) | 1.601658 / 1.504120 (0.097538) | 1.467267 / 1.541195 (-0.073928) | 1.514656 / 1.468490 (0.046166) | 0.563934 / 4.584777 (-4.020843) | 2.513715 / 3.745712 (-1.231997) | 2.813014 / 5.269862 (-2.456847) | 1.773243 / 4.565676 (-2.792433) | 0.063208 / 0.424275 (-0.361067) | 0.004979 / 0.007607 (-0.002628) | 0.360694 / 0.226044 (0.134650) | 3.520578 / 2.268929 (1.251650) | 1.975369 / 55.444624 (-53.469255) | 1.691257 / 6.876477 (-5.185220) | 1.730872 / 2.142072 (-0.411200) | 0.655366 / 4.805227 (-4.149861) | 0.146043 / 6.500664 (-6.354621) | 0.041386 / 0.075469 (-0.034083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979840 / 1.841788 (-0.861948) | 12.456924 / 8.074308 (4.382616) | 10.938595 / 10.191392 (0.747203) | 0.133853 / 0.680424 (-0.546571) | 0.015744 / 0.534201 (-0.518457) | 0.289585 / 0.579283 (-0.289698) | 0.291143 / 0.434364 (-0.143221) | 0.328109 / 0.540337 (-0.212228) | 0.561897 / 1.386936 (-0.825039) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f \"CML watermark\")\n" ]
2023-11-22T17:55:10
2023-11-23T15:19:43
2023-11-23 15:13:28+00:00
CONTRIBUTOR
nan
When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'` ```python from torch import distributed as dist if dist.get_rank() == 0: dataset = process_dataset(*args, **kwargs) objects = [dataset] else: objects = [None] dist.broadcast_object_list(objects, src=0) dataset = objects[0] ```
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6443
Trouble loading files defined in YAML explicitly
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[ "There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ", "wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?" ]
2023-11-22T15:18:10
2023-11-23T09:06:20
NaT
CONTRIBUTOR
nan
Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```
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2006086907
I_kwDODunzps53knT7
6442
Trouble loading image folder with additional features - metadata file ignored
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[ "I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)" ]
2023-11-22T11:01:35
2023-11-24T17:13:03
2023-11-24 17:13:03+00:00
NONE
nan
### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0
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2004985857
I_kwDODunzps53gagB
6441
Trouble Loading a Gated Dataset For User with Granted Permission
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[ "> Also when they try to click the url link for the dataset they get a 404 error.\r\n\r\nThis seems to be a Hub error then (cc @SBrandeis)", "Could you report this to https://discuss.huggingface.co/c/hub/23, providing the URL of the dataset, or at least if the dataset is public or private?", "Thanks for the reply! I've created an issue on the hub's board here: https://discuss.huggingface.co/t/trouble-loading-a-gated-dataset-for-user-with-granted-permission/65565" ]
2023-11-21T19:24:36
2023-12-13T08:27:16
2023-12-13 08:27:16+00:00
NONE
nan
### Describe the bug I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get `FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error. ### Steps to reproduce the bug 1. Grant access to gated dataset for specific users 2. Users accept invitation 3. Users login to hugging face hub using cli login 4. Users run load_dataset ### Expected behavior Dataset is loaded normally for users who were granted access to the gated dataset. ### Environment info datasets==2.15.0
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2004509301
I_kwDODunzps53emJ1
6440
`.map` not hashing under python 3.9
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[ "Tried to upgrade Python to 3.11 - still get this message. A partial solution is to NOT use `num_proc` at all. It will be considerably longer to finish the job.", "Hi! The `model = torch.compile(model)` line is problematic for our hashing logic. We would have to merge https://github.com/huggingface/datasets/pull/5867 to support hashing `torch.compile`-ed models/functions. \r\n\r\nI've started refactoring the hashing logic and plan to incorporate a fix for `torch.compile` as part of it, so this should be addressed soon (probably this or next week). " ]
2023-11-21T15:14:54
2023-11-28T16:29:33
2023-11-28 16:29:33+00:00
NONE
nan
### Describe the bug The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message: `Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ### Steps to reproduce the bug ```python def map_to_pred(batch): """ Perform inference on an audio batch Parameters: batch (dict): A dictionary containing audio data and other related information. Returns: dict: The input batch dictionary with added prediction and transcription fields. """ audio = batch['audio'] input_features = processor( audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features input_features = input_features.to('cuda') with torch.no_grad(): predicted_ids = model.generate(input_features) preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] batch['prediction'] = processor.tokenizer._normalize(preds) batch["transcription"] = processor.tokenizer._normalize(batch['transcription']) return batch MODEL_CARD = "openai/whisper-small" MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1] model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD) processor = AutoProcessor.from_pretrained( MODEL_CARD, language="english", task="transcribe") model = torch.compile(model) dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test") dt = dt.cast_column("audio", Audio(sampling_rate=16000)) result = coraal_dt.map(map_to_pred, num_proc=16) ``` ### Expected behavior Hashed and cached dataset starts inferencing ### Environment info - `transformers` version: 4.35.0 - Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - Safetensors version: 0.4.0 - Accelerate version: 0.24.1 - Accelerate config: not found - PyTorch version (GPU?): 2.1.0 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no
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2002916514
I_kwDODunzps53YhSi
6439
Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding
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2023-11-20T20:07:23
2023-11-20T20:07:37
NaT
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nan
### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1
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2002032804
I_kwDODunzps53VJik
6438
Support GeoParquet
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[ "Thank you, @severo ! I would be more than happy to help in any way I can. I am not familiar with this repo's codebase, but I would be eager to contribute. :)\r\n\r\nFor the preview in Datasets Hub, I think it makes sense to just display the geospatial column as text. If there were a dataset loader, though, I think it should be able to support the geospatial components. Geopandas is probably the most user-friendly interface for that. I'm not sure if it's currently relevant in the context of geoparquet, but I think the pyogrio driver is faster than fiona.\r\n\r\nBut the whole gdal dependency thing can be a real pain. If anything, it would need to be an optional dependency. Maybe it would be best if the loader tries importing relevant geospatial libraries, and in the event of an ImportError, falls back to text for the geometry column.\r\n\r\nPlease let me know if I can be of assistance, and thanks again for creating this Issue. :)", "Just hitting into this same issue too showing GeoParquet files in Datasets Viewer. I tried to implement a custom reader for GeoParquet in https://huggingface.co/datasets/weiji14/clay_vector_embeddings/discussions/1, but it seems like HuggingFace has disabled datasets with custom loading scripts from using the dataset viewer according to https://discuss.huggingface.co/t/dataset-repo-requires-arbitrary-python-code-execution/59346 :frowning_face: \r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/2f84d8ce-91c2-48cb-b72c-547ea8583892)\r\n\r\nI'm thinking now if there's a way to simply map files with GeoParquet extensions (*.gpq, *.geoparquet, etc) to use the Parquet reader. Maybe we could allowlist these geoparquet file extensions at https://github.com/huggingface/datasets/blame/0caf91285116ec910f409e82cc6e1f4cff7496e3/src/datasets/packaged_modules/__init__.py#L30-L51? Having the table columns show up would be a quick win.\r\n\r\nLonger term though, it would certainly be nice if the WKB geometry columns could be displayed in a nicer form. Geopandas' [read_parquet](https://geopandas.org/en/v0.14.1/docs/reference/api/geopandas.read_parquet.html) function is supposedly faster than `pyogrio.read_dataframe` according to https://github.com/geopandas/geopandas/discussions/2724#discussioncomment-4606048, but there's also [`pyogrio.raw.read_arrow`](https://pyogrio.readthedocs.io/en/latest/api.html#pyogrio.raw.read_arrow) now that can read into a `pyarrow.Table` directly.", "Update: It looks like renaming the GeoParquet file to have a file extension of `*.parquet` works (see https://huggingface.co/datasets/weiji14/clay_vector_embeddings). HuggingFace's default parquet reader is able to read the GeoParquet file, though the geometry column is of an unknown type:\r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/9060c300-d595-4409-9ccb-5e0207396883)\r\n\r\nI've opened a quick PR at #6508 to allow files with a `*.geoparquet` or `*.gpq` extension to be read using the default Parquet reader. Let's see how that goes :smile:", "@joshuasundance-swca, @weiji14, If I'm understanding this correctly, the code below wouldn't be recommended to due to dependency headaches? If that's the case, what solution would there be to see the geometry features for .gpq files in huggingfaceHub? \r\n\r\ncode for dataset_loader.py\r\n```\r\nimport geopandas as gpd\r\n# ... (other imports remain the same)\r\n\r\nclass ClayVectorEmbeddings(datasets.ArrowBasedBuilder):\r\n # ... (other parts of the class remain the same)\r\n\r\n def _info(self):\r\n # Read the GeoParquet file to get the schema for the 'geometry' feature\r\n gdf = gpd.read_file(\"path/to/your/geoparquet/file.gpq\") # Replace with your file path\r\n geometry_schema = str(gdf.geometry.dtype)\r\n\r\n return datasets.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=\"Clay Vector Embeddings in GeoParquet format.\",\r\n # This defines the different columns of the dataset and their types\r\n features=datasets.Features(\r\n {\r\n \"source_url\": datasets.Value(dtype=\"string\"),\r\n \"date\": datasets.Value(dtype=\"date32\"),\r\n \"embeddings\": datasets.Value(\"string\"),\r\n \"geometry\": datasets.Value(dtype=geometry_schema), # Use the schema read by GeoPandas\r\n # ... (other features)\r\n }\r\n ),\r\n )\r\n\r\n# ... (rest of the script remains the same)\r\n\r\n```", "Hi @mehrdad-es, I'm not sure if HuggingFace would be keen to add `geopandas` to HuggingFace Hub (maybe a question for @severo?). Having a geometry viewer would be an even bigger task, and if you're thinking of a map-viewer, it might involve some redesign of the website UI. Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.", "I've created https://github.com/huggingface/datasets-server/issues/2416 to discuss the possibility of supporting (vectorial) geospatial columns in the dataset viewer, or in the converted parquet files.\r\n\r\nAt the same time, it would be super interesting to see what is already possible to do with a Hugging Face dataset that hosts geospatial data. \r\n\r\n> Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.\r\n\r\nIt would be awesome to show this inside a [Space](https://huggingface.co/docs/hub/spaces)." ]
2023-11-20T11:54:58
2024-02-07T08:36:51
NaT
CONTRIBUTOR
nan
### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)
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2001272606
I_kwDODunzps53SP8e
6437
Problem in training iterable dataset
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[ "Has anyone ever encountered this problem before?", "`split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n\r\nHowever if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.", "> `split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n> \r\n> However if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.\r\n\r\nThanks for your answer! I finally solve it by using the torch.distributed.algorithms.join.Join. I think maybe some rookie like me would face the same question the day after tomorrow hh.", "Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?", "> Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?\r\n\r\nI think it's none. I have tried barrier() to solve the problem but I failed. Maybe it's a tool for other situation." ]
2023-11-20T03:04:02
2024-05-22T03:14:13
NaT
NONE
nan
### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, **train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, **test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```
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6436
TypeError: <lambda>() takes 0 positional arguments but 1 was given
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[ "This looks like a problem with your environment rather than `datasets`.", "I meet the same problem,\r\nand originally use\r\n```python\r\nlocale.getpreferredencoding = lambda : \"UTF-8\"\r\n```\r\nand change to\r\n```\r\nlocale.getpreferredencoding = lambda x: \"UTF-8\"\r\n```\r\nand it works." ]
2023-11-19T13:10:20
2024-06-25T06:00:31
2023-11-29 16:28:34+00:00
NONE
nan
### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`
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6435
Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
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[ "[This doc section](https://huggingface.co/docs/datasets/main/en/process#multiprocessing) explains how to modify the script to avoid this error.", "@mariosasko thank you very much, i'll check it", "@mariosasko no it does not\r\n\r\n`Dataset.filter() got an unexpected keyword argument 'with_rank'`" ]
2023-11-19T04:21:16
2024-01-27T17:14:20
2023-12-04 16:57:43+00:00
NONE
nan
### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux
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1999554915
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6434
Use `ruff` for formatting
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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) | 0.004293 / 0.011353 (-0.007060) | 0.002953 / 0.011008 (-0.008055) | 0.063712 / 0.038508 (0.025204) | 0.029963 / 0.023109 (0.006854) | 0.248574 / 0.275898 (-0.027324) | 0.272757 / 0.323480 (-0.050723) | 0.003878 / 0.007986 (-0.004108) | 0.002456 / 0.004328 (-0.001872) | 0.047959 / 0.004250 (0.043709) | 0.043277 / 0.037052 (0.006224) | 0.255071 / 0.258489 (-0.003418) | 0.283934 / 0.293841 (-0.009907) | 0.022870 / 0.128546 (-0.105676) | 0.007224 / 0.075646 (-0.068422) | 0.221595 / 0.419271 (-0.197677) | 0.053468 / 0.043533 (0.009935) | 0.249906 / 0.255139 (-0.005233) | 0.274894 / 0.283200 (-0.008305) | 0.017246 / 0.141683 (-0.124437) | 1.112440 / 1.452155 (-0.339714) | 1.167293 / 1.492716 (-0.325424) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092684 / 0.018006 (0.074677) | 0.301721 / 0.000490 (0.301231) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018289 / 0.037411 (-0.019122) | 0.061898 / 0.014526 (0.047372) | 0.072904 / 0.176557 (-0.103653) | 0.118515 / 0.737135 (-0.618621) | 0.074000 / 0.296338 (-0.222338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287044 / 0.215209 (0.071835) | 2.818091 / 2.077655 (0.740436) | 1.502401 / 1.504120 (-0.001719) | 1.374688 / 1.541195 (-0.166506) | 1.410254 / 1.468490 (-0.058236) | 0.407519 / 4.584777 (-4.177258) | 2.379199 / 3.745712 (-1.366513) | 2.585745 / 5.269862 (-2.684117) | 1.562336 / 4.565676 (-3.003341) | 0.045977 / 0.424275 (-0.378299) | 0.004809 / 0.007607 (-0.002798) | 0.347942 / 0.226044 (0.121897) | 3.383318 / 2.268929 (1.114390) | 1.844784 / 55.444624 (-53.599841) | 1.561949 / 6.876477 (-5.314528) | 1.571082 / 2.142072 (-0.570990) | 0.482469 / 4.805227 (-4.322758) | 0.099357 / 6.500664 (-6.401307) | 0.041039 / 0.075469 (-0.034430) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944236 / 1.841788 (-0.897551) | 11.519623 / 8.074308 (3.445315) | 10.353829 / 10.191392 (0.162437) | 0.137530 / 0.680424 (-0.542894) | 0.014454 / 0.534201 (-0.519747) | 0.268657 / 0.579283 (-0.310626) | 0.265165 / 0.434364 (-0.169199) | 0.302626 / 0.540337 (-0.237712) | 0.426923 / 1.386936 (-0.960013) |\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) | 0.004711 / 0.011353 (-0.006641) | 0.002504 / 0.011008 (-0.008504) | 0.047671 / 0.038508 (0.009163) | 0.051147 / 0.023109 (0.028037) | 0.272848 / 0.275898 (-0.003050) | 0.291705 / 0.323480 (-0.031775) | 0.004002 / 0.007986 (-0.003984) | 0.002382 / 0.004328 (-0.001947) | 0.047583 / 0.004250 (0.043332) | 0.038203 / 0.037052 (0.001150) | 0.278536 / 0.258489 (0.020047) | 0.305872 / 0.293841 (0.012031) | 0.023890 / 0.128546 (-0.104657) | 0.006954 / 0.075646 (-0.068693) | 0.053716 / 0.419271 (-0.365556) | 0.032158 / 0.043533 (-0.011375) | 0.273939 / 0.255139 (0.018800) | 0.290722 / 0.283200 (0.007522) | 0.016946 / 0.141683 (-0.124737) | 1.102726 / 1.452155 (-0.349429) | 1.169356 / 1.492716 (-0.323360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092520 / 0.018006 (0.074514) | 0.301949 / 0.000490 (0.301459) | 0.000248 / 0.000200 (0.000048) | 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) | 0.021013 / 0.037411 (-0.016399) | 0.069965 / 0.014526 (0.055439) | 0.080105 / 0.176557 (-0.096451) | 0.119802 / 0.737135 (-0.617334) | 0.081615 / 0.296338 (-0.214724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301170 / 0.215209 (0.085960) | 2.884817 / 2.077655 (0.807162) | 1.596376 / 1.504120 (0.092256) | 1.471205 / 1.541195 (-0.069990) | 1.499061 / 1.468490 (0.030571) | 0.407729 / 4.584777 (-4.177048) | 2.432824 / 3.745712 (-1.312888) | 2.561905 / 5.269862 (-2.707957) | 1.535364 / 4.565676 (-3.030313) | 0.046592 / 0.424275 (-0.377683) | 0.004773 / 0.007607 (-0.002834) | 0.350872 / 0.226044 (0.124828) | 3.474874 / 2.268929 (1.205945) | 1.963114 / 55.444624 (-53.481510) | 1.688213 / 6.876477 (-5.188263) | 1.686325 / 2.142072 (-0.455748) | 0.487151 / 4.805227 (-4.318076) | 0.104253 / 6.500664 (-6.396411) | 0.043499 / 0.075469 (-0.031970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980395 / 1.841788 (-0.861393) | 11.907393 / 8.074308 (3.833085) | 10.983688 / 10.191392 (0.792296) | 0.142875 / 0.680424 (-0.537549) | 0.015375 / 0.534201 (-0.518826) | 0.270043 / 0.579283 (-0.309240) | 0.295092 / 0.434364 (-0.139272) | 0.309466 / 0.540337 (-0.230871) | 0.409812 / 1.386936 (-0.977124) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17f97ca8ec66f6664d3e9b7ceb84fe3ca49a9c18 \"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) | 0.004703 / 0.011353 (-0.006650) | 0.002767 / 0.011008 (-0.008241) | 0.063162 / 0.038508 (0.024654) | 0.052241 / 0.023109 (0.029132) | 0.237138 / 0.275898 (-0.038760) | 0.262793 / 0.323480 (-0.060687) | 0.003873 / 0.007986 (-0.004113) | 0.002433 / 0.004328 (-0.001896) | 0.048647 / 0.004250 (0.044397) | 0.037887 / 0.037052 (0.000834) | 0.244939 / 0.258489 (-0.013551) | 0.304015 / 0.293841 (0.010174) | 0.022859 / 0.128546 (-0.105688) | 0.006763 / 0.075646 (-0.068883) | 0.202728 / 0.419271 (-0.216544) | 0.035369 / 0.043533 (-0.008164) | 0.240785 / 0.255139 (-0.014354) | 0.255109 / 0.283200 (-0.028091) | 0.017951 / 0.141683 (-0.123732) | 1.096103 / 1.452155 (-0.356052) | 1.167662 / 1.492716 (-0.325054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092285 / 0.018006 (0.074279) | 0.300201 / 0.000490 (0.299711) | 0.000222 / 0.000200 (0.000022) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018271 / 0.037411 (-0.019140) | 0.062306 / 0.014526 (0.047780) | 0.072615 / 0.176557 (-0.103942) | 0.119357 / 0.737135 (-0.617779) | 0.073365 / 0.296338 (-0.222974) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278763 / 0.215209 (0.063554) | 2.714943 / 2.077655 (0.637288) | 1.426318 / 1.504120 (-0.077802) | 1.313296 / 1.541195 (-0.227898) | 1.330920 / 1.468490 (-0.137570) | 0.391466 / 4.584777 (-4.193311) | 2.380521 / 3.745712 (-1.365191) | 2.545042 / 5.269862 (-2.724819) | 1.549696 / 4.565676 (-3.015980) | 0.044661 / 0.424275 (-0.379614) | 0.005269 / 0.007607 (-0.002338) | 0.331112 / 0.226044 (0.105068) | 3.241120 / 2.268929 (0.972192) | 1.783771 / 55.444624 (-53.660853) | 1.506205 / 6.876477 (-5.370272) | 1.521062 / 2.142072 (-0.621010) | 0.462339 / 4.805227 (-4.342888) | 0.097646 / 6.500664 (-6.403018) | 0.041365 / 0.075469 (-0.034104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939653 / 1.841788 (-0.902135) | 11.415472 / 8.074308 (3.341164) | 10.338961 / 10.191392 (0.147569) | 0.128543 / 0.680424 (-0.551881) | 0.013997 / 0.534201 (-0.520204) | 0.270034 / 0.579283 (-0.309249) | 0.266766 / 0.434364 (-0.167598) | 0.305290 / 0.540337 (-0.235047) | 0.395969 / 1.386936 (-0.990967) |\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) | 0.004869 / 0.011353 (-0.006484) | 0.002445 / 0.011008 (-0.008563) | 0.051256 / 0.038508 (0.012748) | 0.050871 / 0.023109 (0.027761) | 0.271044 / 0.275898 (-0.004854) | 0.294138 / 0.323480 (-0.029342) | 0.003974 / 0.007986 (-0.004012) | 0.002423 / 0.004328 (-0.001906) | 0.048277 / 0.004250 (0.044027) | 0.039685 / 0.037052 (0.002632) | 0.277092 / 0.258489 (0.018603) | 0.302097 / 0.293841 (0.008256) | 0.024515 / 0.128546 (-0.104031) | 0.006892 / 0.075646 (-0.068754) | 0.053528 / 0.419271 (-0.365744) | 0.032243 / 0.043533 (-0.011290) | 0.272098 / 0.255139 (0.016959) | 0.291678 / 0.283200 (0.008479) | 0.018368 / 0.141683 (-0.123315) | 1.160151 / 1.452155 (-0.292004) | 1.193643 / 1.492716 (-0.299073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096669 / 0.018006 (0.078663) | 0.299043 / 0.000490 (0.298553) | 0.000227 / 0.000200 (0.000027) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021557 / 0.037411 (-0.015855) | 0.069875 / 0.014526 (0.055349) | 0.080952 / 0.176557 (-0.095605) | 0.119509 / 0.737135 (-0.617626) | 0.082030 / 0.296338 (-0.214308) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303062 / 0.215209 (0.087853) | 2.943823 / 2.077655 (0.866169) | 1.607816 / 1.504120 (0.103696) | 1.479773 / 1.541195 (-0.061422) | 1.482663 / 1.468490 (0.014173) | 0.411923 / 4.584777 (-4.172854) | 2.450138 / 3.745712 (-1.295574) | 2.466111 / 5.269862 (-2.803751) | 1.543852 / 4.565676 (-3.021825) | 0.046256 / 0.424275 (-0.378019) | 0.004787 / 0.007607 (-0.002820) | 0.353673 / 0.226044 (0.127628) | 3.528218 / 2.268929 (1.259289) | 1.984663 / 55.444624 (-53.459962) | 1.675785 / 6.876477 (-5.200691) | 1.775646 / 2.142072 (-0.366426) | 0.483277 / 4.805227 (-4.321950) | 0.097781 / 6.500664 (-6.402883) | 0.040291 / 0.075469 (-0.035178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975458 / 1.841788 (-0.866330) | 11.961966 / 8.074308 (3.887658) | 10.558559 / 10.191392 (0.367167) | 0.131372 / 0.680424 (-0.549052) | 0.016156 / 0.534201 (-0.518045) | 0.269254 / 0.579283 (-0.310029) | 0.274896 / 0.434364 (-0.159468) | 0.304672 / 0.540337 (-0.235665) | 0.517652 / 1.386936 (-0.869284) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1a1e7416892dcb71097b47120bc9b26b3d90f06a \"CML watermark\")\n" ]
2023-11-17T16:53:22
2023-11-21T14:19:21
2023-11-21 14:13:13+00:00
COLLABORATOR
nan
Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).
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1999419105
PR_kwDODunzps5fxDoG
6433
Better `tqdm` wrapper
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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) | 0.005070 / 0.011353 (-0.006283) | 0.003251 / 0.011008 (-0.007757) | 0.061528 / 0.038508 (0.023020) | 0.055386 / 0.023109 (0.032276) | 0.248536 / 0.275898 (-0.027362) | 0.272346 / 0.323480 (-0.051134) | 0.003875 / 0.007986 (-0.004111) | 0.002396 / 0.004328 (-0.001933) | 0.047659 / 0.004250 (0.043409) | 0.037448 / 0.037052 (0.000396) | 0.251101 / 0.258489 (-0.007388) | 0.282353 / 0.293841 (-0.011488) | 0.027784 / 0.128546 (-0.100762) | 0.010534 / 0.075646 (-0.065113) | 0.206025 / 0.419271 (-0.213246) | 0.035410 / 0.043533 (-0.008123) | 0.250626 / 0.255139 (-0.004513) | 0.266801 / 0.283200 (-0.016399) | 0.017704 / 0.141683 (-0.123979) | 1.089970 / 1.452155 (-0.362185) | 1.171683 / 1.492716 (-0.321033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092700 / 0.018006 (0.074694) | 0.301314 / 0.000490 (0.300824) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018385 / 0.037411 (-0.019026) | 0.062364 / 0.014526 (0.047838) | 0.075887 / 0.176557 (-0.100670) | 0.119484 / 0.737135 (-0.617651) | 0.074490 / 0.296338 (-0.221849) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283893 / 0.215209 (0.068684) | 2.746772 / 2.077655 (0.669118) | 1.486568 / 1.504120 (-0.017552) | 1.376451 / 1.541195 (-0.164744) | 1.377928 / 1.468490 (-0.090562) | 0.572393 / 4.584777 (-4.012384) | 2.383282 / 3.745712 (-1.362430) | 2.791614 / 5.269862 (-2.478248) | 1.753373 / 4.565676 (-2.812303) | 0.063539 / 0.424275 (-0.360736) | 0.005014 / 0.007607 (-0.002593) | 0.341300 / 0.226044 (0.115256) | 3.376960 / 2.268929 (1.108032) | 1.914162 / 55.444624 (-53.530462) | 1.590188 / 6.876477 (-5.286289) | 1.618420 / 2.142072 (-0.523652) | 0.648723 / 4.805227 (-4.156504) | 0.117745 / 6.500664 (-6.382919) | 0.048858 / 0.075469 (-0.026611) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.944422 / 1.841788 (-0.897366) | 11.603590 / 8.074308 (3.529282) | 10.707000 / 10.191392 (0.515608) | 0.130779 / 0.680424 (-0.549645) | 0.015126 / 0.534201 (-0.519075) | 0.284869 / 0.579283 (-0.294414) | 0.266778 / 0.434364 (-0.167585) | 0.320646 / 0.540337 (-0.219691) | 0.417167 / 1.386936 (-0.969769) |\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) | 0.005384 / 0.011353 (-0.005969) | 0.003311 / 0.011008 (-0.007698) | 0.049933 / 0.038508 (0.011425) | 0.052791 / 0.023109 (0.029681) | 0.277061 / 0.275898 (0.001162) | 0.302149 / 0.323480 (-0.021331) | 0.004006 / 0.007986 (-0.003979) | 0.002394 / 0.004328 (-0.001934) | 0.049020 / 0.004250 (0.044770) | 0.040168 / 0.037052 (0.003116) | 0.278625 / 0.258489 (0.020136) | 0.308641 / 0.293841 (0.014800) | 0.029808 / 0.128546 (-0.098738) | 0.010873 / 0.075646 (-0.064774) | 0.058040 / 0.419271 (-0.361231) | 0.032706 / 0.043533 (-0.010827) | 0.277254 / 0.255139 (0.022115) | 0.295208 / 0.283200 (0.012008) | 0.017769 / 0.141683 (-0.123914) | 1.126416 / 1.452155 (-0.325739) | 1.169046 / 1.492716 (-0.323670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094776 / 0.018006 (0.076770) | 0.306262 / 0.000490 (0.305772) | 0.000223 / 0.000200 (0.000023) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022279 / 0.037411 (-0.015132) | 0.086784 / 0.014526 (0.072258) | 0.082268 / 0.176557 (-0.094289) | 0.120131 / 0.737135 (-0.617004) | 0.082862 / 0.296338 (-0.213476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300565 / 0.215209 (0.085356) | 2.923424 / 2.077655 (0.845769) | 1.594836 / 1.504120 (0.090716) | 1.504323 / 1.541195 (-0.036872) | 1.498495 / 1.468490 (0.030005) | 0.570969 / 4.584777 (-4.013808) | 2.476966 / 3.745712 (-1.268746) | 2.785190 / 5.269862 (-2.484672) | 1.749839 / 4.565676 (-2.815837) | 0.062809 / 0.424275 (-0.361466) | 0.004908 / 0.007607 (-0.002699) | 0.361513 / 0.226044 (0.135469) | 3.587135 / 2.268929 (1.318207) | 1.952030 / 55.444624 (-53.492595) | 1.661552 / 6.876477 (-5.214925) | 1.678673 / 2.142072 (-0.463399) | 0.645083 / 4.805227 (-4.160144) | 0.117098 / 6.500664 (-6.383566) | 0.041630 / 0.075469 (-0.033839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.987883 / 1.841788 (-0.853904) | 12.300764 / 8.074308 (4.226456) | 10.962068 / 10.191392 (0.770675) | 0.143200 / 0.680424 (-0.537224) | 0.015743 / 0.534201 (-0.518458) | 0.289733 / 0.579283 (-0.289550) | 0.276384 / 0.434364 (-0.157979) | 0.328542 / 0.540337 (-0.211795) | 0.552175 / 1.386936 (-0.834761) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81a65a57cf9fd0abdf85b664a144c9a06cb2896d \"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) | 0.005110 / 0.011353 (-0.006243) | 0.003311 / 0.011008 (-0.007697) | 0.061962 / 0.038508 (0.023454) | 0.050250 / 0.023109 (0.027140) | 0.245313 / 0.275898 (-0.030585) | 0.268748 / 0.323480 (-0.054732) | 0.004693 / 0.007986 (-0.003293) | 0.002465 / 0.004328 (-0.001863) | 0.047698 / 0.004250 (0.043447) | 0.037314 / 0.037052 (0.000262) | 0.250370 / 0.258489 (-0.008119) | 0.286023 / 0.293841 (-0.007818) | 0.027891 / 0.128546 (-0.100655) | 0.010574 / 0.075646 (-0.065072) | 0.204895 / 0.419271 (-0.214376) | 0.036014 / 0.043533 (-0.007519) | 0.250959 / 0.255139 (-0.004180) | 0.266710 / 0.283200 (-0.016489) | 0.018492 / 0.141683 (-0.123191) | 1.115340 / 1.452155 (-0.336815) | 1.176488 / 1.492716 (-0.316229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099409 / 0.018006 (0.081402) | 0.310151 / 0.000490 (0.309661) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018132 / 0.037411 (-0.019279) | 0.061820 / 0.014526 (0.047294) | 0.074960 / 0.176557 (-0.101596) | 0.119793 / 0.737135 (-0.617342) | 0.074132 / 0.296338 (-0.222206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286388 / 0.215209 (0.071179) | 2.830791 / 2.077655 (0.753137) | 1.514588 / 1.504120 (0.010468) | 1.376514 / 1.541195 (-0.164681) | 1.405080 / 1.468490 (-0.063410) | 0.555297 / 4.584777 (-4.029480) | 2.364838 / 3.745712 (-1.380874) | 2.806050 / 5.269862 (-2.463812) | 1.756114 / 4.565676 (-2.809562) | 0.062254 / 0.424275 (-0.362022) | 0.005020 / 0.007607 (-0.002588) | 0.346272 / 0.226044 (0.120227) | 3.453195 / 2.268929 (1.184266) | 1.837810 / 55.444624 (-53.606814) | 1.577984 / 6.876477 (-5.298493) | 1.560821 / 2.142072 (-0.581251) | 0.633930 / 4.805227 (-4.171297) | 0.116414 / 6.500664 (-6.384250) | 0.042007 / 0.075469 (-0.033462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941322 / 1.841788 (-0.900466) | 11.740927 / 8.074308 (3.666618) | 10.450543 / 10.191392 (0.259151) | 0.128820 / 0.680424 (-0.551604) | 0.014856 / 0.534201 (-0.519345) | 0.285636 / 0.579283 (-0.293647) | 0.270051 / 0.434364 (-0.164313) | 0.321244 / 0.540337 (-0.219093) | 0.415486 / 1.386936 (-0.971450) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005333 / 0.011353 (-0.006020) | 0.003370 / 0.011008 (-0.007638) | 0.049046 / 0.038508 (0.010538) | 0.055767 / 0.023109 (0.032658) | 0.273463 / 0.275898 (-0.002435) | 0.292909 / 0.323480 (-0.030571) | 0.004102 / 0.007986 (-0.003883) | 0.002460 / 0.004328 (-0.001868) | 0.048025 / 0.004250 (0.043775) | 0.040342 / 0.037052 (0.003290) | 0.275114 / 0.258489 (0.016625) | 0.295988 / 0.293841 (0.002147) | 0.029461 / 0.128546 (-0.099085) | 0.010654 / 0.075646 (-0.064992) | 0.057196 / 0.419271 (-0.362076) | 0.033238 / 0.043533 (-0.010295) | 0.275885 / 0.255139 (0.020746) | 0.288566 / 0.283200 (0.005366) | 0.018058 / 0.141683 (-0.123625) | 1.130513 / 1.452155 (-0.321642) | 1.173608 / 1.492716 (-0.319108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097751 / 0.018006 (0.079745) | 0.312106 / 0.000490 (0.311616) | 0.000232 / 0.000200 (0.000032) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021201 / 0.037411 (-0.016211) | 0.070150 / 0.014526 (0.055624) | 0.081073 / 0.176557 (-0.095484) | 0.119520 / 0.737135 (-0.617615) | 0.084479 / 0.296338 (-0.211859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292322 / 0.215209 (0.077113) | 2.844070 / 2.077655 (0.766415) | 1.581838 / 1.504120 (0.077718) | 1.462665 / 1.541195 (-0.078530) | 1.483013 / 1.468490 (0.014523) | 0.558705 / 4.584777 (-4.026072) | 2.422368 / 3.745712 (-1.323344) | 2.772274 / 5.269862 (-2.497587) | 1.725901 / 4.565676 (-2.839775) | 0.062993 / 0.424275 (-0.361282) | 0.004982 / 0.007607 (-0.002625) | 0.344336 / 0.226044 (0.118292) | 3.425230 / 2.268929 (1.156302) | 1.947199 / 55.444624 (-53.497425) | 1.670362 / 6.876477 (-5.206115) | 1.674112 / 2.142072 (-0.467961) | 0.633857 / 4.805227 (-4.171370) | 0.114837 / 6.500664 (-6.385827) | 0.042558 / 0.075469 (-0.032911) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979474 / 1.841788 (-0.862314) | 12.110856 / 8.074308 (4.036548) | 10.605998 / 10.191392 (0.414606) | 0.130769 / 0.680424 (-0.549654) | 0.016057 / 0.534201 (-0.518144) | 0.296448 / 0.579283 (-0.282835) | 0.278078 / 0.434364 (-0.156286) | 0.320809 / 0.540337 (-0.219528) | 0.570756 / 1.386936 (-0.816180) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeb9727cc680a8f8172a012920bf84f285fec5a0 \"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) | 0.005181 / 0.011353 (-0.006172) | 0.003434 / 0.011008 (-0.007574) | 0.062333 / 0.038508 (0.023825) | 0.058544 / 0.023109 (0.035435) | 0.233794 / 0.275898 (-0.042104) | 0.258774 / 0.323480 (-0.064706) | 0.003869 / 0.007986 (-0.004117) | 0.002478 / 0.004328 (-0.001850) | 0.047871 / 0.004250 (0.043620) | 0.037997 / 0.037052 (0.000945) | 0.241269 / 0.258489 (-0.017220) | 0.270103 / 0.293841 (-0.023738) | 0.027710 / 0.128546 (-0.100836) | 0.010683 / 0.075646 (-0.064963) | 0.213204 / 0.419271 (-0.206067) | 0.036156 / 0.043533 (-0.007377) | 0.240061 / 0.255139 (-0.015078) | 0.253627 / 0.283200 (-0.029573) | 0.017880 / 0.141683 (-0.123803) | 1.102965 / 1.452155 (-0.349189) | 1.176919 / 1.492716 (-0.315797) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093206 / 0.018006 (0.075200) | 0.300960 / 0.000490 (0.300470) | 0.000214 / 0.000200 (0.000014) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019417 / 0.037411 (-0.017994) | 0.061948 / 0.014526 (0.047422) | 0.073560 / 0.176557 (-0.102997) | 0.120682 / 0.737135 (-0.616453) | 0.074925 / 0.296338 (-0.221413) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280157 / 0.215209 (0.064948) | 2.760648 / 2.077655 (0.682994) | 1.482129 / 1.504120 (-0.021991) | 1.364091 / 1.541195 (-0.177104) | 1.415680 / 1.468490 (-0.052810) | 0.564697 / 4.584777 (-4.020080) | 2.364080 / 3.745712 (-1.381633) | 2.794018 / 5.269862 (-2.475844) | 1.752157 / 4.565676 (-2.813520) | 0.062234 / 0.424275 (-0.362041) | 0.004927 / 0.007607 (-0.002680) | 0.337835 / 0.226044 (0.111790) | 3.313819 / 2.268929 (1.044891) | 1.834095 / 55.444624 (-53.610530) | 1.559964 / 6.876477 (-5.316513) | 1.598489 / 2.142072 (-0.543584) | 0.636829 / 4.805227 (-4.168399) | 0.116436 / 6.500664 (-6.384228) | 0.042506 / 0.075469 (-0.032963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951508 / 1.841788 (-0.890280) | 11.599532 / 8.074308 (3.525224) | 10.492355 / 10.191392 (0.300963) | 0.151582 / 0.680424 (-0.528842) | 0.014356 / 0.534201 (-0.519845) | 0.288448 / 0.579283 (-0.290835) | 0.265607 / 0.434364 (-0.168757) | 0.324455 / 0.540337 (-0.215883) | 0.416718 / 1.386936 (-0.970218) |\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) | 0.005489 / 0.011353 (-0.005864) | 0.003481 / 0.011008 (-0.007527) | 0.048952 / 0.038508 (0.010444) | 0.054650 / 0.023109 (0.031540) | 0.280853 / 0.275898 (0.004955) | 0.298089 / 0.323480 (-0.025391) | 0.004762 / 0.007986 (-0.003224) | 0.002500 / 0.004328 (-0.001828) | 0.048503 / 0.004250 (0.044253) | 0.042048 / 0.037052 (0.004995) | 0.281729 / 0.258489 (0.023240) | 0.303625 / 0.293841 (0.009785) | 0.028887 / 0.128546 (-0.099659) | 0.010687 / 0.075646 (-0.064960) | 0.058093 / 0.419271 (-0.361178) | 0.032366 / 0.043533 (-0.011167) | 0.281987 / 0.255139 (0.026848) | 0.295554 / 0.283200 (0.012355) | 0.019242 / 0.141683 (-0.122441) | 1.127760 / 1.452155 (-0.324395) | 1.166868 / 1.492716 (-0.325848) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092367 / 0.018006 (0.074361) | 0.300195 / 0.000490 (0.299706) | 0.000222 / 0.000200 (0.000022) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022062 / 0.037411 (-0.015349) | 0.069955 / 0.014526 (0.055429) | 0.081224 / 0.176557 (-0.095333) | 0.120183 / 0.737135 (-0.616953) | 0.082846 / 0.296338 (-0.213492) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295880 / 0.215209 (0.080671) | 2.902508 / 2.077655 (0.824853) | 1.616311 / 1.504120 (0.112191) | 1.491320 / 1.541195 (-0.049875) | 1.517333 / 1.468490 (0.048843) | 0.566824 / 4.584777 (-4.017953) | 2.428397 / 3.745712 (-1.317315) | 2.807241 / 5.269862 (-2.462620) | 1.786364 / 4.565676 (-2.779312) | 0.065253 / 0.424275 (-0.359022) | 0.004971 / 0.007607 (-0.002636) | 0.350095 / 0.226044 (0.124051) | 3.422226 / 2.268929 (1.153297) | 1.972955 / 55.444624 (-53.471670) | 1.686142 / 6.876477 (-5.190335) | 1.694539 / 2.142072 (-0.447533) | 0.645709 / 4.805227 (-4.159518) | 0.117774 / 6.500664 (-6.382890) | 0.041679 / 0.075469 (-0.033790) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976835 / 1.841788 (-0.864952) | 12.358039 / 8.074308 (4.283730) | 10.774304 / 10.191392 (0.582912) | 0.130442 / 0.680424 (-0.549982) | 0.016071 / 0.534201 (-0.518130) | 0.289911 / 0.579283 (-0.289372) | 0.280693 / 0.434364 (-0.153671) | 0.325598 / 0.540337 (-0.214739) | 0.549618 / 1.386936 (-0.837318) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1570235228b67a15dce1ed535e905edd7442117f \"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) | 0.005176 / 0.011353 (-0.006177) | 0.003297 / 0.011008 (-0.007711) | 0.061673 / 0.038508 (0.023165) | 0.052174 / 0.023109 (0.029065) | 0.245897 / 0.275898 (-0.030001) | 0.273102 / 0.323480 (-0.050377) | 0.003870 / 0.007986 (-0.004115) | 0.002385 / 0.004328 (-0.001943) | 0.047675 / 0.004250 (0.043424) | 0.037722 / 0.037052 (0.000670) | 0.250780 / 0.258489 (-0.007709) | 0.279464 / 0.293841 (-0.014376) | 0.028107 / 0.128546 (-0.100439) | 0.010460 / 0.075646 (-0.065187) | 0.205522 / 0.419271 (-0.213750) | 0.035781 / 0.043533 (-0.007752) | 0.246526 / 0.255139 (-0.008613) | 0.263919 / 0.283200 (-0.019281) | 0.018634 / 0.141683 (-0.123049) | 1.103845 / 1.452155 (-0.348310) | 1.175536 / 1.492716 (-0.317181) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091696 / 0.018006 (0.073690) | 0.301284 / 0.000490 (0.300794) | 0.000213 / 0.000200 (0.000013) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019153 / 0.037411 (-0.018258) | 0.063846 / 0.014526 (0.049320) | 0.073635 / 0.176557 (-0.102922) | 0.119625 / 0.737135 (-0.617511) | 0.075161 / 0.296338 (-0.221177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285637 / 0.215209 (0.070428) | 2.751787 / 2.077655 (0.674132) | 1.465098 / 1.504120 (-0.039022) | 1.341676 / 1.541195 (-0.199519) | 1.390636 / 1.468490 (-0.077854) | 0.567663 / 4.584777 (-4.017114) | 2.378183 / 3.745712 (-1.367529) | 2.801830 / 5.269862 (-2.468032) | 1.750125 / 4.565676 (-2.815551) | 0.063705 / 0.424275 (-0.360570) | 0.004967 / 0.007607 (-0.002640) | 0.373302 / 0.226044 (0.147258) | 3.301847 / 2.268929 (1.032918) | 1.830117 / 55.444624 (-53.614508) | 1.564360 / 6.876477 (-5.312117) | 1.551766 / 2.142072 (-0.590306) | 0.654424 / 4.805227 (-4.150803) | 0.120656 / 6.500664 (-6.380008) | 0.042383 / 0.075469 (-0.033086) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.931815 / 1.841788 (-0.909973) | 11.755904 / 8.074308 (3.681596) | 10.571707 / 10.191392 (0.380315) | 0.131118 / 0.680424 (-0.549306) | 0.015241 / 0.534201 (-0.518960) | 0.287137 / 0.579283 (-0.292146) | 0.265651 / 0.434364 (-0.168713) | 0.329083 / 0.540337 (-0.211254) | 0.417501 / 1.386936 (-0.969435) |\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) | 0.005355 / 0.011353 (-0.005998) | 0.003305 / 0.011008 (-0.007703) | 0.048289 / 0.038508 (0.009781) | 0.059223 / 0.023109 (0.036114) | 0.267213 / 0.275898 (-0.008685) | 0.290151 / 0.323480 (-0.033329) | 0.004683 / 0.007986 (-0.003303) | 0.002413 / 0.004328 (-0.001916) | 0.047982 / 0.004250 (0.043732) | 0.040943 / 0.037052 (0.003891) | 0.270967 / 0.258489 (0.012478) | 0.297644 / 0.293841 (0.003803) | 0.029309 / 0.128546 (-0.099237) | 0.010624 / 0.075646 (-0.065023) | 0.057359 / 0.419271 (-0.361913) | 0.032716 / 0.043533 (-0.010816) | 0.268602 / 0.255139 (0.013463) | 0.286016 / 0.283200 (0.002817) | 0.018578 / 0.141683 (-0.123105) | 1.120275 / 1.452155 (-0.331880) | 1.195514 / 1.492716 (-0.297202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092590 / 0.018006 (0.074584) | 0.302589 / 0.000490 (0.302099) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022439 / 0.037411 (-0.014972) | 0.070914 / 0.014526 (0.056388) | 0.084927 / 0.176557 (-0.091629) | 0.123154 / 0.737135 (-0.613981) | 0.085527 / 0.296338 (-0.210812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292652 / 0.215209 (0.077443) | 2.843736 / 2.077655 (0.766081) | 1.561289 / 1.504120 (0.057169) | 1.439500 / 1.541195 (-0.101695) | 1.485074 / 1.468490 (0.016584) | 0.570520 / 4.584777 (-4.014257) | 2.436611 / 3.745712 (-1.309102) | 2.925600 / 5.269862 (-2.344261) | 1.796518 / 4.565676 (-2.769159) | 0.065075 / 0.424275 (-0.359200) | 0.004995 / 0.007607 (-0.002612) | 0.349976 / 0.226044 (0.123932) | 3.442535 / 2.268929 (1.173607) | 1.919002 / 55.444624 (-53.525622) | 1.659222 / 6.876477 (-5.217255) | 1.648370 / 2.142072 (-0.493703) | 0.643119 / 4.805227 (-4.162108) | 0.118015 / 6.500664 (-6.382649) | 0.041229 / 0.075469 (-0.034240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986226 / 1.841788 (-0.855562) | 12.302487 / 8.074308 (4.228179) | 10.528848 / 10.191392 (0.337456) | 0.143911 / 0.680424 (-0.536513) | 0.015265 / 0.534201 (-0.518936) | 0.287692 / 0.579283 (-0.291591) | 0.277011 / 0.434364 (-0.157353) | 0.327650 / 0.540337 (-0.212688) | 0.552951 / 1.386936 (-0.833985) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0af18e68664db94e863f0dcde4b0f3a7adcc80e7 \"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) | 0.005234 / 0.011353 (-0.006119) | 0.003324 / 0.011008 (-0.007684) | 0.062429 / 0.038508 (0.023921) | 0.051619 / 0.023109 (0.028510) | 0.256850 / 0.275898 (-0.019048) | 0.260566 / 0.323480 (-0.062914) | 0.002914 / 0.007986 (-0.005071) | 0.003093 / 0.004328 (-0.001235) | 0.047947 / 0.004250 (0.043696) | 0.038753 / 0.037052 (0.001701) | 0.246810 / 0.258489 (-0.011679) | 0.275128 / 0.293841 (-0.018713) | 0.027171 / 0.128546 (-0.101375) | 0.010290 / 0.075646 (-0.065356) | 0.206069 / 0.419271 (-0.213203) | 0.035514 / 0.043533 (-0.008019) | 0.240645 / 0.255139 (-0.014494) | 0.259693 / 0.283200 (-0.023507) | 0.019722 / 0.141683 (-0.121961) | 1.128534 / 1.452155 (-0.323620) | 1.139602 / 1.492716 (-0.353115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095837 / 0.018006 (0.077830) | 0.304754 / 0.000490 (0.304264) | 0.000204 / 0.000200 (0.000004) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018349 / 0.037411 (-0.019063) | 0.062763 / 0.014526 (0.048237) | 0.074443 / 0.176557 (-0.102113) | 0.120607 / 0.737135 (-0.616528) | 0.077721 / 0.296338 (-0.218617) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281852 / 0.215209 (0.066643) | 2.770806 / 2.077655 (0.693151) | 1.466255 / 1.504120 (-0.037864) | 1.349611 / 1.541195 (-0.191584) | 1.385463 / 1.468490 (-0.083027) | 0.566489 / 4.584777 (-4.018288) | 2.420932 / 3.745712 (-1.324780) | 2.809397 / 5.269862 (-2.460464) | 1.749734 / 4.565676 (-2.815942) | 0.063407 / 0.424275 (-0.360868) | 0.005038 / 0.007607 (-0.002569) | 0.379121 / 0.226044 (0.153077) | 3.500938 / 2.268929 (1.232010) | 1.852207 / 55.444624 (-53.592417) | 1.570474 / 6.876477 (-5.306002) | 1.555222 / 2.142072 (-0.586850) | 0.657198 / 4.805227 (-4.148030) | 0.119835 / 6.500664 (-6.380829) | 0.042453 / 0.075469 (-0.033016) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949953 / 1.841788 (-0.891835) | 11.736811 / 8.074308 (3.662503) | 10.558049 / 10.191392 (0.366657) | 0.146230 / 0.680424 (-0.534194) | 0.014922 / 0.534201 (-0.519279) | 0.289100 / 0.579283 (-0.290183) | 0.267130 / 0.434364 (-0.167234) | 0.320055 / 0.540337 (-0.220282) | 0.417244 / 1.386936 (-0.969692) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005309 / 0.011353 (-0.006044) | 0.003329 / 0.011008 (-0.007679) | 0.048576 / 0.038508 (0.010068) | 0.055219 / 0.023109 (0.032110) | 0.271522 / 0.275898 (-0.004376) | 0.294435 / 0.323480 (-0.029045) | 0.004018 / 0.007986 (-0.003968) | 0.002456 / 0.004328 (-0.001873) | 0.047939 / 0.004250 (0.043689) | 0.041195 / 0.037052 (0.004143) | 0.274819 / 0.258489 (0.016330) | 0.299407 / 0.293841 (0.005566) | 0.029145 / 0.128546 (-0.099401) | 0.010680 / 0.075646 (-0.064966) | 0.057238 / 0.419271 (-0.362034) | 0.032722 / 0.043533 (-0.010810) | 0.272066 / 0.255139 (0.016927) | 0.289223 / 0.283200 (0.006023) | 0.017826 / 0.141683 (-0.123857) | 1.119079 / 1.452155 (-0.333076) | 1.179109 / 1.492716 (-0.313608) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095662 / 0.018006 (0.077656) | 0.307652 / 0.000490 (0.307162) | 0.000213 / 0.000200 (0.000013) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022263 / 0.037411 (-0.015149) | 0.070224 / 0.014526 (0.055698) | 0.081477 / 0.176557 (-0.095079) | 0.120763 / 0.737135 (-0.616372) | 0.083152 / 0.296338 (-0.213187) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295780 / 0.215209 (0.080571) | 2.926623 / 2.077655 (0.848968) | 1.605901 / 1.504120 (0.101781) | 1.482874 / 1.541195 (-0.058321) | 1.501467 / 1.468490 (0.032977) | 0.569566 / 4.584777 (-4.015211) | 2.474948 / 3.745712 (-1.270764) | 2.831877 / 5.269862 (-2.437985) | 1.761229 / 4.565676 (-2.804448) | 0.064129 / 0.424275 (-0.360147) | 0.004964 / 0.007607 (-0.002643) | 0.350081 / 0.226044 (0.124037) | 3.446766 / 2.268929 (1.177837) | 1.974998 / 55.444624 (-53.469627) | 1.683381 / 6.876477 (-5.193095) | 1.711543 / 2.142072 (-0.430530) | 0.648695 / 4.805227 (-4.156532) | 0.118224 / 6.500664 (-6.382440) | 0.040895 / 0.075469 (-0.034574) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960208 / 1.841788 (-0.881580) | 12.164941 / 8.074308 (4.090633) | 10.860573 / 10.191392 (0.669181) | 0.133525 / 0.680424 (-0.546899) | 0.015643 / 0.534201 (-0.518558) | 0.290898 / 0.579283 (-0.288386) | 0.289612 / 0.434364 (-0.144752) | 0.325836 / 0.540337 (-0.214501) | 0.565592 / 1.386936 (-0.821344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d19a315920c6d4293f8226273d99bf3de5c1d4e \"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) | 0.006097 / 0.011353 (-0.005256) | 0.004386 / 0.011008 (-0.006622) | 0.064481 / 0.038508 (0.025973) | 0.059983 / 0.023109 (0.036873) | 0.268177 / 0.275898 (-0.007721) | 0.296207 / 0.323480 (-0.027273) | 0.002986 / 0.007986 (-0.005000) | 0.002923 / 0.004328 (-0.001406) | 0.048798 / 0.004250 (0.044547) | 0.039945 / 0.037052 (0.002893) | 0.271234 / 0.258489 (0.012745) | 0.295461 / 0.293841 (0.001620) | 0.028771 / 0.128546 (-0.099775) | 0.011104 / 0.075646 (-0.064542) | 0.207471 / 0.419271 (-0.211800) | 0.036955 / 0.043533 (-0.006578) | 0.254761 / 0.255139 (-0.000378) | 0.275933 / 0.283200 (-0.007267) | 0.021232 / 0.141683 (-0.120451) | 1.170771 / 1.452155 (-0.281384) | 1.188900 / 1.492716 (-0.303816) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092328 / 0.018006 (0.074322) | 0.302591 / 0.000490 (0.302102) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019207 / 0.037411 (-0.018204) | 0.070247 / 0.014526 (0.055721) | 0.074963 / 0.176557 (-0.101593) | 0.124301 / 0.737135 (-0.612834) | 0.077356 / 0.296338 (-0.218982) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283321 / 0.215209 (0.068112) | 2.800448 / 2.077655 (0.722793) | 1.510278 / 1.504120 (0.006158) | 1.390353 / 1.541195 (-0.150842) | 1.387881 / 1.468490 (-0.080609) | 0.563927 / 4.584777 (-4.020850) | 2.387753 / 3.745712 (-1.357959) | 2.776655 / 5.269862 (-2.493207) | 1.767383 / 4.565676 (-2.798293) | 0.064864 / 0.424275 (-0.359411) | 0.004999 / 0.007607 (-0.002608) | 0.351173 / 0.226044 (0.125129) | 3.459446 / 2.268929 (1.190517) | 1.873078 / 55.444624 (-53.571547) | 1.602831 / 6.876477 (-5.273646) | 1.595612 / 2.142072 (-0.546460) | 0.648786 / 4.805227 (-4.156441) | 0.118720 / 6.500664 (-6.381944) | 0.042821 / 0.075469 (-0.032649) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970738 / 1.841788 (-0.871049) | 12.273548 / 8.074308 (4.199240) | 11.191375 / 10.191392 (0.999983) | 0.131903 / 0.680424 (-0.548521) | 0.014512 / 0.534201 (-0.519689) | 0.289382 / 0.579283 (-0.289901) | 0.269449 / 0.434364 (-0.164915) | 0.327557 / 0.540337 (-0.212781) | 0.427052 / 1.386936 (-0.959884) |\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) | 0.005472 / 0.011353 (-0.005881) | 0.003380 / 0.011008 (-0.007628) | 0.050677 / 0.038508 (0.012169) | 0.059606 / 0.023109 (0.036497) | 0.275798 / 0.275898 (-0.000100) | 0.303733 / 0.323480 (-0.019747) | 0.004187 / 0.007986 (-0.003799) | 0.002657 / 0.004328 (-0.001672) | 0.048713 / 0.004250 (0.044463) | 0.043501 / 0.037052 (0.006449) | 0.278845 / 0.258489 (0.020356) | 0.305322 / 0.293841 (0.011481) | 0.030665 / 0.128546 (-0.097881) | 0.010600 / 0.075646 (-0.065047) | 0.058923 / 0.419271 (-0.360349) | 0.032936 / 0.043533 (-0.010596) | 0.272835 / 0.255139 (0.017696) | 0.293975 / 0.283200 (0.010775) | 0.018193 / 0.141683 (-0.123490) | 1.144903 / 1.452155 (-0.307251) | 1.192220 / 1.492716 (-0.300497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094519 / 0.018006 (0.076513) | 0.305591 / 0.000490 (0.305101) | 0.000221 / 0.000200 (0.000021) | 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) | 0.022108 / 0.037411 (-0.015303) | 0.070184 / 0.014526 (0.055658) | 0.081640 / 0.176557 (-0.094916) | 0.124661 / 0.737135 (-0.612474) | 0.082229 / 0.296338 (-0.214110) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303710 / 0.215209 (0.088501) | 2.966478 / 2.077655 (0.888824) | 1.646066 / 1.504120 (0.141946) | 1.551454 / 1.541195 (0.010259) | 1.557995 / 1.468490 (0.089505) | 0.577723 / 4.584777 (-4.007054) | 2.510321 / 3.745712 (-1.235391) | 2.951343 / 5.269862 (-2.318519) | 1.857550 / 4.565676 (-2.708127) | 0.064079 / 0.424275 (-0.360196) | 0.004971 / 0.007607 (-0.002636) | 0.359022 / 0.226044 (0.132978) | 3.628716 / 2.268929 (1.359788) | 2.011380 / 55.444624 (-53.433245) | 1.710407 / 6.876477 (-5.166070) | 1.756235 / 2.142072 (-0.385838) | 0.659185 / 4.805227 (-4.146042) | 0.120245 / 6.500664 (-6.380419) | 0.042751 / 0.075469 (-0.032718) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026794 / 1.841788 (-0.814993) | 12.695125 / 8.074308 (4.620816) | 10.864908 / 10.191392 (0.673516) | 0.136128 / 0.680424 (-0.544295) | 0.016824 / 0.534201 (-0.517377) | 0.289717 / 0.579283 (-0.289567) | 0.282919 / 0.434364 (-0.151445) | 0.323345 / 0.540337 (-0.216992) | 0.556375 / 1.386936 (-0.830561) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52207295162f734235b71428d13e6a42c6fdc370 \"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) | 0.005407 / 0.011353 (-0.005946) | 0.003464 / 0.011008 (-0.007544) | 0.062084 / 0.038508 (0.023576) | 0.052582 / 0.023109 (0.029472) | 0.251239 / 0.275898 (-0.024659) | 0.276675 / 0.323480 (-0.046805) | 0.002894 / 0.007986 (-0.005092) | 0.003850 / 0.004328 (-0.000479) | 0.047789 / 0.004250 (0.043538) | 0.038955 / 0.037052 (0.001903) | 0.258333 / 0.258489 (-0.000156) | 0.290103 / 0.293841 (-0.003738) | 0.027291 / 0.128546 (-0.101256) | 0.010575 / 0.075646 (-0.065071) | 0.207208 / 0.419271 (-0.212063) | 0.035848 / 0.043533 (-0.007685) | 0.253918 / 0.255139 (-0.001221) | 0.269870 / 0.283200 (-0.013330) | 0.019830 / 0.141683 (-0.121853) | 1.085332 / 1.452155 (-0.366823) | 1.171385 / 1.492716 (-0.321331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094956 / 0.018006 (0.076950) | 0.301104 / 0.000490 (0.300614) | 0.000204 / 0.000200 (0.000004) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019045 / 0.037411 (-0.018367) | 0.070815 / 0.014526 (0.056289) | 0.073763 / 0.176557 (-0.102794) | 0.120668 / 0.737135 (-0.616467) | 0.075197 / 0.296338 (-0.221141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286072 / 0.215209 (0.070863) | 2.762868 / 2.077655 (0.685213) | 1.504481 / 1.504120 (0.000361) | 1.390301 / 1.541195 (-0.150894) | 1.449571 / 1.468490 (-0.018919) | 0.555598 / 4.584777 (-4.029179) | 2.404975 / 3.745712 (-1.340737) | 2.864359 / 5.269862 (-2.405503) | 1.764913 / 4.565676 (-2.800763) | 0.062956 / 0.424275 (-0.361320) | 0.005116 / 0.007607 (-0.002491) | 0.344027 / 0.226044 (0.117983) | 3.426781 / 2.268929 (1.157852) | 1.891040 / 55.444624 (-53.553584) | 1.599972 / 6.876477 (-5.276505) | 1.603464 / 2.142072 (-0.538608) | 0.638136 / 4.805227 (-4.167091) | 0.117808 / 6.500664 (-6.382857) | 0.043740 / 0.075469 (-0.031730) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.934654 / 1.841788 (-0.907133) | 12.243698 / 8.074308 (4.169390) | 10.566791 / 10.191392 (0.375399) | 0.130440 / 0.680424 (-0.549983) | 0.014019 / 0.534201 (-0.520182) | 0.285453 / 0.579283 (-0.293831) | 0.266121 / 0.434364 (-0.168243) | 0.325962 / 0.540337 (-0.214375) | 0.422181 / 1.386936 (-0.964755) |\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) | 0.005151 / 0.011353 (-0.006202) | 0.003704 / 0.011008 (-0.007304) | 0.049483 / 0.038508 (0.010975) | 0.055147 / 0.023109 (0.032038) | 0.277589 / 0.275898 (0.001691) | 0.301274 / 0.323480 (-0.022206) | 0.004031 / 0.007986 (-0.003955) | 0.002568 / 0.004328 (-0.001760) | 0.048830 / 0.004250 (0.044580) | 0.040391 / 0.037052 (0.003339) | 0.281031 / 0.258489 (0.022541) | 0.304263 / 0.293841 (0.010422) | 0.029237 / 0.128546 (-0.099309) | 0.010598 / 0.075646 (-0.065048) | 0.058089 / 0.419271 (-0.361182) | 0.032529 / 0.043533 (-0.011004) | 0.275761 / 0.255139 (0.020622) | 0.294427 / 0.283200 (0.011227) | 0.017227 / 0.141683 (-0.124456) | 1.138036 / 1.452155 (-0.314119) | 1.201946 / 1.492716 (-0.290770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094241 / 0.018006 (0.076234) | 0.301622 / 0.000490 (0.301132) | 0.000229 / 0.000200 (0.000029) | 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) | 0.022731 / 0.037411 (-0.014680) | 0.071217 / 0.014526 (0.056691) | 0.082619 / 0.176557 (-0.093937) | 0.123308 / 0.737135 (-0.613827) | 0.083552 / 0.296338 (-0.212787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295770 / 0.215209 (0.080561) | 2.886069 / 2.077655 (0.808414) | 1.597686 / 1.504120 (0.093566) | 1.458612 / 1.541195 (-0.082583) | 1.501171 / 1.468490 (0.032680) | 0.575653 / 4.584777 (-4.009124) | 2.444021 / 3.745712 (-1.301691) | 2.860192 / 5.269862 (-2.409669) | 1.758896 / 4.565676 (-2.806780) | 0.063334 / 0.424275 (-0.360941) | 0.004913 / 0.007607 (-0.002694) | 0.341828 / 0.226044 (0.115783) | 3.420310 / 2.268929 (1.151381) | 1.996099 / 55.444624 (-53.448525) | 1.680112 / 6.876477 (-5.196365) | 1.693418 / 2.142072 (-0.448654) | 0.697321 / 4.805227 (-4.107906) | 0.120474 / 6.500664 (-6.380190) | 0.042192 / 0.075469 (-0.033277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975876 / 1.841788 (-0.865912) | 12.174933 / 8.074308 (4.100625) | 10.400906 / 10.191392 (0.209514) | 0.162244 / 0.680424 (-0.518180) | 0.016443 / 0.534201 (-0.517758) | 0.293430 / 0.579283 (-0.285853) | 0.285664 / 0.434364 (-0.148700) | 0.332322 / 0.540337 (-0.208015) | 0.609815 / 1.386936 (-0.777121) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2c417d087d232b5abf9054ffb10305cc06c5440 \"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) | 0.005155 / 0.011353 (-0.006198) | 0.003226 / 0.011008 (-0.007782) | 0.062651 / 0.038508 (0.024143) | 0.051314 / 0.023109 (0.028205) | 0.246075 / 0.275898 (-0.029823) | 0.266859 / 0.323480 (-0.056621) | 0.003895 / 0.007986 (-0.004091) | 0.002462 / 0.004328 (-0.001866) | 0.048097 / 0.004250 (0.043846) | 0.037313 / 0.037052 (0.000261) | 0.253208 / 0.258489 (-0.005281) | 0.280255 / 0.293841 (-0.013585) | 0.027052 / 0.128546 (-0.101494) | 0.010276 / 0.075646 (-0.065370) | 0.205663 / 0.419271 (-0.213608) | 0.035111 / 0.043533 (-0.008422) | 0.253757 / 0.255139 (-0.001382) | 0.265466 / 0.283200 (-0.017733) | 0.017873 / 0.141683 (-0.123810) | 1.118906 / 1.452155 (-0.333249) | 1.176384 / 1.492716 (-0.316332) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094921 / 0.018006 (0.076914) | 0.300459 / 0.000490 (0.299970) | 0.000214 / 0.000200 (0.000014) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018430 / 0.037411 (-0.018981) | 0.062690 / 0.014526 (0.048165) | 0.074215 / 0.176557 (-0.102342) | 0.119969 / 0.737135 (-0.617166) | 0.075846 / 0.296338 (-0.220493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273492 / 0.215209 (0.058283) | 2.667937 / 2.077655 (0.590282) | 1.405912 / 1.504120 (-0.098208) | 1.269041 / 1.541195 (-0.272153) | 1.313461 / 1.468490 (-0.155029) | 0.554633 / 4.584777 (-4.030144) | 2.325552 / 3.745712 (-1.420160) | 2.825580 / 5.269862 (-2.444282) | 1.745432 / 4.565676 (-2.820245) | 0.062497 / 0.424275 (-0.361778) | 0.004935 / 0.007607 (-0.002673) | 0.337045 / 0.226044 (0.111001) | 3.246360 / 2.268929 (0.977432) | 1.775329 / 55.444624 (-53.669296) | 1.491812 / 6.876477 (-5.384665) | 1.499783 / 2.142072 (-0.642290) | 0.636768 / 4.805227 (-4.168459) | 0.116471 / 6.500664 (-6.384193) | 0.041838 / 0.075469 (-0.033631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.937388 / 1.841788 (-0.904400) | 11.950930 / 8.074308 (3.876622) | 10.532062 / 10.191392 (0.340670) | 0.129490 / 0.680424 (-0.550934) | 0.013907 / 0.534201 (-0.520294) | 0.287503 / 0.579283 (-0.291780) | 0.270548 / 0.434364 (-0.163816) | 0.324321 / 0.540337 (-0.216016) | 0.427639 / 1.386936 (-0.959297) |\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) | 0.005272 / 0.011353 (-0.006081) | 0.003413 / 0.011008 (-0.007595) | 0.049800 / 0.038508 (0.011292) | 0.055978 / 0.023109 (0.032868) | 0.274365 / 0.275898 (-0.001533) | 0.293414 / 0.323480 (-0.030066) | 0.003994 / 0.007986 (-0.003992) | 0.002480 / 0.004328 (-0.001848) | 0.048787 / 0.004250 (0.044537) | 0.040520 / 0.037052 (0.003468) | 0.276198 / 0.258489 (0.017709) | 0.301085 / 0.293841 (0.007244) | 0.028352 / 0.128546 (-0.100194) | 0.010631 / 0.075646 (-0.065015) | 0.057103 / 0.419271 (-0.362168) | 0.032277 / 0.043533 (-0.011256) | 0.274472 / 0.255139 (0.019333) | 0.289953 / 0.283200 (0.006754) | 0.018048 / 0.141683 (-0.123635) | 1.120329 / 1.452155 (-0.331826) | 1.175784 / 1.492716 (-0.316932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102519 / 0.018006 (0.084512) | 0.322030 / 0.000490 (0.321540) | 0.000234 / 0.000200 (0.000034) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023084 / 0.037411 (-0.014327) | 0.069592 / 0.014526 (0.055066) | 0.081293 / 0.176557 (-0.095264) | 0.119546 / 0.737135 (-0.617589) | 0.083249 / 0.296338 (-0.213090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294997 / 0.215209 (0.079788) | 2.925517 / 2.077655 (0.847863) | 1.607824 / 1.504120 (0.103705) | 1.469586 / 1.541195 (-0.071608) | 1.492350 / 1.468490 (0.023860) | 0.561351 / 4.584777 (-4.023426) | 2.446741 / 3.745712 (-1.298972) | 2.842588 / 5.269862 (-2.427273) | 1.789189 / 4.565676 (-2.776487) | 0.064064 / 0.424275 (-0.360211) | 0.005011 / 0.007607 (-0.002597) | 0.351059 / 0.226044 (0.125015) | 3.485277 / 2.268929 (1.216348) | 1.981821 / 55.444624 (-53.462803) | 1.671846 / 6.876477 (-5.204631) | 1.702014 / 2.142072 (-0.440058) | 0.645205 / 4.805227 (-4.160023) | 0.117358 / 6.500664 (-6.383306) | 0.041633 / 0.075469 (-0.033836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963281 / 1.841788 (-0.878506) | 12.141256 / 8.074308 (4.066947) | 10.595207 / 10.191392 (0.403815) | 0.130401 / 0.680424 (-0.550023) | 0.015490 / 0.534201 (-0.518710) | 0.284201 / 0.579283 (-0.295082) | 0.280244 / 0.434364 (-0.154120) | 0.323545 / 0.540337 (-0.216792) | 0.561246 / 1.386936 (-0.825690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3193829cf0dd9888c42bd7640a71d9d656cba2a \"CML watermark\")\n" ]
2023-11-17T15:45:15
2023-11-22T16:48:18
2023-11-22 16:42:08+00:00
COLLABORATOR
nan
This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone. Additionally, it improves the doc page about the `datasets`' utilities, and the handling of local `fsspec` paths in `cached_path`. Fix #6409
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1999258140
I_kwDODunzps53KkIc
6432
load_dataset does not load all of the data in my input file
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[ "You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves." ]
2023-11-17T14:28:50
2023-11-22T17:34:58
NaT
NONE
nan
### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1
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6431
Create DatasetNotFoundError and DataFilesNotFoundError
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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) | 0.004459 / 0.011353 (-0.006894) | 0.002883 / 0.011008 (-0.008125) | 0.062434 / 0.038508 (0.023925) | 0.030353 / 0.023109 (0.007244) | 0.256696 / 0.275898 (-0.019202) | 0.280557 / 0.323480 (-0.042923) | 0.003903 / 0.007986 (-0.004083) | 0.002424 / 0.004328 (-0.001905) | 0.048509 / 0.004250 (0.044259) | 0.043583 / 0.037052 (0.006531) | 0.253900 / 0.258489 (-0.004590) | 0.309146 / 0.293841 (0.015305) | 0.023253 / 0.128546 (-0.105294) | 0.007073 / 0.075646 (-0.068573) | 0.204118 / 0.419271 (-0.215154) | 0.056429 / 0.043533 (0.012897) | 0.247331 / 0.255139 (-0.007808) | 0.271581 / 0.283200 (-0.011619) | 0.017021 / 0.141683 (-0.124662) | 1.115057 / 1.452155 (-0.337098) | 1.209947 / 1.492716 (-0.282770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093141 / 0.018006 (0.075134) | 0.295987 / 0.000490 (0.295497) | 0.000221 / 0.000200 (0.000021) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019182 / 0.037411 (-0.018230) | 0.062049 / 0.014526 (0.047523) | 0.073824 / 0.176557 (-0.102733) | 0.120175 / 0.737135 (-0.616960) | 0.074700 / 0.296338 (-0.221639) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280036 / 0.215209 (0.064827) | 2.731512 / 2.077655 (0.653857) | 1.414606 / 1.504120 (-0.089514) | 1.302433 / 1.541195 (-0.238761) | 1.313012 / 1.468490 (-0.155478) | 0.399722 / 4.584777 (-4.185055) | 2.371249 / 3.745712 (-1.374463) | 2.582520 / 5.269862 (-2.687342) | 1.558505 / 4.565676 (-3.007171) | 0.045765 / 0.424275 (-0.378510) | 0.004748 / 0.007607 (-0.002859) | 0.327623 / 0.226044 (0.101578) | 3.258742 / 2.268929 (0.989814) | 1.756798 / 55.444624 (-53.687826) | 1.494551 / 6.876477 (-5.381925) | 1.518161 / 2.142072 (-0.623911) | 0.468560 / 4.805227 (-4.336667) | 0.101034 / 6.500664 (-6.399630) | 0.048259 / 0.075469 (-0.027210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.938146 / 1.841788 (-0.903642) | 11.636387 / 8.074308 (3.562078) | 10.638909 / 10.191392 (0.447517) | 0.128340 / 0.680424 (-0.552084) | 0.015194 / 0.534201 (-0.519007) | 0.275961 / 0.579283 (-0.303322) | 0.264629 / 0.434364 (-0.169735) | 0.308580 / 0.540337 (-0.231758) | 0.433658 / 1.386936 (-0.953278) |\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) | 0.004797 / 0.011353 (-0.006556) | 0.002801 / 0.011008 (-0.008208) | 0.048101 / 0.038508 (0.009593) | 0.056406 / 0.023109 (0.033296) | 0.274966 / 0.275898 (-0.000932) | 0.298310 / 0.323480 (-0.025170) | 0.004115 / 0.007986 (-0.003871) | 0.002437 / 0.004328 (-0.001891) | 0.047921 / 0.004250 (0.043671) | 0.038812 / 0.037052 (0.001760) | 0.279594 / 0.258489 (0.021105) | 0.313703 / 0.293841 (0.019862) | 0.024485 / 0.128546 (-0.104061) | 0.007095 / 0.075646 (-0.068551) | 0.053398 / 0.419271 (-0.365874) | 0.032306 / 0.043533 (-0.011227) | 0.278014 / 0.255139 (0.022875) | 0.301156 / 0.283200 (0.017956) | 0.017353 / 0.141683 (-0.124330) | 1.150168 / 1.452155 (-0.301987) | 1.190822 / 1.492716 (-0.301894) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092162 / 0.018006 (0.074156) | 0.301031 / 0.000490 (0.300541) | 0.000244 / 0.000200 (0.000044) | 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) | 0.020918 / 0.037411 (-0.016494) | 0.072030 / 0.014526 (0.057504) | 0.081813 / 0.176557 (-0.094743) | 0.120233 / 0.737135 (-0.616903) | 0.082874 / 0.296338 (-0.213465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291659 / 0.215209 (0.076450) | 2.841978 / 2.077655 (0.764323) | 1.594207 / 1.504120 (0.090087) | 1.473941 / 1.541195 (-0.067254) | 1.514393 / 1.468490 (0.045903) | 0.393393 / 4.584777 (-4.191384) | 2.443663 / 3.745712 (-1.302050) | 2.545747 / 5.269862 (-2.724114) | 1.521130 / 4.565676 (-3.044546) | 0.046246 / 0.424275 (-0.378030) | 0.004826 / 0.007607 (-0.002781) | 0.340909 / 0.226044 (0.114865) | 3.319474 / 2.268929 (1.050546) | 1.933110 / 55.444624 (-53.511515) | 1.662463 / 6.876477 (-5.214014) | 1.670331 / 2.142072 (-0.471742) | 0.458062 / 4.805227 (-4.347165) | 0.098397 / 6.500664 (-6.402267) | 0.041339 / 0.075469 (-0.034130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973718 / 1.841788 (-0.868070) | 12.095266 / 8.074308 (4.020957) | 10.761212 / 10.191392 (0.569820) | 0.142352 / 0.680424 (-0.538072) | 0.015423 / 0.534201 (-0.518778) | 0.270912 / 0.579283 (-0.308371) | 0.276618 / 0.434364 (-0.157746) | 0.309120 / 0.540337 (-0.231217) | 0.415330 / 1.386936 (-0.971606) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"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) | 0.004676 / 0.011353 (-0.006677) | 0.003101 / 0.011008 (-0.007907) | 0.062260 / 0.038508 (0.023752) | 0.030012 / 0.023109 (0.006903) | 0.253704 / 0.275898 (-0.022194) | 0.276404 / 0.323480 (-0.047075) | 0.004060 / 0.007986 (-0.003926) | 0.002467 / 0.004328 (-0.001861) | 0.047921 / 0.004250 (0.043670) | 0.045760 / 0.037052 (0.008708) | 0.254529 / 0.258489 (-0.003960) | 0.286283 / 0.293841 (-0.007558) | 0.023301 / 0.128546 (-0.105246) | 0.007407 / 0.075646 (-0.068239) | 0.204541 / 0.419271 (-0.214730) | 0.056387 / 0.043533 (0.012854) | 0.252120 / 0.255139 (-0.003019) | 0.275795 / 0.283200 (-0.007404) | 0.018648 / 0.141683 (-0.123034) | 1.113484 / 1.452155 (-0.338671) | 1.168685 / 1.492716 (-0.324031) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098286 / 0.018006 (0.080280) | 0.304619 / 0.000490 (0.304129) | 0.000225 / 0.000200 (0.000025) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019183 / 0.037411 (-0.018229) | 0.062183 / 0.014526 (0.047657) | 0.074288 / 0.176557 (-0.102269) | 0.120576 / 0.737135 (-0.616560) | 0.074833 / 0.296338 (-0.221505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280512 / 0.215209 (0.065303) | 2.770052 / 2.077655 (0.692397) | 1.471234 / 1.504120 (-0.032886) | 1.352080 / 1.541195 (-0.189114) | 1.374518 / 1.468490 (-0.093973) | 0.407108 / 4.584777 (-4.177669) | 2.400581 / 3.745712 (-1.345131) | 2.677507 / 5.269862 (-2.592355) | 1.578042 / 4.565676 (-2.987635) | 0.048539 / 0.424275 (-0.375736) | 0.004905 / 0.007607 (-0.002703) | 0.346676 / 0.226044 (0.120631) | 3.367732 / 2.268929 (1.098803) | 1.844405 / 55.444624 (-53.600220) | 1.576883 / 6.876477 (-5.299594) | 1.666986 / 2.142072 (-0.475086) | 0.495872 / 4.805227 (-4.309355) | 0.103142 / 6.500664 (-6.397522) | 0.044037 / 0.075469 (-0.031432) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980865 / 1.841788 (-0.860923) | 12.268525 / 8.074308 (4.194217) | 10.756554 / 10.191392 (0.565162) | 0.129954 / 0.680424 (-0.550470) | 0.013864 / 0.534201 (-0.520337) | 0.267653 / 0.579283 (-0.311630) | 0.265120 / 0.434364 (-0.169244) | 0.309050 / 0.540337 (-0.231288) | 0.423877 / 1.386936 (-0.963059) |\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) | 0.005074 / 0.011353 (-0.006279) | 0.003001 / 0.011008 (-0.008007) | 0.048271 / 0.038508 (0.009763) | 0.061206 / 0.023109 (0.038097) | 0.279268 / 0.275898 (0.003370) | 0.302592 / 0.323480 (-0.020888) | 0.004177 / 0.007986 (-0.003809) | 0.002452 / 0.004328 (-0.001876) | 0.048259 / 0.004250 (0.044009) | 0.040032 / 0.037052 (0.002979) | 0.281398 / 0.258489 (0.022909) | 0.314121 / 0.293841 (0.020280) | 0.025137 / 0.128546 (-0.103409) | 0.007230 / 0.075646 (-0.068416) | 0.054537 / 0.419271 (-0.364735) | 0.033266 / 0.043533 (-0.010267) | 0.277305 / 0.255139 (0.022166) | 0.295993 / 0.283200 (0.012794) | 0.019278 / 0.141683 (-0.122405) | 1.131700 / 1.452155 (-0.320454) | 1.183848 / 1.492716 (-0.308868) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092258 / 0.018006 (0.074251) | 0.310668 / 0.000490 (0.310178) | 0.000219 / 0.000200 (0.000019) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021838 / 0.037411 (-0.015574) | 0.071382 / 0.014526 (0.056857) | 0.081389 / 0.176557 (-0.095168) | 0.120389 / 0.737135 (-0.616746) | 0.084135 / 0.296338 (-0.212203) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291676 / 0.215209 (0.076467) | 2.840623 / 2.077655 (0.762968) | 1.565748 / 1.504120 (0.061628) | 1.452529 / 1.541195 (-0.088666) | 1.490633 / 1.468490 (0.022143) | 0.402878 / 4.584777 (-4.181899) | 2.486192 / 3.745712 (-1.259520) | 2.520563 / 5.269862 (-2.749299) | 1.518550 / 4.565676 (-3.047127) | 0.047423 / 0.424275 (-0.376852) | 0.004823 / 0.007607 (-0.002784) | 0.353122 / 0.226044 (0.127078) | 3.452136 / 2.268929 (1.183208) | 1.973798 / 55.444624 (-53.470827) | 1.669569 / 6.876477 (-5.206907) | 1.654910 / 2.142072 (-0.487163) | 0.486746 / 4.805227 (-4.318481) | 0.097260 / 6.500664 (-6.403404) | 0.040608 / 0.075469 (-0.034861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989705 / 1.841788 (-0.852083) | 12.114386 / 8.074308 (4.040077) | 11.284551 / 10.191392 (1.093159) | 0.141408 / 0.680424 (-0.539016) | 0.015275 / 0.534201 (-0.518926) | 0.267407 / 0.579283 (-0.311877) | 0.281007 / 0.434364 (-0.153357) | 0.309617 / 0.540337 (-0.230720) | 0.414033 / 1.386936 (-0.972903) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"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) | 0.004888 / 0.011353 (-0.006465) | 0.002775 / 0.011008 (-0.008233) | 0.062000 / 0.038508 (0.023492) | 0.050694 / 0.023109 (0.027584) | 0.257063 / 0.275898 (-0.018835) | 0.282743 / 0.323480 (-0.040736) | 0.002862 / 0.007986 (-0.005124) | 0.002305 / 0.004328 (-0.002023) | 0.049549 / 0.004250 (0.045299) | 0.038754 / 0.037052 (0.001701) | 0.264047 / 0.258489 (0.005558) | 0.310162 / 0.293841 (0.016321) | 0.022901 / 0.128546 (-0.105645) | 0.006894 / 0.075646 (-0.068752) | 0.202467 / 0.419271 (-0.216805) | 0.035901 / 0.043533 (-0.007631) | 0.262344 / 0.255139 (0.007205) | 0.285563 / 0.283200 (0.002364) | 0.017070 / 0.141683 (-0.124613) | 1.113972 / 1.452155 (-0.338182) | 1.176261 / 1.492716 (-0.316455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092912 / 0.018006 (0.074906) | 0.302610 / 0.000490 (0.302120) | 0.000204 / 0.000200 (0.000005) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018232 / 0.037411 (-0.019179) | 0.062367 / 0.014526 (0.047841) | 0.074570 / 0.176557 (-0.101987) | 0.120468 / 0.737135 (-0.616668) | 0.075187 / 0.296338 (-0.221151) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279760 / 0.215209 (0.064551) | 2.715372 / 2.077655 (0.637717) | 1.461636 / 1.504120 (-0.042484) | 1.324220 / 1.541195 (-0.216975) | 1.350724 / 1.468490 (-0.117766) | 0.395648 / 4.584777 (-4.189129) | 2.376548 / 3.745712 (-1.369164) | 2.594662 / 5.269862 (-2.675200) | 1.553528 / 4.565676 (-3.012148) | 0.047875 / 0.424275 (-0.376400) | 0.005287 / 0.007607 (-0.002321) | 0.334734 / 0.226044 (0.108689) | 3.294753 / 2.268929 (1.025825) | 1.797901 / 55.444624 (-53.646724) | 1.510907 / 6.876477 (-5.365570) | 1.536070 / 2.142072 (-0.606003) | 0.474672 / 4.805227 (-4.330555) | 0.099323 / 6.500664 (-6.401341) | 0.041703 / 0.075469 (-0.033766) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947441 / 1.841788 (-0.894347) | 11.451378 / 8.074308 (3.377070) | 10.283213 / 10.191392 (0.091821) | 0.131032 / 0.680424 (-0.549392) | 0.014423 / 0.534201 (-0.519777) | 0.272568 / 0.579283 (-0.306715) | 0.267127 / 0.434364 (-0.167237) | 0.307361 / 0.540337 (-0.232976) | 0.403858 / 1.386936 (-0.983078) |\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) | 0.004836 / 0.011353 (-0.006517) | 0.002544 / 0.011008 (-0.008464) | 0.047979 / 0.038508 (0.009471) | 0.052211 / 0.023109 (0.029102) | 0.273394 / 0.275898 (-0.002504) | 0.291202 / 0.323480 (-0.032277) | 0.004094 / 0.007986 (-0.003891) | 0.002415 / 0.004328 (-0.001914) | 0.048057 / 0.004250 (0.043807) | 0.039756 / 0.037052 (0.002703) | 0.277301 / 0.258489 (0.018812) | 0.297626 / 0.293841 (0.003785) | 0.024641 / 0.128546 (-0.103905) | 0.006957 / 0.075646 (-0.068690) | 0.053574 / 0.419271 (-0.365697) | 0.036532 / 0.043533 (-0.007001) | 0.273753 / 0.255139 (0.018614) | 0.294254 / 0.283200 (0.011054) | 0.022252 / 0.141683 (-0.119431) | 1.128609 / 1.452155 (-0.323546) | 1.217322 / 1.492716 (-0.275394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091050 / 0.018006 (0.073044) | 0.300089 / 0.000490 (0.299600) | 0.000215 / 0.000200 (0.000015) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021423 / 0.037411 (-0.015988) | 0.069892 / 0.014526 (0.055366) | 0.081125 / 0.176557 (-0.095432) | 0.118725 / 0.737135 (-0.618411) | 0.081357 / 0.296338 (-0.214981) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295046 / 0.215209 (0.079837) | 2.868813 / 2.077655 (0.791159) | 1.579613 / 1.504120 (0.075493) | 1.449308 / 1.541195 (-0.091887) | 1.478804 / 1.468490 (0.010314) | 0.416916 / 4.584777 (-4.167861) | 2.461093 / 3.745712 (-1.284619) | 2.449792 / 5.269862 (-2.820070) | 1.573930 / 4.565676 (-2.991746) | 0.046808 / 0.424275 (-0.377467) | 0.004811 / 0.007607 (-0.002796) | 0.352805 / 0.226044 (0.126761) | 3.495034 / 2.268929 (1.226105) | 1.952019 / 55.444624 (-53.492606) | 1.642607 / 6.876477 (-5.233869) | 1.775235 / 2.142072 (-0.366837) | 0.482196 / 4.805227 (-4.323032) | 0.099562 / 6.500664 (-6.401102) | 0.040709 / 0.075469 (-0.034760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972750 / 1.841788 (-0.869038) | 11.905172 / 8.074308 (3.830864) | 10.613847 / 10.191392 (0.422455) | 0.129892 / 0.680424 (-0.550532) | 0.015611 / 0.534201 (-0.518590) | 0.271884 / 0.579283 (-0.307400) | 0.275270 / 0.434364 (-0.159094) | 0.303213 / 0.540337 (-0.237125) | 0.402338 / 1.386936 (-0.984598) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\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) | 0.004826 / 0.011353 (-0.006527) | 0.002979 / 0.011008 (-0.008029) | 0.062055 / 0.038508 (0.023547) | 0.056574 / 0.023109 (0.033465) | 0.244342 / 0.275898 (-0.031556) | 0.278040 / 0.323480 (-0.045439) | 0.004020 / 0.007986 (-0.003965) | 0.002474 / 0.004328 (-0.001855) | 0.048451 / 0.004250 (0.044200) | 0.038633 / 0.037052 (0.001580) | 0.251389 / 0.258489 (-0.007100) | 0.282739 / 0.293841 (-0.011102) | 0.023298 / 0.128546 (-0.105248) | 0.007513 / 0.075646 (-0.068134) | 0.203014 / 0.419271 (-0.216257) | 0.036216 / 0.043533 (-0.007317) | 0.250988 / 0.255139 (-0.004151) | 0.281228 / 0.283200 (-0.001972) | 0.018259 / 0.141683 (-0.123424) | 1.121200 / 1.452155 (-0.330955) | 1.184298 / 1.492716 (-0.308419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093730 / 0.018006 (0.075724) | 0.301716 / 0.000490 (0.301226) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019238 / 0.037411 (-0.018173) | 0.064329 / 0.014526 (0.049803) | 0.075657 / 0.176557 (-0.100899) | 0.122616 / 0.737135 (-0.614519) | 0.077459 / 0.296338 (-0.218880) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280153 / 0.215209 (0.064944) | 2.715488 / 2.077655 (0.637833) | 1.449666 / 1.504120 (-0.054454) | 1.331903 / 1.541195 (-0.209292) | 1.396200 / 1.468490 (-0.072290) | 0.398861 / 4.584777 (-4.185916) | 2.402814 / 3.745712 (-1.342898) | 2.664033 / 5.269862 (-2.605829) | 1.619589 / 4.565676 (-2.946088) | 0.044798 / 0.424275 (-0.379477) | 0.004989 / 0.007607 (-0.002618) | 0.336822 / 0.226044 (0.110777) | 3.245604 / 2.268929 (0.976676) | 1.815633 / 55.444624 (-53.628991) | 1.557975 / 6.876477 (-5.318501) | 1.603655 / 2.142072 (-0.538417) | 0.462980 / 4.805227 (-4.342247) | 0.098340 / 6.500664 (-6.402324) | 0.042750 / 0.075469 (-0.032719) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.973785 / 1.841788 (-0.868003) | 12.379356 / 8.074308 (4.305048) | 10.540164 / 10.191392 (0.348772) | 0.144803 / 0.680424 (-0.535621) | 0.013875 / 0.534201 (-0.520326) | 0.270192 / 0.579283 (-0.309091) | 0.264614 / 0.434364 (-0.169750) | 0.313454 / 0.540337 (-0.226883) | 0.402310 / 1.386936 (-0.984626) |\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) | 0.004987 / 0.011353 (-0.006366) | 0.003017 / 0.011008 (-0.007992) | 0.048592 / 0.038508 (0.010084) | 0.059370 / 0.023109 (0.036261) | 0.277536 / 0.275898 (0.001638) | 0.300592 / 0.323480 (-0.022888) | 0.004870 / 0.007986 (-0.003115) | 0.002452 / 0.004328 (-0.001876) | 0.047972 / 0.004250 (0.043721) | 0.042336 / 0.037052 (0.005283) | 0.277570 / 0.258489 (0.019081) | 0.304739 / 0.293841 (0.010898) | 0.025313 / 0.128546 (-0.103233) | 0.007219 / 0.075646 (-0.068427) | 0.053967 / 0.419271 (-0.365304) | 0.033314 / 0.043533 (-0.010219) | 0.273908 / 0.255139 (0.018769) | 0.291913 / 0.283200 (0.008713) | 0.019440 / 0.141683 (-0.122243) | 1.111047 / 1.452155 (-0.341107) | 1.191276 / 1.492716 (-0.301440) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093985 / 0.018006 (0.075979) | 0.303105 / 0.000490 (0.302615) | 0.000235 / 0.000200 (0.000035) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022226 / 0.037411 (-0.015186) | 0.072151 / 0.014526 (0.057625) | 0.081700 / 0.176557 (-0.094857) | 0.121407 / 0.737135 (-0.615729) | 0.083217 / 0.296338 (-0.213121) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297286 / 0.215209 (0.082077) | 2.913392 / 2.077655 (0.835738) | 1.591758 / 1.504120 (0.087638) | 1.463339 / 1.541195 (-0.077856) | 1.495095 / 1.468490 (0.026605) | 0.414341 / 4.584777 (-4.170436) | 2.412438 / 3.745712 (-1.333275) | 2.611452 / 5.269862 (-2.658410) | 1.658545 / 4.565676 (-2.907132) | 0.047269 / 0.424275 (-0.377007) | 0.004872 / 0.007607 (-0.002735) | 0.350746 / 0.226044 (0.124701) | 3.491482 / 2.268929 (1.222554) | 1.999009 / 55.444624 (-53.445616) | 1.672862 / 6.876477 (-5.203615) | 1.863095 / 2.142072 (-0.278977) | 0.484746 / 4.805227 (-4.320481) | 0.100774 / 6.500664 (-6.399890) | 0.042519 / 0.075469 (-0.032950) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984497 / 1.841788 (-0.857291) | 12.972576 / 8.074308 (4.898268) | 10.886021 / 10.191392 (0.694629) | 0.141639 / 0.680424 (-0.538785) | 0.015726 / 0.534201 (-0.518475) | 0.284160 / 0.579283 (-0.295123) | 0.291437 / 0.434364 (-0.142927) | 0.314121 / 0.540337 (-0.226217) | 0.420439 / 1.386936 (-0.966497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ad7c7767b9cda62113c207f0ff42506a8f27c0 \"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) | 0.004881 / 0.011353 (-0.006472) | 0.002550 / 0.011008 (-0.008458) | 0.062171 / 0.038508 (0.023663) | 0.055341 / 0.023109 (0.032232) | 0.243132 / 0.275898 (-0.032766) | 0.265174 / 0.323480 (-0.058306) | 0.002934 / 0.007986 (-0.005052) | 0.002233 / 0.004328 (-0.002096) | 0.049302 / 0.004250 (0.045052) | 0.039491 / 0.037052 (0.002439) | 0.252776 / 0.258489 (-0.005713) | 0.280923 / 0.293841 (-0.012918) | 0.022585 / 0.128546 (-0.105962) | 0.006888 / 0.075646 (-0.068759) | 0.202751 / 0.419271 (-0.216521) | 0.035250 / 0.043533 (-0.008283) | 0.251745 / 0.255139 (-0.003394) | 0.267431 / 0.283200 (-0.015768) | 0.019486 / 0.141683 (-0.122197) | 1.161783 / 1.452155 (-0.290372) | 1.194254 / 1.492716 (-0.298463) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097772 / 0.018006 (0.079766) | 0.309137 / 0.000490 (0.308647) | 0.000225 / 0.000200 (0.000025) | 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) | 0.018719 / 0.037411 (-0.018693) | 0.062211 / 0.014526 (0.047686) | 0.074291 / 0.176557 (-0.102266) | 0.119436 / 0.737135 (-0.617699) | 0.075519 / 0.296338 (-0.220820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279778 / 0.215209 (0.064569) | 2.730678 / 2.077655 (0.653023) | 1.413922 / 1.504120 (-0.090198) | 1.286747 / 1.541195 (-0.254447) | 1.299835 / 1.468490 (-0.168656) | 0.392516 / 4.584777 (-4.192261) | 2.381816 / 3.745712 (-1.363896) | 2.616944 / 5.269862 (-2.652918) | 1.606152 / 4.565676 (-2.959525) | 0.044867 / 0.424275 (-0.379408) | 0.004915 / 0.007607 (-0.002692) | 0.334078 / 0.226044 (0.108034) | 3.388096 / 2.268929 (1.119167) | 1.756666 / 55.444624 (-53.687958) | 1.497211 / 6.876477 (-5.379266) | 1.496787 / 2.142072 (-0.645285) | 0.469145 / 4.805227 (-4.336082) | 0.097821 / 6.500664 (-6.402843) | 0.041850 / 0.075469 (-0.033619) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956878 / 1.841788 (-0.884910) | 11.520184 / 8.074308 (3.445875) | 10.659216 / 10.191392 (0.467824) | 0.143687 / 0.680424 (-0.536737) | 0.014118 / 0.534201 (-0.520083) | 0.270990 / 0.579283 (-0.308293) | 0.270057 / 0.434364 (-0.164306) | 0.311109 / 0.540337 (-0.229229) | 0.407042 / 1.386936 (-0.979894) |\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) | 0.004816 / 0.011353 (-0.006537) | 0.002898 / 0.011008 (-0.008110) | 0.048540 / 0.038508 (0.010032) | 0.055286 / 0.023109 (0.032176) | 0.279086 / 0.275898 (0.003187) | 0.298950 / 0.323480 (-0.024529) | 0.004090 / 0.007986 (-0.003896) | 0.002497 / 0.004328 (-0.001832) | 0.049160 / 0.004250 (0.044910) | 0.040612 / 0.037052 (0.003560) | 0.287832 / 0.258489 (0.029343) | 0.305617 / 0.293841 (0.011776) | 0.023936 / 0.128546 (-0.104610) | 0.007565 / 0.075646 (-0.068081) | 0.054037 / 0.419271 (-0.365235) | 0.032389 / 0.043533 (-0.011144) | 0.283031 / 0.255139 (0.027892) | 0.295411 / 0.283200 (0.012212) | 0.018466 / 0.141683 (-0.123217) | 1.134660 / 1.452155 (-0.317495) | 1.196212 / 1.492716 (-0.296504) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099961 / 0.018006 (0.081955) | 0.310831 / 0.000490 (0.310342) | 0.000238 / 0.000200 (0.000038) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021566 / 0.037411 (-0.015845) | 0.070255 / 0.014526 (0.055729) | 0.081221 / 0.176557 (-0.095336) | 0.119404 / 0.737135 (-0.617732) | 0.083005 / 0.296338 (-0.213333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302788 / 0.215209 (0.087579) | 2.928876 / 2.077655 (0.851221) | 1.601221 / 1.504120 (0.097101) | 1.485147 / 1.541195 (-0.056047) | 1.508698 / 1.468490 (0.040207) | 0.402783 / 4.584777 (-4.181994) | 2.432151 / 3.745712 (-1.313561) | 2.476848 / 5.269862 (-2.793013) | 1.585487 / 4.565676 (-2.980189) | 0.045965 / 0.424275 (-0.378310) | 0.004818 / 0.007607 (-0.002789) | 0.354847 / 0.226044 (0.128803) | 3.500670 / 2.268929 (1.231742) | 1.951904 / 55.444624 (-53.492720) | 1.675152 / 6.876477 (-5.201325) | 1.795971 / 2.142072 (-0.346101) | 0.470625 / 4.805227 (-4.334602) | 0.126080 / 6.500664 (-6.374584) | 0.040506 / 0.075469 (-0.034963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985251 / 1.841788 (-0.856536) | 12.316710 / 8.074308 (4.242402) | 10.674437 / 10.191392 (0.483045) | 0.133622 / 0.680424 (-0.546802) | 0.016756 / 0.534201 (-0.517445) | 0.269318 / 0.579283 (-0.309965) | 0.282258 / 0.434364 (-0.152106) | 0.309941 / 0.540337 (-0.230396) | 0.403189 / 1.386936 (-0.983747) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08ceb927025575c453228cab31291b74043dba1a \"CML watermark\")\n", "I am merging this PR because we need it by `datasets-server`.", "<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) | 0.004935 / 0.011353 (-0.006418) | 0.002643 / 0.011008 (-0.008365) | 0.064449 / 0.038508 (0.025941) | 0.053110 / 0.023109 (0.030001) | 0.261576 / 0.275898 (-0.014322) | 0.270866 / 0.323480 (-0.052614) | 0.002895 / 0.007986 (-0.005091) | 0.002349 / 0.004328 (-0.001979) | 0.047620 / 0.004250 (0.043370) | 0.038699 / 0.037052 (0.001647) | 0.246663 / 0.258489 (-0.011826) | 0.282021 / 0.293841 (-0.011820) | 0.022807 / 0.128546 (-0.105739) | 0.007242 / 0.075646 (-0.068404) | 0.204236 / 0.419271 (-0.215035) | 0.035429 / 0.043533 (-0.008104) | 0.241684 / 0.255139 (-0.013455) | 0.262343 / 0.283200 (-0.020857) | 0.020036 / 0.141683 (-0.121647) | 1.112687 / 1.452155 (-0.339467) | 1.167086 / 1.492716 (-0.325630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107059 / 0.018006 (0.089053) | 0.301036 / 0.000490 (0.300546) | 0.000224 / 0.000200 (0.000024) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018464 / 0.037411 (-0.018947) | 0.063822 / 0.014526 (0.049296) | 0.073562 / 0.176557 (-0.102994) | 0.120136 / 0.737135 (-0.616999) | 0.074934 / 0.296338 (-0.221405) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275474 / 0.215209 (0.060265) | 2.714239 / 2.077655 (0.636584) | 1.455535 / 1.504120 (-0.048585) | 1.336530 / 1.541195 (-0.204665) | 1.359607 / 1.468490 (-0.108883) | 0.396303 / 4.584777 (-4.188474) | 2.366076 / 3.745712 (-1.379636) | 2.600755 / 5.269862 (-2.669107) | 1.572382 / 4.565676 (-2.993294) | 0.045795 / 0.424275 (-0.378480) | 0.004932 / 0.007607 (-0.002675) | 0.332175 / 0.226044 (0.106130) | 3.257843 / 2.268929 (0.988915) | 1.799021 / 55.444624 (-53.645603) | 1.532813 / 6.876477 (-5.343663) | 1.552279 / 2.142072 (-0.589794) | 0.471369 / 4.805227 (-4.333858) | 0.098931 / 6.500664 (-6.401733) | 0.042735 / 0.075469 (-0.032734) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960779 / 1.841788 (-0.881009) | 11.741631 / 8.074308 (3.667322) | 10.355721 / 10.191392 (0.164329) | 0.129025 / 0.680424 (-0.551399) | 0.013794 / 0.534201 (-0.520407) | 0.267268 / 0.579283 (-0.312015) | 0.265582 / 0.434364 (-0.168782) | 0.306242 / 0.540337 (-0.234095) | 0.400367 / 1.386936 (-0.986569) |\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) | 0.004966 / 0.011353 (-0.006387) | 0.002846 / 0.011008 (-0.008163) | 0.049104 / 0.038508 (0.010596) | 0.055436 / 0.023109 (0.032327) | 0.273892 / 0.275898 (-0.002006) | 0.300207 / 0.323480 (-0.023273) | 0.004017 / 0.007986 (-0.003969) | 0.002465 / 0.004328 (-0.001863) | 0.048088 / 0.004250 (0.043837) | 0.040037 / 0.037052 (0.002984) | 0.279918 / 0.258489 (0.021429) | 0.305378 / 0.293841 (0.011537) | 0.024326 / 0.128546 (-0.104220) | 0.006992 / 0.075646 (-0.068654) | 0.053545 / 0.419271 (-0.365726) | 0.032312 / 0.043533 (-0.011221) | 0.272899 / 0.255139 (0.017760) | 0.289683 / 0.283200 (0.006483) | 0.019121 / 0.141683 (-0.122562) | 1.133296 / 1.452155 (-0.318858) | 1.220989 / 1.492716 (-0.271728) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093193 / 0.018006 (0.075187) | 0.307658 / 0.000490 (0.307168) | 0.000224 / 0.000200 (0.000024) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022906 / 0.037411 (-0.014506) | 0.080931 / 0.014526 (0.066405) | 0.081442 / 0.176557 (-0.095115) | 0.121150 / 0.737135 (-0.615986) | 0.083387 / 0.296338 (-0.212952) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294979 / 0.215209 (0.079770) | 2.900090 / 2.077655 (0.822435) | 1.610061 / 1.504120 (0.105941) | 1.455118 / 1.541195 (-0.086077) | 1.456599 / 1.468490 (-0.011891) | 0.397919 / 4.584777 (-4.186858) | 2.421010 / 3.745712 (-1.324702) | 2.486527 / 5.269862 (-2.783334) | 1.573854 / 4.565676 (-2.991822) | 0.046199 / 0.424275 (-0.378076) | 0.004888 / 0.007607 (-0.002719) | 0.342183 / 0.226044 (0.116139) | 3.392068 / 2.268929 (1.123140) | 1.963688 / 55.444624 (-53.480936) | 1.667611 / 6.876477 (-5.208866) | 1.833706 / 2.142072 (-0.308367) | 0.509421 / 4.805227 (-4.295806) | 0.099669 / 6.500664 (-6.400995) | 0.041004 / 0.075469 (-0.034465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956314 / 1.841788 (-0.885474) | 12.190194 / 8.074308 (4.115886) | 10.417839 / 10.191392 (0.226447) | 0.144139 / 0.680424 (-0.536285) | 0.015841 / 0.534201 (-0.518359) | 0.270436 / 0.579283 (-0.308847) | 0.273952 / 0.434364 (-0.160412) | 0.303018 / 0.540337 (-0.237319) | 0.410163 / 1.386936 (-0.976773) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa8558fc7fe1f9f7675c7c5d21a14d1a19598296 \"CML watermark\")\n" ]
2023-11-16T16:02:55
2023-11-22T15:18:51
2023-11-22 15:12:33+00:00
MEMBER
nan
Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo
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PR_kwDODunzps5fn1r_
6429
Add trust_remote_code argument
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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) | 0.004947 / 0.011353 (-0.006405) | 0.002961 / 0.011008 (-0.008047) | 0.063474 / 0.038508 (0.024966) | 0.030162 / 0.023109 (0.007053) | 0.232388 / 0.275898 (-0.043511) | 0.257654 / 0.323480 (-0.065826) | 0.002969 / 0.007986 (-0.005017) | 0.002336 / 0.004328 (-0.001993) | 0.049724 / 0.004250 (0.045473) | 0.045608 / 0.037052 (0.008555) | 0.236079 / 0.258489 (-0.022410) | 0.267809 / 0.293841 (-0.026032) | 0.023805 / 0.128546 (-0.104741) | 0.007177 / 0.075646 (-0.068470) | 0.202167 / 0.419271 (-0.217104) | 0.056181 / 0.043533 (0.012648) | 0.256464 / 0.255139 (0.001325) | 0.271908 / 0.283200 (-0.011292) | 0.020211 / 0.141683 (-0.121472) | 1.114112 / 1.452155 (-0.338042) | 1.174879 / 1.492716 (-0.317837) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093457 / 0.018006 (0.075451) | 0.307643 / 0.000490 (0.307154) | 0.000212 / 0.000200 (0.000012) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018635 / 0.037411 (-0.018777) | 0.062099 / 0.014526 (0.047573) | 0.073619 / 0.176557 (-0.102938) | 0.119986 / 0.737135 (-0.617149) | 0.075439 / 0.296338 (-0.220899) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280142 / 0.215209 (0.064933) | 2.733790 / 2.077655 (0.656136) | 1.457633 / 1.504120 (-0.046487) | 1.336288 / 1.541195 (-0.204907) | 1.363191 / 1.468490 (-0.105299) | 0.399331 / 4.584777 (-4.185446) | 2.343099 / 3.745712 (-1.402614) | 2.617059 / 5.269862 (-2.652802) | 1.575912 / 4.565676 (-2.989765) | 0.045621 / 0.424275 (-0.378655) | 0.004825 / 0.007607 (-0.002782) | 0.346669 / 0.226044 (0.120625) | 3.225982 / 2.268929 (0.957054) | 1.787067 / 55.444624 (-53.657557) | 1.503883 / 6.876477 (-5.372593) | 1.527593 / 2.142072 (-0.614479) | 0.466806 / 4.805227 (-4.338421) | 0.098537 / 6.500664 (-6.402127) | 0.042028 / 0.075469 (-0.033441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945040 / 1.841788 (-0.896748) | 11.970022 / 8.074308 (3.895714) | 10.261176 / 10.191392 (0.069784) | 0.138231 / 0.680424 (-0.542193) | 0.013933 / 0.534201 (-0.520268) | 0.270640 / 0.579283 (-0.308643) | 0.263185 / 0.434364 (-0.171178) | 0.306686 / 0.540337 (-0.233651) | 0.423164 / 1.386936 (-0.963772) |\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) | 0.004765 / 0.011353 (-0.006588) | 0.003158 / 0.011008 (-0.007850) | 0.047813 / 0.038508 (0.009305) | 0.053363 / 0.023109 (0.030254) | 0.278570 / 0.275898 (0.002671) | 0.291500 / 0.323480 (-0.031980) | 0.003987 / 0.007986 (-0.003998) | 0.002430 / 0.004328 (-0.001898) | 0.048059 / 0.004250 (0.043809) | 0.038595 / 0.037052 (0.001542) | 0.276383 / 0.258489 (0.017894) | 0.304234 / 0.293841 (0.010393) | 0.024402 / 0.128546 (-0.104144) | 0.007303 / 0.075646 (-0.068343) | 0.055091 / 0.419271 (-0.364180) | 0.032735 / 0.043533 (-0.010797) | 0.270905 / 0.255139 (0.015766) | 0.287181 / 0.283200 (0.003981) | 0.018919 / 0.141683 (-0.122764) | 1.153814 / 1.452155 (-0.298341) | 1.197009 / 1.492716 (-0.295707) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093743 / 0.018006 (0.075737) | 0.302877 / 0.000490 (0.302387) | 0.000223 / 0.000200 (0.000023) | 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) | 0.021279 / 0.037411 (-0.016133) | 0.070886 / 0.014526 (0.056360) | 0.081628 / 0.176557 (-0.094928) | 0.119721 / 0.737135 (-0.617414) | 0.083093 / 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) | 0.297788 / 0.215209 (0.082579) | 2.915235 / 2.077655 (0.837580) | 1.587580 / 1.504120 (0.083460) | 1.461699 / 1.541195 (-0.079495) | 1.520609 / 1.468490 (0.052119) | 0.398363 / 4.584777 (-4.186413) | 2.408415 / 3.745712 (-1.337297) | 2.552776 / 5.269862 (-2.717086) | 1.508219 / 4.565676 (-3.057457) | 0.045884 / 0.424275 (-0.378391) | 0.004842 / 0.007607 (-0.002765) | 0.341376 / 0.226044 (0.115331) | 3.420192 / 2.268929 (1.151264) | 1.974938 / 55.444624 (-53.469686) | 1.678283 / 6.876477 (-5.198194) | 1.702439 / 2.142072 (-0.439633) | 0.467056 / 4.805227 (-4.338172) | 0.098684 / 6.500664 (-6.401980) | 0.041052 / 0.075469 (-0.034417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990145 / 1.841788 (-0.851643) | 12.143198 / 8.074308 (4.068890) | 10.911039 / 10.191392 (0.719647) | 0.130384 / 0.680424 (-0.550040) | 0.015602 / 0.534201 (-0.518599) | 0.270799 / 0.579283 (-0.308484) | 0.279060 / 0.434364 (-0.155304) | 0.315108 / 0.540337 (-0.225230) | 0.413576 / 1.386936 (-0.973360) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d99b8225e28cca88ed9c2d9b1d8e0342762c4ece \"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) | 0.004911 / 0.011353 (-0.006442) | 0.002808 / 0.011008 (-0.008200) | 0.061367 / 0.038508 (0.022859) | 0.050154 / 0.023109 (0.027045) | 0.250403 / 0.275898 (-0.025495) | 0.273831 / 0.323480 (-0.049649) | 0.002914 / 0.007986 (-0.005071) | 0.002493 / 0.004328 (-0.001836) | 0.048288 / 0.004250 (0.044037) | 0.039219 / 0.037052 (0.002167) | 0.260043 / 0.258489 (0.001554) | 0.288177 / 0.293841 (-0.005664) | 0.023123 / 0.128546 (-0.105423) | 0.006981 / 0.075646 (-0.068666) | 0.201306 / 0.419271 (-0.217965) | 0.035670 / 0.043533 (-0.007863) | 0.255237 / 0.255139 (0.000098) | 0.283701 / 0.283200 (0.000502) | 0.019349 / 0.141683 (-0.122334) | 1.100963 / 1.452155 (-0.351192) | 1.152725 / 1.492716 (-0.339992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106350 / 0.018006 (0.088344) | 0.300577 / 0.000490 (0.300087) | 0.000206 / 0.000200 (0.000006) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019028 / 0.037411 (-0.018384) | 0.062643 / 0.014526 (0.048118) | 0.072771 / 0.176557 (-0.103786) | 0.119873 / 0.737135 (-0.617263) | 0.074470 / 0.296338 (-0.221869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287032 / 0.215209 (0.071823) | 2.826134 / 2.077655 (0.748480) | 1.507362 / 1.504120 (0.003242) | 1.382929 / 1.541195 (-0.158266) | 1.385361 / 1.468490 (-0.083129) | 0.412081 / 4.584777 (-4.172696) | 2.384289 / 3.745712 (-1.361423) | 2.551316 / 5.269862 (-2.718546) | 1.562954 / 4.565676 (-3.002722) | 0.046669 / 0.424275 (-0.377606) | 0.004804 / 0.007607 (-0.002803) | 0.337751 / 0.226044 (0.111707) | 3.378894 / 2.268929 (1.109965) | 1.848817 / 55.444624 (-53.595807) | 1.564560 / 6.876477 (-5.311917) | 1.579577 / 2.142072 (-0.562496) | 0.484531 / 4.805227 (-4.320697) | 0.101157 / 6.500664 (-6.399507) | 0.042272 / 0.075469 (-0.033197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948289 / 1.841788 (-0.893498) | 11.490877 / 8.074308 (3.416569) | 10.492787 / 10.191392 (0.301395) | 0.128575 / 0.680424 (-0.551849) | 0.013716 / 0.534201 (-0.520485) | 0.271075 / 0.579283 (-0.308208) | 0.269749 / 0.434364 (-0.164615) | 0.306378 / 0.540337 (-0.233959) | 0.400204 / 1.386936 (-0.986732) |\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) | 0.004821 / 0.011353 (-0.006532) | 0.002773 / 0.011008 (-0.008235) | 0.048934 / 0.038508 (0.010426) | 0.049490 / 0.023109 (0.026380) | 0.271107 / 0.275898 (-0.004791) | 0.291472 / 0.323480 (-0.032008) | 0.004734 / 0.007986 (-0.003252) | 0.002437 / 0.004328 (-0.001892) | 0.048840 / 0.004250 (0.044590) | 0.039757 / 0.037052 (0.002704) | 0.276037 / 0.258489 (0.017548) | 0.298220 / 0.293841 (0.004379) | 0.024595 / 0.128546 (-0.103952) | 0.007320 / 0.075646 (-0.068327) | 0.054693 / 0.419271 (-0.364578) | 0.032672 / 0.043533 (-0.010861) | 0.271555 / 0.255139 (0.016416) | 0.287685 / 0.283200 (0.004485) | 0.017159 / 0.141683 (-0.124524) | 1.118496 / 1.452155 (-0.333659) | 1.177389 / 1.492716 (-0.315327) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090469 / 0.018006 (0.072463) | 0.306014 / 0.000490 (0.305525) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021452 / 0.037411 (-0.015960) | 0.070014 / 0.014526 (0.055488) | 0.081917 / 0.176557 (-0.094639) | 0.120615 / 0.737135 (-0.616520) | 0.081745 / 0.296338 (-0.214593) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294049 / 0.215209 (0.078840) | 2.886802 / 2.077655 (0.809147) | 1.607817 / 1.504120 (0.103697) | 1.474172 / 1.541195 (-0.067023) | 1.474744 / 1.468490 (0.006254) | 0.398178 / 4.584777 (-4.186599) | 2.455908 / 3.745712 (-1.289804) | 2.463003 / 5.269862 (-2.806858) | 1.560402 / 4.565676 (-3.005275) | 0.046208 / 0.424275 (-0.378067) | 0.004862 / 0.007607 (-0.002745) | 0.350862 / 0.226044 (0.124817) | 3.463958 / 2.268929 (1.195030) | 1.934696 / 55.444624 (-53.509928) | 1.660090 / 6.876477 (-5.216387) | 1.770920 / 2.142072 (-0.371153) | 0.468409 / 4.805227 (-4.336819) | 0.096812 / 6.500664 (-6.403852) | 0.040580 / 0.075469 (-0.034889) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978102 / 1.841788 (-0.863686) | 11.943265 / 8.074308 (3.868957) | 10.684995 / 10.191392 (0.493603) | 0.131554 / 0.680424 (-0.548870) | 0.015608 / 0.534201 (-0.518593) | 0.271449 / 0.579283 (-0.307834) | 0.282485 / 0.434364 (-0.151879) | 0.302376 / 0.540337 (-0.237962) | 0.524908 / 1.386936 (-0.862028) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bb0b21e37a57257a7d428f8744c862ca92c0c7e \"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) | 0.004926 / 0.011353 (-0.006427) | 0.003020 / 0.011008 (-0.007988) | 0.061899 / 0.038508 (0.023391) | 0.063836 / 0.023109 (0.040726) | 0.239252 / 0.275898 (-0.036646) | 0.268320 / 0.323480 (-0.055160) | 0.003939 / 0.007986 (-0.004046) | 0.002557 / 0.004328 (-0.001772) | 0.048469 / 0.004250 (0.044219) | 0.038707 / 0.037052 (0.001655) | 0.247563 / 0.258489 (-0.010926) | 0.281171 / 0.293841 (-0.012670) | 0.023564 / 0.128546 (-0.104983) | 0.007699 / 0.075646 (-0.067948) | 0.207561 / 0.419271 (-0.211710) | 0.036362 / 0.043533 (-0.007171) | 0.248324 / 0.255139 (-0.006814) | 0.269673 / 0.283200 (-0.013527) | 0.018841 / 0.141683 (-0.122842) | 1.123407 / 1.452155 (-0.328748) | 1.170422 / 1.492716 (-0.322295) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096278 / 0.018006 (0.078272) | 0.311477 / 0.000490 (0.310988) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019470 / 0.037411 (-0.017942) | 0.071888 / 0.014526 (0.057362) | 0.074264 / 0.176557 (-0.102292) | 0.124413 / 0.737135 (-0.612723) | 0.075602 / 0.296338 (-0.220737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284755 / 0.215209 (0.069546) | 2.770789 / 2.077655 (0.693135) | 1.478276 / 1.504120 (-0.025843) | 1.375287 / 1.541195 (-0.165907) | 1.398032 / 1.468490 (-0.070458) | 0.420457 / 4.584777 (-4.164320) | 2.445929 / 3.745712 (-1.299783) | 2.819548 / 5.269862 (-2.450313) | 1.628506 / 4.565676 (-2.937171) | 0.047687 / 0.424275 (-0.376588) | 0.004861 / 0.007607 (-0.002746) | 0.340173 / 0.226044 (0.114129) | 3.340703 / 2.268929 (1.071774) | 1.882803 / 55.444624 (-53.561821) | 1.587206 / 6.876477 (-5.289271) | 1.645298 / 2.142072 (-0.496774) | 0.490957 / 4.805227 (-4.314270) | 0.102779 / 6.500664 (-6.397885) | 0.048372 / 0.075469 (-0.027098) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958311 / 1.841788 (-0.883477) | 12.354981 / 8.074308 (4.280673) | 10.864826 / 10.191392 (0.673434) | 0.149053 / 0.680424 (-0.531371) | 0.015078 / 0.534201 (-0.519123) | 0.270117 / 0.579283 (-0.309166) | 0.274495 / 0.434364 (-0.159869) | 0.307584 / 0.540337 (-0.232753) | 0.405603 / 1.386936 (-0.981333) |\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) | 0.004996 / 0.011353 (-0.006357) | 0.002995 / 0.011008 (-0.008014) | 0.047897 / 0.038508 (0.009389) | 0.056413 / 0.023109 (0.033303) | 0.277669 / 0.275898 (0.001771) | 0.300679 / 0.323480 (-0.022801) | 0.004094 / 0.007986 (-0.003892) | 0.002519 / 0.004328 (-0.001810) | 0.049536 / 0.004250 (0.045285) | 0.042341 / 0.037052 (0.005288) | 0.281533 / 0.258489 (0.023044) | 0.306771 / 0.293841 (0.012930) | 0.025379 / 0.128546 (-0.103167) | 0.007495 / 0.075646 (-0.068152) | 0.054453 / 0.419271 (-0.364818) | 0.032616 / 0.043533 (-0.010917) | 0.277844 / 0.255139 (0.022705) | 0.296265 / 0.283200 (0.013065) | 0.019462 / 0.141683 (-0.122221) | 1.115841 / 1.452155 (-0.336313) | 1.169662 / 1.492716 (-0.323054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095459 / 0.018006 (0.077453) | 0.301590 / 0.000490 (0.301100) | 0.000230 / 0.000200 (0.000030) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022182 / 0.037411 (-0.015229) | 0.085367 / 0.014526 (0.070842) | 0.084006 / 0.176557 (-0.092550) | 0.121260 / 0.737135 (-0.615876) | 0.084137 / 0.296338 (-0.212202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.310335 / 0.215209 (0.095126) | 3.002531 / 2.077655 (0.924876) | 1.642282 / 1.504120 (0.138162) | 1.573044 / 1.541195 (0.031849) | 1.572076 / 1.468490 (0.103586) | 0.422037 / 4.584777 (-4.162740) | 2.495295 / 3.745712 (-1.250417) | 2.523707 / 5.269862 (-2.746155) | 1.725824 / 4.565676 (-2.839853) | 0.047814 / 0.424275 (-0.376461) | 0.004868 / 0.007607 (-0.002739) | 0.352833 / 0.226044 (0.126789) | 3.477241 / 2.268929 (1.208313) | 1.983888 / 55.444624 (-53.460736) | 1.696883 / 6.876477 (-5.179594) | 1.831665 / 2.142072 (-0.310407) | 0.502976 / 4.805227 (-4.302251) | 0.101264 / 6.500664 (-6.399400) | 0.041779 / 0.075469 (-0.033690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981629 / 1.841788 (-0.860159) | 12.550634 / 8.074308 (4.476326) | 11.113382 / 10.191392 (0.921990) | 0.136565 / 0.680424 (-0.543859) | 0.016742 / 0.534201 (-0.517459) | 0.274316 / 0.579283 (-0.304967) | 0.284687 / 0.434364 (-0.149676) | 0.309966 / 0.540337 (-0.230372) | 0.557990 / 1.386936 (-0.828946) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0c30facb87af83107a645eeffcd18c0775afe11 \"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) | 0.004980 / 0.011353 (-0.006373) | 0.002786 / 0.011008 (-0.008222) | 0.062460 / 0.038508 (0.023952) | 0.051811 / 0.023109 (0.028702) | 0.231734 / 0.275898 (-0.044164) | 0.254075 / 0.323480 (-0.069405) | 0.002884 / 0.007986 (-0.005102) | 0.002317 / 0.004328 (-0.002011) | 0.049044 / 0.004250 (0.044793) | 0.038984 / 0.037052 (0.001931) | 0.241193 / 0.258489 (-0.017296) | 0.272091 / 0.293841 (-0.021750) | 0.023098 / 0.128546 (-0.105448) | 0.007190 / 0.075646 (-0.068456) | 0.201409 / 0.419271 (-0.217863) | 0.036100 / 0.043533 (-0.007433) | 0.238185 / 0.255139 (-0.016954) | 0.257127 / 0.283200 (-0.026072) | 0.019542 / 0.141683 (-0.122141) | 1.127925 / 1.452155 (-0.324230) | 1.174354 / 1.492716 (-0.318362) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099608 / 0.018006 (0.081601) | 0.315046 / 0.000490 (0.314556) | 0.000282 / 0.000200 (0.000082) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018710 / 0.037411 (-0.018701) | 0.062557 / 0.014526 (0.048031) | 0.074021 / 0.176557 (-0.102536) | 0.119670 / 0.737135 (-0.617465) | 0.076491 / 0.296338 (-0.219847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282940 / 0.215209 (0.067731) | 2.788542 / 2.077655 (0.710887) | 1.496039 / 1.504120 (-0.008080) | 1.367542 / 1.541195 (-0.173653) | 1.393705 / 1.468490 (-0.074785) | 0.405910 / 4.584777 (-4.178867) | 2.422544 / 3.745712 (-1.323168) | 2.602822 / 5.269862 (-2.667039) | 1.586853 / 4.565676 (-2.978823) | 0.045440 / 0.424275 (-0.378836) | 0.004792 / 0.007607 (-0.002815) | 0.342059 / 0.226044 (0.116015) | 3.366880 / 2.268929 (1.097952) | 1.810566 / 55.444624 (-53.634058) | 1.527112 / 6.876477 (-5.349364) | 1.548906 / 2.142072 (-0.593166) | 0.479491 / 4.805227 (-4.325736) | 0.099807 / 6.500664 (-6.400857) | 0.041951 / 0.075469 (-0.033518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953723 / 1.841788 (-0.888065) | 11.837240 / 8.074308 (3.762932) | 10.562979 / 10.191392 (0.371587) | 0.145064 / 0.680424 (-0.535360) | 0.014285 / 0.534201 (-0.519916) | 0.270605 / 0.579283 (-0.308678) | 0.264086 / 0.434364 (-0.170278) | 0.308000 / 0.540337 (-0.232337) | 0.403916 / 1.386936 (-0.983020) |\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) | 0.004796 / 0.011353 (-0.006557) | 0.002997 / 0.011008 (-0.008011) | 0.048702 / 0.038508 (0.010193) | 0.053377 / 0.023109 (0.030267) | 0.271852 / 0.275898 (-0.004046) | 0.293366 / 0.323480 (-0.030114) | 0.004041 / 0.007986 (-0.003945) | 0.002459 / 0.004328 (-0.001869) | 0.048197 / 0.004250 (0.043947) | 0.040094 / 0.037052 (0.003042) | 0.275837 / 0.258489 (0.017348) | 0.301174 / 0.293841 (0.007333) | 0.024433 / 0.128546 (-0.104113) | 0.007203 / 0.075646 (-0.068444) | 0.054080 / 0.419271 (-0.365192) | 0.033237 / 0.043533 (-0.010295) | 0.271177 / 0.255139 (0.016038) | 0.293062 / 0.283200 (0.009862) | 0.018399 / 0.141683 (-0.123284) | 1.149527 / 1.452155 (-0.302628) | 1.202717 / 1.492716 (-0.290000) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093168 / 0.018006 (0.075162) | 0.290536 / 0.000490 (0.290046) | 0.000290 / 0.000200 (0.000090) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021191 / 0.037411 (-0.016221) | 0.069990 / 0.014526 (0.055465) | 0.080636 / 0.176557 (-0.095920) | 0.120151 / 0.737135 (-0.616984) | 0.082944 / 0.296338 (-0.213395) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289673 / 0.215209 (0.074463) | 2.828419 / 2.077655 (0.750764) | 1.590741 / 1.504120 (0.086621) | 1.480969 / 1.541195 (-0.060226) | 1.512761 / 1.468490 (0.044271) | 0.398328 / 4.584777 (-4.186449) | 2.441134 / 3.745712 (-1.304578) | 2.487606 / 5.269862 (-2.782256) | 1.586604 / 4.565676 (-2.979073) | 0.045578 / 0.424275 (-0.378697) | 0.004842 / 0.007607 (-0.002766) | 0.344556 / 0.226044 (0.118512) | 3.395982 / 2.268929 (1.127053) | 1.963354 / 55.444624 (-53.481271) | 1.680496 / 6.876477 (-5.195980) | 1.827916 / 2.142072 (-0.314157) | 0.476203 / 4.805227 (-4.329024) | 0.098016 / 6.500664 (-6.402648) | 0.041234 / 0.075469 (-0.034235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977820 / 1.841788 (-0.863968) | 12.139614 / 8.074308 (4.065306) | 10.643071 / 10.191392 (0.451679) | 0.130928 / 0.680424 (-0.549496) | 0.015341 / 0.534201 (-0.518860) | 0.271304 / 0.579283 (-0.307979) | 0.284671 / 0.434364 (-0.149693) | 0.306210 / 0.540337 (-0.234128) | 0.546498 / 1.386936 (-0.840438) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bf7408a171db4a744d1760a9e32ba21deb8d41d \"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) | 0.004748 / 0.011353 (-0.006605) | 0.002942 / 0.011008 (-0.008066) | 0.061298 / 0.038508 (0.022790) | 0.052873 / 0.023109 (0.029764) | 0.250573 / 0.275898 (-0.025325) | 0.270636 / 0.323480 (-0.052844) | 0.002925 / 0.007986 (-0.005061) | 0.003126 / 0.004328 (-0.001203) | 0.047340 / 0.004250 (0.043090) | 0.038662 / 0.037052 (0.001609) | 0.252151 / 0.258489 (-0.006338) | 0.284700 / 0.293841 (-0.009141) | 0.025145 / 0.128546 (-0.103402) | 0.007075 / 0.075646 (-0.068572) | 0.200501 / 0.419271 (-0.218771) | 0.035623 / 0.043533 (-0.007910) | 0.249657 / 0.255139 (-0.005482) | 0.272384 / 0.283200 (-0.010815) | 0.018331 / 0.141683 (-0.123351) | 1.095064 / 1.452155 (-0.357091) | 1.145304 / 1.492716 (-0.347412) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092548 / 0.018006 (0.074542) | 0.299338 / 0.000490 (0.298848) | 0.000212 / 0.000200 (0.000012) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018723 / 0.037411 (-0.018688) | 0.062226 / 0.014526 (0.047700) | 0.072840 / 0.176557 (-0.103717) | 0.120073 / 0.737135 (-0.617063) | 0.074536 / 0.296338 (-0.221802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284862 / 0.215209 (0.069653) | 2.791842 / 2.077655 (0.714188) | 1.506481 / 1.504120 (0.002361) | 1.368952 / 1.541195 (-0.172243) | 1.372555 / 1.468490 (-0.095935) | 0.408292 / 4.584777 (-4.176485) | 2.381155 / 3.745712 (-1.364558) | 2.613617 / 5.269862 (-2.656244) | 1.575892 / 4.565676 (-2.989785) | 0.047526 / 0.424275 (-0.376749) | 0.004792 / 0.007607 (-0.002815) | 0.344818 / 0.226044 (0.118773) | 3.344965 / 2.268929 (1.076036) | 1.883659 / 55.444624 (-53.560965) | 1.596039 / 6.876477 (-5.280437) | 1.584410 / 2.142072 (-0.557662) | 0.486672 / 4.805227 (-4.318555) | 0.101464 / 6.500664 (-6.399200) | 0.041824 / 0.075469 (-0.033645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930491 / 1.841788 (-0.911296) | 11.636526 / 8.074308 (3.562218) | 10.371829 / 10.191392 (0.180437) | 0.138181 / 0.680424 (-0.542243) | 0.014307 / 0.534201 (-0.519894) | 0.268322 / 0.579283 (-0.310961) | 0.264173 / 0.434364 (-0.170191) | 0.303649 / 0.540337 (-0.236688) | 0.399958 / 1.386936 (-0.986978) |\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) | 0.004802 / 0.011353 (-0.006551) | 0.002861 / 0.011008 (-0.008147) | 0.048843 / 0.038508 (0.010335) | 0.053887 / 0.023109 (0.030778) | 0.278690 / 0.275898 (0.002792) | 0.302729 / 0.323480 (-0.020751) | 0.003929 / 0.007986 (-0.004057) | 0.002376 / 0.004328 (-0.001953) | 0.048146 / 0.004250 (0.043896) | 0.039842 / 0.037052 (0.002790) | 0.281595 / 0.258489 (0.023106) | 0.305813 / 0.293841 (0.011972) | 0.024214 / 0.128546 (-0.104333) | 0.007201 / 0.075646 (-0.068446) | 0.053604 / 0.419271 (-0.365667) | 0.032841 / 0.043533 (-0.010691) | 0.276168 / 0.255139 (0.021029) | 0.293869 / 0.283200 (0.010669) | 0.017550 / 0.141683 (-0.124132) | 1.121508 / 1.452155 (-0.330647) | 1.177694 / 1.492716 (-0.315022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091805 / 0.018006 (0.073799) | 0.299026 / 0.000490 (0.298536) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021094 / 0.037411 (-0.016318) | 0.069769 / 0.014526 (0.055243) | 0.081191 / 0.176557 (-0.095366) | 0.118884 / 0.737135 (-0.618252) | 0.081955 / 0.296338 (-0.214383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292159 / 0.215209 (0.076950) | 2.874473 / 2.077655 (0.796819) | 1.614695 / 1.504120 (0.110575) | 1.492123 / 1.541195 (-0.049071) | 1.505293 / 1.468490 (0.036803) | 0.394498 / 4.584777 (-4.190279) | 2.455539 / 3.745712 (-1.290173) | 2.458184 / 5.269862 (-2.811677) | 1.569108 / 4.565676 (-2.996569) | 0.046576 / 0.424275 (-0.377699) | 0.005093 / 0.007607 (-0.002514) | 0.346142 / 0.226044 (0.120098) | 3.398171 / 2.268929 (1.129242) | 1.971953 / 55.444624 (-53.472672) | 1.695275 / 6.876477 (-5.181201) | 1.840511 / 2.142072 (-0.301562) | 0.465932 / 4.805227 (-4.339295) | 0.098578 / 6.500664 (-6.402086) | 0.040456 / 0.075469 (-0.035013) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977636 / 1.841788 (-0.864152) | 12.083585 / 8.074308 (4.009277) | 10.509082 / 10.191392 (0.317690) | 0.130717 / 0.680424 (-0.549707) | 0.015958 / 0.534201 (-0.518243) | 0.273504 / 0.579283 (-0.305780) | 0.276498 / 0.434364 (-0.157866) | 0.306139 / 0.540337 (-0.234199) | 0.522521 / 1.386936 (-0.864415) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e17dd8acec9a958ba82a5f753276b842eaadf52 \"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) | 0.004859 / 0.011353 (-0.006493) | 0.002423 / 0.011008 (-0.008585) | 0.060969 / 0.038508 (0.022461) | 0.048758 / 0.023109 (0.025649) | 0.245400 / 0.275898 (-0.030498) | 0.263686 / 0.323480 (-0.059794) | 0.002852 / 0.007986 (-0.005134) | 0.002273 / 0.004328 (-0.002055) | 0.047648 / 0.004250 (0.043398) | 0.038310 / 0.037052 (0.001258) | 0.249849 / 0.258489 (-0.008640) | 0.279305 / 0.293841 (-0.014536) | 0.022897 / 0.128546 (-0.105649) | 0.006882 / 0.075646 (-0.068764) | 0.202793 / 0.419271 (-0.216478) | 0.034557 / 0.043533 (-0.008976) | 0.252147 / 0.255139 (-0.002992) | 0.267414 / 0.283200 (-0.015785) | 0.019956 / 0.141683 (-0.121727) | 1.106181 / 1.452155 (-0.345973) | 1.158423 / 1.492716 (-0.334293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.086848 / 0.018006 (0.068842) | 0.295235 / 0.000490 (0.294745) | 0.000211 / 0.000200 (0.000011) | 0.000041 / 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) | 0.018209 / 0.037411 (-0.019203) | 0.061967 / 0.014526 (0.047441) | 0.071551 / 0.176557 (-0.105005) | 0.117525 / 0.737135 (-0.619611) | 0.073401 / 0.296338 (-0.222937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.272388 / 0.215209 (0.057179) | 2.689797 / 2.077655 (0.612143) | 1.440897 / 1.504120 (-0.063223) | 1.334689 / 1.541195 (-0.206505) | 1.356395 / 1.468490 (-0.112095) | 0.387201 / 4.584777 (-4.197576) | 2.342908 / 3.745712 (-1.402804) | 2.480156 / 5.269862 (-2.789706) | 1.512342 / 4.565676 (-3.053335) | 0.042324 / 0.424275 (-0.381951) | 0.004744 / 0.007607 (-0.002863) | 0.323568 / 0.226044 (0.097523) | 3.190021 / 2.268929 (0.921093) | 1.765046 / 55.444624 (-53.679578) | 1.513958 / 6.876477 (-5.362519) | 1.504943 / 2.142072 (-0.637129) | 0.452302 / 4.805227 (-4.352925) | 0.094728 / 6.500664 (-6.405936) | 0.038641 / 0.075469 (-0.036828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939721 / 1.841788 (-0.902067) | 11.174180 / 8.074308 (3.099872) | 10.046717 / 10.191392 (-0.144675) | 0.124877 / 0.680424 (-0.555547) | 0.013687 / 0.534201 (-0.520514) | 0.261002 / 0.579283 (-0.318282) | 0.267349 / 0.434364 (-0.167015) | 0.306545 / 0.540337 (-0.233792) | 0.389322 / 1.386936 (-0.997614) |\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) | 0.004702 / 0.011353 (-0.006651) | 0.002431 / 0.011008 (-0.008577) | 0.046138 / 0.038508 (0.007630) | 0.048356 / 0.023109 (0.025246) | 0.272154 / 0.275898 (-0.003744) | 0.292676 / 0.323480 (-0.030804) | 0.003870 / 0.007986 (-0.004115) | 0.002294 / 0.004328 (-0.002035) | 0.048129 / 0.004250 (0.043879) | 0.039026 / 0.037052 (0.001974) | 0.273900 / 0.258489 (0.015411) | 0.295927 / 0.293841 (0.002086) | 0.024044 / 0.128546 (-0.104502) | 0.006906 / 0.075646 (-0.068740) | 0.053268 / 0.419271 (-0.366004) | 0.032360 / 0.043533 (-0.011173) | 0.273470 / 0.255139 (0.018331) | 0.286207 / 0.283200 (0.003007) | 0.017580 / 0.141683 (-0.124103) | 1.091064 / 1.452155 (-0.361091) | 1.159645 / 1.492716 (-0.333071) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087149 / 0.018006 (0.069143) | 0.293489 / 0.000490 (0.293000) | 0.000217 / 0.000200 (0.000017) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021779 / 0.037411 (-0.015632) | 0.066453 / 0.014526 (0.051928) | 0.078517 / 0.176557 (-0.098039) | 0.117317 / 0.737135 (-0.619819) | 0.079828 / 0.296338 (-0.216511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287605 / 0.215209 (0.072396) | 2.811094 / 2.077655 (0.733439) | 1.572474 / 1.504120 (0.068354) | 1.450294 / 1.541195 (-0.090900) | 1.456052 / 1.468490 (-0.012438) | 0.402095 / 4.584777 (-4.182682) | 2.444709 / 3.745712 (-1.301003) | 2.390837 / 5.269862 (-2.879024) | 1.530519 / 4.565676 (-3.035157) | 0.043520 / 0.424275 (-0.380755) | 0.004788 / 0.007607 (-0.002819) | 0.337436 / 0.226044 (0.111391) | 3.326111 / 2.268929 (1.057182) | 1.889273 / 55.444624 (-53.555352) | 1.624423 / 6.876477 (-5.252054) | 1.715766 / 2.142072 (-0.426307) | 0.484570 / 4.805227 (-4.320657) | 0.091691 / 6.500664 (-6.408973) | 0.038278 / 0.075469 (-0.037191) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961708 / 1.841788 (-0.880079) | 11.496471 / 8.074308 (3.422162) | 10.211589 / 10.191392 (0.020197) | 0.127584 / 0.680424 (-0.552840) | 0.015178 / 0.534201 (-0.519023) | 0.267290 / 0.579283 (-0.311993) | 0.259305 / 0.434364 (-0.175059) | 0.303433 / 0.540337 (-0.236905) | 0.508016 / 1.386936 (-0.878920) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72880aa8a3e4b49438db72b13fb9a2541331820b \"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) | 0.004558 / 0.011353 (-0.006795) | 0.002563 / 0.011008 (-0.008445) | 0.061314 / 0.038508 (0.022806) | 0.049312 / 0.023109 (0.026203) | 0.240988 / 0.275898 (-0.034910) | 0.260548 / 0.323480 (-0.062932) | 0.002817 / 0.007986 (-0.005169) | 0.002904 / 0.004328 (-0.001425) | 0.048515 / 0.004250 (0.044264) | 0.037511 / 0.037052 (0.000459) | 0.244880 / 0.258489 (-0.013609) | 0.276118 / 0.293841 (-0.017723) | 0.022636 / 0.128546 (-0.105910) | 0.006694 / 0.075646 (-0.068953) | 0.201336 / 0.419271 (-0.217936) | 0.035228 / 0.043533 (-0.008305) | 0.242424 / 0.255139 (-0.012715) | 0.260178 / 0.283200 (-0.023022) | 0.017836 / 0.141683 (-0.123847) | 1.122296 / 1.452155 (-0.329859) | 1.189024 / 1.492716 (-0.303692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090051 / 0.018006 (0.072045) | 0.298562 / 0.000490 (0.298073) | 0.000216 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018228 / 0.037411 (-0.019184) | 0.062379 / 0.014526 (0.047853) | 0.073482 / 0.176557 (-0.103075) | 0.120341 / 0.737135 (-0.616794) | 0.073868 / 0.296338 (-0.222470) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280195 / 0.215209 (0.064986) | 2.743333 / 2.077655 (0.665678) | 1.470078 / 1.504120 (-0.034042) | 1.335874 / 1.541195 (-0.205321) | 1.342961 / 1.468490 (-0.125529) | 0.409203 / 4.584777 (-4.175574) | 2.392217 / 3.745712 (-1.353495) | 2.544161 / 5.269862 (-2.725701) | 1.544016 / 4.565676 (-3.021660) | 0.059485 / 0.424275 (-0.364790) | 0.004833 / 0.007607 (-0.002775) | 0.335114 / 0.226044 (0.109070) | 3.289009 / 2.268929 (1.020080) | 1.854666 / 55.444624 (-53.589959) | 1.566282 / 6.876477 (-5.310195) | 1.561287 / 2.142072 (-0.580786) | 0.484961 / 4.805227 (-4.320267) | 0.099651 / 6.500664 (-6.401013) | 0.041408 / 0.075469 (-0.034061) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941743 / 1.841788 (-0.900044) | 11.165692 / 8.074308 (3.091383) | 10.236693 / 10.191392 (0.045301) | 0.129694 / 0.680424 (-0.550730) | 0.014879 / 0.534201 (-0.519322) | 0.275120 / 0.579283 (-0.304163) | 0.263822 / 0.434364 (-0.170542) | 0.306429 / 0.540337 (-0.233909) | 0.397611 / 1.386936 (-0.989325) |\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) | 0.004714 / 0.011353 (-0.006639) | 0.002430 / 0.011008 (-0.008578) | 0.047644 / 0.038508 (0.009136) | 0.049710 / 0.023109 (0.026601) | 0.271950 / 0.275898 (-0.003948) | 0.290996 / 0.323480 (-0.032483) | 0.003888 / 0.007986 (-0.004097) | 0.002367 / 0.004328 (-0.001962) | 0.047623 / 0.004250 (0.043372) | 0.039574 / 0.037052 (0.002522) | 0.274540 / 0.258489 (0.016051) | 0.298065 / 0.293841 (0.004224) | 0.024677 / 0.128546 (-0.103869) | 0.006844 / 0.075646 (-0.068802) | 0.053180 / 0.419271 (-0.366091) | 0.032391 / 0.043533 (-0.011141) | 0.273222 / 0.255139 (0.018083) | 0.290336 / 0.283200 (0.007136) | 0.017911 / 0.141683 (-0.123772) | 1.105879 / 1.452155 (-0.346276) | 1.176979 / 1.492716 (-0.315737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089563 / 0.018006 (0.071557) | 0.296392 / 0.000490 (0.295903) | 0.000214 / 0.000200 (0.000014) | 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) | 0.021588 / 0.037411 (-0.015824) | 0.069951 / 0.014526 (0.055425) | 0.080397 / 0.176557 (-0.096160) | 0.118772 / 0.737135 (-0.618363) | 0.080356 / 0.296338 (-0.215983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288492 / 0.215209 (0.073283) | 2.839553 / 2.077655 (0.761898) | 1.597504 / 1.504120 (0.093384) | 1.475001 / 1.541195 (-0.066193) | 1.481561 / 1.468490 (0.013071) | 0.411851 / 4.584777 (-4.172926) | 2.397322 / 3.745712 (-1.348390) | 2.444078 / 5.269862 (-2.825784) | 1.557106 / 4.565676 (-3.008571) | 0.047159 / 0.424275 (-0.377116) | 0.004842 / 0.007607 (-0.002765) | 0.346221 / 0.226044 (0.120177) | 3.387900 / 2.268929 (1.118972) | 1.962167 / 55.444624 (-53.482457) | 1.675017 / 6.876477 (-5.201460) | 1.788745 / 2.142072 (-0.353328) | 0.488063 / 4.805227 (-4.317164) | 0.098878 / 6.500664 (-6.401786) | 0.040369 / 0.075469 (-0.035100) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977999 / 1.841788 (-0.863789) | 11.671558 / 8.074308 (3.597250) | 10.327847 / 10.191392 (0.136455) | 0.129317 / 0.680424 (-0.551107) | 0.015600 / 0.534201 (-0.518601) | 0.267967 / 0.579283 (-0.311316) | 0.273811 / 0.434364 (-0.160553) | 0.301749 / 0.540337 (-0.238588) | 0.515493 / 1.386936 (-0.871443) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5394939b0b3d124674f938e1f1cd9e8de3cbdbf7 \"CML watermark\")\n", "I added tests and docs @mariosasko @albertvillanova let le know what you think !", "<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) | 0.004867 / 0.011353 (-0.006486) | 0.002952 / 0.011008 (-0.008056) | 0.062008 / 0.038508 (0.023500) | 0.055279 / 0.023109 (0.032170) | 0.248160 / 0.275898 (-0.027738) | 0.276173 / 0.323480 (-0.047307) | 0.003945 / 0.007986 (-0.004041) | 0.002371 / 0.004328 (-0.001958) | 0.048385 / 0.004250 (0.044134) | 0.038997 / 0.037052 (0.001945) | 0.257465 / 0.258489 (-0.001024) | 0.286920 / 0.293841 (-0.006921) | 0.023031 / 0.128546 (-0.105515) | 0.007075 / 0.075646 (-0.068571) | 0.201897 / 0.419271 (-0.217375) | 0.035637 / 0.043533 (-0.007896) | 0.252050 / 0.255139 (-0.003089) | 0.272580 / 0.283200 (-0.010620) | 0.018578 / 0.141683 (-0.123105) | 1.129427 / 1.452155 (-0.322727) | 1.172182 / 1.492716 (-0.320534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091806 / 0.018006 (0.073800) | 0.298632 / 0.000490 (0.298143) | 0.000202 / 0.000200 (0.000002) | 0.000047 / 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) | 0.019123 / 0.037411 (-0.018288) | 0.062603 / 0.014526 (0.048077) | 0.074352 / 0.176557 (-0.102205) | 0.120431 / 0.737135 (-0.616704) | 0.074622 / 0.296338 (-0.221717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276019 / 0.215209 (0.060810) | 2.701610 / 2.077655 (0.623955) | 1.398388 / 1.504120 (-0.105732) | 1.270902 / 1.541195 (-0.270292) | 1.307992 / 1.468490 (-0.160499) | 0.396350 / 4.584777 (-4.188427) | 2.351064 / 3.745712 (-1.394648) | 2.606229 / 5.269862 (-2.663632) | 1.591075 / 4.565676 (-2.974601) | 0.046429 / 0.424275 (-0.377846) | 0.004832 / 0.007607 (-0.002775) | 0.327887 / 0.226044 (0.101843) | 3.277847 / 2.268929 (1.008918) | 1.767210 / 55.444624 (-53.677414) | 1.483997 / 6.876477 (-5.392479) | 1.515689 / 2.142072 (-0.626383) | 0.471326 / 4.805227 (-4.333902) | 0.098821 / 6.500664 (-6.401843) | 0.041914 / 0.075469 (-0.033555) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956278 / 1.841788 (-0.885510) | 11.924373 / 8.074308 (3.850065) | 10.493926 / 10.191392 (0.302534) | 0.140214 / 0.680424 (-0.540210) | 0.013679 / 0.534201 (-0.520522) | 0.270304 / 0.579283 (-0.308979) | 0.266518 / 0.434364 (-0.167846) | 0.310113 / 0.540337 (-0.230224) | 0.399811 / 1.386936 (-0.987125) |\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) | 0.004793 / 0.011353 (-0.006560) | 0.002879 / 0.011008 (-0.008130) | 0.048632 / 0.038508 (0.010124) | 0.051413 / 0.023109 (0.028304) | 0.272704 / 0.275898 (-0.003194) | 0.291541 / 0.323480 (-0.031939) | 0.003913 / 0.007986 (-0.004072) | 0.002387 / 0.004328 (-0.001941) | 0.049045 / 0.004250 (0.044795) | 0.040164 / 0.037052 (0.003112) | 0.273052 / 0.258489 (0.014563) | 0.300139 / 0.293841 (0.006298) | 0.024225 / 0.128546 (-0.104321) | 0.007060 / 0.075646 (-0.068587) | 0.054360 / 0.419271 (-0.364911) | 0.032882 / 0.043533 (-0.010650) | 0.270295 / 0.255139 (0.015157) | 0.312253 / 0.283200 (0.029054) | 0.017413 / 0.141683 (-0.124270) | 1.137306 / 1.452155 (-0.314849) | 1.203705 / 1.492716 (-0.289011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091083 / 0.018006 (0.073077) | 0.301607 / 0.000490 (0.301117) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021753 / 0.037411 (-0.015658) | 0.069693 / 0.014526 (0.055167) | 0.080481 / 0.176557 (-0.096075) | 0.118581 / 0.737135 (-0.618555) | 0.082231 / 0.296338 (-0.214108) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300014 / 0.215209 (0.084805) | 2.885934 / 2.077655 (0.808279) | 1.594120 / 1.504120 (0.090000) | 1.472312 / 1.541195 (-0.068883) | 1.491663 / 1.468490 (0.023173) | 0.412946 / 4.584777 (-4.171831) | 2.494168 / 3.745712 (-1.251544) | 2.527987 / 5.269862 (-2.741875) | 1.589187 / 4.565676 (-2.976490) | 0.046594 / 0.424275 (-0.377681) | 0.004810 / 0.007607 (-0.002797) | 0.345496 / 0.226044 (0.119452) | 3.428850 / 2.268929 (1.159921) | 1.952696 / 55.444624 (-53.491929) | 1.663285 / 6.876477 (-5.213191) | 1.822187 / 2.142072 (-0.319885) | 0.483798 / 4.805227 (-4.321430) | 0.101403 / 6.500664 (-6.399261) | 0.041773 / 0.075469 (-0.033696) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974247 / 1.841788 (-0.867541) | 12.459980 / 8.074308 (4.385672) | 10.354792 / 10.191392 (0.163400) | 0.129083 / 0.680424 (-0.551341) | 0.015225 / 0.534201 (-0.518976) | 0.267673 / 0.579283 (-0.311610) | 0.281011 / 0.434364 (-0.153352) | 0.303054 / 0.540337 (-0.237283) | 0.405719 / 1.386936 (-0.981217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33dc51fc1a8122b842bb7839ff0eda32f173c325 \"CML watermark\")\n", "I switched to using `deepmind/code_contests` in examples in the docs to avoid having to pass trust_remote_code, and remove the DEFAULT naming stuff :)", "<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) | 0.005169 / 0.011353 (-0.006184) | 0.003066 / 0.011008 (-0.007942) | 0.068884 / 0.038508 (0.030376) | 0.060345 / 0.023109 (0.037236) | 0.243050 / 0.275898 (-0.032848) | 0.265523 / 0.323480 (-0.057957) | 0.002918 / 0.007986 (-0.005067) | 0.002495 / 0.004328 (-0.001834) | 0.051538 / 0.004250 (0.047288) | 0.040010 / 0.037052 (0.002957) | 0.249603 / 0.258489 (-0.008886) | 0.287955 / 0.293841 (-0.005886) | 0.024003 / 0.128546 (-0.104543) | 0.007111 / 0.075646 (-0.068535) | 0.205041 / 0.419271 (-0.214231) | 0.036296 / 0.043533 (-0.007237) | 0.246135 / 0.255139 (-0.009004) | 0.268801 / 0.283200 (-0.014399) | 0.018451 / 0.141683 (-0.123232) | 1.130387 / 1.452155 (-0.321767) | 1.162041 / 1.492716 (-0.330675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096370 / 0.018006 (0.078364) | 0.309867 / 0.000490 (0.309377) | 0.000229 / 0.000200 (0.000029) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018688 / 0.037411 (-0.018723) | 0.062859 / 0.014526 (0.048333) | 0.076383 / 0.176557 (-0.100173) | 0.120385 / 0.737135 (-0.616750) | 0.080192 / 0.296338 (-0.216147) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282994 / 0.215209 (0.067785) | 2.742341 / 2.077655 (0.664686) | 1.432041 / 1.504120 (-0.072079) | 1.303282 / 1.541195 (-0.237913) | 1.347198 / 1.468490 (-0.121292) | 0.399145 / 4.584777 (-4.185632) | 2.359766 / 3.745712 (-1.385947) | 2.753577 / 5.269862 (-2.516285) | 1.639953 / 4.565676 (-2.925724) | 0.047111 / 0.424275 (-0.377164) | 0.004946 / 0.007607 (-0.002661) | 0.338857 / 0.226044 (0.112813) | 3.328709 / 2.268929 (1.059781) | 1.794729 / 55.444624 (-53.649895) | 1.508514 / 6.876477 (-5.367963) | 1.550737 / 2.142072 (-0.591335) | 0.484227 / 4.805227 (-4.321000) | 0.101001 / 6.500664 (-6.399663) | 0.042792 / 0.075469 (-0.032677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.956471 / 1.841788 (-0.885317) | 12.031362 / 8.074308 (3.957054) | 10.512914 / 10.191392 (0.321522) | 0.141841 / 0.680424 (-0.538583) | 0.014343 / 0.534201 (-0.519858) | 0.273916 / 0.579283 (-0.305367) | 0.266150 / 0.434364 (-0.168214) | 0.312020 / 0.540337 (-0.228317) | 0.410465 / 1.386936 (-0.976471) |\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) | 0.004945 / 0.011353 (-0.006408) | 0.003288 / 0.011008 (-0.007720) | 0.048247 / 0.038508 (0.009739) | 0.057892 / 0.023109 (0.034783) | 0.269741 / 0.275898 (-0.006157) | 0.293728 / 0.323480 (-0.029752) | 0.004789 / 0.007986 (-0.003197) | 0.002477 / 0.004328 (-0.001852) | 0.047825 / 0.004250 (0.043575) | 0.040780 / 0.037052 (0.003727) | 0.273355 / 0.258489 (0.014865) | 0.300057 / 0.293841 (0.006216) | 0.024481 / 0.128546 (-0.104066) | 0.007285 / 0.075646 (-0.068361) | 0.053046 / 0.419271 (-0.366226) | 0.032342 / 0.043533 (-0.011190) | 0.272293 / 0.255139 (0.017154) | 0.290842 / 0.283200 (0.007642) | 0.017546 / 0.141683 (-0.124137) | 1.155816 / 1.452155 (-0.296339) | 1.195839 / 1.492716 (-0.296878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094177 / 0.018006 (0.076170) | 0.305122 / 0.000490 (0.304632) | 0.000237 / 0.000200 (0.000037) | 0.000063 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021817 / 0.037411 (-0.015595) | 0.070711 / 0.014526 (0.056185) | 0.084028 / 0.176557 (-0.092528) | 0.120160 / 0.737135 (-0.616975) | 0.083085 / 0.296338 (-0.213254) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289127 / 0.215209 (0.073918) | 2.826365 / 2.077655 (0.748710) | 1.582910 / 1.504120 (0.078790) | 1.472796 / 1.541195 (-0.068399) | 1.497491 / 1.468490 (0.029000) | 0.412276 / 4.584777 (-4.172501) | 2.430692 / 3.745712 (-1.315020) | 2.556444 / 5.269862 (-2.713418) | 1.625782 / 4.565676 (-2.939895) | 0.047921 / 0.424275 (-0.376354) | 0.004809 / 0.007607 (-0.002798) | 0.345569 / 0.226044 (0.119524) | 3.417785 / 2.268929 (1.148856) | 1.959223 / 55.444624 (-53.485401) | 1.672765 / 6.876477 (-5.203712) | 1.852444 / 2.142072 (-0.289628) | 0.489225 / 4.805227 (-4.316002) | 0.100624 / 6.500664 (-6.400040) | 0.041242 / 0.075469 (-0.034227) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971130 / 1.841788 (-0.870658) | 12.652204 / 8.074308 (4.577896) | 10.661821 / 10.191392 (0.470429) | 0.147636 / 0.680424 (-0.532787) | 0.015738 / 0.534201 (-0.518463) | 0.272763 / 0.579283 (-0.306520) | 0.282623 / 0.434364 (-0.151741) | 0.341303 / 0.540337 (-0.199035) | 0.412149 / 1.386936 (-0.974787) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9499908c97ceef1792f69b71e93e36602880a4ae \"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) | 0.004589 / 0.011353 (-0.006764) | 0.002730 / 0.011008 (-0.008279) | 0.061862 / 0.038508 (0.023353) | 0.050945 / 0.023109 (0.027836) | 0.240776 / 0.275898 (-0.035122) | 0.266000 / 0.323480 (-0.057480) | 0.003823 / 0.007986 (-0.004162) | 0.002345 / 0.004328 (-0.001983) | 0.047821 / 0.004250 (0.043571) | 0.037813 / 0.037052 (0.000761) | 0.251075 / 0.258489 (-0.007415) | 0.279430 / 0.293841 (-0.014411) | 0.022957 / 0.128546 (-0.105590) | 0.007294 / 0.075646 (-0.068353) | 0.206092 / 0.419271 (-0.213180) | 0.035308 / 0.043533 (-0.008225) | 0.247197 / 0.255139 (-0.007942) | 0.264988 / 0.283200 (-0.018212) | 0.017588 / 0.141683 (-0.124095) | 1.093291 / 1.452155 (-0.358864) | 1.165477 / 1.492716 (-0.327240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.104057 / 0.018006 (0.086051) | 0.303424 / 0.000490 (0.302934) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019040 / 0.037411 (-0.018371) | 0.063161 / 0.014526 (0.048635) | 0.085333 / 0.176557 (-0.091224) | 0.155973 / 0.737135 (-0.581162) | 0.077528 / 0.296338 (-0.218810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276104 / 0.215209 (0.060895) | 2.738174 / 2.077655 (0.660519) | 1.479484 / 1.504120 (-0.024636) | 1.354094 / 1.541195 (-0.187100) | 1.385312 / 1.468490 (-0.083178) | 0.401398 / 4.584777 (-4.183379) | 2.368503 / 3.745712 (-1.377209) | 2.586405 / 5.269862 (-2.683457) | 1.573978 / 4.565676 (-2.991699) | 0.046969 / 0.424275 (-0.377306) | 0.004874 / 0.007607 (-0.002733) | 0.334028 / 0.226044 (0.107984) | 3.269645 / 2.268929 (1.000717) | 1.834528 / 55.444624 (-53.610096) | 1.559883 / 6.876477 (-5.316594) | 1.581380 / 2.142072 (-0.560693) | 0.479580 / 4.805227 (-4.325647) | 0.099077 / 6.500664 (-6.401587) | 0.041166 / 0.075469 (-0.034303) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.918810 / 1.841788 (-0.922978) | 11.505017 / 8.074308 (3.430709) | 10.331934 / 10.191392 (0.140542) | 0.128079 / 0.680424 (-0.552345) | 0.013716 / 0.534201 (-0.520485) | 0.271567 / 0.579283 (-0.307716) | 0.264846 / 0.434364 (-0.169518) | 0.305245 / 0.540337 (-0.235092) | 0.401391 / 1.386936 (-0.985546) |\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) | 0.004860 / 0.011353 (-0.006493) | 0.002854 / 0.011008 (-0.008155) | 0.048327 / 0.038508 (0.009819) | 0.051377 / 0.023109 (0.028268) | 0.264344 / 0.275898 (-0.011554) | 0.286800 / 0.323480 (-0.036680) | 0.003969 / 0.007986 (-0.004016) | 0.002415 / 0.004328 (-0.001914) | 0.048498 / 0.004250 (0.044247) | 0.040399 / 0.037052 (0.003347) | 0.267254 / 0.258489 (0.008765) | 0.292049 / 0.293841 (-0.001792) | 0.024730 / 0.128546 (-0.103817) | 0.007275 / 0.075646 (-0.068371) | 0.053725 / 0.419271 (-0.365546) | 0.033142 / 0.043533 (-0.010391) | 0.265418 / 0.255139 (0.010279) | 0.286242 / 0.283200 (0.003042) | 0.017824 / 0.141683 (-0.123859) | 1.135978 / 1.452155 (-0.316176) | 1.192506 / 1.492716 (-0.300210) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091907 / 0.018006 (0.073900) | 0.307152 / 0.000490 (0.306663) | 0.000223 / 0.000200 (0.000023) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021909 / 0.037411 (-0.015502) | 0.070676 / 0.014526 (0.056150) | 0.081651 / 0.176557 (-0.094906) | 0.120915 / 0.737135 (-0.616220) | 0.085882 / 0.296338 (-0.210456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288008 / 0.215209 (0.072799) | 2.861352 / 2.077655 (0.783697) | 1.539045 / 1.504120 (0.034925) | 1.412175 / 1.541195 (-0.129019) | 1.421236 / 1.468490 (-0.047254) | 0.404921 / 4.584777 (-4.179856) | 2.480211 / 3.745712 (-1.265501) | 2.473083 / 5.269862 (-2.796779) | 1.558894 / 4.565676 (-3.006783) | 0.046692 / 0.424275 (-0.377584) | 0.004802 / 0.007607 (-0.002805) | 0.346046 / 0.226044 (0.120001) | 3.464387 / 2.268929 (1.195459) | 1.937298 / 55.444624 (-53.507326) | 1.593701 / 6.876477 (-5.282776) | 1.730688 / 2.142072 (-0.411385) | 0.481069 / 4.805227 (-4.324158) | 0.098991 / 6.500664 (-6.401673) | 0.040491 / 0.075469 (-0.034978) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967809 / 1.841788 (-0.873979) | 11.952335 / 8.074308 (3.878027) | 10.616711 / 10.191392 (0.425319) | 0.128938 / 0.680424 (-0.551486) | 0.015455 / 0.534201 (-0.518746) | 0.272100 / 0.579283 (-0.307183) | 0.278275 / 0.434364 (-0.156089) | 0.309711 / 0.540337 (-0.230627) | 0.411026 / 1.386936 (-0.975910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#495bc04226a67983f523d12d42b680172f8d4893 \"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) | 0.008470 / 0.011353 (-0.002883) | 0.003201 / 0.011008 (-0.007808) | 0.063193 / 0.038508 (0.024685) | 0.064174 / 0.023109 (0.041064) | 0.248316 / 0.275898 (-0.027582) | 0.281598 / 0.323480 (-0.041882) | 0.004076 / 0.007986 (-0.003909) | 0.002397 / 0.004328 (-0.001932) | 0.048834 / 0.004250 (0.044584) | 0.056517 / 0.037052 (0.019465) | 0.254164 / 0.258489 (-0.004326) | 0.289800 / 0.293841 (-0.004041) | 0.031092 / 0.128546 (-0.097454) | 0.010885 / 0.075646 (-0.064762) | 0.219198 / 0.419271 (-0.200073) | 0.040087 / 0.043533 (-0.003446) | 0.250900 / 0.255139 (-0.004239) | 0.267787 / 0.283200 (-0.015413) | 0.019666 / 0.141683 (-0.122017) | 1.114960 / 1.452155 (-0.337194) | 1.266675 / 1.492716 (-0.226041) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091429 / 0.018006 (0.073422) | 0.301804 / 0.000490 (0.301314) | 0.000212 / 0.000200 (0.000012) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021053 / 0.037411 (-0.016358) | 0.062407 / 0.014526 (0.047881) | 0.073166 / 0.176557 (-0.103391) | 0.119642 / 0.737135 (-0.617493) | 0.074771 / 0.296338 (-0.221567) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278582 / 0.215209 (0.063373) | 2.773023 / 2.077655 (0.695368) | 1.459977 / 1.504120 (-0.044143) | 1.330453 / 1.541195 (-0.210742) | 1.372797 / 1.468490 (-0.095693) | 0.628845 / 4.584777 (-3.955932) | 3.428779 / 3.745712 (-0.316933) | 3.138967 / 5.269862 (-2.130895) | 2.126891 / 4.565676 (-2.438785) | 0.062340 / 0.424275 (-0.361935) | 0.004939 / 0.007607 (-0.002668) | 0.336058 / 0.226044 (0.110014) | 3.463741 / 2.268929 (1.194813) | 1.847504 / 55.444624 (-53.597120) | 1.984173 / 6.876477 (-4.892304) | 1.602962 / 2.142072 (-0.539110) | 0.637683 / 4.805227 (-4.167545) | 0.117898 / 6.500664 (-6.382766) | 0.043308 / 0.075469 (-0.032161) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.087773 / 1.841788 (-0.754014) | 14.959526 / 8.074308 (6.885218) | 10.886003 / 10.191392 (0.694611) | 0.163385 / 0.680424 (-0.517039) | 0.016679 / 0.534201 (-0.517522) | 0.351913 / 0.579283 (-0.227370) | 0.359007 / 0.434364 (-0.075357) | 0.323824 / 0.540337 (-0.216513) | 0.549268 / 1.386936 (-0.837668) |\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) | 0.005265 / 0.011353 (-0.006088) | 0.003367 / 0.011008 (-0.007641) | 0.062741 / 0.038508 (0.024233) | 0.068463 / 0.023109 (0.045354) | 0.258497 / 0.275898 (-0.017401) | 0.355360 / 0.323480 (0.031880) | 0.003910 / 0.007986 (-0.004075) | 0.002399 / 0.004328 (-0.001929) | 0.055564 / 0.004250 (0.051313) | 0.039644 / 0.037052 (0.002591) | 0.258313 / 0.258489 (-0.000176) | 0.328927 / 0.293841 (0.035086) | 0.035634 / 0.128546 (-0.092912) | 0.010378 / 0.075646 (-0.065268) | 0.073109 / 0.419271 (-0.346163) | 0.039752 / 0.043533 (-0.003781) | 0.258237 / 0.255139 (0.003098) | 0.330329 / 0.283200 (0.047129) | 0.023924 / 0.141683 (-0.117759) | 1.198639 / 1.452155 (-0.253515) | 1.202307 / 1.492716 (-0.290409) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091297 / 0.018006 (0.073290) | 0.298729 / 0.000490 (0.298240) | 0.000210 / 0.000200 (0.000010) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022381 / 0.037411 (-0.015030) | 0.070226 / 0.014526 (0.055700) | 0.080549 / 0.176557 (-0.096007) | 0.119677 / 0.737135 (-0.617458) | 0.082612 / 0.296338 (-0.213727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289270 / 0.215209 (0.074061) | 2.853830 / 2.077655 (0.776175) | 1.528938 / 1.504120 (0.024818) | 1.398429 / 1.541195 (-0.142766) | 1.472465 / 1.468490 (0.003975) | 0.779015 / 4.584777 (-3.805762) | 3.287724 / 3.745712 (-0.457988) | 3.020908 / 5.269862 (-2.248953) | 1.926094 / 4.565676 (-2.639583) | 0.063163 / 0.424275 (-0.361112) | 0.005175 / 0.007607 (-0.002432) | 0.342884 / 0.226044 (0.116840) | 3.476837 / 2.268929 (1.207908) | 1.880683 / 55.444624 (-53.563942) | 1.613845 / 6.876477 (-5.262632) | 1.624734 / 2.142072 (-0.517338) | 0.626220 / 4.805227 (-4.179007) | 0.114976 / 6.500664 (-6.385689) | 0.040670 / 0.075469 (-0.034799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.116815 / 1.841788 (-0.724973) | 15.388426 / 8.074308 (7.314118) | 10.825276 / 10.191392 (0.633884) | 0.172659 / 0.680424 (-0.507765) | 0.015468 / 0.534201 (-0.518733) | 0.285552 / 0.579283 (-0.293731) | 0.346886 / 0.434364 (-0.087478) | 0.348696 / 0.540337 (-0.191641) | 0.729335 / 1.386936 (-0.657601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7bbf346dc268b8084dee406b2a6e2b96d44bc3b \"CML watermark\")\n" ]
2023-11-16T12:12:54
2023-11-28T16:10:39
2023-11-28 16:03:43+00:00
MEMBER
nan
Draft about adding `trust_remote_code` to `load_dataset`. ```python ds = load_dataset(..., trust_remote_code=True) # run remote code (current default) ``` It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course). ```python # in the future ds = load_dataset(...) # prompt the user to check the code before running it (future default) ds = load_dataset(..., trust_remote_code=True) # run remote code ds = load_dataset(..., trust_remote_code=False) # disallow remote code ``` Related to https://github.com/huggingface/datasets/issues/6400 Will do a separate PR to use the parquet export when possible
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6428
Set dev version
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6428). 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) | 0.004839 / 0.011353 (-0.006514) | 0.002928 / 0.011008 (-0.008080) | 0.061730 / 0.038508 (0.023221) | 0.030523 / 0.023109 (0.007414) | 0.252679 / 0.275898 (-0.023219) | 0.281597 / 0.323480 (-0.041883) | 0.003025 / 0.007986 (-0.004961) | 0.002374 / 0.004328 (-0.001955) | 0.048134 / 0.004250 (0.043884) | 0.045843 / 0.037052 (0.008791) | 0.256274 / 0.258489 (-0.002215) | 0.288704 / 0.293841 (-0.005137) | 0.023486 / 0.128546 (-0.105060) | 0.007186 / 0.075646 (-0.068461) | 0.202519 / 0.419271 (-0.216753) | 0.058192 / 0.043533 (0.014659) | 0.256448 / 0.255139 (0.001309) | 0.279417 / 0.283200 (-0.003783) | 0.019942 / 0.141683 (-0.121740) | 1.100954 / 1.452155 (-0.351201) | 1.168183 / 1.492716 (-0.324533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091314 / 0.018006 (0.073308) | 0.298614 / 0.000490 (0.298124) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018071 / 0.037411 (-0.019340) | 0.062265 / 0.014526 (0.047740) | 0.073228 / 0.176557 (-0.103328) | 0.119163 / 0.737135 (-0.617972) | 0.074717 / 0.296338 (-0.221622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273906 / 0.215209 (0.058697) | 2.683995 / 2.077655 (0.606340) | 1.418773 / 1.504120 (-0.085347) | 1.310473 / 1.541195 (-0.230722) | 1.303152 / 1.468490 (-0.165339) | 0.390846 / 4.584777 (-4.193931) | 2.346407 / 3.745712 (-1.399305) | 2.582945 / 5.269862 (-2.686916) | 1.569549 / 4.565676 (-2.996128) | 0.044893 / 0.424275 (-0.379383) | 0.004754 / 0.007607 (-0.002853) | 0.323491 / 0.226044 (0.097447) | 3.229736 / 2.268929 (0.960808) | 1.783551 / 55.444624 (-53.661074) | 1.499685 / 6.876477 (-5.376792) | 1.515826 / 2.142072 (-0.626246) | 0.475768 / 4.805227 (-4.329460) | 0.099579 / 6.500664 (-6.401085) | 0.042709 / 0.075469 (-0.032760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.926120 / 1.841788 (-0.915667) | 11.597189 / 8.074308 (3.522881) | 10.327055 / 10.191392 (0.135663) | 0.127479 / 0.680424 (-0.552945) | 0.014844 / 0.534201 (-0.519357) | 0.261181 / 0.579283 (-0.318102) | 0.258407 / 0.434364 (-0.175957) | 0.303192 / 0.540337 (-0.237146) | 0.416665 / 1.386936 (-0.970271) |\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) | 0.004759 / 0.011353 (-0.006594) | 0.002780 / 0.011008 (-0.008228) | 0.047991 / 0.038508 (0.009483) | 0.052263 / 0.023109 (0.029153) | 0.261228 / 0.275898 (-0.014670) | 0.287779 / 0.323480 (-0.035701) | 0.003961 / 0.007986 (-0.004024) | 0.002357 / 0.004328 (-0.001971) | 0.047755 / 0.004250 (0.043505) | 0.038066 / 0.037052 (0.001014) | 0.269502 / 0.258489 (0.011013) | 0.298348 / 0.293841 (0.004507) | 0.024398 / 0.128546 (-0.104149) | 0.007189 / 0.075646 (-0.068457) | 0.053356 / 0.419271 (-0.365915) | 0.032459 / 0.043533 (-0.011074) | 0.266389 / 0.255139 (0.011250) | 0.305367 / 0.283200 (0.022168) | 0.017629 / 0.141683 (-0.124054) | 1.145789 / 1.452155 (-0.306366) | 1.204778 / 1.492716 (-0.287938) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091347 / 0.018006 (0.073341) | 0.298671 / 0.000490 (0.298181) | 0.000229 / 0.000200 (0.000029) | 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) | 0.021374 / 0.037411 (-0.016037) | 0.068869 / 0.014526 (0.054344) | 0.080443 / 0.176557 (-0.096113) | 0.118759 / 0.737135 (-0.618376) | 0.081646 / 0.296338 (-0.214692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295274 / 0.215209 (0.080065) | 2.889349 / 2.077655 (0.811695) | 1.561020 / 1.504120 (0.056900) | 1.425025 / 1.541195 (-0.116170) | 1.495446 / 1.468490 (0.026956) | 0.403825 / 4.584777 (-4.180952) | 2.404905 / 3.745712 (-1.340807) | 2.590104 / 5.269862 (-2.679758) | 1.570559 / 4.565676 (-2.995118) | 0.046342 / 0.424275 (-0.377933) | 0.004799 / 0.007607 (-0.002809) | 0.349981 / 0.226044 (0.123937) | 3.437341 / 2.268929 (1.168412) | 1.948155 / 55.444624 (-53.496469) | 1.637765 / 6.876477 (-5.238711) | 1.671521 / 2.142072 (-0.470551) | 0.479500 / 4.805227 (-4.325727) | 0.098305 / 6.500664 (-6.402359) | 0.040864 / 0.075469 (-0.034605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979986 / 1.841788 (-0.861801) | 12.169722 / 8.074308 (4.095413) | 11.297345 / 10.191392 (1.105953) | 0.129123 / 0.680424 (-0.551301) | 0.015389 / 0.534201 (-0.518812) | 0.270964 / 0.579283 (-0.308319) | 0.269590 / 0.434364 (-0.164774) | 0.310662 / 0.540337 (-0.229675) | 0.406272 / 1.386936 (-0.980664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31873f1e9acbe013e6d396d1ed5492db8cd59dd3 \"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) | 0.004620 / 0.011353 (-0.006733) | 0.002971 / 0.011008 (-0.008038) | 0.062864 / 0.038508 (0.024355) | 0.028743 / 0.023109 (0.005634) | 0.246729 / 0.275898 (-0.029169) | 0.271165 / 0.323480 (-0.052315) | 0.003930 / 0.007986 (-0.004056) | 0.002422 / 0.004328 (-0.001906) | 0.047430 / 0.004250 (0.043180) | 0.044895 / 0.037052 (0.007843) | 0.249128 / 0.258489 (-0.009361) | 0.283384 / 0.293841 (-0.010457) | 0.023288 / 0.128546 (-0.105259) | 0.007241 / 0.075646 (-0.068405) | 0.207551 / 0.419271 (-0.211720) | 0.055008 / 0.043533 (0.011475) | 0.252781 / 0.255139 (-0.002358) | 0.296924 / 0.283200 (0.013724) | 0.017860 / 0.141683 (-0.123822) | 1.094597 / 1.452155 (-0.357558) | 1.162314 / 1.492716 (-0.330402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091423 / 0.018006 (0.073417) | 0.302833 / 0.000490 (0.302343) | 0.000242 / 0.000200 (0.000042) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018143 / 0.037411 (-0.019268) | 0.066371 / 0.014526 (0.051845) | 0.072774 / 0.176557 (-0.103783) | 0.119062 / 0.737135 (-0.618073) | 0.102836 / 0.296338 (-0.193502) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280117 / 0.215209 (0.064908) | 2.757955 / 2.077655 (0.680301) | 1.494994 / 1.504120 (-0.009126) | 1.375325 / 1.541195 (-0.165870) | 1.384179 / 1.468490 (-0.084311) | 0.399824 / 4.584777 (-4.184953) | 2.368575 / 3.745712 (-1.377137) | 2.574035 / 5.269862 (-2.695827) | 1.548738 / 4.565676 (-3.016939) | 0.045841 / 0.424275 (-0.378434) | 0.004799 / 0.007607 (-0.002808) | 0.331522 / 0.226044 (0.105478) | 3.324471 / 2.268929 (1.055543) | 1.838637 / 55.444624 (-53.605987) | 1.562854 / 6.876477 (-5.313623) | 1.581736 / 2.142072 (-0.560336) | 0.468832 / 4.805227 (-4.336396) | 0.099309 / 6.500664 (-6.401355) | 0.042078 / 0.075469 (-0.033391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.928468 / 1.841788 (-0.913320) | 11.331143 / 8.074308 (3.256835) | 10.296213 / 10.191392 (0.104821) | 0.138912 / 0.680424 (-0.541511) | 0.014044 / 0.534201 (-0.520157) | 0.267293 / 0.579283 (-0.311991) | 0.267267 / 0.434364 (-0.167097) | 0.306560 / 0.540337 (-0.233778) | 0.423926 / 1.386936 (-0.963010) |\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) | 0.004842 / 0.011353 (-0.006511) | 0.002917 / 0.011008 (-0.008091) | 0.048263 / 0.038508 (0.009755) | 0.051453 / 0.023109 (0.028344) | 0.278330 / 0.275898 (0.002432) | 0.298569 / 0.323480 (-0.024911) | 0.003936 / 0.007986 (-0.004049) | 0.002479 / 0.004328 (-0.001850) | 0.048281 / 0.004250 (0.044031) | 0.038925 / 0.037052 (0.001872) | 0.285258 / 0.258489 (0.026769) | 0.313701 / 0.293841 (0.019860) | 0.024916 / 0.128546 (-0.103630) | 0.007142 / 0.075646 (-0.068504) | 0.053634 / 0.419271 (-0.365638) | 0.032842 / 0.043533 (-0.010690) | 0.279373 / 0.255139 (0.024234) | 0.295844 / 0.283200 (0.012644) | 0.018142 / 0.141683 (-0.123541) | 1.136960 / 1.452155 (-0.315195) | 1.184438 / 1.492716 (-0.308278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090271 / 0.018006 (0.072264) | 0.299940 / 0.000490 (0.299450) | 0.000234 / 0.000200 (0.000034) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021175 / 0.037411 (-0.016237) | 0.070924 / 0.014526 (0.056398) | 0.080584 / 0.176557 (-0.095972) | 0.119278 / 0.737135 (-0.617857) | 0.082361 / 0.296338 (-0.213977) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298312 / 0.215209 (0.083103) | 2.895361 / 2.077655 (0.817706) | 1.616120 / 1.504120 (0.112001) | 1.484444 / 1.541195 (-0.056750) | 1.541893 / 1.468490 (0.073403) | 0.409968 / 4.584777 (-4.174809) | 2.423639 / 3.745712 (-1.322073) | 2.585122 / 5.269862 (-2.684740) | 1.540343 / 4.565676 (-3.025333) | 0.046604 / 0.424275 (-0.377671) | 0.004742 / 0.007607 (-0.002865) | 0.341659 / 0.226044 (0.115614) | 3.409259 / 2.268929 (1.140330) | 2.007068 / 55.444624 (-53.437556) | 1.681348 / 6.876477 (-5.195129) | 1.719253 / 2.142072 (-0.422819) | 0.482301 / 4.805227 (-4.322926) | 0.099619 / 6.500664 (-6.401045) | 0.041247 / 0.075469 (-0.034222) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971783 / 1.841788 (-0.870004) | 12.208000 / 8.074308 (4.133692) | 10.948230 / 10.191392 (0.756838) | 0.131824 / 0.680424 (-0.548599) | 0.015696 / 0.534201 (-0.518505) | 0.272265 / 0.579283 (-0.307018) | 0.276093 / 0.434364 (-0.158270) | 0.305897 / 0.540337 (-0.234441) | 0.411632 / 1.386936 (-0.975304) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bf75fe522c6fedd16d00b4a928f613dee11f73c \"CML watermark\")\n" ]
2023-11-16T08:12:55
2023-11-16T08:19:39
2023-11-16 08:13:28+00:00
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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) | 0.004331 / 0.011353 (-0.007022) | 0.002573 / 0.011008 (-0.008435) | 0.061002 / 0.038508 (0.022494) | 0.029259 / 0.023109 (0.006149) | 0.242983 / 0.275898 (-0.032915) | 0.267629 / 0.323480 (-0.055851) | 0.003906 / 0.007986 (-0.004080) | 0.002383 / 0.004328 (-0.001946) | 0.047574 / 0.004250 (0.043323) | 0.042153 / 0.037052 (0.005101) | 0.245821 / 0.258489 (-0.012668) | 0.276479 / 0.293841 (-0.017362) | 0.022498 / 0.128546 (-0.106049) | 0.006775 / 0.075646 (-0.068871) | 0.201795 / 0.419271 (-0.217477) | 0.052443 / 0.043533 (0.008910) | 0.248320 / 0.255139 (-0.006819) | 0.261964 / 0.283200 (-0.021235) | 0.016764 / 0.141683 (-0.124919) | 1.118702 / 1.452155 (-0.333453) | 1.203079 / 1.492716 (-0.289638) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088808 / 0.018006 (0.070801) | 0.296526 / 0.000490 (0.296037) | 0.000203 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018816 / 0.037411 (-0.018595) | 0.062295 / 0.014526 (0.047769) | 0.075228 / 0.176557 (-0.101329) | 0.119916 / 0.737135 (-0.617219) | 0.077206 / 0.296338 (-0.219132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276723 / 0.215209 (0.061514) | 2.711431 / 2.077655 (0.633776) | 1.425590 / 1.504120 (-0.078530) | 1.301383 / 1.541195 (-0.239812) | 1.316314 / 1.468490 (-0.152176) | 0.402709 / 4.584777 (-4.182068) | 2.347229 / 3.745712 (-1.398483) | 2.596937 / 5.269862 (-2.672925) | 1.560658 / 4.565676 (-3.005018) | 0.046162 / 0.424275 (-0.378113) | 0.004760 / 0.007607 (-0.002848) | 0.330522 / 0.226044 (0.104478) | 3.244072 / 2.268929 (0.975143) | 1.747603 / 55.444624 (-53.697021) | 1.475534 / 6.876477 (-5.400943) | 1.485135 / 2.142072 (-0.656938) | 0.476794 / 4.805227 (-4.328433) | 0.098496 / 6.500664 (-6.402168) | 0.040740 / 0.075469 (-0.034729) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.939020 / 1.841788 (-0.902768) | 11.235187 / 8.074308 (3.160878) | 10.194975 / 10.191392 (0.003583) | 0.126241 / 0.680424 (-0.554182) | 0.013990 / 0.534201 (-0.520211) | 0.269149 / 0.579283 (-0.310134) | 0.256950 / 0.434364 (-0.177414) | 0.301282 / 0.540337 (-0.239056) | 0.421490 / 1.386936 (-0.965446) |\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) | 0.004956 / 0.011353 (-0.006397) | 0.002478 / 0.011008 (-0.008530) | 0.047773 / 0.038508 (0.009265) | 0.050076 / 0.023109 (0.026967) | 0.261915 / 0.275898 (-0.013983) | 0.282553 / 0.323480 (-0.040927) | 0.003881 / 0.007986 (-0.004105) | 0.002329 / 0.004328 (-0.001999) | 0.048091 / 0.004250 (0.043841) | 0.038188 / 0.037052 (0.001135) | 0.265502 / 0.258489 (0.007013) | 0.292568 / 0.293841 (-0.001273) | 0.024172 / 0.128546 (-0.104374) | 0.006865 / 0.075646 (-0.068781) | 0.053199 / 0.419271 (-0.366072) | 0.032201 / 0.043533 (-0.011332) | 0.265774 / 0.255139 (0.010635) | 0.277954 / 0.283200 (-0.005245) | 0.017798 / 0.141683 (-0.123885) | 1.121503 / 1.452155 (-0.330652) | 1.176319 / 1.492716 (-0.316398) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087027 / 0.018006 (0.069020) | 0.296182 / 0.000490 (0.295693) | 0.000216 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020990 / 0.037411 (-0.016421) | 0.069693 / 0.014526 (0.055168) | 0.081098 / 0.176557 (-0.095459) | 0.117760 / 0.737135 (-0.619375) | 0.081493 / 0.296338 (-0.214845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295078 / 0.215209 (0.079869) | 2.876602 / 2.077655 (0.798947) | 1.558011 / 1.504120 (0.053891) | 1.426715 / 1.541195 (-0.114480) | 1.443785 / 1.468490 (-0.024705) | 0.400826 / 4.584777 (-4.183951) | 2.378903 / 3.745712 (-1.366810) | 2.473128 / 5.269862 (-2.796734) | 1.500785 / 4.565676 (-3.064891) | 0.045438 / 0.424275 (-0.378837) | 0.004953 / 0.007607 (-0.002654) | 0.348182 / 0.226044 (0.122137) | 3.427751 / 2.268929 (1.158822) | 1.925173 / 55.444624 (-53.519451) | 1.633354 / 6.876477 (-5.243123) | 1.651573 / 2.142072 (-0.490499) | 0.473260 / 4.805227 (-4.331968) | 0.097613 / 6.500664 (-6.403051) | 0.040196 / 0.075469 (-0.035273) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951780 / 1.841788 (-0.890008) | 11.709342 / 8.074308 (3.635034) | 10.571831 / 10.191392 (0.380439) | 0.134344 / 0.680424 (-0.546079) | 0.022116 / 0.534201 (-0.512084) | 0.269651 / 0.579283 (-0.309632) | 0.272310 / 0.434364 (-0.162054) | 0.306434 / 0.540337 (-0.233903) | 0.408320 / 1.386936 (-0.978616) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"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) | 0.004402 / 0.011353 (-0.006951) | 0.002732 / 0.011008 (-0.008277) | 0.062799 / 0.038508 (0.024291) | 0.029155 / 0.023109 (0.006046) | 0.241925 / 0.275898 (-0.033973) | 0.275694 / 0.323480 (-0.047786) | 0.003989 / 0.007986 (-0.003997) | 0.002528 / 0.004328 (-0.001801) | 0.048410 / 0.004250 (0.044160) | 0.043729 / 0.037052 (0.006677) | 0.248843 / 0.258489 (-0.009646) | 0.282980 / 0.293841 (-0.010860) | 0.023828 / 0.128546 (-0.104718) | 0.006972 / 0.075646 (-0.068675) | 0.213222 / 0.419271 (-0.206049) | 0.054883 / 0.043533 (0.011350) | 0.251353 / 0.255139 (-0.003786) | 0.269818 / 0.283200 (-0.013381) | 0.016906 / 0.141683 (-0.124777) | 1.114109 / 1.452155 (-0.338045) | 1.162942 / 1.492716 (-0.329774) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093724 / 0.018006 (0.075718) | 0.301989 / 0.000490 (0.301499) | 0.000213 / 0.000200 (0.000014) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018245 / 0.037411 (-0.019166) | 0.062237 / 0.014526 (0.047712) | 0.075644 / 0.176557 (-0.100913) | 0.119655 / 0.737135 (-0.617480) | 0.074525 / 0.296338 (-0.221814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.274534 / 0.215209 (0.059324) | 2.683678 / 2.077655 (0.606024) | 1.453306 / 1.504120 (-0.050814) | 1.347630 / 1.541195 (-0.193564) | 1.352875 / 1.468490 (-0.115615) | 0.398425 / 4.584777 (-4.186352) | 2.375738 / 3.745712 (-1.369974) | 2.591573 / 5.269862 (-2.678289) | 1.555527 / 4.565676 (-3.010150) | 0.045656 / 0.424275 (-0.378619) | 0.004898 / 0.007607 (-0.002709) | 0.330591 / 0.226044 (0.104547) | 3.247638 / 2.268929 (0.978710) | 1.816676 / 55.444624 (-53.627948) | 1.531754 / 6.876477 (-5.344723) | 1.543196 / 2.142072 (-0.598877) | 0.472489 / 4.805227 (-4.332739) | 0.099311 / 6.500664 (-6.401353) | 0.042139 / 0.075469 (-0.033330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.945472 / 1.841788 (-0.896316) | 11.476550 / 8.074308 (3.402242) | 10.281157 / 10.191392 (0.089765) | 0.141062 / 0.680424 (-0.539362) | 0.013634 / 0.534201 (-0.520567) | 0.268778 / 0.579283 (-0.310505) | 0.263542 / 0.434364 (-0.170822) | 0.307918 / 0.540337 (-0.232420) | 0.421231 / 1.386936 (-0.965705) |\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) | 0.005090 / 0.011353 (-0.006263) | 0.003135 / 0.011008 (-0.007873) | 0.048058 / 0.038508 (0.009550) | 0.052898 / 0.023109 (0.029789) | 0.273233 / 0.275898 (-0.002665) | 0.299516 / 0.323480 (-0.023964) | 0.004126 / 0.007986 (-0.003860) | 0.002331 / 0.004328 (-0.001997) | 0.047627 / 0.004250 (0.043376) | 0.039076 / 0.037052 (0.002023) | 0.276625 / 0.258489 (0.018136) | 0.308180 / 0.293841 (0.014340) | 0.024929 / 0.128546 (-0.103618) | 0.007396 / 0.075646 (-0.068251) | 0.053408 / 0.419271 (-0.365863) | 0.032896 / 0.043533 (-0.010637) | 0.275412 / 0.255139 (0.020273) | 0.292014 / 0.283200 (0.008814) | 0.018336 / 0.141683 (-0.123347) | 1.123565 / 1.452155 (-0.328589) | 1.175382 / 1.492716 (-0.317334) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093799 / 0.018006 (0.075793) | 0.304219 / 0.000490 (0.303729) | 0.000231 / 0.000200 (0.000031) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021034 / 0.037411 (-0.016377) | 0.069961 / 0.014526 (0.055435) | 0.080311 / 0.176557 (-0.096246) | 0.118603 / 0.737135 (-0.618532) | 0.084003 / 0.296338 (-0.212335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305610 / 0.215209 (0.090401) | 2.962027 / 2.077655 (0.884372) | 1.598604 / 1.504120 (0.094484) | 1.476227 / 1.541195 (-0.064967) | 1.528960 / 1.468490 (0.060470) | 0.404545 / 4.584777 (-4.180232) | 2.423147 / 3.745712 (-1.322565) | 2.516632 / 5.269862 (-2.753229) | 1.529000 / 4.565676 (-3.036677) | 0.045780 / 0.424275 (-0.378495) | 0.004784 / 0.007607 (-0.002823) | 0.358836 / 0.226044 (0.132792) | 3.508782 / 2.268929 (1.239853) | 1.954513 / 55.444624 (-53.490111) | 1.672824 / 6.876477 (-5.203653) | 1.683482 / 2.142072 (-0.458590) | 0.479014 / 4.805227 (-4.326213) | 0.098325 / 6.500664 (-6.402340) | 0.040934 / 0.075469 (-0.034536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974770 / 1.841788 (-0.867017) | 11.956137 / 8.074308 (3.881829) | 10.956458 / 10.191392 (0.765066) | 0.141800 / 0.680424 (-0.538624) | 0.015439 / 0.534201 (-0.518762) | 0.271783 / 0.579283 (-0.307500) | 0.278058 / 0.434364 (-0.156306) | 0.305823 / 0.540337 (-0.234514) | 0.415677 / 1.386936 (-0.971259) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"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) | 0.004483 / 0.011353 (-0.006870) | 0.002560 / 0.011008 (-0.008448) | 0.061428 / 0.038508 (0.022920) | 0.029460 / 0.023109 (0.006351) | 0.238971 / 0.275898 (-0.036927) | 0.271768 / 0.323480 (-0.051712) | 0.003970 / 0.007986 (-0.004016) | 0.002408 / 0.004328 (-0.001921) | 0.047755 / 0.004250 (0.043505) | 0.043358 / 0.037052 (0.006306) | 0.245543 / 0.258489 (-0.012946) | 0.278230 / 0.293841 (-0.015611) | 0.023819 / 0.128546 (-0.104727) | 0.006856 / 0.075646 (-0.068790) | 0.204603 / 0.419271 (-0.214668) | 0.054521 / 0.043533 (0.010989) | 0.246277 / 0.255139 (-0.008862) | 0.271230 / 0.283200 (-0.011969) | 0.017283 / 0.141683 (-0.124400) | 1.088955 / 1.452155 (-0.363200) | 1.245141 / 1.492716 (-0.247575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091534 / 0.018006 (0.073528) | 0.299517 / 0.000490 (0.299027) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018105 / 0.037411 (-0.019306) | 0.061860 / 0.014526 (0.047334) | 0.074494 / 0.176557 (-0.102063) | 0.120107 / 0.737135 (-0.617029) | 0.073406 / 0.296338 (-0.222932) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278140 / 0.215209 (0.062931) | 2.746208 / 2.077655 (0.668553) | 1.476264 / 1.504120 (-0.027856) | 1.356498 / 1.541195 (-0.184697) | 1.362998 / 1.468490 (-0.105492) | 0.401884 / 4.584777 (-4.182893) | 2.409836 / 3.745712 (-1.335877) | 2.579087 / 5.269862 (-2.690775) | 1.545021 / 4.565676 (-3.020656) | 0.046001 / 0.424275 (-0.378274) | 0.004812 / 0.007607 (-0.002795) | 0.339767 / 0.226044 (0.113722) | 3.341599 / 2.268929 (1.072670) | 1.821498 / 55.444624 (-53.623127) | 1.559311 / 6.876477 (-5.317166) | 1.570368 / 2.142072 (-0.571704) | 0.472688 / 4.805227 (-4.332539) | 0.099549 / 6.500664 (-6.401115) | 0.041644 / 0.075469 (-0.033825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951988 / 1.841788 (-0.889799) | 11.371459 / 8.074308 (3.297150) | 10.229446 / 10.191392 (0.038054) | 0.128105 / 0.680424 (-0.552319) | 0.014418 / 0.534201 (-0.519783) | 0.268615 / 0.579283 (-0.310668) | 0.263956 / 0.434364 (-0.170407) | 0.302213 / 0.540337 (-0.238125) | 0.427224 / 1.386936 (-0.959712) |\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) | 0.005150 / 0.011353 (-0.006203) | 0.002557 / 0.011008 (-0.008451) | 0.048092 / 0.038508 (0.009584) | 0.050522 / 0.023109 (0.027413) | 0.272195 / 0.275898 (-0.003703) | 0.294191 / 0.323480 (-0.029289) | 0.004098 / 0.007986 (-0.003887) | 0.002350 / 0.004328 (-0.001978) | 0.048682 / 0.004250 (0.044432) | 0.038381 / 0.037052 (0.001328) | 0.275530 / 0.258489 (0.017041) | 0.303991 / 0.293841 (0.010150) | 0.024734 / 0.128546 (-0.103812) | 0.006926 / 0.075646 (-0.068720) | 0.053683 / 0.419271 (-0.365588) | 0.032675 / 0.043533 (-0.010858) | 0.272816 / 0.255139 (0.017677) | 0.291754 / 0.283200 (0.008554) | 0.018290 / 0.141683 (-0.123392) | 1.127696 / 1.452155 (-0.324459) | 1.187080 / 1.492716 (-0.305636) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091224 / 0.018006 (0.073218) | 0.288838 / 0.000490 (0.288348) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069846 / 0.014526 (0.055320) | 0.079962 / 0.176557 (-0.096594) | 0.118575 / 0.737135 (-0.618560) | 0.080223 / 0.296338 (-0.216115) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290835 / 0.215209 (0.075626) | 2.831787 / 2.077655 (0.754133) | 1.587728 / 1.504120 (0.083608) | 1.461939 / 1.541195 (-0.079256) | 1.495257 / 1.468490 (0.026767) | 0.397653 / 4.584777 (-4.187124) | 2.399903 / 3.745712 (-1.345809) | 2.527615 / 5.269862 (-2.742247) | 1.501555 / 4.565676 (-3.064121) | 0.045742 / 0.424275 (-0.378533) | 0.004797 / 0.007607 (-0.002811) | 0.339139 / 0.226044 (0.113094) | 3.358340 / 2.268929 (1.089412) | 1.968955 / 55.444624 (-53.475670) | 1.663598 / 6.876477 (-5.212879) | 1.673995 / 2.142072 (-0.468078) | 0.463444 / 4.805227 (-4.341783) | 0.098008 / 6.500664 (-6.402656) | 0.040836 / 0.075469 (-0.034633) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974033 / 1.841788 (-0.867755) | 11.863206 / 8.074308 (3.788897) | 10.892389 / 10.191392 (0.700997) | 0.128884 / 0.680424 (-0.551540) | 0.015319 / 0.534201 (-0.518882) | 0.268931 / 0.579283 (-0.310353) | 0.274148 / 0.434364 (-0.160216) | 0.305407 / 0.540337 (-0.234930) | 0.410574 / 1.386936 (-0.976362) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n" ]
2023-11-16T07:37:20
2023-11-16T08:12:12
2023-11-16 07:43:05+00:00
MEMBER
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