Datasets:

BanUrsus

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github-issues_chapter5_section4

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[ "_The documentation is not available anymore as the PR was closed or merged._", "By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009256 / 0.011353 (-0.002097) | 0.005033 / 0.011008 (-0.005975) | 0.099346 / 0.038508 (0.060838) | 0.035204 / 0.023109 (0.012095) | 0.303017 / 0.275898 (0.027119) | 0.335632 / 0.323480 (0.012152) | 0.007953 / 0.007986 (-0.000033) | 0.005806 / 0.004328 (0.001477) | 0.076121 / 0.004250 (0.071871) | 0.041164 / 0.037052 (0.004112) | 0.305536 / 0.258489 (0.047047) | 0.348452 / 0.293841 (0.054611) | 0.037704 / 0.128546 (-0.090842) | 0.011982 / 0.075646 (-0.063664) | 0.333264 / 0.419271 (-0.086008) | 0.047738 / 0.043533 (0.004205) | 0.310126 / 0.255139 (0.054987) | 0.318719 / 0.283200 (0.035519) | 0.098933 / 0.141683 (-0.042750) | 1.421058 / 1.452155 (-0.031096) | 1.554771 / 1.492716 (0.062054) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258627 / 0.018006 (0.240620) | 0.450814 / 0.000490 (0.450324) | 0.011288 / 0.000200 (0.011088) | 0.000136 / 0.000054 (0.000081) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027004 / 0.037411 (-0.010407) | 0.109067 / 0.014526 (0.094541) | 0.120401 / 0.176557 (-0.056155) | 0.158336 / 0.737135 (-0.578799) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401847 / 0.215209 (0.186638) | 4.006003 / 2.077655 (1.928348) | 1.806342 / 1.504120 (0.302223) | 1.619751 / 1.541195 (0.078556) | 1.709660 / 1.468490 (0.241170) | 0.692444 / 4.584777 (-3.892333) | 3.853691 / 3.745712 (0.107979) | 2.143910 / 5.269862 (-3.125951) | 1.471600 / 4.565676 (-3.094076) | 0.084589 / 0.424275 (-0.339686) | 0.012276 / 0.007607 (0.004669) | 0.506529 / 0.226044 (0.280485) | 5.028361 / 2.268929 (2.759432) | 2.277660 / 55.444624 (-53.166964) | 1.930365 / 6.876477 (-4.946112) | 1.965494 / 2.142072 (-0.176579) | 0.826752 / 4.805227 (-3.978475) | 0.165050 / 6.500664 (-6.335614) | 0.062702 / 0.075469 (-0.012767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234539 / 1.841788 (-0.607249) | 15.067401 / 8.074308 (6.993093) | 14.041920 / 10.191392 (3.850528) | 0.162590 / 0.680424 (-0.517834) | 0.028941 / 0.534201 (-0.505260) | 0.438518 / 0.579283 (-0.140765) | 0.443787 / 0.434364 (0.009423) | 0.516671 / 0.540337 (-0.023666) | 0.609036 / 1.386936 (-0.777900) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007535 / 0.011353 (-0.003818) | 0.005283 / 0.011008 (-0.005725) | 0.097116 / 0.038508 (0.058608) | 0.033357 / 0.023109 (0.010247) | 0.383398 / 0.275898 (0.107500) | 0.425516 / 0.323480 (0.102037) | 0.006039 / 0.007986 (-0.001947) | 0.004074 / 0.004328 (-0.000255) | 0.073207 / 0.004250 (0.068956) | 0.052153 / 0.037052 (0.015101) | 0.386385 / 0.258489 (0.127896) | 0.429900 / 0.293841 (0.136059) | 0.038341 / 0.128546 (-0.090205) | 0.012417 / 0.075646 (-0.063230) | 0.333859 / 0.419271 (-0.085413) | 0.051157 / 0.043533 (0.007625) | 0.395022 / 0.255139 (0.139883) | 0.402462 / 0.283200 (0.119262) | 0.105207 / 0.141683 (-0.036475) | 1.510679 / 1.452155 (0.058524) | 1.584205 / 1.492716 (0.091489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225805 / 0.018006 (0.207799) | 0.452109 / 0.000490 (0.451619) | 0.000429 / 0.000200 (0.000229) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029653 / 0.037411 (-0.007759) | 0.112609 / 0.014526 (0.098083) | 0.121828 / 0.176557 (-0.054728) | 0.159003 / 0.737135 (-0.578133) | 0.129306 / 0.296338 (-0.167033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453001 / 0.215209 (0.237792) | 4.514882 / 2.077655 (2.437228) | 2.277494 / 1.504120 (0.773374) | 2.073870 / 1.541195 (0.532675) | 2.153346 / 1.468490 (0.684856) | 0.698363 / 4.584777 (-3.886414) | 3.921763 / 3.745712 (0.176051) | 2.123133 / 5.269862 (-3.146729) | 1.347618 / 4.565676 (-3.218058) | 0.085654 / 0.424275 (-0.338621) | 0.012059 / 0.007607 (0.004452) | 0.568183 / 0.226044 (0.342139) | 5.720047 / 2.268929 (3.451119) | 2.777973 / 55.444624 (-52.666651) | 2.453426 / 6.876477 (-4.423051) | 2.523977 / 2.142072 (0.381905) | 0.841979 / 4.805227 (-3.963248) | 0.167958 / 6.500664 (-6.332706) | 0.064929 / 0.075469 (-0.010540) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235297 / 1.841788 (-0.606491) | 15.883598 / 8.074308 (7.809290) | 14.395328 / 10.191392 (4.203936) | 0.162401 / 0.680424 (-0.518022) | 0.017806 / 0.534201 (-0.516394) | 0.423853 / 0.579283 (-0.155430) | 0.423266 / 0.434364 (-0.011098) | 0.490351 / 0.540337 (-0.049986) | 0.588116 / 1.386936 (-0.798820) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010759 / 0.011353 (-0.000594) | 0.005748 / 0.011008 (-0.005260) | 0.119195 / 0.038508 (0.080687) | 0.033476 / 0.023109 (0.010367) | 0.364081 / 0.275898 (0.088183) | 0.422456 / 0.323480 (0.098976) | 0.009780 / 0.007986 (0.001795) | 0.006170 / 0.004328 (0.001841) | 0.093242 / 0.004250 (0.088991) | 0.041049 / 0.037052 (0.003997) | 0.372132 / 0.258489 (0.113643) | 0.442501 / 0.293841 (0.148660) | 0.054889 / 0.128546 (-0.073657) | 0.018302 / 0.075646 (-0.057345) | 0.378899 / 0.419271 (-0.040373) | 0.058455 / 0.043533 (0.014922) | 0.356141 / 0.255139 (0.101002) | 0.400866 / 0.283200 (0.117666) | 0.103384 / 0.141683 (-0.038299) | 1.629867 / 1.452155 (0.177713) | 1.693939 / 1.492716 (0.201222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240484 / 0.018006 (0.222478) | 0.509137 / 0.000490 (0.508648) | 0.000450 / 0.000200 (0.000250) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025856 / 0.037411 (-0.011555) | 0.113214 / 0.014526 (0.098689) | 0.119420 / 0.176557 (-0.057136) | 0.158663 / 0.737135 (-0.578473) | 0.123542 / 0.296338 (-0.172797) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.555900 / 0.215209 (0.340691) | 5.580295 / 2.077655 (3.502640) | 2.216640 / 1.504120 (0.712520) | 1.904944 / 1.541195 (0.363749) | 1.865839 / 1.468490 (0.397349) | 1.158325 / 4.584777 (-3.426452) | 5.097420 / 3.745712 (1.351708) | 2.881775 / 5.269862 (-2.388087) | 2.068896 / 4.565676 (-2.496780) | 0.129028 / 0.424275 (-0.295247) | 0.014075 / 0.007607 (0.006468) | 0.698874 / 0.226044 (0.472830) | 7.131225 / 2.268929 (4.862296) | 2.901686 / 55.444624 (-52.542939) | 2.186146 / 6.876477 (-4.690330) | 2.251172 / 2.142072 (0.109100) | 1.342264 / 4.805227 (-3.462963) | 0.232045 / 6.500664 (-6.268619) | 0.073520 / 0.075469 (-0.001949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.431314 / 1.841788 (-0.410474) | 16.313055 / 8.074308 (8.238747) | 18.451552 / 10.191392 (8.260160) | 0.232875 / 0.680424 (-0.447549) | 0.042170 / 0.534201 (-0.492031) | 0.495261 / 0.579283 (-0.084022) | 0.582901 / 0.434364 (0.148537) | 0.582049 / 0.540337 (0.041712) | 0.681122 / 1.386936 (-0.705814) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008131 / 0.011353 (-0.003222) | 0.006162 / 0.011008 (-0.004847) | 0.113721 / 0.038508 (0.075213) | 0.030797 / 0.023109 (0.007688) | 0.413108 / 0.275898 (0.137210) | 0.449968 / 0.323480 (0.126488) | 0.006126 / 0.007986 (-0.001860) | 0.004848 / 0.004328 (0.000519) | 0.085465 / 0.004250 (0.081214) | 0.045817 / 0.037052 (0.008764) | 0.419360 / 0.258489 (0.160871) | 0.489077 / 0.293841 (0.195236) | 0.050841 / 0.128546 (-0.077705) | 0.020646 / 0.075646 (-0.055000) | 0.379838 / 0.419271 (-0.039434) | 0.068897 / 0.043533 (0.025365) | 0.422182 / 0.255139 (0.167043) | 0.435529 / 0.283200 (0.152330) | 0.115299 / 0.141683 (-0.026384) | 1.655134 / 1.452155 (0.202979) | 1.835198 / 1.492716 (0.342481) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207041 / 0.018006 (0.189034) | 0.491263 / 0.000490 (0.490773) | 0.003554 / 0.000200 (0.003354) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030830 / 0.037411 (-0.006582) | 0.127003 / 0.014526 (0.112477) | 0.142901 / 0.176557 (-0.033656) | 0.177570 / 0.737135 (-0.559565) | 0.137758 / 0.296338 (-0.158580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632820 / 0.215209 (0.417611) | 6.215535 / 2.077655 (4.137880) | 2.615310 / 1.504120 (1.111190) | 2.261431 / 1.541195 (0.720236) | 2.220570 / 1.468490 (0.752080) | 1.215820 / 4.584777 (-3.368957) | 5.247680 / 3.745712 (1.501968) | 3.120054 / 5.269862 (-2.149807) | 1.950947 / 4.565676 (-2.614730) | 0.149980 / 0.424275 (-0.274295) | 0.015241 / 0.007607 (0.007634) | 0.879714 / 0.226044 (0.653670) | 7.941913 / 2.268929 (5.672984) | 3.512456 / 55.444624 (-51.932168) | 2.693833 / 6.876477 (-4.182644) | 2.772780 / 2.142072 (0.630708) | 1.459581 / 4.805227 (-3.345646) | 0.264820 / 6.500664 (-6.235844) | 0.076698 / 0.075469 (0.001228) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.437719 / 1.841788 (-0.404068) | 16.750309 / 8.074308 (8.676001) | 18.646776 / 10.191392 (8.455384) | 0.227858 / 0.680424 (-0.452566) | 0.024239 / 0.534201 (-0.509962) | 0.486172 / 0.579283 (-0.093111) | 0.574731 / 0.434364 (0.140367) | 0.557776 / 0.540337 (0.017439) | 0.672921 / 1.386936 (-0.714015) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n" ]

"2023-01-10T08:43:19Z"

"2023-01-18T17:12:17Z"

"2023-01-18T14:09:02Z"

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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) | 0.008451 / 0.011353 (-0.002902) | 0.005056 / 0.011008 (-0.005952) | 0.086367 / 0.038508 (0.047859) | 0.068030 / 0.023109 (0.044920) | 0.358812 / 0.275898 (0.082914) | 0.385790 / 0.323480 (0.062310) | 0.005608 / 0.007986 (-0.002378) | 0.004262 / 0.004328 (-0.000067) | 0.066618 / 0.004250 (0.062368) | 0.053901 / 0.037052 (0.016849) | 0.398456 / 0.258489 (0.139967) | 0.391681 / 0.293841 (0.097840) | 0.046743 / 0.128546 (-0.081804) | 0.014118 / 0.075646 (-0.061528) | 0.308479 / 0.419271 (-0.110793) | 0.064214 / 0.043533 (0.020681) | 0.367940 / 0.255139 (0.112801) | 0.387204 / 0.283200 (0.104004) | 0.036093 / 0.141683 (-0.105590) | 1.534182 / 1.452155 (0.082027) | 1.598357 / 1.492716 (0.105641) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265910 / 0.018006 (0.247904) | 0.589453 / 0.000490 (0.588963) | 0.004881 / 0.000200 (0.004681) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032540 / 0.037411 (-0.004872) | 0.083153 / 0.014526 (0.068627) | 0.098960 / 0.176557 (-0.077597) | 0.162044 / 0.737135 (-0.575091) | 0.093602 / 0.296338 (-0.202736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517056 / 0.215209 (0.301847) | 5.167908 / 2.077655 (3.090253) | 2.359856 / 1.504120 (0.855736) | 2.092448 / 1.541195 (0.551253) | 2.100270 / 1.468490 (0.631780) | 0.742321 / 4.584777 (-3.842456) | 4.845010 / 3.745712 (1.099298) | 4.361808 / 5.269862 (-0.908054) | 2.621941 / 4.565676 (-1.943736) | 0.094907 / 0.424275 (-0.329369) | 0.009357 / 0.007607 (0.001750) | 0.719859 / 0.226044 (0.493814) | 6.929731 / 2.268929 (4.660802) | 3.240862 / 55.444624 (-52.203763) | 2.700817 / 6.876477 (-4.175659) | 2.904600 / 2.142072 (0.762527) | 0.924930 / 4.805227 (-3.880298) | 0.194390 / 6.500664 (-6.306274) | 0.078331 / 0.075469 (0.002862) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.539347 / 1.841788 (-0.302441) | 22.696358 / 8.074308 (14.622050) | 18.791692 / 10.191392 (8.600300) | 0.221376 / 0.680424 (-0.459048) | 0.029824 / 0.534201 (-0.504377) | 0.455604 / 0.579283 (-0.123679) | 0.573169 / 0.434364 (0.138805) | 0.507109 / 0.540337 (-0.033228) | 0.730986 / 1.386936 (-0.655950) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009308 / 0.011353 (-0.002045) | 0.005027 / 0.011008 (-0.005982) | 0.074094 / 0.038508 (0.035586) | 0.068277 / 0.023109 (0.045168) | 0.412716 / 0.275898 (0.136818) | 0.446883 / 0.323480 (0.123403) | 0.005864 / 0.007986 (-0.002122) | 0.003753 / 0.004328 (-0.000575) | 0.072575 / 0.004250 (0.068325) | 0.064434 / 0.037052 (0.027382) | 0.445395 / 0.258489 (0.186906) | 0.464520 / 0.293841 (0.170679) | 0.045303 / 0.128546 (-0.083243) | 0.013120 / 0.075646 (-0.062527) | 0.077830 / 0.419271 (-0.341441) | 0.057303 / 0.043533 (0.013770) | 0.420845 / 0.255139 (0.165706) | 0.431308 / 0.283200 (0.148109) | 0.033908 / 0.141683 (-0.107775) | 1.577667 / 1.452155 (0.125512) | 1.677321 / 1.492716 (0.184604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305855 / 0.018006 (0.287849) | 0.601442 / 0.000490 (0.600953) | 0.010722 / 0.000200 (0.010522) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029202 / 0.037411 (-0.008209) | 0.094576 / 0.014526 (0.080050) | 0.106734 / 0.176557 (-0.069822) | 0.168114 / 0.737135 (-0.569021) | 0.107241 / 0.296338 (-0.189098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643634 / 0.215209 (0.428425) | 6.391757 / 2.077655 (4.314103) | 3.011679 / 1.504120 (1.507559) | 2.379711 / 1.541195 (0.838517) | 2.387444 / 1.468490 (0.918954) | 0.823460 / 4.584777 (-3.761317) | 4.882240 / 3.745712 (1.136528) | 4.091170 / 5.269862 (-1.178691) | 2.688761 / 4.565676 (-1.876915) | 0.094555 / 0.424275 (-0.329720) | 0.008464 / 0.007607 (0.000857) | 0.665949 / 0.226044 (0.439905) | 6.948237 / 2.268929 (4.679309) | 3.384894 / 55.444624 (-52.059730) | 2.675570 / 6.876477 (-4.200907) | 3.073045 / 2.142072 (0.930973) | 0.969780 / 4.805227 (-3.835447) | 0.205859 / 6.500664 (-6.294805) | 0.072548 / 0.075469 (-0.002922) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.563869 / 1.841788 (-0.277919) | 22.431392 / 8.074308 (14.357084) | 19.434811 / 10.191392 (9.243419) | 0.255135 / 0.680424 (-0.425289) | 0.027799 / 0.534201 (-0.506402) | 0.427713 / 0.579283 (-0.151570) | 0.527030 / 0.434364 (0.092666) | 0.503660 / 0.540337 (-0.036678) | 0.730996 / 1.386936 (-0.655940) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06c1940953807dbde4bc18af64bd3d87234edf00 \"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) | 0.007597 / 0.011353 (-0.003756) | 0.004492 / 0.011008 (-0.006516) | 0.103779 / 0.038508 (0.065271) | 0.079287 / 0.023109 (0.056178) | 0.389651 / 0.275898 (0.113753) | 0.421955 / 0.323480 (0.098475) | 0.006023 / 0.007986 (-0.001963) | 0.003727 / 0.004328 (-0.000602) | 0.078604 / 0.004250 (0.074354) | 0.060810 / 0.037052 (0.023758) | 0.412170 / 0.258489 (0.153681) | 0.436218 / 0.293841 (0.142377) | 0.037282 / 0.128546 (-0.091264) | 0.010341 / 0.075646 (-0.065305) | 0.357652 / 0.419271 (-0.061620) | 0.063320 / 0.043533 (0.019788) | 0.389454 / 0.255139 (0.134315) | 0.433073 / 0.283200 (0.149874) | 0.028449 / 0.141683 (-0.113234) | 1.894107 / 1.452155 (0.441952) | 1.954190 / 1.492716 (0.461474) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224477 / 0.018006 (0.206471) | 0.510878 / 0.000490 (0.510388) | 0.005013 / 0.000200 (0.004813) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032976 / 0.037411 (-0.004436) | 0.101073 / 0.014526 (0.086547) | 0.113990 / 0.176557 (-0.062566) | 0.183499 / 0.737135 (-0.553636) | 0.114283 / 0.296338 (-0.182056) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473242 / 0.215209 (0.258033) | 4.719800 / 2.077655 (2.642146) | 2.318732 / 1.504120 (0.814612) | 2.102336 / 1.541195 (0.561141) | 2.143618 / 1.468490 (0.675128) | 0.594122 / 4.584777 (-3.990654) | 4.265961 / 3.745712 (0.520249) | 3.794635 / 5.269862 (-1.475226) | 2.394506 / 4.565676 (-2.171170) | 0.070091 / 0.424275 (-0.354184) | 0.009222 / 0.007607 (0.001614) | 0.564496 / 0.226044 (0.338452) | 5.644348 / 2.268929 (3.375419) | 2.934395 / 55.444624 (-52.510229) | 2.429076 / 6.876477 (-4.447401) | 2.592010 / 2.142072 (0.449937) | 0.713371 / 4.805227 (-4.091856) | 0.165019 / 6.500664 (-6.335646) | 0.075913 / 0.075469 (0.000444) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.570836 / 1.841788 (-0.270951) | 22.569763 / 8.074308 (14.495455) | 17.159658 / 10.191392 (6.968266) | 0.185716 / 0.680424 (-0.494708) | 0.021938 / 0.534201 (-0.512263) | 0.487204 / 0.579283 (-0.092079) | 0.472776 / 0.434364 (0.038412) | 0.565052 / 0.540337 (0.024714) | 0.763322 / 1.386936 (-0.623614) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007810 / 0.011353 (-0.003543) | 0.005140 / 0.011008 (-0.005869) | 0.079018 / 0.038508 (0.040510) | 0.080899 / 0.023109 (0.057790) | 0.489213 / 0.275898 (0.213315) | 0.525334 / 0.323480 (0.201854) | 0.006992 / 0.007986 (-0.000994) | 0.003729 / 0.004328 (-0.000599) | 0.079277 / 0.004250 (0.075026) | 0.064883 / 0.037052 (0.027831) | 0.496718 / 0.258489 (0.238229) | 0.534976 / 0.293841 (0.241135) | 0.038790 / 0.128546 (-0.089756) | 0.010122 / 0.075646 (-0.065524) | 0.087669 / 0.419271 (-0.331603) | 0.057959 / 0.043533 (0.014426) | 0.490611 / 0.255139 (0.235472) | 0.518376 / 0.283200 (0.235176) | 0.026561 / 0.141683 (-0.115122) | 1.843241 / 1.452155 (0.391086) | 1.952367 / 1.492716 (0.459651) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289799 / 0.018006 (0.271792) | 0.486999 / 0.000490 (0.486509) | 0.017481 / 0.000200 (0.017281) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037662 / 0.037411 (0.000250) | 0.113238 / 0.014526 (0.098712) | 0.123918 / 0.176557 (-0.052638) | 0.190484 / 0.737135 (-0.546652) | 0.126473 / 0.296338 (-0.169865) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.530622 / 0.215209 (0.315413) | 5.292093 / 2.077655 (3.214438) | 2.819354 / 1.504120 (1.315234) | 2.609821 / 1.541195 (1.068626) | 2.680090 / 1.468490 (1.211600) | 0.603490 / 4.584777 (-3.981287) | 4.344541 / 3.745712 (0.598828) | 3.874001 / 5.269862 (-1.395861) | 2.445302 / 4.565676 (-2.120375) | 0.071173 / 0.424275 (-0.353102) | 0.009131 / 0.007607 (0.001524) | 0.627273 / 0.226044 (0.401229) | 6.278637 / 2.268929 (4.009709) | 3.433762 / 55.444624 (-52.010862) | 2.973400 / 6.876477 (-3.903077) | 3.188165 / 2.142072 (1.046093) | 0.722824 / 4.805227 (-4.082404) | 0.165154 / 6.500664 (-6.335510) | 0.075268 / 0.075469 (-0.000202) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.652994 / 1.841788 (-0.188794) | 23.309030 / 8.074308 (15.234722) | 18.135649 / 10.191392 (7.944257) | 0.177543 / 0.680424 (-0.502881) | 0.024784 / 0.534201 (-0.509417) | 0.489952 / 0.579283 (-0.089331) | 0.485368 / 0.434364 (0.051004) | 0.580583 / 0.540337 (0.040246) | 0.787843 / 1.386936 (-0.599093) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5982039f7814a204fe532240ca6aabe72430d834 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "A bug in `FixedSizeArray.flatten` in `PyArrow<10.0.0` makes CI fail. Colab installs 9.0.0 by default, so we should be able to set the minimal version to `10.0.0` soon. Keeping this PR as a draft in the meantime.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6283" ]

"2023-09-04T16:21:23Z"

"2023-10-05T15:25:05Z"

"2023-10-05T15:24:34Z"

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[ "Before addressing this PR, we should be sure about the issue. See my comment in:\r\n- https://github.com/huggingface/datasets/issues/4961#issuecomment-1243376247", "Once we know 2022.8.2 works, I'm closing this PR, as the corresponding issue." ]

"2022-09-09T17:57:56Z"

"2022-09-12T14:33:04Z"

"2022-09-12T14:33:04Z"

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[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example.", "Any recent updates on this ? " ]

"2023-06-23T14:34:20Z"

"2023-12-08T09:04:04Z"

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[ "Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ", "sorry I misunderstood length of dataset with dataloader, closed. thanks " ]

"2020-11-30T12:23:39Z"

"2020-11-30T12:37:27Z"

"2020-11-30T12:37:26Z"

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[ "For reference:\r\nhttps://github.com/huggingface/transformers/issues/15514\r\nhttps://github.com/huggingface/huggingface_hub/issues/650", "Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https://huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint", "Yes, I tested it successfully this morning. Thanks." ]

"2022-02-04T10:49:06Z"

"2022-02-22T11:08:07Z"

"2022-02-22T11:08:07Z"

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"2020-12-03T22:16:31Z"

"2020-12-04T09:55:15Z"

"2020-12-04T09:55:15Z"

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[ "Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py#L62-L77), you can see how the Features were originally specified. \r\n\r\nFeel free to use it as a template, customize it and pass it to `load_dataset` using the parameter `features`.", "ah got it, thanks!" ]

"2021-06-25T04:32:23Z"

"2021-06-25T21:07:57Z"

"2021-06-25T21:07:56Z"

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"2022-11-25T15:21:32Z"

"2022-11-29T08:21:25Z"

"2022-11-29T08:21:25Z"

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[ "It's been fixed in this commit: https://github.com/huggingface/datasets/commit/549110e08238b3716a5904667095fb003acda54e\r\n\r\nBasically #2246 broke querying an index with a simple iterable.\r\nWith the fix, it's again possible to use iterables and we can keep RandIter as it is.\r\n\r\nClosing since the fix is already on master" ]

"2021-04-27T14:35:56Z"

"2021-04-28T17:39:27Z"

"2021-04-28T17:39:27Z"

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"2020-12-17T20:06:29Z"

"2020-12-18T09:43:58Z"

"2020-12-18T09:43:57Z"

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"2021-08-16T07:11:40Z"

"2021-08-16T10:34:00Z"

"2021-08-16T10:34:00Z"

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[ "@lhoestq Refactored into new branch, please review :) ", "The `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine", "merging since the CI is fixed on master" ]

"2020-12-09T16:52:16Z"

"2020-12-09T18:02:27Z"

"2020-12-09T18:02:27Z"

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[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint.", "Maybe we can add\r\n```python\r\npaths-ignore:\r\n - \"docs/**\"\r\n```\r\nto `ci.yml` and `benchmarks.yml`. The other supporting files are not modified often, so leaving them out is fine." ]

"2023-05-26T10:46:52Z"

"2023-09-07T15:52:36Z"

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[ "The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.", "I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n 198 predictions = self.data[\"predictions\"]\r\n 199 references = self.data[\"references\"]\r\n--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n 201 return output\r\n 202 \r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)\r\n 101 return pearson_and_spearman(predictions, references)\r\n 102 elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103 return acc_and_f1(predictions, references)\r\n 104 elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n 105 return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)\r\n 60 def acc_and_f1(preds, labels):\r\n 61 acc = simple_accuracy(preds, labels)\r\n---> 62 f1 = f1_score(y_true=labels, y_pred=preds)\r\n 63 return {\r\n 64 \"accuracy\": acc,\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n 1097 pos_label=pos_label, average=average,\r\n 1098 sample_weight=sample_weight,\r\n-> 1099 zero_division=zero_division)\r\n 1100 \r\n 1101 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n 1224 warn_for=('f-score',),\r\n 1225 sample_weight=sample_weight,\r\n-> 1226 zero_division=zero_division)\r\n 1227 return f\r\n 1228 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n 1482 raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n 1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484 pos_label)\r\n 1485 \r\n 1486 # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n 1314 raise ValueError(\"Target is %s but average='binary'. Please \"\r\n 1315 \"choose another average setting, one of %r.\"\r\n-> 1316 % (y_type, average_options))\r\n 1317 elif pos_label not in (None, 1):\r\n 1318 warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```", "MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).", "Closing this one. Feel free to re-open if you have other questions :)" ]

"2020-06-29T16:53:48Z"

"2020-07-09T09:13:34Z"

"2020-07-09T09:13:34Z"

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"2022-01-25T14:39:45Z"

"2022-01-25T14:55:46Z"

"2022-01-25T14:55:46Z"

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"2020-12-01T10:52:18Z"

"2020-12-02T09:42:35Z"

"2020-12-02T09:42:09Z"

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[ "Hi @kradonneoh, thanks for reporting.\r\n\r\nPlease note that to work with audio datasets (and specifically with MP3 files) we have detailed installation instructions in our docs: https://huggingface.co/docs/datasets/installation#audio\r\n- one of the requirements is torchaudio<0.12.0\r\n\r\nLet us know if the problem persists after having followed them.", "I saw that and have followed it (hence the Expected Behavior section of the bug report). \r\n\r\nIs there no intention of updating to the latest version? It does limit the version of `torch` I can use, which isn’t ideal.", "@kradonneoh hey! actually with `ffmpeg4` loading of mp3 files should work, so this is a not expected behavior and we need to investigate it. It works on my side with `torchaudio==0.13` and `ffmpeg==4.2.7`. Which `torchaudio` version do you use?\r\n\r\n`datasets` should support decoding of mp3 files with `torchaudio` when its version is `>0.12` but as you noted it requires `ffmpeg>4`, we need to fix this in the documentation, thank you for pointing to this! \r\n\r\nBut according to your traceback it seems that it tries to use [`libsndfile`](https://github.com/libsndfile/libsndfile) backend for mp3 decoding. And `libsndfile` library supports mp3 decoding starting from version 1.1.0 which on Linux has to be compiled from source for now afaik. \r\n\r\nfyi - we are aiming at getting rid of `torchaudio` dependency at all by the next major library release in favor of `libsndfile` too.", "We now decode MP3 with `soundfile`, so I'm closing this issue" ]

"2023-01-31T21:25:33Z"

"2023-03-02T16:25:14Z"

"2023-03-02T16:25:13Z"

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[ "Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path/to/data/dir\")\r\n```\r\n\r\nLet me know what you think", "Yep that would be sweet - closing for now as we found a workaround. ", "@lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to seeing.", "Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw/image-folder', data_files='PetImages/')\r\n```", "Bumping this one following our recent discussion @mariosasko @nateraw :)" ]

"2021-06-15T22:43:33Z"

"2022-03-01T16:29:44Z"

"2022-03-01T16:29:44Z"

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"2020-12-08T22:25:04Z"

"2020-12-08T23:11:55Z"

"2020-12-08T23:11:55Z"

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[ "This was fixed in https://github.com/huggingface/datasets/pull/6247. You can find the fix in the `main` version of the docs", "Ah great, thanks :)" ]

"2023-10-24T11:01:21Z"

"2023-10-25T13:05:21Z"

"2023-10-25T13:05:21Z"

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

"2022-12-05T16:06:09Z"

"2022-12-06T08:25:17Z"

"2022-12-06T08:22:49Z"

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[ "PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though", "Grand, thanks for this!" ]

"2020-11-18T14:40:54Z"

"2020-11-18T15:01:30Z"

"2020-11-18T15:01:30Z"

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"2020-12-08T11:08:28Z"

"2020-12-08T14:02:13Z"

"2020-12-08T14:02:13Z"

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[ "Thanks for opening a discussion :)\r\n\r\nNote that you can use `.remove_columns(...)` to keep only the ones you're interested in before calling `.to_pandas()`", "Yes I can do that thank you!\r\n\r\nDo you think that conceptually my example should work? If not, I'm happy to close this issue. \r\n\r\nIf yes, I can start working on it.", "Hi! Instead of `with_format(columns=['a', 'b']).to_pandas()`, use `with_format(\"pandas\", columns=[\"a\", \"b\"])` for easy conversion of the parts of the dataset to pandas via indexing/slicing.\r\n\r\nThe full code:\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})\r\npandas_df = ds.with_format(\"pandas\", columns=['a', 'b'])[:]\r\n```", "Ahhhh Thank you!\r\n\r\nclosing then :)" ]

"2022-06-10T20:25:31Z"

"2022-06-15T17:41:41Z"

"2022-06-15T17:41:41Z"

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[ "The expression \"train+test\" concatenates the splits.\r\n\r\nThe individual splits as separate datasets can be obtained as follows:\r\n```python\r\ntrain_ds, test_ds = load_dataset(\"<dataset_name>\", split=[\"train\", \"test\"])\r\ntrain_10pct_ds, test_10pct_ds = load_dataset(\"<dataset_name>\", split=[\"train[:10%]\", \"test[:%10]\"])\r\n```" ]

"2023-10-19T16:09:22Z"

"2023-11-30T16:21:15Z"

"2023-11-30T16:21:15Z"

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[ "```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset reddit/default (download: 2.93 GiB, generated: 17.64 GiB, post-processed: Unknown size, total: 20.57 GiB) to /Volumes/My Passport for Mac/og-chat-data/reddit/default/1.0.0/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969...\r\nDownloading: 13%\r\n403M/3.14G [44:39<2:27:09, 310kB/s]\r\n---------------------------------------------------------------------------\r\ntimeout Traceback (most recent call last)\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in _error_catcher(self)\r\n 437 try:\r\n--> 438 yield\r\n 439 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in read(self, amt, decode_content, cache_content)\r\n 518 cache_content = False\r\n--> 519 data = self._fp.read(amt) if not fp_closed else b\"\"\r\n 520 if (\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/http/client.py in read(self, amt)\r\n 458 b = bytearray(amt)\r\n--> 459 n = self.readinto(b)\r\n 460 return memoryview(b)[:n].tobytes()\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/http/client.py in readinto(self, b)\r\n 502 # (for example, reading in 1k chunks)\r\n--> 503 n = self.fp.readinto(b)\r\n 504 if not n and b:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/socket.py in readinto(self, b)\r\n 703 try:\r\n--> 704 return self._sock.recv_into(b)\r\n 705 except timeout:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/ssl.py in recv_into(self, buffer, nbytes, flags)\r\n 1240 self.__class__)\r\n-> 1241 return self.read(nbytes, buffer)\r\n 1242 else:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/ssl.py in read(self, len, buffer)\r\n 1098 if buffer is not None:\r\n-> 1099 return self._sslobj.read(len, buffer)\r\n 1100 else:\r\n\r\ntimeout: The read operation timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadTimeoutError Traceback (most recent call last)\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/requests/models.py in generate()\r\n 757 try:\r\n--> 758 for chunk in self.raw.stream(chunk_size, decode_content=True):\r\n 759 yield chunk\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in stream(self, amt, decode_content)\r\n 575 while not is_fp_closed(self._fp):\r\n--> 576 data = self.read(amt=amt, decode_content=decode_content)\r\n 577 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in read(self, amt, decode_content, cache_content)\r\n 540 # Content-Length are caught.\r\n--> 541 raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n 542 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/contextlib.py in __exit__(self, type, value, traceback)\r\n 134 try:\r\n--> 135 self.gen.throw(type, value, traceback)\r\n 136 except StopIteration as exc:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in _error_catcher(self)\r\n 442 # there is yet no clean way to get at it from this context.\r\n--> 443 raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\r\n 444 \r\n\r\nReadTimeoutError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError Traceback (most recent call last)\r\n/var/folders/3f/md0t9sgj6rz8xy01fskttqdc0000gn/T/ipykernel_89016/1133441872.py in <module>\r\n 1 from datasets import load_dataset\r\n 2 \r\n----> 3 dataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"/Volumes/My Passport for Mac/og-chat-data\")\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 669 split_dict = SplitDict(dataset_name=self.name)\r\n 670 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 671 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 672 \r\n 673 # Checksums verification\r\n\r\n~/.cache/huggingface/modules/datasets_modules/datasets/reddit/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969/reddit.py in _split_generators(self, dl_manager)\r\n 73 def _split_generators(self, dl_manager):\r\n 74 \"\"\"Returns SplitGenerators.\"\"\"\r\n---> 75 dl_path = dl_manager.download_and_extract(_URL)\r\n 76 return [\r\n 77 datasets.SplitGenerator(\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 287 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 288 \"\"\"\r\n--> 289 return self.extract(self.download(url_or_urls))\r\n 290 \r\n 291 def get_recorded_sizes_checksums(self):\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in download(self, url_or_urls)\r\n 195 \r\n 196 start_time = datetime.now()\r\n--> 197 downloaded_path_or_paths = map_nested(\r\n 198 download_func,\r\n 199 url_or_urls,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 194 # Singleton\r\n 195 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196 return function(data_struct)\r\n 197 \r\n 198 disable_tqdm = bool(logger.getEffectiveLevel() > logging.INFO) or not utils.is_progress_bar_enabled()\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in _download(self, url_or_filename, download_config)\r\n 218 # append the relative path to the base_path\r\n 219 url_or_filename = url_or_path_join(self._base_path, url_or_filename)\r\n--> 220 return cached_path(url_or_filename, download_config=download_config)\r\n 221 \r\n 222 def iter_archive(self, path):\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n 286 if is_remote_url(url_or_filename):\r\n 287 # URL, so get it from the cache (downloading if necessary)\r\n--> 288 output_path = get_from_cache(\r\n 289 url_or_filename,\r\n 290 cache_dir=cache_dir,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n 643 ftp_get(url, temp_file)\r\n 644 else:\r\n--> 645 http_get(\r\n 646 url,\r\n 647 temp_file,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)\r\n 451 disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n 452 )\r\n--> 453 for chunk in response.iter_content(chunk_size=1024):\r\n 454 if chunk: # filter out keep-alive new chunks\r\n 455 progress.update(len(chunk))\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/requests/models.py in generate()\r\n 763 raise ContentDecodingError(e)\r\n 764 except ReadTimeoutError as e:\r\n--> 765 raise ConnectionError(e)\r\n 766 else:\r\n 767 # Standard file-like object.\r\n\r\nConnectionError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n```", "Hey @lhoestq should I try to fix this issue ?", "It also doesn't seem to be \"smart caching\" and I received an error about a file not being found...", "To be clear, the error I get when I try to \"re-instantiate\" the download after failure is: \r\n```\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: <HOME>/.cache/huggingface/datasets/downloads/1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c/corpus-webis-tldr-17.json'\r\n```", "Here is a new error:\r\n```\r\nConnectionError: Couldn't reach https://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1\r\n```", "Hi ! Since https://github.com/huggingface/datasets/pull/2803 we've changed the time out from 10sec to 100sec.\r\nThis should prevent the `ReadTimeoutError`. Feel free to try it out by installing `datasets` from source\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```\r\n\r\nWhen re-running your code you said you get a `OSError`, could you try deleting the file at the path returned by the error ? (the one after `[Errno 20] Not a directory:`). Ideally when a download fails you should be able to re-run it without error; there might be an issue here.\r\n\r\nFinally not sure what we can do about `ConnectionError`, this must be an issue from zenodo. If it happens you simply need to try again\r\n", "@lhoestq thanks for the update. The directory specified by the OSError ie. \r\n```\r\n1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c/corpus-webis-tldr-17.json \r\n```\r\n was not actually in that directory so I can't delete it. ", "Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?\r\nThis way the download manager will know that it has to uncompress the data again", "It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. ", "Great ! The timeout change will be available in the next release of `datasets` :)" ]

"2021-08-20T02:52:36Z"

"2021-09-08T14:52:02Z"

"2021-09-08T14:52:02Z"

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[]

[ "can you also run `make style` to format the code ?", "I'll come back to this one in sometime :) @lhoestq ", "Also if you would use `xml.etree.ElementTree` to parse the XML it would be awesome, because right now you're using an external dependency `xmltodict `", "> Also if you would use xml.etree.ElementTree to parse the XML it would be awesome, because right now you're using an external dependency xmltodict\r\n\r\nIts pseudo xml so elementtree fails. xmltodict seems to be working quite good for this. do we have examples of pseudo xml datasets?", "for the other pseudo xml the text is parsed manually", "Can you add `xmltodict` to the test dependencies in setup.py please to fix the CI please ?", "Also can you add the dataset card with the tags and run `make style` ?", "Hi :) have you had a chance to fix the test dependency and apply `make style` ?\r\n\r\nFeel fee to ping me when it's ready for a review", "Thanks for your contribution, @abhishekkrthakur. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help." ]

"2020-12-07T09:57:15Z"

"2023-09-24T09:40:59Z"

"2022-10-03T09:39:43Z"

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[]

[ "I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ", "Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n", "Yes it worked, thank you!" ]

"2020-07-20T14:52:02Z"

"2020-07-20T15:39:32Z"

"2020-07-20T15:39:32Z"

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

"2022-09-29T11:37:58Z"

"2022-09-30T07:13:38Z"

"2022-09-29T12:07:29Z"

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

"2022-07-18T08:41:31Z"

"2022-07-18T14:43:59Z"

"2022-07-18T14:31:47Z"

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"2022-03-23T14:34:52Z"

"2022-03-23T15:43:43Z"

"2022-03-23T15:43:43Z"

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[]

"2020-12-13T21:59:02Z"

"2020-12-23T11:20:40Z"

"2020-12-23T11:20:39Z"

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[]

"2021-10-23T01:38:35Z"

"2021-10-25T14:27:36Z"

"2021-10-25T14:03:48Z"

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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" ]

"2023-12-11T11:09:16Z"

"2023-12-11T11:41:34Z"

"2023-12-11T11:41:34Z"

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"2023-04-26T14:55:23Z"

"2023-04-26T14:55:23Z"

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[ "Good point!\r\n\r\nI guess we could add this to `write_batch` as well (before using `self._schema` in the first line of this method)?" ]

"2020-05-26T21:59:48Z"

"2020-05-27T09:07:54Z"

"2020-05-27T09:07:53Z"

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[ "Thanks for reporting, I'm looking into it :)", "Fixed by #2950." ]

"2021-09-16T01:14:02Z"

"2021-09-20T16:23:22Z"

"2021-09-20T16:23:21Z"

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[ "Hi @AmitMY, \r\n\r\nWere you able to figure this out?", "I did not.\r\nWith all the limitations this repo currently has, I had to create a repo of my own using tfds to mitigate them. \r\nhttps://github.com/sign-language-processing/datasets/tree/master/sign_language_datasets/datasets/dgs_corpus\r\n\r\nClosing as I don't know how to support this PR further" ]

"2020-11-06T10:14:43Z"

"2021-03-23T06:18:55Z"

"2021-03-23T06:18:55Z"

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[ "@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?", "This dataset is now hosted on the Hub here: https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0" ]

"2023-01-18T13:07:05Z"

"2023-07-21T14:26:10Z"

"2023-07-21T14:26:09Z"

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[]

"2022-06-22T16:35:23Z"

"2022-06-23T10:13:44Z"

"2022-06-23T10:13:44Z"

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[ "ah didn't read well - it's all good", "or maybe it isn't ? `-c huggingface -c conda-forge` installs from HF or from conda-forge ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010196 / 0.011353 (-0.001157) | 0.005531 / 0.011008 (-0.005477) | 0.104601 / 0.038508 (0.066093) | 0.041322 / 0.023109 (0.018213) | 0.302080 / 0.275898 (0.026182) | 0.396579 / 0.323480 (0.073099) | 0.008874 / 0.007986 (0.000888) | 0.004482 / 0.004328 (0.000153) | 0.077487 / 0.004250 (0.073236) | 0.051113 / 0.037052 (0.014061) | 0.321850 / 0.258489 (0.063361) | 0.354946 / 0.293841 (0.061105) | 0.039822 / 0.128546 (-0.088724) | 0.012622 / 0.075646 (-0.063024) | 0.337898 / 0.419271 (-0.081374) | 0.048372 / 0.043533 (0.004839) | 0.299646 / 0.255139 (0.044507) | 0.321113 / 0.283200 (0.037914) | 0.114780 / 0.141683 (-0.026903) | 1.475750 / 1.452155 (0.023595) | 1.496307 / 1.492716 (0.003590) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311443 / 0.018006 (0.293437) | 0.567268 / 0.000490 (0.566778) | 0.006149 / 0.000200 (0.005950) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029407 / 0.037411 (-0.008004) | 0.118611 / 0.014526 (0.104085) | 0.122247 / 0.176557 (-0.054309) | 0.164770 / 0.737135 (-0.572365) | 0.128561 / 0.296338 (-0.167778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399185 / 0.215209 (0.183976) | 3.972995 / 2.077655 (1.895340) | 1.764638 / 1.504120 (0.260518) | 1.574058 / 1.541195 (0.032863) | 1.741695 / 1.468490 (0.273205) | 0.705664 / 4.584777 (-3.879113) | 3.915399 / 3.745712 (0.169686) | 2.310154 / 5.269862 (-2.959707) | 1.554067 / 4.565676 (-3.011610) | 0.087133 / 0.424275 (-0.337142) | 0.012393 / 0.007607 (0.004786) | 0.510758 / 0.226044 (0.284713) | 5.114906 / 2.268929 (2.845977) | 2.304473 / 55.444624 (-53.140152) | 1.960768 / 6.876477 (-4.915709) | 2.092263 / 2.142072 (-0.049810) | 0.867973 / 4.805227 (-3.937255) | 0.170000 / 6.500664 (-6.330664) | 0.068358 / 0.075469 (-0.007111) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.211022 / 1.841788 (-0.630765) | 16.777269 / 8.074308 (8.702961) | 15.272659 / 10.191392 (5.081267) | 0.182149 / 0.680424 (-0.498274) | 0.029577 / 0.534201 (-0.504624) | 0.446590 / 0.579283 (-0.132693) | 0.454724 / 0.434364 (0.020360) | 0.541938 / 0.540337 (0.001601) | 0.640886 / 1.386936 (-0.746050) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008441 / 0.011353 (-0.002912) | 0.006105 / 0.011008 (-0.004904) | 0.100349 / 0.038508 (0.061841) | 0.040675 / 0.023109 (0.017565) | 0.381775 / 0.275898 (0.105877) | 0.425246 / 0.323480 (0.101767) | 0.007197 / 0.007986 (-0.000789) | 0.004972 / 0.004328 (0.000644) | 0.075346 / 0.004250 (0.071096) | 0.065339 / 0.037052 (0.028286) | 0.379340 / 0.258489 (0.120851) | 0.435646 / 0.293841 (0.141805) | 0.038891 / 0.128546 (-0.089656) | 0.013079 / 0.075646 (-0.062568) | 0.339273 / 0.419271 (-0.079999) | 0.057478 / 0.043533 (0.013945) | 0.373516 / 0.255139 (0.118377) | 0.402388 / 0.283200 (0.119189) | 0.123145 / 0.141683 (-0.018538) | 1.503765 / 1.452155 (0.051610) | 1.609797 / 1.492716 (0.117081) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.420354 / 0.018006 (0.402348) | 0.589272 / 0.000490 (0.588782) | 0.045861 / 0.000200 (0.045662) | 0.000527 / 0.000054 (0.000473) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033918 / 0.037411 (-0.003493) | 0.128041 / 0.014526 (0.113515) | 0.130274 / 0.176557 (-0.046283) | 0.180605 / 0.737135 (-0.556530) | 0.136377 / 0.296338 (-0.159962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440343 / 0.215209 (0.225133) | 4.390264 / 2.077655 (2.312610) | 2.218738 / 1.504120 (0.714618) | 2.052399 / 1.541195 (0.511204) | 2.231912 / 1.468490 (0.763422) | 0.716805 / 4.584777 (-3.867972) | 3.909277 / 3.745712 (0.163565) | 2.302121 / 5.269862 (-2.967740) | 1.419454 / 4.565676 (-3.146222) | 0.088067 / 0.424275 (-0.336208) | 0.012994 / 0.007607 (0.005387) | 0.548267 / 0.226044 (0.322223) | 5.462973 / 2.268929 (3.194044) | 2.768414 / 55.444624 (-52.676210) | 2.489320 / 6.876477 (-4.387157) | 2.569546 / 2.142072 (0.427474) | 0.853135 / 4.805227 (-3.952092) | 0.170618 / 6.500664 (-6.330046) | 0.069908 / 0.075469 (-0.005562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304726 / 1.841788 (-0.537062) | 17.335977 / 8.074308 (9.261669) | 15.088319 / 10.191392 (4.896927) | 0.190893 / 0.680424 (-0.489531) | 0.018133 / 0.534201 (-0.516068) | 0.429324 / 0.579283 (-0.149959) | 0.439212 / 0.434364 (0.004848) | 0.545312 / 0.540337 (0.004975) | 0.663972 / 1.386936 (-0.722964) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7505adc37498f5e0cb3dd4c13bbb06696afdda5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]

"2023-01-26T10:03:01Z"

"2023-09-24T10:06:59Z"

"2023-01-26T18:29:37Z"

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[ "Fixed with PR" ]

"2023-08-09T15:15:03Z"

"2023-08-16T04:43:36Z"

"2023-08-16T04:43:29Z"

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[ "Hi @rabeehk , I am unable to reproduce your problem locally.\r\nCan you try emptying the cache (removing the content of `/idiap/temp/rkarimi/cache_home_1/datasets`) and retry ?", "Hi,\r\nthanks, I did remove the cache and still the same error here\r\n\r\n```\r\n>>> a = datasets.load_dataset(\"imdb\", split=\"train\")\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nDownloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads\r\nTraceback (most recent call last): \r\n File \"<stdin>\", line 1, in <module>\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 476, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 558, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 73, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=4902716, num_examples=3680, dataset_name='imdb')}]\r\n```\r\n\r\ndatasets version\r\n```\r\ndatasets 1.1.2 <pip>\r\ntensorflow-datasets 4.1.0 <pip>\r\n\r\n```", "resolved with moving to version 1.1.3" ]

"2020-12-08T10:47:36Z"

"2020-12-24T17:38:09Z"

"2020-12-24T17:38:09Z"

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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[ "Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.", "Facing the same issue, is there any way to overtake this issue until it will be fixed? ", "You can clear your extraction cache in the meantime (by default at `~/.cache/huggingface/datasets/downloads/extracted`)" ]

"2021-09-14T09:45:26Z"

"2021-10-06T09:37:19Z"

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"2022-08-25T09:14:17Z"

"2022-09-22T14:39:02Z"

"2022-08-25T13:43:34Z"

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[]

[]

[ "Close #1872" ]

"2021-02-16T11:45:00Z"

"2021-03-30T14:01:03Z"

"2021-02-16T11:58:57Z"

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[ "Same as #1698, can you add a task tag for dialogue-modeling (under sequence-modeling) :) ?" ]

"2021-01-07T08:22:33Z"

"2021-01-07T13:34:28Z"

"2021-01-07T13:34:28Z"

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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 " ]

"2023-12-12T08:09:18Z"

"2023-12-12T08:36:22Z"

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[ "Hi @danshirron, \r\n`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration.", "@bhavitvyamalik @lhoestq , just tried the above and got:\r\n>>> a=datasets.load_dataset('c4','en.realnewslike')\r\nDownloading: 3.29kB [00:00, 1.66MB/s] \r\nDownloading: 2.40MB [00:00, 12.6MB/s] \r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py\", line 819, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py\", line 701, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1049, in __init__\r\n super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 268, in __init__\r\n self.config, self.config_id = self._create_builder_config(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 360, in _create_builder_config\r\n raise ValueError(\r\nValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']\r\n>>> \r\n\r\ndatasets version is 1.11.0\r\n", "I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`. \r\n\r\nI tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up!", "It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks" ]

"2021-08-05T08:16:58Z"

"2021-08-08T19:44:34Z"

"2021-08-08T19:44:34Z"

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"2022-03-07T10:06:42Z"

"2022-05-20T14:13:43Z"

"2022-03-07T10:07:24Z"

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"2020-10-14T20:44:14Z"

"2020-10-15T09:27:43Z"

"2020-10-15T09:27:42Z"

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"2021-02-18T07:39:41Z"

"2021-02-18T15:07:20Z"

"2021-02-18T09:39:21Z"

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[ "Hi @StellaAthena, thanks for reporting.\r\n\r\nPlease note, that our `datasets` library performs several operations in order to load a dataset, among them:\r\n- it downloads all the required files: for C4 \"en\", 378.69 GB of JSON GZIPped files\r\n- it parses their content to generate the dataset\r\n- it caches the dataset in an Arrow file: for C4 \"en\", this file size is 1.87 TB\r\n- it memory-maps the Arrow file\r\n\r\nIf it suits your use case, you might load this dataset in streaming mode:\r\n- no Arrow file is generated\r\n- you can iterate over elements immediately (no need to wait to download all the entire files)\r\n\r\n```python\r\nIn [45]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"c4\", \"en\", split=\"train\", streaming=True)\r\n ...: for item in ds:\r\n ...: print(item)\r\n ...: break\r\n ...: \r\n{'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'}\r\n```\r\nI hope this is useful for your use case." ]

"2022-04-07T17:41:30Z"

"2023-06-26T22:01:29Z"

"2023-06-26T22:01:29Z"

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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) | 0.006053 / 0.011353 (-0.005299) | 0.003532 / 0.011008 (-0.007476) | 0.081930 / 0.038508 (0.043422) | 0.059043 / 0.023109 (0.035934) | 0.322785 / 0.275898 (0.046887) | 0.378158 / 0.323480 (0.054678) | 0.004709 / 0.007986 (-0.003277) | 0.002907 / 0.004328 (-0.001421) | 0.061516 / 0.004250 (0.057266) | 0.047209 / 0.037052 (0.010157) | 0.346885 / 0.258489 (0.088396) | 0.381011 / 0.293841 (0.087170) | 0.027491 / 0.128546 (-0.101055) | 0.008014 / 0.075646 (-0.067632) | 0.260663 / 0.419271 (-0.158608) | 0.045427 / 0.043533 (0.001894) | 0.315277 / 0.255139 (0.060138) | 0.377902 / 0.283200 (0.094703) | 0.021371 / 0.141683 (-0.120311) | 1.416350 / 1.452155 (-0.035804) | 1.483345 / 1.492716 (-0.009372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203660 / 0.018006 (0.185654) | 0.569081 / 0.000490 (0.568591) | 0.002742 / 0.000200 (0.002542) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023456 / 0.037411 (-0.013955) | 0.073954 / 0.014526 (0.059428) | 0.082991 / 0.176557 (-0.093566) | 0.144781 / 0.737135 (-0.592354) | 0.083346 / 0.296338 (-0.212992) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.391542 / 0.215209 (0.176333) | 3.909505 / 2.077655 (1.831850) | 1.862234 / 1.504120 (0.358114) | 1.676076 / 1.541195 (0.134881) | 1.727595 / 1.468490 (0.259105) | 0.501769 / 4.584777 (-4.083008) | 3.083697 / 3.745712 (-0.662016) | 2.819751 / 5.269862 (-2.450111) | 1.867265 / 4.565676 (-2.698411) | 0.057575 / 0.424275 (-0.366700) | 0.006478 / 0.007607 (-0.001129) | 0.466684 / 0.226044 (0.240640) | 4.657982 / 2.268929 (2.389054) | 2.347052 / 55.444624 (-53.097573) | 1.964688 / 6.876477 (-4.911789) | 2.077821 / 2.142072 (-0.064252) | 0.590591 / 4.805227 (-4.214636) | 0.124585 / 6.500664 (-6.376079) | 0.059468 / 0.075469 (-0.016001) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223484 / 1.841788 (-0.618304) | 18.104638 / 8.074308 (10.030330) | 13.755126 / 10.191392 (3.563734) | 0.143158 / 0.680424 (-0.537266) | 0.017147 / 0.534201 (-0.517054) | 0.337427 / 0.579283 (-0.241856) | 0.352270 / 0.434364 (-0.082094) | 0.383718 / 0.540337 (-0.156619) | 0.534973 / 1.386936 (-0.851963) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006039 / 0.011353 (-0.005314) | 0.003735 / 0.011008 (-0.007274) | 0.061954 / 0.038508 (0.023446) | 0.061786 / 0.023109 (0.038677) | 0.429420 / 0.275898 (0.153522) | 0.457629 / 0.323480 (0.134149) | 0.004748 / 0.007986 (-0.003237) | 0.002843 / 0.004328 (-0.001485) | 0.061811 / 0.004250 (0.057560) | 0.048740 / 0.037052 (0.011687) | 0.430066 / 0.258489 (0.171577) | 0.465971 / 0.293841 (0.172130) | 0.027577 / 0.128546 (-0.100969) | 0.007981 / 0.075646 (-0.067665) | 0.067580 / 0.419271 (-0.351692) | 0.042058 / 0.043533 (-0.001475) | 0.428412 / 0.255139 (0.173273) | 0.451054 / 0.283200 (0.167855) | 0.020850 / 0.141683 (-0.120833) | 1.453907 / 1.452155 (0.001752) | 1.509914 / 1.492716 (0.017197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237713 / 0.018006 (0.219707) | 0.418064 / 0.000490 (0.417575) | 0.006411 / 0.000200 (0.006211) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024950 / 0.037411 (-0.012462) | 0.076806 / 0.014526 (0.062281) | 0.085237 / 0.176557 (-0.091320) | 0.137940 / 0.737135 (-0.599196) | 0.086266 / 0.296338 (-0.210072) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418666 / 0.215209 (0.203457) | 4.160547 / 2.077655 (2.082893) | 2.135671 / 1.504120 (0.631551) | 1.964985 / 1.541195 (0.423790) | 2.009447 / 1.468490 (0.540957) | 0.501377 / 4.584777 (-4.083400) | 3.064293 / 3.745712 (-0.681419) | 2.827153 / 5.269862 (-2.442709) | 1.854698 / 4.565676 (-2.710978) | 0.057662 / 0.424275 (-0.366613) | 0.006829 / 0.007607 (-0.000778) | 0.496730 / 0.226044 (0.270686) | 4.964663 / 2.268929 (2.695735) | 2.583133 / 55.444624 (-52.861491) | 2.329700 / 6.876477 (-4.546776) | 2.415521 / 2.142072 (0.273449) | 0.591973 / 4.805227 (-4.213255) | 0.126801 / 6.500664 (-6.373863) | 0.062811 / 0.075469 (-0.012659) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.348575 / 1.841788 (-0.493212) | 18.282861 / 8.074308 (10.208553) | 13.734056 / 10.191392 (3.542664) | 0.154987 / 0.680424 (-0.525437) | 0.016996 / 0.534201 (-0.517205) | 0.335264 / 0.579283 (-0.244019) | 0.356907 / 0.434364 (-0.077456) | 0.399185 / 0.540337 (-0.141152) | 0.540209 / 1.386936 (-0.846727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887bef1217e0f4441d57bf0f4d1e806df12f2c50 \"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) | 0.006768 / 0.011353 (-0.004585) | 0.004250 / 0.011008 (-0.006758) | 0.086780 / 0.038508 (0.048272) | 0.080872 / 0.023109 (0.057762) | 0.309281 / 0.275898 (0.033383) | 0.352293 / 0.323480 (0.028814) | 0.005604 / 0.007986 (-0.002382) | 0.003544 / 0.004328 (-0.000784) | 0.066910 / 0.004250 (0.062659) | 0.055568 / 0.037052 (0.018516) | 0.314931 / 0.258489 (0.056442) | 0.366026 / 0.293841 (0.072185) | 0.031247 / 0.128546 (-0.097300) | 0.008860 / 0.075646 (-0.066786) | 0.293210 / 0.419271 (-0.126061) | 0.052868 / 0.043533 (0.009335) | 0.316769 / 0.255139 (0.061630) | 0.352128 / 0.283200 (0.068929) | 0.025492 / 0.141683 (-0.116190) | 1.478379 / 1.452155 (0.026224) | 1.573652 / 1.492716 (0.080936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294975 / 0.018006 (0.276968) | 0.615093 / 0.000490 (0.614603) | 0.004279 / 0.000200 (0.004079) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031557 / 0.037411 (-0.005855) | 0.085026 / 0.014526 (0.070500) | 0.101221 / 0.176557 (-0.075336) | 0.157432 / 0.737135 (-0.579703) | 0.102350 / 0.296338 (-0.193988) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384158 / 0.215209 (0.168949) | 3.826656 / 2.077655 (1.749001) | 1.873510 / 1.504120 (0.369390) | 1.721913 / 1.541195 (0.180718) | 1.848779 / 1.468490 (0.380289) | 0.485128 / 4.584777 (-4.099649) | 3.656660 / 3.745712 (-0.089052) | 3.441964 / 5.269862 (-1.827898) | 2.150611 / 4.565676 (-2.415066) | 0.056869 / 0.424275 (-0.367406) | 0.007382 / 0.007607 (-0.000225) | 0.458751 / 0.226044 (0.232707) | 4.585028 / 2.268929 (2.316099) | 2.439538 / 55.444624 (-53.005086) | 2.116959 / 6.876477 (-4.759518) | 2.459220 / 2.142072 (0.317147) | 0.580907 / 4.805227 (-4.224321) | 0.134502 / 6.500664 (-6.366162) | 0.062528 / 0.075469 (-0.012941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251006 / 1.841788 (-0.590782) | 20.755849 / 8.074308 (12.681541) | 14.456950 / 10.191392 (4.265558) | 0.167074 / 0.680424 (-0.513350) | 0.018482 / 0.534201 (-0.515719) | 0.395867 / 0.579283 (-0.183416) | 0.415620 / 0.434364 (-0.018744) | 0.462247 / 0.540337 (-0.078090) | 0.645762 / 1.386936 (-0.741174) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007050 / 0.011353 (-0.004303) | 0.004421 / 0.011008 (-0.006587) | 0.065312 / 0.038508 (0.026804) | 0.089790 / 0.023109 (0.066681) | 0.366318 / 0.275898 (0.090420) | 0.403542 / 0.323480 (0.080062) | 0.005695 / 0.007986 (-0.002290) | 0.003642 / 0.004328 (-0.000687) | 0.064540 / 0.004250 (0.060289) | 0.060933 / 0.037052 (0.023881) | 0.369004 / 0.258489 (0.110515) | 0.408056 / 0.293841 (0.114215) | 0.032124 / 0.128546 (-0.096422) | 0.008960 / 0.075646 (-0.066686) | 0.071267 / 0.419271 (-0.348005) | 0.049745 / 0.043533 (0.006212) | 0.367203 / 0.255139 (0.112064) | 0.383009 / 0.283200 (0.099809) | 0.025330 / 0.141683 (-0.116353) | 1.518290 / 1.452155 (0.066135) | 1.581738 / 1.492716 (0.089022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.338281 / 0.018006 (0.320275) | 0.538195 / 0.000490 (0.537706) | 0.008498 / 0.000200 (0.008298) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033279 / 0.037411 (-0.004133) | 0.093233 / 0.014526 (0.078707) | 0.106019 / 0.176557 (-0.070538) | 0.161262 / 0.737135 (-0.575874) | 0.109935 / 0.296338 (-0.186404) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411563 / 0.215209 (0.196354) | 4.102149 / 2.077655 (2.024495) | 2.108513 / 1.504120 (0.604393) | 1.945344 / 1.541195 (0.404150) | 2.066964 / 1.468490 (0.598474) | 0.482771 / 4.584777 (-4.102006) | 3.659160 / 3.745712 (-0.086552) | 3.420833 / 5.269862 (-1.849029) | 2.147276 / 4.565676 (-2.418400) | 0.056957 / 0.424275 (-0.367318) | 0.007898 / 0.007607 (0.000290) | 0.482401 / 0.226044 (0.256357) | 4.821044 / 2.268929 (2.552115) | 2.567993 / 55.444624 (-52.876631) | 2.336165 / 6.876477 (-4.540312) | 2.545066 / 2.142072 (0.402994) | 0.580888 / 4.805227 (-4.224339) | 0.134092 / 6.500664 (-6.366572) | 0.062681 / 0.075469 (-0.012788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.379124 / 1.841788 (-0.462664) | 21.627949 / 8.074308 (13.553641) | 15.064818 / 10.191392 (4.873426) | 0.169707 / 0.680424 (-0.510716) | 0.018671 / 0.534201 (-0.515530) | 0.400496 / 0.579283 (-0.178787) | 0.415542 / 0.434364 (-0.018822) | 0.484351 / 0.540337 (-0.055986) | 0.646046 / 1.386936 (-0.740890) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) | 0.007113 / 0.011353 (-0.004240) | 0.004436 / 0.011008 (-0.006572) | 0.087422 / 0.038508 (0.048914) | 0.085996 / 0.023109 (0.062887) | 0.311772 / 0.275898 (0.035873) | 0.353281 / 0.323480 (0.029801) | 0.004562 / 0.007986 (-0.003423) | 0.003840 / 0.004328 (-0.000488) | 0.066500 / 0.004250 (0.062250) | 0.061293 / 0.037052 (0.024241) | 0.328840 / 0.258489 (0.070351) | 0.365587 / 0.293841 (0.071746) | 0.031802 / 0.128546 (-0.096744) | 0.008881 / 0.075646 (-0.066765) | 0.289671 / 0.419271 (-0.129601) | 0.053348 / 0.043533 (0.009816) | 0.307822 / 0.255139 (0.052683) | 0.342559 / 0.283200 (0.059360) | 0.025760 / 0.141683 (-0.115923) | 1.509944 / 1.452155 (0.057789) | 1.556634 / 1.492716 (0.063918) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282036 / 0.018006 (0.264029) | 0.608350 / 0.000490 (0.607860) | 0.004843 / 0.000200 (0.004643) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029810 / 0.037411 (-0.007601) | 0.086215 / 0.014526 (0.071689) | 0.102200 / 0.176557 (-0.074356) | 0.158051 / 0.737135 (-0.579084) | 0.103083 / 0.296338 (-0.193255) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392119 / 0.215209 (0.176910) | 3.895796 / 2.077655 (1.818141) | 1.921118 / 1.504120 (0.416998) | 1.754271 / 1.541195 (0.213076) | 1.880991 / 1.468490 (0.412501) | 0.481158 / 4.584777 (-4.103618) | 3.609210 / 3.745712 (-0.136502) | 3.412018 / 5.269862 (-1.857843) | 2.131710 / 4.565676 (-2.433967) | 0.057122 / 0.424275 (-0.367153) | 0.007444 / 0.007607 (-0.000163) | 0.468880 / 0.226044 (0.242835) | 4.682441 / 2.268929 (2.413512) | 2.505613 / 55.444624 (-52.939012) | 2.149655 / 6.876477 (-4.726822) | 2.465904 / 2.142072 (0.323832) | 0.578877 / 4.805227 (-4.226350) | 0.133504 / 6.500664 (-6.367160) | 0.061422 / 0.075469 (-0.014047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269395 / 1.841788 (-0.572393) | 21.107558 / 8.074308 (13.033250) | 15.318502 / 10.191392 (5.127110) | 0.165273 / 0.680424 (-0.515151) | 0.018783 / 0.534201 (-0.515418) | 0.396259 / 0.579283 (-0.183024) | 0.412907 / 0.434364 (-0.021457) | 0.465723 / 0.540337 (-0.074615) | 0.638414 / 1.386936 (-0.748522) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007083 / 0.011353 (-0.004270) | 0.004216 / 0.011008 (-0.006793) | 0.065362 / 0.038508 (0.026854) | 0.095454 / 0.023109 (0.072345) | 0.364220 / 0.275898 (0.088322) | 0.417650 / 0.323480 (0.094170) | 0.006114 / 0.007986 (-0.001872) | 0.003577 / 0.004328 (-0.000751) | 0.064830 / 0.004250 (0.060579) | 0.062535 / 0.037052 (0.025483) | 0.381844 / 0.258489 (0.123355) | 0.418996 / 0.293841 (0.125155) | 0.031386 / 0.128546 (-0.097160) | 0.008913 / 0.075646 (-0.066733) | 0.070860 / 0.419271 (-0.348411) | 0.049132 / 0.043533 (0.005599) | 0.360406 / 0.255139 (0.105267) | 0.392407 / 0.283200 (0.109207) | 0.024611 / 0.141683 (-0.117072) | 1.509051 / 1.452155 (0.056896) | 1.570288 / 1.492716 (0.077572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.368611 / 0.018006 (0.350605) | 0.537587 / 0.000490 (0.537098) | 0.028056 / 0.000200 (0.027856) | 0.000317 / 0.000054 (0.000262) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031570 / 0.037411 (-0.005841) | 0.088985 / 0.014526 (0.074460) | 0.105268 / 0.176557 (-0.071288) | 0.156724 / 0.737135 (-0.580412) | 0.105266 / 0.296338 (-0.191073) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413861 / 0.215209 (0.198652) | 4.127001 / 2.077655 (2.049347) | 2.112114 / 1.504120 (0.607994) | 1.945200 / 1.541195 (0.404005) | 2.083031 / 1.468490 (0.614540) | 0.488086 / 4.584777 (-4.096691) | 3.565584 / 3.745712 (-0.180128) | 3.380782 / 5.269862 (-1.889079) | 2.103481 / 4.565676 (-2.462195) | 0.058203 / 0.424275 (-0.366072) | 0.007996 / 0.007607 (0.000389) | 0.487986 / 0.226044 (0.261941) | 4.871023 / 2.268929 (2.602095) | 2.584632 / 55.444624 (-52.859992) | 2.240103 / 6.876477 (-4.636374) | 2.555165 / 2.142072 (0.413092) | 0.591950 / 4.805227 (-4.213278) | 0.134919 / 6.500664 (-6.365745) | 0.062868 / 0.075469 (-0.012601) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369731 / 1.841788 (-0.472057) | 21.497888 / 8.074308 (13.423580) | 14.555054 / 10.191392 (4.363662) | 0.168768 / 0.680424 (-0.511656) | 0.018837 / 0.534201 (-0.515364) | 0.394512 / 0.579283 (-0.184771) | 0.405459 / 0.434364 (-0.028905) | 0.475479 / 0.540337 (-0.064858) | 0.631994 / 1.386936 (-0.754942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) | 0.009072 / 0.011353 (-0.002280) | 0.004894 / 0.011008 (-0.006114) | 0.108790 / 0.038508 (0.070282) | 0.081783 / 0.023109 (0.058674) | 0.381963 / 0.275898 (0.106064) | 0.450700 / 0.323480 (0.127220) | 0.006961 / 0.007986 (-0.001025) | 0.004035 / 0.004328 (-0.000293) | 0.081420 / 0.004250 (0.077169) | 0.058029 / 0.037052 (0.020976) | 0.437453 / 0.258489 (0.178964) | 0.472607 / 0.293841 (0.178766) | 0.048663 / 0.128546 (-0.079884) | 0.013512 / 0.075646 (-0.062134) | 0.406009 / 0.419271 (-0.013262) | 0.067616 / 0.043533 (0.024084) | 0.383641 / 0.255139 (0.128502) | 0.456734 / 0.283200 (0.173534) | 0.033391 / 0.141683 (-0.108292) | 1.753529 / 1.452155 (0.301375) | 1.859831 / 1.492716 (0.367115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215128 / 0.018006 (0.197122) | 0.538261 / 0.000490 (0.537771) | 0.005430 / 0.000200 (0.005230) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032664 / 0.037411 (-0.004748) | 0.093465 / 0.014526 (0.078939) | 0.106637 / 0.176557 (-0.069919) | 0.173642 / 0.737135 (-0.563494) | 0.113944 / 0.296338 (-0.182394) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629212 / 0.215209 (0.414003) | 6.116729 / 2.077655 (4.039075) | 2.818000 / 1.504120 (1.313880) | 2.515317 / 1.541195 (0.974122) | 2.466588 / 1.468490 (0.998098) | 0.850815 / 4.584777 (-3.733962) | 5.051292 / 3.745712 (1.305579) | 4.472138 / 5.269862 (-0.797724) | 2.968317 / 4.565676 (-1.597360) | 0.100173 / 0.424275 (-0.324102) | 0.008407 / 0.007607 (0.000800) | 0.743972 / 0.226044 (0.517928) | 7.397619 / 2.268929 (5.128690) | 3.596681 / 55.444624 (-51.847943) | 2.854674 / 6.876477 (-4.021803) | 3.114274 / 2.142072 (0.972201) | 1.064879 / 4.805227 (-3.740348) | 0.215981 / 6.500664 (-6.284683) | 0.078159 / 0.075469 (0.002690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.543291 / 1.841788 (-0.298497) | 23.244641 / 8.074308 (15.170333) | 20.784610 / 10.191392 (10.593218) | 0.222002 / 0.680424 (-0.458422) | 0.028584 / 0.534201 (-0.505617) | 0.478563 / 0.579283 (-0.100720) | 0.556101 / 0.434364 (0.121737) | 0.547446 / 0.540337 (0.007109) | 0.764318 / 1.386936 (-0.622618) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008651 / 0.011353 (-0.002702) | 0.004925 / 0.011008 (-0.006083) | 0.078995 / 0.038508 (0.040487) | 0.092878 / 0.023109 (0.069769) | 0.485615 / 0.275898 (0.209717) | 0.532157 / 0.323480 (0.208677) | 0.008228 / 0.007986 (0.000243) | 0.004777 / 0.004328 (0.000449) | 0.076892 / 0.004250 (0.072642) | 0.066905 / 0.037052 (0.029853) | 0.465497 / 0.258489 (0.207008) | 0.520153 / 0.293841 (0.226312) | 0.047357 / 0.128546 (-0.081189) | 0.016870 / 0.075646 (-0.058776) | 0.090481 / 0.419271 (-0.328791) | 0.060774 / 0.043533 (0.017241) | 0.474368 / 0.255139 (0.219229) | 0.503981 / 0.283200 (0.220781) | 0.036025 / 0.141683 (-0.105658) | 1.769939 / 1.452155 (0.317784) | 1.851518 / 1.492716 (0.358802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265947 / 0.018006 (0.247941) | 0.532317 / 0.000490 (0.531828) | 0.004997 / 0.000200 (0.004797) | 0.000130 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034112 / 0.037411 (-0.003299) | 0.102290 / 0.014526 (0.087764) | 0.109989 / 0.176557 (-0.066567) | 0.182813 / 0.737135 (-0.554323) | 0.111774 / 0.296338 (-0.184565) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584893 / 0.215209 (0.369684) | 6.138505 / 2.077655 (4.060850) | 2.925761 / 1.504120 (1.421641) | 2.607320 / 1.541195 (1.066125) | 2.655827 / 1.468490 (1.187337) | 0.871140 / 4.584777 (-3.713637) | 5.051171 / 3.745712 (1.305459) | 4.708008 / 5.269862 (-0.561854) | 3.027485 / 4.565676 (-1.538191) | 0.100970 / 0.424275 (-0.323305) | 0.009640 / 0.007607 (0.002033) | 0.747818 / 0.226044 (0.521774) | 7.539930 / 2.268929 (5.271001) | 3.611693 / 55.444624 (-51.832931) | 2.924087 / 6.876477 (-3.952390) | 3.141993 / 2.142072 (0.999920) | 1.062921 / 4.805227 (-3.742306) | 0.213185 / 6.500664 (-6.287479) | 0.077146 / 0.075469 (0.001677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669182 / 1.841788 (-0.172606) | 23.810242 / 8.074308 (15.735934) | 21.220649 / 10.191392 (11.029257) | 0.212639 / 0.680424 (-0.467785) | 0.026705 / 0.534201 (-0.507496) | 0.469231 / 0.579283 (-0.110053) | 0.551672 / 0.434364 (0.117308) | 0.575043 / 0.540337 (0.034706) | 0.767511 / 1.386936 (-0.619425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n" ]

"2023-08-08T15:43:56Z"

"2023-08-08T16:08:22Z"

"2023-08-08T15:49:06Z"

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"2022-05-22T04:57:19Z"

"2022-05-23T06:36:27Z"

"2022-05-23T06:36:27Z"

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"2020-10-27T16:37:08Z"

"2020-10-27T17:25:26Z"

"2020-10-27T17:25:25Z"

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"2020-10-01T15:18:58Z"

"2020-10-02T07:48:19Z"

"2020-10-02T07:48:18Z"

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"2020-04-30T16:38:29Z"

"2020-04-30T20:12:04Z"

"2020-04-30T20:12:03Z"

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[ "Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n predictions: Predicted labels, as returned by a model.\r\n references: Ground truth labels.\r\n labels: The set of labels to include when average != 'binary', and\r\n their order if average is None. Labels present in the data can\r\n be excluded, for example to calculate a multiclass average ignoring\r\n a majority negative class, while labels not present in the data will\r\n result in 0 components in a macro average. For multilabel targets,\r\n labels are column indices. By default, all labels in y_true and\r\n y_pred are used in sorted order.\r\n average: This parameter is required for multiclass/multilabel targets.\r\n If None, the scores for each class are returned. Otherwise, this\r\n determines the type of averaging performed on the data:\r\n binary: Only report results for the class specified by pos_label.\r\n This is applicable only if targets (y_{true,pred}) are binary.\r\n micro: Calculate metrics globally by counting the total true positives,\r\n false negatives and false positives.\r\n macro: Calculate metrics for each label, and find their unweighted mean.\r\n This does not take label imbalance into account.\r\n weighted: Calculate metrics for each label, and find their average\r\n weighted by support (the number of true instances for each label).\r\n This alters ‘macro’ to account for label imbalance; it can result\r\n in an F-score that is not between precision and recall.\r\n samples: Calculate metrics for each instance, and find their average\r\n (only meaningful for multilabel classification).\r\n sample_weight: Sample weights.\r\n\r\nReturns:\r\n precision: Precision score.\r\n\r\nExamples:\r\n\r\n >>> precision_metric = datasets.load_metric(\"precision\")\r\n >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n >>> print(results)\r\n {'precision': 1.0}\r\n\r\n >>> predictions = [0, 2, 1, 0, 0, 1]\r\n >>> references = [0, 1, 2, 0, 1, 2]\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n >>> print(results)\r\n {'precision': 0.3333333333333333}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n >>> print(results)\r\n {'precision': array([0.66666667, 0. , 0. ])}\r\n```\r\n" ]

"2022-01-27T10:14:16Z"

"2022-02-24T09:02:18Z"

"2022-02-24T09:02:17Z"

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[ "And we're already at PR number 3,000 ! ^^", "Thank you so much for fixing this @lhoestq 😍 ! I just tested the branch out and it works like a charm!" ]

"2021-10-01T17:47:22Z"

"2021-10-01T18:05:00Z"

"2021-10-01T17:54:23Z"

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[ "Summary of CircleCI errors:\r\n\r\n- **XSum**: missing 6 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', and 'source_datasets'\r\n- **Yelp_polarity**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'", "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-05-12T21:02:08Z"

"2022-05-16T15:51:02Z"

"2022-05-16T15:42:59Z"

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[ "Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.", "Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\r\n\r\nRegarding `column_names`, I agree we should add this property to `IterableDataset`. In the meantime, you can use `list(dataset.features.keys())` instead.", "Thanks! That's great news.\n\nOn Thu, Dec 22, 2022, 07:48 Mario Šaško ***@***.***> wrote:\n\n> Hi! fn_kwargs was added to IterableDataset.map in datasets 2.5.0, so\n> please update your installation (pip install -U datasets) to use it.\n>\n> Regarding column_names, I agree we should add this property to\n> IterableDataset. In the meantime, you can use\n> list(dataset.features.keys()) instead.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5383#issuecomment-1362993633>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAHD6N2EQUFEOUFDW3VHSILWORZ45ANCNFSM6AAAAAATGKWVGM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "I'm marking this issue as a \"good first issue\", as it makes sense to have `IterableDataset.column_names` in the API. Besides the case when `features` are `None` (e.g., `features` are `None` after `map`), in which we can also return `column_names` as `None`, adding this property should be straightforward,", "Hi @mariosasko, I can work on this if that's ok?", "Yes! I've assigned you the issue." ]

"2022-12-22T05:27:02Z"

"2023-03-13T19:03:33Z"

"2023-03-13T19:03:33Z"

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[ "I'm a bit frustrated now to get this right.", "Hey @zhongpeixiang!\r\nReally nice addition here!\r\n\r\nDid you officially joined the sprint by posting [on the forum thread](https://discuss.huggingface.co/t/open-to-the-community-one-week-team-effort-to-reach-v2-0-of-hf-datasets-library/2176) and joining our slack?\r\nI can't seem to find you there! Should I add you directly with your gmail address?", "> Hey @zhongpeixiang!\r\n> Really nice addition here!\r\n> \r\n> Did you officially joined the sprint by posting [on the forum thread](https://discuss.huggingface.co/t/open-to-the-community-one-week-team-effort-to-reach-v2-0-of-hf-datasets-library/2176) and joining our slack?\r\n> I can't seem to find you there! Should I add you directly with your gmail address?\r\n\r\nThank you for the invitation. This initiative is awesome. Sadly I’m occupied by my thesis writing this month. Good luck 🤗", "As you want @zhongpeixiang (I was maybe not clear but that just mean that by posting on the forum thread that you participated in the current event you will get a special gift (a tee-shirt) for the contribution that you have already done here :-) Nothing more to do)", "> As you want @zhongpeixiang (I was maybe not clear but that just mean that by posting on the forum thread that you participated in the current event you will get a special gift (a tee-shirt) for the contribution that you have already done here :-) Nothing more to do)\r\n\r\nOh, I misunderstood the post. I'm glad to join." ]

"2020-12-03T02:46:08Z"

"2020-12-04T10:58:19Z"

"2020-12-03T16:15:06Z"

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"2021-05-07T13:49:42Z"

"2021-05-07T17:20:48Z"

"2021-05-07T17:20:48Z"

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[ "Connection error failed. Need rerun", "merging since the CI is fixed on master" ]

"2020-12-11T08:20:29Z"

"2020-12-17T11:40:59Z"

"2020-12-17T11:40:59Z"

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[ "Could you update the languages tags before we merge @VasudevGupta7 ?", "done.", "thanks !" ]

"2020-12-05T23:44:33Z"

"2020-12-09T09:38:50Z"

"2020-12-09T09:38:50Z"

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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" ]

"2023-11-29T13:18:44Z"

"2023-12-12T23:30:33Z"

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[]

[ "After some more tests I've realized that this \"issue\" is due to the `numpy.float64` to `float` conversion, but when defining a function named `compute_metrics` as it follows:\r\n\r\n```python\r\ndef compute_metrics(eval_preds):\r\n metric = load_metric(\"matthews_correlation\")\r\n logits, labels = eval_preds\r\n predictions = np.argmax(logits, axis=1)\r\n return metric.compute(predictions=predictions, references=labels)\r\n```\r\n\r\nIt fails when the evaluation metrics are computed in the `Trainer` with the same error code `AttributeError: 'float' object has no attribute 'item'` as the output is not a `numpy.float64`... Maybe I'm doing something wrong, not sure!", "Ok after some more experiments I've realized that it's an issue from my side, at first I thought it was due to `fp16=True` in `TrainingArguments`, but in the end that may not be the issue, so I'll close this for now and check later, since the mistake is on my side :weary: Sorry for the inconvenience!" ]

"2021-09-24T15:55:21Z"

"2021-09-25T08:06:07Z"

"2021-09-25T08:06:07Z"

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"2020-05-12T11:25:29Z"

"2020-05-12T11:27:35Z"

"2020-05-12T11:27:34Z"

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