Datasets:

mdroth

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github_issues_300

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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) | 0.008634 / 0.011353 (-0.002719) | 0.005147 / 0.011008 (-0.005861) | 0.102865 / 0.038508 (0.064357) | 0.080245 / 0.023109 (0.057136) | 0.401288 / 0.275898 (0.125390) | 0.419708 / 0.323480 (0.096228) | 0.006342 / 0.007986 (-0.001644) | 0.003998 / 0.004328 (-0.000330) | 0.078880 / 0.004250 (0.074630) | 0.068199 / 0.037052 (0.031147) | 0.389573 / 0.258489 (0.131084) | 0.417292 / 0.293841 (0.123451) | 0.048856 / 0.128546 (-0.079691) | 0.014165 / 0.075646 (-0.061481) | 0.348063 / 0.419271 (-0.071209) | 0.067547 / 0.043533 (0.024014) | 0.402251 / 0.255139 (0.147112) | 0.419478 / 0.283200 (0.136278) | 0.034846 / 0.141683 (-0.106837) | 1.773493 / 1.452155 (0.321338) | 1.930546 / 1.492716 (0.437830) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211835 / 0.018006 (0.193829) | 0.545311 / 0.000490 (0.544821) | 0.006766 / 0.000200 (0.006566) | 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) | 0.035406 / 0.037411 (-0.002006) | 0.100769 / 0.014526 (0.086243) | 0.108667 / 0.176557 (-0.067890) | 0.193099 / 0.737135 (-0.544036) | 0.113539 / 0.296338 (-0.182799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586935 / 0.215209 (0.371726) | 5.895245 / 2.077655 (3.817591) | 2.528375 / 1.504120 (1.024255) | 2.228617 / 1.541195 (0.687423) | 2.295799 / 1.468490 (0.827309) | 0.859272 / 4.584777 (-3.725505) | 5.033434 / 3.745712 (1.287722) | 7.546587 / 5.269862 (2.276726) | 4.457137 / 4.565676 (-0.108539) | 0.099626 / 0.424275 (-0.324649) | 0.009296 / 0.007607 (0.001689) | 0.713498 / 0.226044 (0.487454) | 7.409385 / 2.268929 (5.140456) | 3.361418 / 55.444624 (-52.083206) | 2.681111 / 6.876477 (-4.195366) | 2.849598 / 2.142072 (0.707526) | 1.114863 / 4.805227 (-3.690364) | 0.215494 / 6.500664 (-6.285170) | 0.075807 / 0.075469 (0.000338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606458 / 1.841788 (-0.235330) | 23.751096 / 8.074308 (15.676788) | 21.279110 / 10.191392 (11.087718) | 0.220785 / 0.680424 (-0.459639) | 0.032688 / 0.534201 (-0.501513) | 0.530948 / 0.579283 (-0.048335) | 0.630056 / 0.434364 (0.195693) | 0.572743 / 0.540337 (0.032405) | 0.771853 / 1.386936 (-0.615083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008693 / 0.011353 (-0.002660) | 0.004750 / 0.011008 (-0.006259) | 0.079764 / 0.038508 (0.041256) | 0.082096 / 0.023109 (0.058987) | 0.467198 / 0.275898 (0.191300) | 0.532361 / 0.323480 (0.208881) | 0.005836 / 0.007986 (-0.002149) | 0.004333 / 0.004328 (0.000005) | 0.080444 / 0.004250 (0.076194) | 0.065883 / 0.037052 (0.028831) | 0.464871 / 0.258489 (0.206382) | 0.575026 / 0.293841 (0.281185) | 0.057807 / 0.128546 (-0.070739) | 0.017462 / 0.075646 (-0.058185) | 0.093667 / 0.419271 (-0.325605) | 0.071466 / 0.043533 (0.027933) | 0.495846 / 0.255139 (0.240707) | 0.526100 / 0.283200 (0.242900) | 0.034852 / 0.141683 (-0.106831) | 1.884152 / 1.452155 (0.431998) | 1.922681 / 1.492716 (0.429965) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250969 / 0.018006 (0.232963) | 0.504979 / 0.000490 (0.504489) | 0.000466 / 0.000200 (0.000266) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032411 / 0.037411 (-0.005000) | 0.093184 / 0.014526 (0.078658) | 0.110798 / 0.176557 (-0.065759) | 0.165741 / 0.737135 (-0.571394) | 0.111022 / 0.296338 (-0.185317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.661284 / 0.215209 (0.446075) | 6.622388 / 2.077655 (4.544733) | 3.095705 / 1.504120 (1.591585) | 2.745698 / 1.541195 (1.204503) | 2.694103 / 1.468490 (1.225612) | 0.862154 / 4.584777 (-3.722623) | 5.109985 / 3.745712 (1.364273) | 5.040362 / 5.269862 (-0.229499) | 3.072837 / 4.565676 (-1.492840) | 0.110421 / 0.424275 (-0.313854) | 0.008476 / 0.007607 (0.000869) | 0.910020 / 0.226044 (0.683975) | 8.123626 / 2.268929 (5.854698) | 3.813811 / 55.444624 (-51.630813) | 3.017244 / 6.876477 (-3.859232) | 3.061222 / 2.142072 (0.919150) | 1.073548 / 4.805227 (-3.731680) | 0.216327 / 6.500664 (-6.284338) | 0.072977 / 0.075469 (-0.002492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.722482 / 1.841788 (-0.119305) | 23.706716 / 8.074308 (15.632407) | 23.192134 / 10.191392 (13.000742) | 0.276733 / 0.680424 (-0.403691) | 0.033538 / 0.534201 (-0.500663) | 0.602083 / 0.579283 (0.022799) | 0.578718 / 0.434364 (0.144354) | 0.558311 / 0.540337 (0.017974) | 0.740341 / 1.386936 (-0.646595) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac575b8ed57dac60d7ba33a616894f38601f84a \"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) | 0.006862 / 0.011353 (-0.004491) | 0.004223 / 0.011008 (-0.006786) | 0.085931 / 0.038508 (0.047423) | 0.081437 / 0.023109 (0.058328) | 0.349542 / 0.275898 (0.073644) | 0.379881 / 0.323480 (0.056401) | 0.005651 / 0.007986 (-0.002334) | 0.003662 / 0.004328 (-0.000666) | 0.065251 / 0.004250 (0.061001) | 0.061599 / 0.037052 (0.024547) | 0.359681 / 0.258489 (0.101192) | 0.392502 / 0.293841 (0.098661) | 0.031300 / 0.128546 (-0.097246) | 0.008591 / 0.075646 (-0.067055) | 0.288577 / 0.419271 (-0.130694) | 0.062920 / 0.043533 (0.019388) | 0.348989 / 0.255139 (0.093850) | 0.362769 / 0.283200 (0.079569) | 0.030087 / 0.141683 (-0.111596) | 1.480748 / 1.452155 (0.028594) | 1.580413 / 1.492716 (0.087697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205804 / 0.018006 (0.187798) | 0.455386 / 0.000490 (0.454897) | 0.003134 / 0.000200 (0.002934) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030252 / 0.037411 (-0.007159) | 0.087566 / 0.014526 (0.073041) | 0.098209 / 0.176557 (-0.078347) | 0.155816 / 0.737135 (-0.581319) | 0.098938 / 0.296338 (-0.197401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386688 / 0.215209 (0.171479) | 3.852777 / 2.077655 (1.775123) | 1.938688 / 1.504120 (0.434568) | 1.779234 / 1.541195 (0.238039) | 1.864262 / 1.468490 (0.395772) | 0.482472 / 4.584777 (-4.102305) | 3.658060 / 3.745712 (-0.087652) | 5.206489 / 5.269862 (-0.063373) | 3.262498 / 4.565676 (-1.303179) | 0.057523 / 0.424275 (-0.366752) | 0.007365 / 0.007607 (-0.000242) | 0.466886 / 0.226044 (0.240841) | 4.671026 / 2.268929 (2.402097) | 2.380357 / 55.444624 (-53.064268) | 2.096590 / 6.876477 (-4.779887) | 2.274415 / 2.142072 (0.132342) | 0.579705 / 4.805227 (-4.225522) | 0.134522 / 6.500664 (-6.366142) | 0.062232 / 0.075469 (-0.013237) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245965 / 1.841788 (-0.595823) | 20.115180 / 8.074308 (12.040872) | 14.602983 / 10.191392 (4.411591) | 0.146890 / 0.680424 (-0.533533) | 0.018424 / 0.534201 (-0.515777) | 0.393941 / 0.579283 (-0.185342) | 0.413785 / 0.434364 (-0.020579) | 0.453344 / 0.540337 (-0.086993) | 0.655446 / 1.386936 (-0.731490) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006807 / 0.011353 (-0.004546) | 0.004083 / 0.011008 (-0.006925) | 0.065389 / 0.038508 (0.026881) | 0.081056 / 0.023109 (0.057947) | 0.362823 / 0.275898 (0.086925) | 0.401928 / 0.323480 (0.078448) | 0.005452 / 0.007986 (-0.002533) | 0.003413 / 0.004328 (-0.000915) | 0.065238 / 0.004250 (0.060987) | 0.057264 / 0.037052 (0.020211) | 0.375713 / 0.258489 (0.117224) | 0.407858 / 0.293841 (0.114017) | 0.031580 / 0.128546 (-0.096966) | 0.008643 / 0.075646 (-0.067003) | 0.071693 / 0.419271 (-0.347578) | 0.049392 / 0.043533 (0.005859) | 0.370194 / 0.255139 (0.115055) | 0.384647 / 0.283200 (0.101447) | 0.024805 / 0.141683 (-0.116877) | 1.509511 / 1.452155 (0.057356) | 1.560193 / 1.492716 (0.067477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234442 / 0.018006 (0.216436) | 0.458818 / 0.000490 (0.458329) | 0.000407 / 0.000200 (0.000207) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031661 / 0.037411 (-0.005750) | 0.093143 / 0.014526 (0.078618) | 0.102205 / 0.176557 (-0.074352) | 0.155850 / 0.737135 (-0.581286) | 0.104345 / 0.296338 (-0.191994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419641 / 0.215209 (0.204432) | 4.200808 / 2.077655 (2.123153) | 2.218227 / 1.504120 (0.714107) | 2.052604 / 1.541195 (0.511409) | 2.150611 / 1.468490 (0.682121) | 0.482665 / 4.584777 (-4.102112) | 3.606541 / 3.745712 (-0.139172) | 3.310637 / 5.269862 (-1.959224) | 2.070200 / 4.565676 (-2.495476) | 0.056586 / 0.424275 (-0.367689) | 0.007826 / 0.007607 (0.000218) | 0.491037 / 0.226044 (0.264992) | 4.901538 / 2.268929 (2.632610) | 2.676402 / 55.444624 (-52.768223) | 2.363935 / 6.876477 (-4.512542) | 2.587813 / 2.142072 (0.445741) | 0.579302 / 4.805227 (-4.225926) | 0.132792 / 6.500664 (-6.367873) | 0.061865 / 0.075469 (-0.013604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354315 / 1.841788 (-0.487473) | 20.874516 / 8.074308 (12.800208) | 14.863559 / 10.191392 (4.672167) | 0.183635 / 0.680424 (-0.496789) | 0.018636 / 0.534201 (-0.515565) | 0.395317 / 0.579283 (-0.183966) | 0.410598 / 0.434364 (-0.023766) | 0.476485 / 0.540337 (-0.063853) | 0.643246 / 1.386936 (-0.743690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4472a8795c603a95eef7c8f15cb04f1290cc8d11 \"CML watermark\")\n" ]

"2023-07-17T11:03:09"

"2023-07-18T16:17:52"

"2023-07-18T16:08:42"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). 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) | 0.006232 / 0.011353 (-0.005121) | 0.003788 / 0.011008 (-0.007220) | 0.100014 / 0.038508 (0.061506) | 0.036488 / 0.023109 (0.013379) | 0.306255 / 0.275898 (0.030357) | 0.363337 / 0.323480 (0.039857) | 0.004765 / 0.007986 (-0.003221) | 0.002935 / 0.004328 (-0.001394) | 0.078897 / 0.004250 (0.074647) | 0.052221 / 0.037052 (0.015169) | 0.315169 / 0.258489 (0.056680) | 0.353050 / 0.293841 (0.059209) | 0.029059 / 0.128546 (-0.099488) | 0.008599 / 0.075646 (-0.067047) | 0.318770 / 0.419271 (-0.100502) | 0.046631 / 0.043533 (0.003098) | 0.303728 / 0.255139 (0.048589) | 0.332379 / 0.283200 (0.049180) | 0.021164 / 0.141683 (-0.120519) | 1.576963 / 1.452155 (0.124808) | 1.629575 / 1.492716 (0.136859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204246 / 0.018006 (0.186240) | 0.426600 / 0.000490 (0.426110) | 0.004336 / 0.000200 (0.004136) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024039 / 0.037411 (-0.013372) | 0.098240 / 0.014526 (0.083715) | 0.108889 / 0.176557 (-0.067668) | 0.170827 / 0.737135 (-0.566308) | 0.111288 / 0.296338 (-0.185051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418103 / 0.215209 (0.202894) | 4.190759 / 2.077655 (2.113104) | 1.875978 / 1.504120 (0.371858) | 1.679198 / 1.541195 (0.138003) | 1.737965 / 1.468490 (0.269474) | 0.556660 / 4.584777 (-4.028117) | 3.413800 / 3.745712 (-0.331912) | 3.004999 / 5.269862 (-2.264862) | 1.464030 / 4.565676 (-3.101647) | 0.067338 / 0.424275 (-0.356937) | 0.011486 / 0.007607 (0.003879) | 0.522589 / 0.226044 (0.296544) | 5.214653 / 2.268929 (2.945724) | 2.316903 / 55.444624 (-53.127722) | 1.991941 / 6.876477 (-4.884536) | 2.110601 / 2.142072 (-0.031471) | 0.665400 / 4.805227 (-4.139828) | 0.135755 / 6.500664 (-6.364910) | 0.065980 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197269 / 1.841788 (-0.644519) | 14.085205 / 8.074308 (6.010897) | 14.083360 / 10.191392 (3.891968) | 0.148054 / 0.680424 (-0.532369) | 0.016548 / 0.534201 (-0.517653) | 0.371538 / 0.579283 (-0.207745) | 0.391068 / 0.434364 (-0.043296) | 0.430589 / 0.540337 (-0.109748) | 0.529319 / 1.386936 (-0.857617) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006214 / 0.011353 (-0.005138) | 0.003846 / 0.011008 (-0.007162) | 0.078559 / 0.038508 (0.040051) | 0.037855 / 0.023109 (0.014745) | 0.437479 / 0.275898 (0.161581) | 0.497588 / 0.323480 (0.174108) | 0.003491 / 0.007986 (-0.004494) | 0.003900 / 0.004328 (-0.000428) | 0.078443 / 0.004250 (0.074193) | 0.048019 / 0.037052 (0.010967) | 0.452076 / 0.258489 (0.193587) | 0.494597 / 0.293841 (0.200756) | 0.028127 / 0.128546 (-0.100419) | 0.008549 / 0.075646 (-0.067098) | 0.082977 / 0.419271 (-0.336295) | 0.043133 / 0.043533 (-0.000400) | 0.441342 / 0.255139 (0.186203) | 0.464339 / 0.283200 (0.181139) | 0.020110 / 0.141683 (-0.121573) | 1.485181 / 1.452155 (0.033026) | 1.532019 / 1.492716 (0.039302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228014 / 0.018006 (0.210007) | 0.416887 / 0.000490 (0.416397) | 0.001133 / 0.000200 (0.000933) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026452 / 0.037411 (-0.010960) | 0.104328 / 0.014526 (0.089802) | 0.110045 / 0.176557 (-0.066511) | 0.164725 / 0.737135 (-0.572410) | 0.116348 / 0.296338 (-0.179990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483502 / 0.215209 (0.268293) | 4.829814 / 2.077655 (2.752159) | 2.505271 / 1.504120 (1.001151) | 2.305819 / 1.541195 (0.764624) | 2.348633 / 1.468490 (0.880143) | 0.562316 / 4.584777 (-4.022461) | 3.426425 / 3.745712 (-0.319287) | 1.737934 / 5.269862 (-3.531927) | 1.042616 / 4.565676 (-3.523061) | 0.068088 / 0.424275 (-0.356187) | 0.011735 / 0.007607 (0.004128) | 0.586339 / 0.226044 (0.360295) | 5.861283 / 2.268929 (3.592354) | 2.953956 / 55.444624 (-52.490668) | 2.626611 / 6.876477 (-4.249865) | 2.687978 / 2.142072 (0.545906) | 0.672748 / 4.805227 (-4.132479) | 0.137231 / 6.500664 (-6.363433) | 0.068149 / 0.075469 (-0.007320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.323139 / 1.841788 (-0.518649) | 14.503102 / 8.074308 (6.428794) | 14.092102 / 10.191392 (3.900710) | 0.165395 / 0.680424 (-0.515028) | 0.016898 / 0.534201 (-0.517303) | 0.366905 / 0.579283 (-0.212378) | 0.396671 / 0.434364 (-0.037692) | 0.421831 / 0.540337 (-0.118506) | 0.514075 / 1.386936 (-0.872861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.004624 / 0.011008 (-0.006384) | 0.123426 / 0.038508 (0.084918) | 0.052209 / 0.023109 (0.029100) | 0.341084 / 0.275898 (0.065186) | 0.421905 / 0.323480 (0.098425) | 0.005768 / 0.007986 (-0.002217) | 0.003647 / 0.004328 (-0.000682) | 0.085569 / 0.004250 (0.081319) | 0.070473 / 0.037052 (0.033421) | 0.356626 / 0.258489 (0.098136) | 0.407413 / 0.293841 (0.113572) | 0.038800 / 0.128546 (-0.089746) | 0.010289 / 0.075646 (-0.065357) | 0.462707 / 0.419271 (0.043436) | 0.060390 / 0.043533 (0.016858) | 0.349805 / 0.255139 (0.094666) | 0.355288 / 0.283200 (0.072088) | 0.025364 / 0.141683 (-0.116318) | 1.745720 / 1.452155 (0.293565) | 1.852764 / 1.492716 (0.360048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290582 / 0.018006 (0.272576) | 0.480044 / 0.000490 (0.479554) | 0.007658 / 0.000200 (0.007458) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031529 / 0.037411 (-0.005882) | 0.130441 / 0.014526 (0.115915) | 0.147653 / 0.176557 (-0.028904) | 0.215935 / 0.737135 (-0.521200) | 0.149871 / 0.296338 (-0.146467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461662 / 0.215209 (0.246453) | 4.570353 / 2.077655 (2.492698) | 2.104416 / 1.504120 (0.600297) | 1.936974 / 1.541195 (0.395779) | 2.139167 / 1.468490 (0.670677) | 0.645100 / 4.584777 (-3.939677) | 4.361536 / 3.745712 (0.615824) | 2.155960 / 5.269862 (-3.113902) | 1.207854 / 4.565676 (-3.357822) | 0.080162 / 0.424275 (-0.344113) | 0.014265 / 0.007607 (0.006658) | 0.606294 / 0.226044 (0.380250) | 5.928093 / 2.268929 (3.659165) | 2.701811 / 55.444624 (-52.742813) | 2.344490 / 6.876477 (-4.531987) | 2.435997 / 2.142072 (0.293925) | 0.761020 / 4.805227 (-4.044207) | 0.165860 / 6.500664 (-6.334804) | 0.075666 / 0.075469 (0.000197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.427318 / 1.841788 (-0.414469) | 17.327468 / 8.074308 (9.253160) | 15.323065 / 10.191392 (5.131673) | 0.178518 / 0.680424 (-0.501905) | 0.020888 / 0.534201 (-0.513313) | 0.497891 / 0.579283 (-0.081393) | 0.487717 / 0.434364 (0.053353) | 0.581430 / 0.540337 (0.041093) | 0.703430 / 1.386936 (-0.683506) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007954 / 0.011353 (-0.003399) | 0.004442 / 0.011008 (-0.006566) | 0.090950 / 0.038508 (0.052442) | 0.054282 / 0.023109 (0.031173) | 0.424474 / 0.275898 (0.148576) | 0.531770 / 0.323480 (0.208290) | 0.004492 / 0.007986 (-0.003493) | 0.004745 / 0.004328 (0.000416) | 0.088213 / 0.004250 (0.083962) | 0.063967 / 0.037052 (0.026914) | 0.454256 / 0.258489 (0.195767) | 0.502870 / 0.293841 (0.209029) | 0.038203 / 0.128546 (-0.090343) | 0.010327 / 0.075646 (-0.065319) | 0.097809 / 0.419271 (-0.321463) | 0.062136 / 0.043533 (0.018604) | 0.426148 / 0.255139 (0.171009) | 0.467812 / 0.283200 (0.184612) | 0.029148 / 0.141683 (-0.112535) | 1.762307 / 1.452155 (0.310152) | 1.814238 / 1.492716 (0.321521) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195676 / 0.018006 (0.177670) | 0.475382 / 0.000490 (0.474892) | 0.003070 / 0.000200 (0.002870) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033945 / 0.037411 (-0.003466) | 0.134666 / 0.014526 (0.120140) | 0.147585 / 0.176557 (-0.028971) | 0.209472 / 0.737135 (-0.527664) | 0.154471 / 0.296338 (-0.141867) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518132 / 0.215209 (0.302923) | 5.103423 / 2.077655 (3.025768) | 2.565207 / 1.504120 (1.061087) | 2.389454 / 1.541195 (0.848259) | 2.391706 / 1.468490 (0.923216) | 0.606463 / 4.584777 (-3.978314) | 4.392227 / 3.745712 (0.646515) | 2.067121 / 5.269862 (-3.202741) | 1.217551 / 4.565676 (-3.348125) | 0.074304 / 0.424275 (-0.349971) | 0.013418 / 0.007607 (0.005811) | 0.623327 / 0.226044 (0.397282) | 6.340233 / 2.268929 (4.071304) | 3.153948 / 55.444624 (-52.290677) | 2.824548 / 6.876477 (-4.051929) | 2.938402 / 2.142072 (0.796329) | 0.774305 / 4.805227 (-4.030922) | 0.170681 / 6.500664 (-6.329983) | 0.075895 / 0.075469 (0.000426) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473491 / 1.841788 (-0.368296) | 17.372294 / 8.074308 (9.297986) | 15.550201 / 10.191392 (5.358809) | 0.191402 / 0.680424 (-0.489022) | 0.021401 / 0.534201 (-0.512800) | 0.484377 / 0.579283 (-0.094906) | 0.488844 / 0.434364 (0.054480) | 0.563336 / 0.540337 (0.022999) | 0.694210 / 1.386936 (-0.692726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n" ]

"2023-06-14T16:26:34"

"2023-06-14T16:34:55"

"2023-06-14T16:26:51"

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"2023-05-26T20:02:14"

"2023-05-30T17:53:31"

"2023-05-30T17:53:31"

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[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]

"2023-06-14T17:59:35"

"2023-06-14T18:04:16"

"2023-06-14T18:04:16"

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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) | 0.006038 / 0.011353 (-0.005315) | 0.003797 / 0.011008 (-0.007211) | 0.097686 / 0.038508 (0.059178) | 0.035235 / 0.023109 (0.012126) | 0.317294 / 0.275898 (0.041396) | 0.377682 / 0.323480 (0.054202) | 0.003485 / 0.007986 (-0.004501) | 0.003603 / 0.004328 (-0.000725) | 0.077268 / 0.004250 (0.073017) | 0.054649 / 0.037052 (0.017597) | 0.322293 / 0.258489 (0.063804) | 0.372277 / 0.293841 (0.078436) | 0.027927 / 0.128546 (-0.100619) | 0.008495 / 0.075646 (-0.067151) | 0.313078 / 0.419271 (-0.106193) | 0.046974 / 0.043533 (0.003441) | 0.313848 / 0.255139 (0.058709) | 0.338454 / 0.283200 (0.055255) | 0.020462 / 0.141683 (-0.121221) | 1.473027 / 1.452155 (0.020873) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221429 / 0.018006 (0.203423) | 0.412044 / 0.000490 (0.411555) | 0.005866 / 0.000200 (0.005666) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022870 / 0.037411 (-0.014541) | 0.099129 / 0.014526 (0.084603) | 0.103463 / 0.176557 (-0.073094) | 0.164969 / 0.737135 (-0.572166) | 0.110000 / 0.296338 (-0.186339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431311 / 0.215209 (0.216102) | 4.293562 / 2.077655 (2.215907) | 1.961209 / 1.504120 (0.457089) | 1.733680 / 1.541195 (0.192485) | 1.793171 / 1.468490 (0.324681) | 0.568566 / 4.584777 (-4.016211) | 3.401794 / 3.745712 (-0.343918) | 1.827949 / 5.269862 (-3.441913) | 1.055963 / 4.565676 (-3.509714) | 0.068459 / 0.424275 (-0.355816) | 0.011586 / 0.007607 (0.003979) | 0.533936 / 0.226044 (0.307891) | 5.347637 / 2.268929 (3.078708) | 2.378056 / 55.444624 (-53.066569) | 2.032159 / 6.876477 (-4.844318) | 2.159064 / 2.142072 (0.016991) | 0.674528 / 4.805227 (-4.130699) | 0.136859 / 6.500664 (-6.363805) | 0.066629 / 0.075469 (-0.008840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218084 / 1.841788 (-0.623704) | 14.141710 / 8.074308 (6.067402) | 13.588415 / 10.191392 (3.397023) | 0.155104 / 0.680424 (-0.525320) | 0.017160 / 0.534201 (-0.517041) | 0.375558 / 0.579283 (-0.203725) | 0.386293 / 0.434364 (-0.048071) | 0.459476 / 0.540337 (-0.080862) | 0.548561 / 1.386936 (-0.838375) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005878 / 0.011353 (-0.005475) | 0.003750 / 0.011008 (-0.007259) | 0.077720 / 0.038508 (0.039212) | 0.034955 / 0.023109 (0.011846) | 0.357480 / 0.275898 (0.081582) | 0.418210 / 0.323480 (0.094730) | 0.004566 / 0.007986 (-0.003419) | 0.002918 / 0.004328 (-0.001410) | 0.076517 / 0.004250 (0.072266) | 0.050202 / 0.037052 (0.013150) | 0.368166 / 0.258489 (0.109677) | 0.415681 / 0.293841 (0.121840) | 0.029496 / 0.128546 (-0.099050) | 0.008547 / 0.075646 (-0.067099) | 0.083037 / 0.419271 (-0.336234) | 0.045001 / 0.043533 (0.001468) | 0.356503 / 0.255139 (0.101364) | 0.383747 / 0.283200 (0.100547) | 0.025071 / 0.141683 (-0.116612) | 1.541985 / 1.452155 (0.089830) | 1.594710 / 1.492716 (0.101994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204491 / 0.018006 (0.186484) | 0.408686 / 0.000490 (0.408196) | 0.002505 / 0.000200 (0.002305) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024446 / 0.037411 (-0.012965) | 0.101432 / 0.014526 (0.086906) | 0.108105 / 0.176557 (-0.068452) | 0.161195 / 0.737135 (-0.575940) | 0.112671 / 0.296338 (-0.183667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459697 / 0.215209 (0.244488) | 4.570071 / 2.077655 (2.492416) | 2.211547 / 1.504120 (0.707427) | 1.996651 / 1.541195 (0.455457) | 2.015621 / 1.468490 (0.547131) | 0.567423 / 4.584777 (-4.017354) | 3.408027 / 3.745712 (-0.337685) | 2.913824 / 5.269862 (-2.356038) | 1.423223 / 4.565676 (-3.142453) | 0.068740 / 0.424275 (-0.355535) | 0.010997 / 0.007607 (0.003390) | 0.567340 / 0.226044 (0.341296) | 5.666280 / 2.268929 (3.397351) | 2.804934 / 55.444624 (-52.639690) | 2.430761 / 6.876477 (-4.445716) | 2.451820 / 2.142072 (0.309748) | 0.681926 / 4.805227 (-4.123301) | 0.137761 / 6.500664 (-6.362903) | 0.067173 / 0.075469 (-0.008296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329853 / 1.841788 (-0.511934) | 14.436232 / 8.074308 (6.361924) | 14.398645 / 10.191392 (4.207253) | 0.147421 / 0.680424 (-0.533002) | 0.016743 / 0.534201 (-0.517458) | 0.364964 / 0.579283 (-0.214319) | 0.387072 / 0.434364 (-0.047292) | 0.423892 / 0.540337 (-0.116445) | 0.521304 / 1.386936 (-0.865632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"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) | 0.006463 / 0.011353 (-0.004889) | 0.003923 / 0.011008 (-0.007086) | 0.102096 / 0.038508 (0.063588) | 0.040230 / 0.023109 (0.017121) | 0.384688 / 0.275898 (0.108789) | 0.445574 / 0.323480 (0.122094) | 0.003590 / 0.007986 (-0.004395) | 0.004023 / 0.004328 (-0.000306) | 0.080125 / 0.004250 (0.075875) | 0.057406 / 0.037052 (0.020354) | 0.395049 / 0.258489 (0.136560) | 0.438065 / 0.293841 (0.144224) | 0.028963 / 0.128546 (-0.099583) | 0.008693 / 0.075646 (-0.066954) | 0.317158 / 0.419271 (-0.102114) | 0.047930 / 0.043533 (0.004397) | 0.382442 / 0.255139 (0.127303) | 0.410665 / 0.283200 (0.127466) | 0.020127 / 0.141683 (-0.121555) | 1.558554 / 1.452155 (0.106400) | 1.590959 / 1.492716 (0.098242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208826 / 0.018006 (0.190820) | 0.432037 / 0.000490 (0.431547) | 0.006509 / 0.000200 (0.006309) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023460 / 0.037411 (-0.013951) | 0.099070 / 0.014526 (0.084545) | 0.105771 / 0.176557 (-0.070785) | 0.166683 / 0.737135 (-0.570452) | 0.108755 / 0.296338 (-0.187583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424324 / 0.215209 (0.209115) | 4.225696 / 2.077655 (2.148042) | 1.910955 / 1.504120 (0.406835) | 1.704493 / 1.541195 (0.163298) | 1.782784 / 1.468490 (0.314293) | 0.562927 / 4.584777 (-4.021850) | 3.380163 / 3.745712 (-0.365550) | 1.779641 / 5.269862 (-3.490221) | 1.029134 / 4.565676 (-3.536543) | 0.068325 / 0.424275 (-0.355950) | 0.011528 / 0.007607 (0.003921) | 0.530141 / 0.226044 (0.304097) | 5.323443 / 2.268929 (3.054514) | 2.346956 / 55.444624 (-53.097668) | 2.013335 / 6.876477 (-4.863142) | 2.118531 / 2.142072 (-0.023541) | 0.675206 / 4.805227 (-4.130021) | 0.135473 / 6.500664 (-6.365191) | 0.064804 / 0.075469 (-0.010665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240179 / 1.841788 (-0.601608) | 14.692449 / 8.074308 (6.618141) | 13.672223 / 10.191392 (3.480831) | 0.147748 / 0.680424 (-0.532676) | 0.017119 / 0.534201 (-0.517082) | 0.369481 / 0.579283 (-0.209802) | 0.390133 / 0.434364 (-0.044231) | 0.458768 / 0.540337 (-0.081569) | 0.548989 / 1.386936 (-0.837947) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006319 / 0.011353 (-0.005034) | 0.003975 / 0.011008 (-0.007033) | 0.077886 / 0.038508 (0.039378) | 0.038322 / 0.023109 (0.015213) | 0.379851 / 0.275898 (0.103953) | 0.456749 / 0.323480 (0.133269) | 0.005320 / 0.007986 (-0.002665) | 0.003135 / 0.004328 (-0.001194) | 0.078272 / 0.004250 (0.074022) | 0.059919 / 0.037052 (0.022866) | 0.430062 / 0.258489 (0.171573) | 0.477432 / 0.293841 (0.183591) | 0.029713 / 0.128546 (-0.098833) | 0.008704 / 0.075646 (-0.066942) | 0.082488 / 0.419271 (-0.336784) | 0.044667 / 0.043533 (0.001134) | 0.354910 / 0.255139 (0.099771) | 0.434637 / 0.283200 (0.151438) | 0.026402 / 0.141683 (-0.115281) | 1.528825 / 1.452155 (0.076671) | 1.548209 / 1.492716 (0.055493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237988 / 0.018006 (0.219982) | 0.420402 / 0.000490 (0.419913) | 0.003098 / 0.000200 (0.002898) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011159) | 0.106137 / 0.014526 (0.091611) | 0.110273 / 0.176557 (-0.066284) | 0.165316 / 0.737135 (-0.571819) | 0.115720 / 0.296338 (-0.180619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454244 / 0.215209 (0.239035) | 4.526018 / 2.077655 (2.448364) | 2.395985 / 1.504120 (0.891865) | 2.234822 / 1.541195 (0.693627) | 2.370235 / 1.468490 (0.901745) | 0.567607 / 4.584777 (-4.017169) | 3.650156 / 3.745712 (-0.095556) | 3.360094 / 5.269862 (-1.909768) | 1.415252 / 4.565676 (-3.150424) | 0.068012 / 0.424275 (-0.356263) | 0.011135 / 0.007607 (0.003528) | 0.561967 / 0.226044 (0.335923) | 5.621819 / 2.268929 (3.352890) | 2.676912 / 55.444624 (-52.767712) | 2.338306 / 6.876477 (-4.538171) | 2.430888 / 2.142072 (0.288815) | 0.684576 / 4.805227 (-4.120651) | 0.138923 / 6.500664 (-6.361741) | 0.069933 / 0.075469 (-0.005536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313383 / 1.841788 (-0.528405) | 15.125088 / 8.074308 (7.050780) | 14.801501 / 10.191392 (4.610109) | 0.134235 / 0.680424 (-0.546189) | 0.017058 / 0.534201 (-0.517143) | 0.365166 / 0.579283 (-0.214117) | 0.395415 / 0.434364 (-0.038949) | 0.419355 / 0.540337 (-0.120983) | 0.513411 / 1.386936 (-0.873525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n" ]

"2023-06-30T13:15:50"

"2023-06-30T14:18:32"

"2023-06-30T14:11:24"

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"2023-06-26T10:33:42"

"2023-06-26T10:33:42"

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[ "i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?", "@lhoestq ", "cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset builder, and therefore contains the SparkContext itself.\r\n\r\nI think it can be fixed by defining `create_cache_and_write_probe` outside the Spark dataset builder, and pass a `partial(create_cache_and_write_probe, cache_dir=self._cache_dir)` to `mapPartitions`", "Just saw this; thanks for flagging! Your proposed solution sounds good. I can prepare a PR", "@maddiedawson can you show me the demo ,so i can test in local .before your PR" ]

"2023-06-19T05:30:35"

"2023-07-24T11:55:46"

"2023-07-24T11:55:46"

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

"2023-07-22T05:06:43"

"2023-07-24T19:29:55"

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[ "Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```" ]

"2023-05-29T08:09:19"

"2023-05-30T17:35:33"

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

"2023-07-26T11:09:31"

"2023-07-26T12:34:45"

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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) | 0.008499 / 0.011353 (-0.002854) | 0.006155 / 0.011008 (-0.004853) | 0.124032 / 0.038508 (0.085524) | 0.037337 / 0.023109 (0.014228) | 0.389274 / 0.275898 (0.113376) | 0.427736 / 0.323480 (0.104257) | 0.006929 / 0.007986 (-0.001057) | 0.005017 / 0.004328 (0.000689) | 0.096356 / 0.004250 (0.092105) | 0.055694 / 0.037052 (0.018642) | 0.391417 / 0.258489 (0.132928) | 0.448098 / 0.293841 (0.154257) | 0.042442 / 0.128546 (-0.086105) | 0.013456 / 0.075646 (-0.062190) | 0.423502 / 0.419271 (0.004230) | 0.062919 / 0.043533 (0.019386) | 0.384317 / 0.255139 (0.129178) | 0.410851 / 0.283200 (0.127652) | 0.112807 / 0.141683 (-0.028875) | 1.746050 / 1.452155 (0.293895) | 1.977974 / 1.492716 (0.485257) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306382 / 0.018006 (0.288375) | 0.620310 / 0.000490 (0.619820) | 0.009309 / 0.000200 (0.009109) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026900 / 0.037411 (-0.010511) | 0.140125 / 0.014526 (0.125599) | 0.136295 / 0.176557 (-0.040261) | 0.207721 / 0.737135 (-0.529414) | 0.146328 / 0.296338 (-0.150011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616712 / 0.215209 (0.401503) | 6.237820 / 2.077655 (4.160166) | 2.503809 / 1.504120 (0.999689) | 2.129739 / 1.541195 (0.588544) | 2.160768 / 1.468490 (0.692277) | 0.971273 / 4.584777 (-3.613504) | 5.687161 / 3.745712 (1.941449) | 2.738148 / 5.269862 (-2.531713) | 1.692695 / 4.565676 (-2.872981) | 0.113701 / 0.424275 (-0.310574) | 0.014809 / 0.007607 (0.007202) | 0.774795 / 0.226044 (0.548750) | 7.660012 / 2.268929 (5.391083) | 3.253036 / 55.444624 (-52.191588) | 2.607498 / 6.876477 (-4.268979) | 2.681678 / 2.142072 (0.539606) | 1.095275 / 4.805227 (-3.709952) | 0.239078 / 6.500664 (-6.261586) | 0.081034 / 0.075469 (0.005565) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.574547 / 1.841788 (-0.267240) | 18.323566 / 8.074308 (10.249258) | 19.274482 / 10.191392 (9.083090) | 0.210275 / 0.680424 (-0.470149) | 0.031843 / 0.534201 (-0.502358) | 0.514843 / 0.579283 (-0.064440) | 0.633782 / 0.434364 (0.199418) | 0.588569 / 0.540337 (0.048232) | 0.721401 / 1.386936 (-0.665535) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008866 / 0.011353 (-0.002487) | 0.006460 / 0.011008 (-0.004548) | 0.121337 / 0.038508 (0.082829) | 0.033896 / 0.023109 (0.010786) | 0.455702 / 0.275898 (0.179804) | 0.509685 / 0.323480 (0.186205) | 0.007650 / 0.007986 (-0.000336) | 0.005578 / 0.004328 (0.001250) | 0.098505 / 0.004250 (0.094255) | 0.056122 / 0.037052 (0.019069) | 0.478483 / 0.258489 (0.219994) | 0.560008 / 0.293841 (0.266167) | 0.044926 / 0.128546 (-0.083620) | 0.014562 / 0.075646 (-0.061085) | 0.115027 / 0.419271 (-0.304244) | 0.066494 / 0.043533 (0.022961) | 0.463434 / 0.255139 (0.208296) | 0.513856 / 0.283200 (0.230656) | 0.126436 / 0.141683 (-0.015247) | 1.874729 / 1.452155 (0.422575) | 1.925080 / 1.492716 (0.432364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012672 / 0.018006 (-0.005334) | 0.615797 / 0.000490 (0.615307) | 0.001606 / 0.000200 (0.001406) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031104 / 0.037411 (-0.006307) | 0.130107 / 0.014526 (0.115581) | 0.140587 / 0.176557 (-0.035970) | 0.205081 / 0.737135 (-0.532054) | 0.144068 / 0.296338 (-0.152270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.646549 / 0.215209 (0.431340) | 6.403962 / 2.077655 (4.326307) | 2.812594 / 1.504120 (1.308474) | 2.478480 / 1.541195 (0.937285) | 2.552385 / 1.468490 (1.083895) | 0.991987 / 4.584777 (-3.592790) | 5.777917 / 3.745712 (2.032205) | 5.697830 / 5.269862 (0.427969) | 2.370583 / 4.565676 (-2.195094) | 0.109905 / 0.424275 (-0.314370) | 0.013801 / 0.007607 (0.006193) | 0.799932 / 0.226044 (0.573888) | 8.155672 / 2.268929 (5.886743) | 3.711662 / 55.444624 (-51.732963) | 3.042164 / 6.876477 (-3.834312) | 3.073549 / 2.142072 (0.931477) | 1.137515 / 4.805227 (-3.667712) | 0.231266 / 6.500664 (-6.269398) | 0.080893 / 0.075469 (0.005424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669210 / 1.841788 (-0.172577) | 18.747144 / 8.074308 (10.672836) | 21.084589 / 10.191392 (10.893197) | 0.241379 / 0.680424 (-0.439045) | 0.029473 / 0.534201 (-0.504728) | 0.524605 / 0.579283 (-0.054678) | 0.622852 / 0.434364 (0.188488) | 0.604941 / 0.540337 (0.064604) | 0.715978 / 1.386936 (-0.670958) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005973 / 0.011353 (-0.005380) | 0.004389 / 0.011008 (-0.006620) | 0.096076 / 0.038508 (0.057568) | 0.031569 / 0.023109 (0.008460) | 0.328300 / 0.275898 (0.052402) | 0.359356 / 0.323480 (0.035876) | 0.005378 / 0.007986 (-0.002607) | 0.003703 / 0.004328 (-0.000625) | 0.075251 / 0.004250 (0.071000) | 0.042340 / 0.037052 (0.005287) | 0.346103 / 0.258489 (0.087614) | 0.379896 / 0.293841 (0.086055) | 0.027493 / 0.128546 (-0.101053) | 0.009033 / 0.075646 (-0.066613) | 0.327829 / 0.419271 (-0.091442) | 0.064074 / 0.043533 (0.020541) | 0.337703 / 0.255139 (0.082564) | 0.355335 / 0.283200 (0.072136) | 0.101179 / 0.141683 (-0.040504) | 1.471738 / 1.452155 (0.019584) | 1.539031 / 1.492716 (0.046315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194097 / 0.018006 (0.176091) | 0.434190 / 0.000490 (0.433701) | 0.005730 / 0.000200 (0.005530) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025634 / 0.037411 (-0.011778) | 0.105080 / 0.014526 (0.090555) | 0.116508 / 0.176557 (-0.060049) | 0.173867 / 0.737135 (-0.563269) | 0.117749 / 0.296338 (-0.178590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401566 / 0.215209 (0.186357) | 4.003558 / 2.077655 (1.925903) | 1.802756 / 1.504120 (0.298636) | 1.604222 / 1.541195 (0.063027) | 1.656617 / 1.468490 (0.188127) | 0.523385 / 4.584777 (-4.061392) | 3.744292 / 3.745712 (-0.001420) | 1.794295 / 5.269862 (-3.475567) | 1.044690 / 4.565676 (-3.520987) | 0.064992 / 0.424275 (-0.359284) | 0.011542 / 0.007607 (0.003935) | 0.507830 / 0.226044 (0.281785) | 5.061574 / 2.268929 (2.792645) | 2.252896 / 55.444624 (-53.191729) | 1.912551 / 6.876477 (-4.963926) | 2.073510 / 2.142072 (-0.068562) | 0.642148 / 4.805227 (-4.163079) | 0.140151 / 6.500664 (-6.360513) | 0.062623 / 0.075469 (-0.012846) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180367 / 1.841788 (-0.661421) | 14.263475 / 8.074308 (6.189167) | 12.917251 / 10.191392 (2.725859) | 0.143815 / 0.680424 (-0.536608) | 0.017286 / 0.534201 (-0.516915) | 0.388411 / 0.579283 (-0.190872) | 0.430512 / 0.434364 (-0.003851) | 0.466595 / 0.540337 (-0.073742) | 0.564545 / 1.386936 (-0.822391) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006059 / 0.011353 (-0.005294) | 0.004419 / 0.011008 (-0.006590) | 0.074206 / 0.038508 (0.035697) | 0.031180 / 0.023109 (0.008071) | 0.380031 / 0.275898 (0.104133) | 0.410373 / 0.323480 (0.086893) | 0.005397 / 0.007986 (-0.002589) | 0.003952 / 0.004328 (-0.000376) | 0.074426 / 0.004250 (0.070176) | 0.046256 / 0.037052 (0.009203) | 0.385543 / 0.258489 (0.127054) | 0.430724 / 0.293841 (0.136883) | 0.028052 / 0.128546 (-0.100494) | 0.008810 / 0.075646 (-0.066836) | 0.080749 / 0.419271 (-0.338522) | 0.046746 / 0.043533 (0.003214) | 0.380325 / 0.255139 (0.125186) | 0.398901 / 0.283200 (0.115701) | 0.099607 / 0.141683 (-0.042076) | 1.433343 / 1.452155 (-0.018812) | 1.520447 / 1.492716 (0.027730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202232 / 0.018006 (0.184225) | 0.431342 / 0.000490 (0.430852) | 0.001020 / 0.000200 (0.000820) | 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) | 0.028762 / 0.037411 (-0.008649) | 0.111777 / 0.014526 (0.097251) | 0.119283 / 0.176557 (-0.057273) | 0.168151 / 0.737135 (-0.568985) | 0.126093 / 0.296338 (-0.170245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442689 / 0.215209 (0.227480) | 4.369202 / 2.077655 (2.291547) | 2.167703 / 1.504120 (0.663583) | 1.960580 / 1.541195 (0.419385) | 2.001459 / 1.468490 (0.532969) | 0.527169 / 4.584777 (-4.057608) | 3.738987 / 3.745712 (-0.006726) | 1.819002 / 5.269862 (-3.450860) | 1.082786 / 4.565676 (-3.482891) | 0.066209 / 0.424275 (-0.358066) | 0.011549 / 0.007607 (0.003942) | 0.545959 / 0.226044 (0.319915) | 5.466655 / 2.268929 (3.197727) | 2.671448 / 55.444624 (-52.773176) | 2.340968 / 6.876477 (-4.535509) | 2.358805 / 2.142072 (0.216733) | 0.649456 / 4.805227 (-4.155771) | 0.142009 / 6.500664 (-6.358655) | 0.064199 / 0.075469 (-0.011270) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259819 / 1.841788 (-0.581969) | 14.456988 / 8.074308 (6.382680) | 14.478982 / 10.191392 (4.287590) | 0.163156 / 0.680424 (-0.517268) | 0.017090 / 0.534201 (-0.517111) | 0.391339 / 0.579283 (-0.187944) | 0.422021 / 0.434364 (-0.012343) | 0.465340 / 0.540337 (-0.074997) | 0.564517 / 1.386936 (-0.822419) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]

"2023-06-07T16:09:39"

"2023-06-09T10:16:58"

"2023-06-09T09:53:16"

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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) | 0.007108 / 0.011353 (-0.004245) | 0.004446 / 0.011008 (-0.006562) | 0.084013 / 0.038508 (0.045505) | 0.084271 / 0.023109 (0.061162) | 0.324496 / 0.275898 (0.048598) | 0.347783 / 0.323480 (0.024303) | 0.004382 / 0.007986 (-0.003604) | 0.005200 / 0.004328 (0.000872) | 0.065117 / 0.004250 (0.060866) | 0.063368 / 0.037052 (0.026316) | 0.328731 / 0.258489 (0.070242) | 0.356676 / 0.293841 (0.062835) | 0.031155 / 0.128546 (-0.097392) | 0.008672 / 0.075646 (-0.066975) | 0.287573 / 0.419271 (-0.131698) | 0.053692 / 0.043533 (0.010160) | 0.308796 / 0.255139 (0.053657) | 0.330521 / 0.283200 (0.047321) | 0.025010 / 0.141683 (-0.116672) | 1.498968 / 1.452155 (0.046813) | 1.552096 / 1.492716 (0.059380) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263580 / 0.018006 (0.245574) | 0.559765 / 0.000490 (0.559275) | 0.003450 / 0.000200 (0.003250) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029403 / 0.037411 (-0.008008) | 0.088154 / 0.014526 (0.073628) | 0.100372 / 0.176557 (-0.076185) | 0.157777 / 0.737135 (-0.579359) | 0.102273 / 0.296338 (-0.194066) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.387027 / 0.215209 (0.171818) | 3.854260 / 2.077655 (1.776605) | 1.875159 / 1.504120 (0.371039) | 1.703734 / 1.541195 (0.162539) | 1.814305 / 1.468490 (0.345815) | 0.482524 / 4.584777 (-4.102253) | 3.463602 / 3.745712 (-0.282110) | 4.004766 / 5.269862 (-1.265095) | 2.406751 / 4.565676 (-2.158925) | 0.057069 / 0.424275 (-0.367206) | 0.007448 / 0.007607 (-0.000159) | 0.465801 / 0.226044 (0.239757) | 4.636700 / 2.268929 (2.367771) | 2.329475 / 55.444624 (-53.115150) | 1.998330 / 6.876477 (-4.878146) | 2.264617 / 2.142072 (0.122544) | 0.577998 / 4.805227 (-4.227230) | 0.130846 / 6.500664 (-6.369818) | 0.059713 / 0.075469 (-0.015756) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275931 / 1.841788 (-0.565857) | 20.396288 / 8.074308 (12.321980) | 13.875242 / 10.191392 (3.683850) | 0.164367 / 0.680424 (-0.516057) | 0.018573 / 0.534201 (-0.515628) | 0.397516 / 0.579283 (-0.181767) | 0.398977 / 0.434364 (-0.035387) | 0.462386 / 0.540337 (-0.077951) | 0.610129 / 1.386936 (-0.776807) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006912 / 0.011353 (-0.004441) | 0.004212 / 0.011008 (-0.006797) | 0.065707 / 0.038508 (0.027199) | 0.090435 / 0.023109 (0.067325) | 0.380539 / 0.275898 (0.104641) | 0.412692 / 0.323480 (0.089212) | 0.005545 / 0.007986 (-0.002441) | 0.003657 / 0.004328 (-0.000672) | 0.065380 / 0.004250 (0.061130) | 0.062901 / 0.037052 (0.025848) | 0.385931 / 0.258489 (0.127442) | 0.416272 / 0.293841 (0.122431) | 0.031974 / 0.128546 (-0.096572) | 0.008783 / 0.075646 (-0.066863) | 0.071424 / 0.419271 (-0.347847) | 0.049454 / 0.043533 (0.005921) | 0.374231 / 0.255139 (0.119092) | 0.386530 / 0.283200 (0.103331) | 0.025404 / 0.141683 (-0.116279) | 1.469869 / 1.452155 (0.017715) | 1.548629 / 1.492716 (0.055913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218413 / 0.018006 (0.200406) | 0.573863 / 0.000490 (0.573373) | 0.004156 / 0.000200 (0.003956) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032610 / 0.037411 (-0.004801) | 0.088270 / 0.014526 (0.073744) | 0.106821 / 0.176557 (-0.069735) | 0.164498 / 0.737135 (-0.572638) | 0.106881 / 0.296338 (-0.189457) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433730 / 0.215209 (0.218520) | 4.323902 / 2.077655 (2.246247) | 2.308607 / 1.504120 (0.804487) | 2.138888 / 1.541195 (0.597693) | 2.246760 / 1.468490 (0.778269) | 0.486863 / 4.584777 (-4.097914) | 3.561826 / 3.745712 (-0.183886) | 5.592685 / 5.269862 (0.322824) | 3.318560 / 4.565676 (-1.247116) | 0.057348 / 0.424275 (-0.366927) | 0.007434 / 0.007607 (-0.000174) | 0.506767 / 0.226044 (0.280723) | 5.083097 / 2.268929 (2.814168) | 2.780618 / 55.444624 (-52.664006) | 2.456924 / 6.876477 (-4.419553) | 2.564184 / 2.142072 (0.422112) | 0.580693 / 4.805227 (-4.224534) | 0.134471 / 6.500664 (-6.366194) | 0.062883 / 0.075469 (-0.012586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346618 / 1.841788 (-0.495169) | 20.547998 / 8.074308 (12.473690) | 14.404159 / 10.191392 (4.212767) | 0.176612 / 0.680424 (-0.503812) | 0.018372 / 0.534201 (-0.515829) | 0.395636 / 0.579283 (-0.183647) | 0.410661 / 0.434364 (-0.023703) | 0.468782 / 0.540337 (-0.071555) | 0.637476 / 1.386936 (-0.749460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0172d4dac0ca823e8bd293cfd4d28e78d92efe42 \"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) | 0.009896 / 0.011353 (-0.001457) | 0.004658 / 0.011008 (-0.006351) | 0.101185 / 0.038508 (0.062677) | 0.075480 / 0.023109 (0.052371) | 0.410620 / 0.275898 (0.134722) | 0.470639 / 0.323480 (0.147159) | 0.007042 / 0.007986 (-0.000943) | 0.003909 / 0.004328 (-0.000419) | 0.079676 / 0.004250 (0.075425) | 0.066921 / 0.037052 (0.029869) | 0.423624 / 0.258489 (0.165135) | 0.473008 / 0.293841 (0.179167) | 0.048492 / 0.128546 (-0.080054) | 0.012833 / 0.075646 (-0.062813) | 0.335286 / 0.419271 (-0.083985) | 0.083506 / 0.043533 (0.039973) | 0.401918 / 0.255139 (0.146779) | 0.467975 / 0.283200 (0.184775) | 0.050025 / 0.141683 (-0.091658) | 1.679392 / 1.452155 (0.227237) | 1.852812 / 1.492716 (0.360095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248067 / 0.018006 (0.230061) | 0.584818 / 0.000490 (0.584328) | 0.021558 / 0.000200 (0.021358) | 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) | 0.028572 / 0.037411 (-0.008839) | 0.097212 / 0.014526 (0.082686) | 0.121675 / 0.176557 (-0.054881) | 0.186597 / 0.737135 (-0.550538) | 0.122285 / 0.296338 (-0.174053) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586279 / 0.215209 (0.371070) | 5.634402 / 2.077655 (3.556747) | 2.560648 / 1.504120 (1.056528) | 2.288796 / 1.541195 (0.747601) | 2.402580 / 1.468490 (0.934090) | 0.801453 / 4.584777 (-3.783324) | 5.036654 / 3.745712 (1.290942) | 8.319972 / 5.269862 (3.050110) | 4.665620 / 4.565676 (0.099944) | 0.107292 / 0.424275 (-0.316983) | 0.009206 / 0.007607 (0.001599) | 0.766505 / 0.226044 (0.540461) | 7.333784 / 2.268929 (5.064856) | 3.601875 / 55.444624 (-51.842749) | 2.886388 / 6.876477 (-3.990089) | 3.231797 / 2.142072 (1.089725) | 1.179509 / 4.805227 (-3.625718) | 0.224656 / 6.500664 (-6.276008) | 0.084749 / 0.075469 (0.009280) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.772345 / 1.841788 (-0.069443) | 24.138788 / 8.074308 (16.064480) | 20.712416 / 10.191392 (10.521024) | 0.254655 / 0.680424 (-0.425769) | 0.028858 / 0.534201 (-0.505343) | 0.499314 / 0.579283 (-0.079969) | 0.605797 / 0.434364 (0.171433) | 0.567628 / 0.540337 (0.027290) | 0.752288 / 1.386936 (-0.634648) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010134 / 0.011353 (-0.001219) | 0.004630 / 0.011008 (-0.006378) | 0.082282 / 0.038508 (0.043774) | 0.081722 / 0.023109 (0.058613) | 0.465018 / 0.275898 (0.189120) | 0.516392 / 0.323480 (0.192912) | 0.006618 / 0.007986 (-0.001368) | 0.004310 / 0.004328 (-0.000018) | 0.078990 / 0.004250 (0.074739) | 0.077729 / 0.037052 (0.040677) | 0.464892 / 0.258489 (0.206403) | 0.510551 / 0.293841 (0.216710) | 0.050750 / 0.128546 (-0.077796) | 0.014402 / 0.075646 (-0.061244) | 0.092587 / 0.419271 (-0.326685) | 0.074769 / 0.043533 (0.031237) | 0.468591 / 0.255139 (0.213452) | 0.508138 / 0.283200 (0.224938) | 0.047774 / 0.141683 (-0.093909) | 1.798354 / 1.452155 (0.346199) | 1.851431 / 1.492716 (0.358714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282528 / 0.018006 (0.264522) | 0.588286 / 0.000490 (0.587797) | 0.004892 / 0.000200 (0.004692) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037048 / 0.037411 (-0.000364) | 0.101513 / 0.014526 (0.086987) | 0.133238 / 0.176557 (-0.043319) | 0.234799 / 0.737135 (-0.502336) | 0.120636 / 0.296338 (-0.175703) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615377 / 0.215209 (0.400168) | 6.225717 / 2.077655 (4.148062) | 2.974137 / 1.504120 (1.470018) | 2.642168 / 1.541195 (1.100973) | 2.706051 / 1.468490 (1.237561) | 0.837171 / 4.584777 (-3.747606) | 5.143368 / 3.745712 (1.397656) | 4.560241 / 5.269862 (-0.709621) | 2.838375 / 4.565676 (-1.727301) | 0.092505 / 0.424275 (-0.331770) | 0.008962 / 0.007607 (0.001355) | 0.726361 / 0.226044 (0.500317) | 7.323998 / 2.268929 (5.055070) | 3.650531 / 55.444624 (-51.794094) | 2.960886 / 6.876477 (-3.915591) | 3.003889 / 2.142072 (0.861816) | 0.979264 / 4.805227 (-3.825963) | 0.204531 / 6.500664 (-6.296133) | 0.078285 / 0.075469 (0.002816) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774225 / 1.841788 (-0.067563) | 26.399536 / 8.074308 (18.325228) | 22.312890 / 10.191392 (12.121498) | 0.244651 / 0.680424 (-0.435773) | 0.026950 / 0.534201 (-0.507251) | 0.493037 / 0.579283 (-0.086246) | 0.620399 / 0.434364 (0.186036) | 0.748985 / 0.540337 (0.208648) | 0.799766 / 1.386936 (-0.587170) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49ac2864177ec4fb34c43b59a6e49de1f21f973 \"CML watermark\")\n" ]

"2023-07-12T15:46:12"

"2023-07-12T16:29:26"

"2023-07-12T16:18:04"

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"2023-07-17T16:39:26"

"2023-07-17T16:40:37"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6072). 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) | 0.007617 / 0.011353 (-0.003736) | 0.004580 / 0.011008 (-0.006428) | 0.100913 / 0.038508 (0.062405) | 0.087703 / 0.023109 (0.064594) | 0.424159 / 0.275898 (0.148261) | 0.467195 / 0.323480 (0.143715) | 0.006890 / 0.007986 (-0.001096) | 0.003765 / 0.004328 (-0.000564) | 0.077513 / 0.004250 (0.073262) | 0.064889 / 0.037052 (0.027837) | 0.422349 / 0.258489 (0.163860) | 0.477391 / 0.293841 (0.183550) | 0.036025 / 0.128546 (-0.092522) | 0.009939 / 0.075646 (-0.065707) | 0.342409 / 0.419271 (-0.076862) | 0.061568 / 0.043533 (0.018035) | 0.431070 / 0.255139 (0.175931) | 0.462008 / 0.283200 (0.178809) | 0.027480 / 0.141683 (-0.114203) | 1.802271 / 1.452155 (0.350116) | 1.861336 / 1.492716 (0.368620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255806 / 0.018006 (0.237800) | 0.507969 / 0.000490 (0.507479) | 0.010060 / 0.000200 (0.009860) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032286 / 0.037411 (-0.005125) | 0.104468 / 0.014526 (0.089942) | 0.112707 / 0.176557 (-0.063850) | 0.181285 / 0.737135 (-0.555850) | 0.113180 / 0.296338 (-0.183158) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449265 / 0.215209 (0.234056) | 4.465941 / 2.077655 (2.388287) | 2.177889 / 1.504120 (0.673769) | 1.969864 / 1.541195 (0.428669) | 2.077502 / 1.468490 (0.609011) | 0.561607 / 4.584777 (-4.023170) | 4.281873 / 3.745712 (0.536161) | 4.975352 / 5.269862 (-0.294510) | 2.907121 / 4.565676 (-1.658555) | 0.070205 / 0.424275 (-0.354070) | 0.009164 / 0.007607 (0.001557) | 0.581921 / 0.226044 (0.355876) | 5.538667 / 2.268929 (3.269739) | 2.798853 / 55.444624 (-52.645771) | 2.314015 / 6.876477 (-4.562462) | 2.584836 / 2.142072 (0.442763) | 0.672333 / 4.805227 (-4.132894) | 0.153828 / 6.500664 (-6.346836) | 0.069757 / 0.075469 (-0.005712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559670 / 1.841788 (-0.282118) | 23.994639 / 8.074308 (15.920331) | 16.856160 / 10.191392 (6.664768) | 0.195555 / 0.680424 (-0.484869) | 0.021586 / 0.534201 (-0.512615) | 0.469295 / 0.579283 (-0.109989) | 0.481582 / 0.434364 (0.047218) | 0.588667 / 0.540337 (0.048329) | 0.734347 / 1.386936 (-0.652589) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009614 / 0.011353 (-0.001739) | 0.004616 / 0.011008 (-0.006392) | 0.077223 / 0.038508 (0.038715) | 0.103074 / 0.023109 (0.079965) | 0.447834 / 0.275898 (0.171936) | 0.524696 / 0.323480 (0.201216) | 0.007120 / 0.007986 (-0.000866) | 0.003890 / 0.004328 (-0.000438) | 0.076406 / 0.004250 (0.072156) | 0.073488 / 0.037052 (0.036436) | 0.466221 / 0.258489 (0.207732) | 0.532206 / 0.293841 (0.238365) | 0.037596 / 0.128546 (-0.090950) | 0.010029 / 0.075646 (-0.065617) | 0.084313 / 0.419271 (-0.334959) | 0.060088 / 0.043533 (0.016555) | 0.437792 / 0.255139 (0.182653) | 0.512850 / 0.283200 (0.229650) | 0.032424 / 0.141683 (-0.109259) | 1.762130 / 1.452155 (0.309975) | 1.946097 / 1.492716 (0.453381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250774 / 0.018006 (0.232768) | 0.506869 / 0.000490 (0.506379) | 0.008232 / 0.000200 (0.008032) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037779 / 0.037411 (0.000368) | 0.111933 / 0.014526 (0.097407) | 0.122385 / 0.176557 (-0.054172) | 0.190372 / 0.737135 (-0.546763) | 0.122472 / 0.296338 (-0.173866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488502 / 0.215209 (0.273293) | 4.878114 / 2.077655 (2.800459) | 2.504144 / 1.504120 (1.000024) | 2.321077 / 1.541195 (0.779883) | 2.416797 / 1.468490 (0.948307) | 0.583582 / 4.584777 (-4.001195) | 4.277896 / 3.745712 (0.532184) | 3.874780 / 5.269862 (-1.395082) | 2.540099 / 4.565676 (-2.025577) | 0.068734 / 0.424275 (-0.355541) | 0.009158 / 0.007607 (0.001550) | 0.578401 / 0.226044 (0.352357) | 5.763354 / 2.268929 (3.494426) | 3.167771 / 55.444624 (-52.276853) | 2.675220 / 6.876477 (-4.201257) | 2.920927 / 2.142072 (0.778855) | 0.673948 / 4.805227 (-4.131280) | 0.157908 / 6.500664 (-6.342756) | 0.071672 / 0.075469 (-0.003797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.635120 / 1.841788 (-0.206668) | 24.853480 / 8.074308 (16.779172) | 17.162978 / 10.191392 (6.971586) | 0.209577 / 0.680424 (-0.470847) | 0.030110 / 0.534201 (-0.504091) | 0.546970 / 0.579283 (-0.032313) | 0.581912 / 0.434364 (0.147548) | 0.571460 / 0.540337 (0.031123) | 0.823411 / 1.386936 (-0.563525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006674 / 0.011353 (-0.004679) | 0.004198 / 0.011008 (-0.006810) | 0.084859 / 0.038508 (0.046351) | 0.076065 / 0.023109 (0.052955) | 0.316065 / 0.275898 (0.040167) | 0.352097 / 0.323480 (0.028617) | 0.005610 / 0.007986 (-0.002376) | 0.003600 / 0.004328 (-0.000729) | 0.064921 / 0.004250 (0.060671) | 0.054493 / 0.037052 (0.017441) | 0.318125 / 0.258489 (0.059636) | 0.370183 / 0.293841 (0.076342) | 0.031141 / 0.128546 (-0.097405) | 0.008755 / 0.075646 (-0.066891) | 0.288241 / 0.419271 (-0.131030) | 0.052379 / 0.043533 (0.008846) | 0.328147 / 0.255139 (0.073008) | 0.347548 / 0.283200 (0.064348) | 0.024393 / 0.141683 (-0.117290) | 1.480646 / 1.452155 (0.028492) | 1.575867 / 1.492716 (0.083151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268978 / 0.018006 (0.250971) | 0.586470 / 0.000490 (0.585980) | 0.003190 / 0.000200 (0.002990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030595 / 0.037411 (-0.006816) | 0.083037 / 0.014526 (0.068511) | 0.103706 / 0.176557 (-0.072850) | 0.164104 / 0.737135 (-0.573031) | 0.104536 / 0.296338 (-0.191802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382274 / 0.215209 (0.167065) | 3.811878 / 2.077655 (1.734223) | 1.840098 / 1.504120 (0.335978) | 1.670949 / 1.541195 (0.129754) | 1.763755 / 1.468490 (0.295264) | 0.479526 / 4.584777 (-4.105251) | 3.544443 / 3.745712 (-0.201269) | 3.263004 / 5.269862 (-2.006858) | 2.092801 / 4.565676 (-2.472875) | 0.057167 / 0.424275 (-0.367108) | 0.007450 / 0.007607 (-0.000157) | 0.463731 / 0.226044 (0.237686) | 4.624630 / 2.268929 (2.355701) | 2.327078 / 55.444624 (-53.117546) | 1.977734 / 6.876477 (-4.898743) | 2.237152 / 2.142072 (0.095079) | 0.573210 / 4.805227 (-4.232018) | 0.132095 / 6.500664 (-6.368569) | 0.060283 / 0.075469 (-0.015186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243404 / 1.841788 (-0.598384) | 20.306778 / 8.074308 (12.232470) | 14.561660 / 10.191392 (4.370268) | 0.170826 / 0.680424 (-0.509598) | 0.018574 / 0.534201 (-0.515627) | 0.392367 / 0.579283 (-0.186916) | 0.402918 / 0.434364 (-0.031446) | 0.476629 / 0.540337 (-0.063708) | 0.653709 / 1.386936 (-0.733227) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006562 / 0.011353 (-0.004791) | 0.004092 / 0.011008 (-0.006916) | 0.065951 / 0.038508 (0.027443) | 0.078090 / 0.023109 (0.054981) | 0.369679 / 0.275898 (0.093781) | 0.411442 / 0.323480 (0.087962) | 0.005646 / 0.007986 (-0.002339) | 0.003537 / 0.004328 (-0.000791) | 0.066024 / 0.004250 (0.061773) | 0.058947 / 0.037052 (0.021895) | 0.389219 / 0.258489 (0.130730) | 0.414200 / 0.293841 (0.120359) | 0.030372 / 0.128546 (-0.098174) | 0.008631 / 0.075646 (-0.067015) | 0.071692 / 0.419271 (-0.347580) | 0.048035 / 0.043533 (0.004502) | 0.376960 / 0.255139 (0.121821) | 0.389847 / 0.283200 (0.106648) | 0.023940 / 0.141683 (-0.117743) | 1.487633 / 1.452155 (0.035479) | 1.561680 / 1.492716 (0.068964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301467 / 0.018006 (0.283461) | 0.544159 / 0.000490 (0.543669) | 0.000408 / 0.000200 (0.000208) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030939 / 0.037411 (-0.006472) | 0.087432 / 0.014526 (0.072906) | 0.103263 / 0.176557 (-0.073293) | 0.154551 / 0.737135 (-0.582585) | 0.104631 / 0.296338 (-0.191707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422348 / 0.215209 (0.207139) | 4.206003 / 2.077655 (2.128348) | 2.212619 / 1.504120 (0.708499) | 2.049616 / 1.541195 (0.508421) | 2.139093 / 1.468490 (0.670603) | 0.489647 / 4.584777 (-4.095130) | 3.523291 / 3.745712 (-0.222422) | 3.277657 / 5.269862 (-1.992205) | 2.111353 / 4.565676 (-2.454324) | 0.057597 / 0.424275 (-0.366679) | 0.007675 / 0.007607 (0.000068) | 0.493068 / 0.226044 (0.267023) | 4.939493 / 2.268929 (2.670565) | 2.695995 / 55.444624 (-52.748630) | 2.374904 / 6.876477 (-4.501573) | 2.600110 / 2.142072 (0.458038) | 0.586306 / 4.805227 (-4.218921) | 0.134137 / 6.500664 (-6.366527) | 0.061897 / 0.075469 (-0.013572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330628 / 1.841788 (-0.511160) | 20.557964 / 8.074308 (12.483656) | 14.251632 / 10.191392 (4.060240) | 0.148772 / 0.680424 (-0.531652) | 0.018383 / 0.534201 (-0.515817) | 0.392552 / 0.579283 (-0.186731) | 0.403959 / 0.434364 (-0.030405) | 0.462154 / 0.540337 (-0.078184) | 0.608832 / 1.386936 (-0.778104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"CML watermark\")\n" ]

"2023-07-26T10:44:23"

"2023-07-26T13:01:27"

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[]

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"2023-06-26T10:33:42"

"2023-06-26T10:33:42"

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[ "The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset(\"audiofolder\", ...)` work." ]

"2023-07-18T10:16:34"

"2023-07-18T16:18:39"

"2023-07-18T16:18:39"

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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) | 0.006635 / 0.011353 (-0.004718) | 0.004439 / 0.011008 (-0.006570) | 0.107831 / 0.038508 (0.069323) | 0.035664 / 0.023109 (0.012555) | 0.393733 / 0.275898 (0.117835) | 0.418336 / 0.323480 (0.094856) | 0.005739 / 0.007986 (-0.002247) | 0.005737 / 0.004328 (0.001408) | 0.079820 / 0.004250 (0.075569) | 0.045402 / 0.037052 (0.008349) | 0.396108 / 0.258489 (0.137619) | 0.422951 / 0.293841 (0.129110) | 0.030506 / 0.128546 (-0.098040) | 0.009785 / 0.075646 (-0.065861) | 0.375302 / 0.419271 (-0.043969) | 0.054355 / 0.043533 (0.010823) | 0.399652 / 0.255139 (0.144513) | 0.410825 / 0.283200 (0.127625) | 0.109238 / 0.141683 (-0.032445) | 1.687532 / 1.452155 (0.235378) | 1.736829 / 1.492716 (0.244113) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226514 / 0.018006 (0.208508) | 0.487010 / 0.000490 (0.486520) | 0.006436 / 0.000200 (0.006236) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029097 / 0.037411 (-0.008315) | 0.122979 / 0.014526 (0.108453) | 0.129454 / 0.176557 (-0.047103) | 0.194006 / 0.737135 (-0.543129) | 0.137968 / 0.296338 (-0.158370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466425 / 0.215209 (0.251216) | 4.627307 / 2.077655 (2.549652) | 2.108840 / 1.504120 (0.604720) | 1.882547 / 1.541195 (0.341353) | 1.891077 / 1.468490 (0.422587) | 0.590646 / 4.584777 (-3.994131) | 4.176918 / 3.745712 (0.431205) | 2.071475 / 5.269862 (-3.198386) | 1.173815 / 4.565676 (-3.391862) | 0.075330 / 0.424275 (-0.348945) | 0.012944 / 0.007607 (0.005337) | 0.587080 / 0.226044 (0.361036) | 5.827053 / 2.268929 (3.558125) | 2.694258 / 55.444624 (-52.750366) | 2.276997 / 6.876477 (-4.599480) | 2.329678 / 2.142072 (0.187605) | 0.721860 / 4.805227 (-4.083367) | 0.159238 / 6.500664 (-6.341426) | 0.073013 / 0.075469 (-0.002456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345396 / 1.841788 (-0.496391) | 16.619283 / 8.074308 (8.544975) | 14.754754 / 10.191392 (4.563362) | 0.180784 / 0.680424 (-0.499639) | 0.020376 / 0.534201 (-0.513825) | 0.451010 / 0.579283 (-0.128273) | 0.481524 / 0.434364 (0.047160) | 0.564777 / 0.540337 (0.024440) | 0.683232 / 1.386936 (-0.703704) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007243 / 0.011353 (-0.004110) | 0.005262 / 0.011008 (-0.005746) | 0.084090 / 0.038508 (0.045581) | 0.037429 / 0.023109 (0.014320) | 0.404038 / 0.275898 (0.128140) | 0.445040 / 0.323480 (0.121560) | 0.006220 / 0.007986 (-0.001766) | 0.004256 / 0.004328 (-0.000072) | 0.083794 / 0.004250 (0.079544) | 0.052655 / 0.037052 (0.015603) | 0.414083 / 0.258489 (0.155594) | 0.458190 / 0.293841 (0.164349) | 0.032719 / 0.128546 (-0.095828) | 0.010063 / 0.075646 (-0.065583) | 0.092281 / 0.419271 (-0.326990) | 0.053888 / 0.043533 (0.010355) | 0.407813 / 0.255139 (0.152674) | 0.431692 / 0.283200 (0.148493) | 0.119799 / 0.141683 (-0.021884) | 1.709853 / 1.452155 (0.257698) | 1.771592 / 1.492716 (0.278876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246540 / 0.018006 (0.228534) | 0.483199 / 0.000490 (0.482709) | 0.002514 / 0.000200 (0.002315) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031576 / 0.037411 (-0.005835) | 0.130020 / 0.014526 (0.115495) | 0.140285 / 0.176557 (-0.036272) | 0.196164 / 0.737135 (-0.540972) | 0.143924 / 0.296338 (-0.152414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488549 / 0.215209 (0.273340) | 4.888055 / 2.077655 (2.810400) | 2.389163 / 1.504120 (0.885043) | 2.184626 / 1.541195 (0.643431) | 2.260227 / 1.468490 (0.791737) | 0.601331 / 4.584777 (-3.983446) | 4.386159 / 3.745712 (0.640447) | 3.345814 / 5.269862 (-1.924048) | 1.734360 / 4.565676 (-2.831317) | 0.073199 / 0.424275 (-0.351076) | 0.012397 / 0.007607 (0.004790) | 0.601411 / 0.226044 (0.375366) | 6.135000 / 2.268929 (3.866072) | 2.930169 / 55.444624 (-52.514456) | 2.532631 / 6.876477 (-4.343845) | 2.619351 / 2.142072 (0.477279) | 0.740954 / 4.805227 (-4.064274) | 0.162936 / 6.500664 (-6.337728) | 0.073885 / 0.075469 (-0.001585) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502493 / 1.841788 (-0.339294) | 17.026756 / 8.074308 (8.952448) | 15.880958 / 10.191392 (5.689566) | 0.167261 / 0.680424 (-0.513163) | 0.020347 / 0.534201 (-0.513854) | 0.452902 / 0.579283 (-0.126381) | 0.481614 / 0.434364 (0.047250) | 0.539893 / 0.540337 (-0.000445) | 0.653401 / 1.386936 (-0.733535) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"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) | 0.008268 / 0.011353 (-0.003084) | 0.005538 / 0.011008 (-0.005470) | 0.126136 / 0.038508 (0.087628) | 0.046100 / 0.023109 (0.022991) | 0.366882 / 0.275898 (0.090984) | 0.408912 / 0.323480 (0.085432) | 0.007090 / 0.007986 (-0.000895) | 0.004820 / 0.004328 (0.000491) | 0.091432 / 0.004250 (0.087181) | 0.058390 / 0.037052 (0.021338) | 0.368787 / 0.258489 (0.110298) | 0.419429 / 0.293841 (0.125588) | 0.034958 / 0.128546 (-0.093588) | 0.010526 / 0.075646 (-0.065120) | 0.463063 / 0.419271 (0.043791) | 0.070544 / 0.043533 (0.027011) | 0.366182 / 0.255139 (0.111043) | 0.390851 / 0.283200 (0.107652) | 0.128377 / 0.141683 (-0.013306) | 1.819385 / 1.452155 (0.367231) | 1.928834 / 1.492716 (0.436117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228413 / 0.018006 (0.210407) | 0.485511 / 0.000490 (0.485021) | 0.005395 / 0.000200 (0.005195) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035209 / 0.037411 (-0.002203) | 0.144492 / 0.014526 (0.129967) | 0.150467 / 0.176557 (-0.026089) | 0.223861 / 0.737135 (-0.513274) | 0.156363 / 0.296338 (-0.139975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517751 / 0.215209 (0.302542) | 5.150438 / 2.077655 (3.072783) | 2.483601 / 1.504120 (0.979481) | 2.279786 / 1.541195 (0.738592) | 2.374510 / 1.468490 (0.906020) | 0.637547 / 4.584777 (-3.947230) | 4.845393 / 3.745712 (1.099681) | 2.241554 / 5.269862 (-3.028307) | 1.290105 / 4.565676 (-3.275572) | 0.079791 / 0.424275 (-0.344484) | 0.014915 / 0.007607 (0.007308) | 0.640468 / 0.226044 (0.414423) | 6.394810 / 2.268929 (4.125881) | 3.012748 / 55.444624 (-52.431876) | 2.625565 / 6.876477 (-4.250912) | 2.792435 / 2.142072 (0.650363) | 0.782284 / 4.805227 (-4.022944) | 0.171628 / 6.500664 (-6.329036) | 0.081714 / 0.075469 (0.006245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.592411 / 1.841788 (-0.249377) | 18.999604 / 8.074308 (10.925295) | 18.469946 / 10.191392 (8.278554) | 0.200878 / 0.680424 (-0.479546) | 0.021595 / 0.534201 (-0.512606) | 0.519247 / 0.579283 (-0.060036) | 0.534940 / 0.434364 (0.100576) | 0.656325 / 0.540337 (0.115987) | 0.789658 / 1.386936 (-0.597278) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008093 / 0.011353 (-0.003260) | 0.005524 / 0.011008 (-0.005484) | 0.092339 / 0.038508 (0.053831) | 0.045619 / 0.023109 (0.022510) | 0.449376 / 0.275898 (0.173478) | 0.478587 / 0.323480 (0.155107) | 0.006978 / 0.007986 (-0.001007) | 0.004622 / 0.004328 (0.000294) | 0.090618 / 0.004250 (0.086368) | 0.059321 / 0.037052 (0.022269) | 0.450989 / 0.258489 (0.192500) | 0.491652 / 0.293841 (0.197811) | 0.033308 / 0.128546 (-0.095238) | 0.010677 / 0.075646 (-0.064969) | 0.099836 / 0.419271 (-0.319435) | 0.055937 / 0.043533 (0.012404) | 0.440560 / 0.255139 (0.185421) | 0.475305 / 0.283200 (0.192105) | 0.130829 / 0.141683 (-0.010854) | 1.857943 / 1.452155 (0.405789) | 1.989534 / 1.492716 (0.496818) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244715 / 0.018006 (0.226709) | 0.482866 / 0.000490 (0.482377) | 0.001100 / 0.000200 (0.000900) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036288 / 0.037411 (-0.001124) | 0.147903 / 0.014526 (0.133377) | 0.154141 / 0.176557 (-0.022416) | 0.221863 / 0.737135 (-0.515272) | 0.162319 / 0.296338 (-0.134019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.536972 / 0.215209 (0.321763) | 5.382866 / 2.077655 (3.305211) | 2.719575 / 1.504120 (1.215456) | 2.516596 / 1.541195 (0.975401) | 2.699602 / 1.468490 (1.231112) | 0.639886 / 4.584777 (-3.944891) | 5.109746 / 3.745712 (1.364034) | 2.260206 / 5.269862 (-3.009656) | 1.305506 / 4.565676 (-3.260170) | 0.080262 / 0.424275 (-0.344013) | 0.014801 / 0.007607 (0.007194) | 0.661228 / 0.226044 (0.435184) | 6.596485 / 2.268929 (4.327557) | 3.226114 / 55.444624 (-52.218510) | 2.859776 / 6.876477 (-4.016701) | 3.059355 / 2.142072 (0.917282) | 0.793413 / 4.805227 (-4.011814) | 0.176521 / 6.500664 (-6.324143) | 0.084062 / 0.075469 (0.008593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.642085 / 1.841788 (-0.199703) | 20.355459 / 8.074308 (12.281151) | 17.979620 / 10.191392 (7.788228) | 0.229329 / 0.680424 (-0.451094) | 0.025681 / 0.534201 (-0.508520) | 0.534142 / 0.579283 (-0.045141) | 0.623439 / 0.434364 (0.189075) | 0.621938 / 0.540337 (0.081601) | 0.759038 / 1.386936 (-0.627898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"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) | 0.007703 / 0.011353 (-0.003649) | 0.005362 / 0.011008 (-0.005646) | 0.113111 / 0.038508 (0.074602) | 0.038891 / 0.023109 (0.015782) | 0.348938 / 0.275898 (0.073040) | 0.398079 / 0.323480 (0.074599) | 0.006707 / 0.007986 (-0.001278) | 0.004489 / 0.004328 (0.000160) | 0.087194 / 0.004250 (0.082943) | 0.054268 / 0.037052 (0.017216) | 0.359949 / 0.258489 (0.101460) | 0.402959 / 0.293841 (0.109118) | 0.032508 / 0.128546 (-0.096038) | 0.010224 / 0.075646 (-0.065422) | 0.387007 / 0.419271 (-0.032264) | 0.058971 / 0.043533 (0.015439) | 0.345085 / 0.255139 (0.089946) | 0.384306 / 0.283200 (0.101107) | 0.122253 / 0.141683 (-0.019430) | 1.706353 / 1.452155 (0.254199) | 1.840780 / 1.492716 (0.348063) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254374 / 0.018006 (0.236368) | 0.497387 / 0.000490 (0.496897) | 0.012294 / 0.000200 (0.012094) | 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) | 0.030902 / 0.037411 (-0.006509) | 0.132098 / 0.014526 (0.117573) | 0.140311 / 0.176557 (-0.036245) | 0.205887 / 0.737135 (-0.531249) | 0.143992 / 0.296338 (-0.152347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467367 / 0.215209 (0.252158) | 4.669936 / 2.077655 (2.592281) | 2.155358 / 1.504120 (0.651238) | 1.984132 / 1.541195 (0.442937) | 2.102352 / 1.468490 (0.633861) | 0.607014 / 4.584777 (-3.977763) | 4.396479 / 3.745712 (0.650767) | 4.666056 / 5.269862 (-0.603806) | 2.176649 / 4.565676 (-2.389028) | 0.072657 / 0.424275 (-0.351619) | 0.012367 / 0.007607 (0.004759) | 0.569706 / 0.226044 (0.343661) | 5.749083 / 2.268929 (3.480154) | 2.640824 / 55.444624 (-52.803801) | 2.310253 / 6.876477 (-4.566224) | 2.486748 / 2.142072 (0.344676) | 0.737891 / 4.805227 (-4.067336) | 0.163507 / 6.500664 (-6.337157) | 0.075776 / 0.075469 (0.000307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362710 / 1.841788 (-0.479078) | 17.010705 / 8.074308 (8.936396) | 15.084231 / 10.191392 (4.892839) | 0.218274 / 0.680424 (-0.462150) | 0.019555 / 0.534201 (-0.514646) | 0.456013 / 0.579283 (-0.123270) | 0.502772 / 0.434364 (0.068408) | 0.581480 / 0.540337 (0.041142) | 0.686952 / 1.386936 (-0.699984) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007976 / 0.011353 (-0.003377) | 0.005141 / 0.011008 (-0.005868) | 0.086629 / 0.038508 (0.048121) | 0.039553 / 0.023109 (0.016444) | 0.433028 / 0.275898 (0.157130) | 0.463444 / 0.323480 (0.139964) | 0.006967 / 0.007986 (-0.001018) | 0.005814 / 0.004328 (0.001485) | 0.086266 / 0.004250 (0.082015) | 0.055384 / 0.037052 (0.018332) | 0.428733 / 0.258489 (0.170243) | 0.475670 / 0.293841 (0.181829) | 0.032872 / 0.128546 (-0.095674) | 0.010664 / 0.075646 (-0.064983) | 0.094357 / 0.419271 (-0.324915) | 0.058386 / 0.043533 (0.014854) | 0.431114 / 0.255139 (0.175975) | 0.441728 / 0.283200 (0.158528) | 0.131942 / 0.141683 (-0.009740) | 1.782214 / 1.452155 (0.330060) | 1.843185 / 1.492716 (0.350469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247047 / 0.018006 (0.229041) | 0.488931 / 0.000490 (0.488441) | 0.002657 / 0.000200 (0.002457) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033893 / 0.037411 (-0.003518) | 0.131021 / 0.014526 (0.116495) | 0.142892 / 0.176557 (-0.033665) | 0.200955 / 0.737135 (-0.536180) | 0.151329 / 0.296338 (-0.145010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521138 / 0.215209 (0.305929) | 5.085207 / 2.077655 (3.007552) | 2.652901 / 1.504120 (1.148781) | 2.401545 / 1.541195 (0.860350) | 2.553461 / 1.468490 (1.084971) | 0.615347 / 4.584777 (-3.969430) | 4.448038 / 3.745712 (0.702326) | 2.049997 / 5.269862 (-3.219865) | 1.190602 / 4.565676 (-3.375075) | 0.073356 / 0.424275 (-0.350919) | 0.013685 / 0.007607 (0.006078) | 0.626705 / 0.226044 (0.400660) | 6.391941 / 2.268929 (4.123012) | 3.218864 / 55.444624 (-52.225760) | 2.858808 / 6.876477 (-4.017669) | 3.005808 / 2.142072 (0.863736) | 0.740725 / 4.805227 (-4.064502) | 0.161904 / 6.500664 (-6.338760) | 0.073727 / 0.075469 (-0.001742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.488623 / 1.841788 (-0.353164) | 17.584367 / 8.074308 (9.510059) | 16.281818 / 10.191392 (6.090426) | 0.164482 / 0.680424 (-0.515942) | 0.020197 / 0.534201 (-0.514003) | 0.456750 / 0.579283 (-0.122533) | 0.501156 / 0.434364 (0.066792) | 0.549779 / 0.540337 (0.009442) | 0.650156 / 1.386936 (-0.736780) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"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) | 0.008337 / 0.011353 (-0.003016) | 0.005911 / 0.011008 (-0.005097) | 0.129037 / 0.038508 (0.090529) | 0.046071 / 0.023109 (0.022962) | 0.418657 / 0.275898 (0.142759) | 0.490340 / 0.323480 (0.166860) | 0.006387 / 0.007986 (-0.001598) | 0.004724 / 0.004328 (0.000396) | 0.097953 / 0.004250 (0.093702) | 0.069025 / 0.037052 (0.031972) | 0.431178 / 0.258489 (0.172689) | 0.458363 / 0.293841 (0.164522) | 0.049341 / 0.128546 (-0.079205) | 0.014637 / 0.075646 (-0.061009) | 0.439800 / 0.419271 (0.020529) | 0.069905 / 0.043533 (0.026373) | 0.406775 / 0.255139 (0.151636) | 0.441989 / 0.283200 (0.158790) | 0.046009 / 0.141683 (-0.095674) | 1.847630 / 1.452155 (0.395475) | 1.904067 / 1.492716 (0.411351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288305 / 0.018006 (0.270299) | 0.594547 / 0.000490 (0.594058) | 0.005600 / 0.000200 (0.005400) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033847 / 0.037411 (-0.003564) | 0.125139 / 0.014526 (0.110613) | 0.147982 / 0.176557 (-0.028574) | 0.208396 / 0.737135 (-0.528739) | 0.144005 / 0.296338 (-0.152334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669175 / 0.215209 (0.453966) | 6.605289 / 2.077655 (4.527634) | 2.720468 / 1.504120 (1.216348) | 2.341355 / 1.541195 (0.800160) | 2.402069 / 1.468490 (0.933578) | 0.939303 / 4.584777 (-3.645474) | 5.718545 / 3.745712 (1.972833) | 2.856235 / 5.269862 (-2.413627) | 1.821555 / 4.565676 (-2.744121) | 0.105473 / 0.424275 (-0.318802) | 0.014490 / 0.007607 (0.006883) | 0.774349 / 0.226044 (0.548305) | 8.065048 / 2.268929 (5.796120) | 3.508482 / 55.444624 (-51.936143) | 2.822881 / 6.876477 (-4.053596) | 2.962947 / 2.142072 (0.820875) | 1.138944 / 4.805227 (-3.666284) | 0.248414 / 6.500664 (-6.252250) | 0.095665 / 0.075469 (0.020196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.688231 / 1.841788 (-0.153557) | 18.673305 / 8.074308 (10.598997) | 22.768663 / 10.191392 (12.577271) | 0.211238 / 0.680424 (-0.469186) | 0.031380 / 0.534201 (-0.502821) | 0.517175 / 0.579283 (-0.062108) | 0.626437 / 0.434364 (0.192073) | 0.624225 / 0.540337 (0.083888) | 0.743746 / 1.386936 (-0.643191) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008888 / 0.011353 (-0.002464) | 0.005491 / 0.011008 (-0.005517) | 0.105013 / 0.038508 (0.066505) | 0.049456 / 0.023109 (0.026347) | 0.528989 / 0.275898 (0.253091) | 0.651871 / 0.323480 (0.328391) | 0.006683 / 0.007986 (-0.001302) | 0.004365 / 0.004328 (0.000037) | 0.098161 / 0.004250 (0.093911) | 0.075615 / 0.037052 (0.038563) | 0.543746 / 0.258489 (0.285257) | 0.650855 / 0.293841 (0.357014) | 0.050220 / 0.128546 (-0.078327) | 0.014471 / 0.075646 (-0.061175) | 0.115903 / 0.419271 (-0.303368) | 0.065925 / 0.043533 (0.022392) | 0.527797 / 0.255139 (0.272658) | 0.543834 / 0.283200 (0.260634) | 0.043005 / 0.141683 (-0.098678) | 1.842846 / 1.452155 (0.390691) | 1.970615 / 1.492716 (0.477899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287350 / 0.018006 (0.269343) | 0.591139 / 0.000490 (0.590649) | 0.006423 / 0.000200 (0.006223) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034594 / 0.037411 (-0.002818) | 0.137155 / 0.014526 (0.122629) | 0.154662 / 0.176557 (-0.021894) | 0.217834 / 0.737135 (-0.519301) | 0.159642 / 0.296338 (-0.136696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664288 / 0.215209 (0.449079) | 6.926912 / 2.077655 (4.849257) | 3.028957 / 1.504120 (1.524837) | 2.625178 / 1.541195 (1.083983) | 2.725316 / 1.468490 (1.256826) | 1.015715 / 4.584777 (-3.569062) | 5.834694 / 3.745712 (2.088982) | 5.105269 / 5.269862 (-0.164593) | 2.316194 / 4.565676 (-2.249483) | 0.113802 / 0.424275 (-0.310473) | 0.014079 / 0.007607 (0.006472) | 0.893727 / 0.226044 (0.667683) | 8.577701 / 2.268929 (6.308772) | 3.706907 / 55.444624 (-51.737717) | 3.087530 / 6.876477 (-3.788947) | 3.295004 / 2.142072 (1.152931) | 1.204172 / 4.805227 (-3.601055) | 0.248720 / 6.500664 (-6.251944) | 0.107208 / 0.075469 (0.031739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.800058 / 1.841788 (-0.041730) | 19.253646 / 8.074308 (11.179338) | 22.590804 / 10.191392 (12.399412) | 0.270687 / 0.680424 (-0.409737) | 0.028678 / 0.534201 (-0.505522) | 0.534670 / 0.579283 (-0.044613) | 0.642881 / 0.434364 (0.208518) | 0.615521 / 0.540337 (0.075184) | 0.723733 / 1.386936 (-0.663203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2591cd45a002a06bd551343ec785abf16f1433e2 \"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) | 0.017236 / 0.011353 (0.005883) | 0.005341 / 0.011008 (-0.005667) | 0.131471 / 0.038508 (0.092963) | 0.048868 / 0.023109 (0.025758) | 0.448942 / 0.275898 (0.173044) | 0.498721 / 0.323480 (0.175241) | 0.006825 / 0.007986 (-0.001161) | 0.004587 / 0.004328 (0.000259) | 0.104142 / 0.004250 (0.099891) | 0.075521 / 0.037052 (0.038469) | 0.439538 / 0.258489 (0.181049) | 0.498720 / 0.293841 (0.204879) | 0.051352 / 0.128546 (-0.077194) | 0.015070 / 0.075646 (-0.060576) | 0.441752 / 0.419271 (0.022480) | 0.089166 / 0.043533 (0.045633) | 0.428909 / 0.255139 (0.173770) | 0.446648 / 0.283200 (0.163448) | 0.042371 / 0.141683 (-0.099312) | 1.993948 / 1.452155 (0.541793) | 2.065756 / 1.492716 (0.573039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257279 / 0.018006 (0.239273) | 0.575453 / 0.000490 (0.574964) | 0.004120 / 0.000200 (0.003920) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034012 / 0.037411 (-0.003399) | 0.141737 / 0.014526 (0.127211) | 0.145241 / 0.176557 (-0.031316) | 0.226196 / 0.737135 (-0.510939) | 0.149526 / 0.296338 (-0.146813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665762 / 0.215209 (0.450553) | 6.683737 / 2.077655 (4.606083) | 2.869485 / 1.504120 (1.365365) | 2.462808 / 1.541195 (0.921613) | 2.526808 / 1.468490 (1.058318) | 0.957518 / 4.584777 (-3.627259) | 5.926261 / 3.745712 (2.180548) | 5.027822 / 5.269862 (-0.242040) | 2.643185 / 4.565676 (-1.922491) | 0.117014 / 0.424275 (-0.307261) | 0.015142 / 0.007607 (0.007535) | 0.835694 / 0.226044 (0.609650) | 8.427356 / 2.268929 (6.158427) | 3.649597 / 55.444624 (-51.795027) | 2.989607 / 6.876477 (-3.886870) | 3.043160 / 2.142072 (0.901088) | 1.158872 / 4.805227 (-3.646355) | 0.240456 / 6.500664 (-6.260208) | 0.089196 / 0.075469 (0.013726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.689361 / 1.841788 (-0.152427) | 18.842158 / 8.074308 (10.767850) | 22.604249 / 10.191392 (12.412857) | 0.248487 / 0.680424 (-0.431936) | 0.029668 / 0.534201 (-0.504533) | 0.536283 / 0.579283 (-0.043001) | 0.663253 / 0.434364 (0.228890) | 0.622973 / 0.540337 (0.082635) | 0.735297 / 1.386936 (-0.651639) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009296 / 0.011353 (-0.002057) | 0.005955 / 0.011008 (-0.005053) | 0.105723 / 0.038508 (0.067215) | 0.051184 / 0.023109 (0.028074) | 0.527095 / 0.275898 (0.251197) | 0.631697 / 0.323480 (0.308217) | 0.006577 / 0.007986 (-0.001408) | 0.004452 / 0.004328 (0.000124) | 0.105921 / 0.004250 (0.101670) | 0.071951 / 0.037052 (0.034899) | 0.572518 / 0.258489 (0.314029) | 0.623957 / 0.293841 (0.330116) | 0.050861 / 0.128546 (-0.077686) | 0.014897 / 0.075646 (-0.060749) | 0.122013 / 0.419271 (-0.297258) | 0.067194 / 0.043533 (0.023661) | 0.530352 / 0.255139 (0.275213) | 0.563912 / 0.283200 (0.280712) | 0.034756 / 0.141683 (-0.106927) | 1.961580 / 1.452155 (0.509425) | 2.052412 / 1.492716 (0.559696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304996 / 0.018006 (0.286990) | 0.584899 / 0.000490 (0.584409) | 0.010444 / 0.000200 (0.010244) | 0.000134 / 0.000054 (0.000080) |\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 (-0.004871) | 0.137349 / 0.014526 (0.122823) | 0.146233 / 0.176557 (-0.030323) | 0.206978 / 0.737135 (-0.530157) | 0.154380 / 0.296338 (-0.141959) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.705438 / 0.215209 (0.490229) | 7.042159 / 2.077655 (4.964504) | 3.285501 / 1.504120 (1.781381) | 2.904710 / 1.541195 (1.363515) | 2.952838 / 1.468490 (1.484348) | 0.987784 / 4.584777 (-3.596993) | 5.949550 / 3.745712 (2.203838) | 2.927148 / 5.269862 (-2.342714) | 1.870054 / 4.565676 (-2.695622) | 0.119548 / 0.424275 (-0.304727) | 0.014565 / 0.007607 (0.006958) | 0.858311 / 0.226044 (0.632266) | 8.721679 / 2.268929 (6.452750) | 4.100825 / 55.444624 (-51.343800) | 3.358093 / 6.876477 (-3.518383) | 3.499637 / 2.142072 (1.357564) | 1.208932 / 4.805227 (-3.596295) | 0.232961 / 6.500664 (-6.267703) | 0.089727 / 0.075469 (0.014258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.780143 / 1.841788 (-0.061645) | 19.074991 / 8.074308 (11.000683) | 21.218487 / 10.191392 (11.027095) | 0.258690 / 0.680424 (-0.421734) | 0.029514 / 0.534201 (-0.504687) | 0.541764 / 0.579283 (-0.037519) | 0.640603 / 0.434364 (0.206239) | 0.635336 / 0.540337 (0.094999) | 0.756309 / 1.386936 (-0.630627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b525c199e6352aa8aac55f1dcddeb55a80db373 \"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) | 0.009619 / 0.011353 (-0.001734) | 0.005683 / 0.011008 (-0.005325) | 0.136971 / 0.038508 (0.098463) | 0.051607 / 0.023109 (0.028497) | 0.439716 / 0.275898 (0.163818) | 0.486193 / 0.323480 (0.162713) | 0.006304 / 0.007986 (-0.001681) | 0.004489 / 0.004328 (0.000160) | 0.103837 / 0.004250 (0.099587) | 0.082954 / 0.037052 (0.045901) | 0.447286 / 0.258489 (0.188797) | 0.495434 / 0.293841 (0.201593) | 0.049244 / 0.128546 (-0.079302) | 0.015176 / 0.075646 (-0.060470) | 0.444406 / 0.419271 (0.025134) | 0.074766 / 0.043533 (0.031233) | 0.438585 / 0.255139 (0.183446) | 0.438232 / 0.283200 (0.155032) | 0.043372 / 0.141683 (-0.098311) | 2.057286 / 1.452155 (0.605131) | 2.049540 / 1.492716 (0.556824) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298038 / 0.018006 (0.280031) | 0.630771 / 0.000490 (0.630281) | 0.008287 / 0.000200 (0.008087) | 0.000123 / 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) | 0.033637 / 0.037411 (-0.003775) | 0.128327 / 0.014526 (0.113801) | 0.150672 / 0.176557 (-0.025885) | 0.228521 / 0.737135 (-0.508614) | 0.142733 / 0.296338 (-0.153606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629072 / 0.215209 (0.413863) | 6.612047 / 2.077655 (4.534392) | 2.715594 / 1.504120 (1.211474) | 2.327823 / 1.541195 (0.786628) | 2.417508 / 1.468490 (0.949018) | 0.959134 / 4.584777 (-3.625643) | 5.669921 / 3.745712 (1.924209) | 2.977920 / 5.269862 (-2.291941) | 1.814564 / 4.565676 (-2.751112) | 0.120233 / 0.424275 (-0.304042) | 0.015859 / 0.007607 (0.008252) | 0.822618 / 0.226044 (0.596574) | 8.440306 / 2.268929 (6.171377) | 3.721611 / 55.444624 (-51.723013) | 2.954867 / 6.876477 (-3.921610) | 3.135364 / 2.142072 (0.993292) | 1.226475 / 4.805227 (-3.578752) | 0.246658 / 6.500664 (-6.254006) | 0.093920 / 0.075469 (0.018451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.665631 / 1.841788 (-0.176157) | 19.136369 / 8.074308 (11.062061) | 23.659564 / 10.191392 (13.468172) | 0.273430 / 0.680424 (-0.406994) | 0.028180 / 0.534201 (-0.506021) | 0.559588 / 0.579283 (-0.019695) | 0.649203 / 0.434364 (0.214840) | 0.647113 / 0.540337 (0.106776) | 0.737978 / 1.386936 (-0.648958) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009104 / 0.011353 (-0.002249) | 0.006838 / 0.011008 (-0.004171) | 0.104516 / 0.038508 (0.066008) | 0.047986 / 0.023109 (0.024877) | 0.521849 / 0.275898 (0.245951) | 0.586281 / 0.323480 (0.262801) | 0.006225 / 0.007986 (-0.001760) | 0.005713 / 0.004328 (0.001384) | 0.111507 / 0.004250 (0.107257) | 0.072320 / 0.037052 (0.035267) | 0.551061 / 0.258489 (0.292572) | 0.628034 / 0.293841 (0.334193) | 0.055417 / 0.128546 (-0.073129) | 0.019613 / 0.075646 (-0.056034) | 0.123958 / 0.419271 (-0.295314) | 0.066132 / 0.043533 (0.022600) | 0.504461 / 0.255139 (0.249322) | 0.560428 / 0.283200 (0.277229) | 0.036098 / 0.141683 (-0.105585) | 1.927398 / 1.452155 (0.475243) | 2.015952 / 1.492716 (0.523235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313065 / 0.018006 (0.295059) | 0.609174 / 0.000490 (0.608684) | 0.008755 / 0.000200 (0.008555) | 0.000120 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040042 / 0.037411 (0.002630) | 0.136053 / 0.014526 (0.121527) | 0.143406 / 0.176557 (-0.033150) | 0.213080 / 0.737135 (-0.524055) | 0.154730 / 0.296338 (-0.141609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.692706 / 0.215209 (0.477497) | 6.952968 / 2.077655 (4.875314) | 3.232023 / 1.504120 (1.727903) | 2.835450 / 1.541195 (1.294256) | 2.933821 / 1.468490 (1.465331) | 0.984712 / 4.584777 (-3.600065) | 6.127651 / 3.745712 (2.381939) | 2.956781 / 5.269862 (-2.313081) | 1.879928 / 4.565676 (-2.685748) | 0.111069 / 0.424275 (-0.313206) | 0.014598 / 0.007607 (0.006991) | 0.871486 / 0.226044 (0.645442) | 8.588500 / 2.268929 (6.319572) | 3.910740 / 55.444624 (-51.533885) | 3.115781 / 6.876477 (-3.760695) | 3.222367 / 2.142072 (1.080294) | 1.229680 / 4.805227 (-3.575547) | 0.232092 / 6.500664 (-6.268572) | 0.097717 / 0.075469 (0.022248) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774193 / 1.841788 (-0.067595) | 19.863087 / 8.074308 (11.788779) | 24.058856 / 10.191392 (13.867464) | 0.214917 / 0.680424 (-0.465507) | 0.028771 / 0.534201 (-0.505430) | 0.544548 / 0.579283 (-0.034735) | 0.655882 / 0.434364 (0.221518) | 0.629110 / 0.540337 (0.088773) | 0.749246 / 1.386936 (-0.637690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4a5ea6a42dcfef1577288b51beeccc0eb124cee \"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) | 0.007075 / 0.011353 (-0.004278) | 0.005195 / 0.011008 (-0.005813) | 0.113043 / 0.038508 (0.074535) | 0.038442 / 0.023109 (0.015333) | 0.336310 / 0.275898 (0.060412) | 0.381888 / 0.323480 (0.058409) | 0.005990 / 0.007986 (-0.001996) | 0.003893 / 0.004328 (-0.000435) | 0.093123 / 0.004250 (0.088872) | 0.058449 / 0.037052 (0.021397) | 0.359463 / 0.258489 (0.100974) | 0.427485 / 0.293841 (0.133644) | 0.041454 / 0.128546 (-0.087092) | 0.013016 / 0.075646 (-0.062630) | 0.372849 / 0.419271 (-0.046422) | 0.059386 / 0.043533 (0.015853) | 0.381398 / 0.255139 (0.126259) | 0.367603 / 0.283200 (0.084403) | 0.033907 / 0.141683 (-0.107775) | 1.628903 / 1.452155 (0.176749) | 1.764131 / 1.492716 (0.271415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298329 / 0.018006 (0.280322) | 0.593030 / 0.000490 (0.592540) | 0.007653 / 0.000200 (0.007453) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025445 / 0.037411 (-0.011966) | 0.112062 / 0.014526 (0.097536) | 0.119863 / 0.176557 (-0.056693) | 0.178389 / 0.737135 (-0.558746) | 0.129934 / 0.296338 (-0.166404) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.532834 / 0.215209 (0.317625) | 5.250908 / 2.077655 (3.173253) | 2.086920 / 1.504120 (0.582800) | 1.799745 / 1.541195 (0.258550) | 1.909648 / 1.468490 (0.441158) | 0.825382 / 4.584777 (-3.759395) | 5.268304 / 3.745712 (1.522592) | 2.533347 / 5.269862 (-2.736515) | 1.730187 / 4.565676 (-2.835490) | 0.099824 / 0.424275 (-0.324451) | 0.012969 / 0.007607 (0.005362) | 0.732234 / 0.226044 (0.506189) | 6.989066 / 2.268929 (4.720138) | 2.873486 / 55.444624 (-52.571138) | 2.274351 / 6.876477 (-4.602125) | 2.311060 / 2.142072 (0.168987) | 1.125366 / 4.805227 (-3.679861) | 0.214522 / 6.500664 (-6.286142) | 0.077579 / 0.075469 (0.002110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670950 / 1.841788 (-0.170838) | 18.131528 / 8.074308 (10.057220) | 21.277823 / 10.191392 (11.086431) | 0.238807 / 0.680424 (-0.441617) | 0.032251 / 0.534201 (-0.501950) | 0.503859 / 0.579283 (-0.075424) | 0.604825 / 0.434364 (0.170461) | 0.555623 / 0.540337 (0.015286) | 0.647301 / 1.386936 (-0.739635) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010857 / 0.011353 (-0.000496) | 0.005581 / 0.011008 (-0.005427) | 0.094346 / 0.038508 (0.055838) | 0.053084 / 0.023109 (0.029975) | 0.457586 / 0.275898 (0.181688) | 0.545475 / 0.323480 (0.221995) | 0.006761 / 0.007986 (-0.001225) | 0.005094 / 0.004328 (0.000765) | 0.095509 / 0.004250 (0.091258) | 0.077182 / 0.037052 (0.040130) | 0.498717 / 0.258489 (0.240228) | 0.542433 / 0.293841 (0.248592) | 0.051547 / 0.128546 (-0.076999) | 0.014633 / 0.075646 (-0.061014) | 0.106843 / 0.419271 (-0.312428) | 0.068459 / 0.043533 (0.024926) | 0.435793 / 0.255139 (0.180654) | 0.475484 / 0.283200 (0.192285) | 0.039495 / 0.141683 (-0.102188) | 1.684906 / 1.452155 (0.232751) | 1.798693 / 1.492716 (0.305976) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279853 / 0.018006 (0.261847) | 0.601016 / 0.000490 (0.600526) | 0.002055 / 0.000200 (0.001855) | 0.000219 / 0.000054 (0.000165) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030935 / 0.037411 (-0.006477) | 0.121197 / 0.014526 (0.106671) | 0.143360 / 0.176557 (-0.033197) | 0.200862 / 0.737135 (-0.536274) | 0.138656 / 0.296338 (-0.157683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613904 / 0.215209 (0.398695) | 6.155422 / 2.077655 (4.077767) | 2.777238 / 1.504120 (1.273118) | 2.473045 / 1.541195 (0.931851) | 2.604470 / 1.468490 (1.135980) | 0.898871 / 4.584777 (-3.685906) | 5.739666 / 3.745712 (1.993954) | 4.719822 / 5.269862 (-0.550040) | 2.727354 / 4.565676 (-1.838322) | 0.108232 / 0.424275 (-0.316043) | 0.013632 / 0.007607 (0.006025) | 0.771802 / 0.226044 (0.545757) | 7.987466 / 2.268929 (5.718537) | 3.609856 / 55.444624 (-51.834768) | 2.974421 / 6.876477 (-3.902056) | 2.956567 / 2.142072 (0.814495) | 1.093792 / 4.805227 (-3.711435) | 0.213369 / 6.500664 (-6.287295) | 0.084486 / 0.075469 (0.009017) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.693855 / 1.841788 (-0.147933) | 18.055027 / 8.074308 (9.980719) | 21.397964 / 10.191392 (11.206571) | 0.240549 / 0.680424 (-0.439875) | 0.031212 / 0.534201 (-0.502989) | 0.513657 / 0.579283 (-0.065626) | 0.651348 / 0.434364 (0.216985) | 0.603740 / 0.540337 (0.063402) | 0.752287 / 1.386936 (-0.634649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f38d00dd40a444ae54c18caa28304ae36b9c3 \"CML watermark\")\n" ]

"2023-06-13T13:03:19"

"2023-06-27T16:47:51"

"2023-06-27T16:38:32"

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[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.", "Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.", "> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails.", "@mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here." ]

"2023-06-19T14:16:14"

"2023-07-26T15:13:53"

"2023-07-26T15:13:53"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006573 / 0.011353 (-0.004780) | 0.004014 / 0.011008 (-0.006994) | 0.084999 / 0.038508 (0.046491) | 0.074965 / 0.023109 (0.051855) | 0.313294 / 0.275898 (0.037396) | 0.349678 / 0.323480 (0.026198) | 0.005401 / 0.007986 (-0.002585) | 0.003401 / 0.004328 (-0.000927) | 0.065363 / 0.004250 (0.061112) | 0.057159 / 0.037052 (0.020107) | 0.313260 / 0.258489 (0.054771) | 0.354654 / 0.293841 (0.060813) | 0.030895 / 0.128546 (-0.097651) | 0.008605 / 0.075646 (-0.067042) | 0.289190 / 0.419271 (-0.130081) | 0.052474 / 0.043533 (0.008942) | 0.316193 / 0.255139 (0.061054) | 0.339966 / 0.283200 (0.056767) | 0.024112 / 0.141683 (-0.117571) | 1.515606 / 1.452155 (0.063452) | 1.571428 / 1.492716 (0.078711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203284 / 0.018006 (0.185278) | 0.452720 / 0.000490 (0.452230) | 0.003891 / 0.000200 (0.003691) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028992 / 0.037411 (-0.008419) | 0.083170 / 0.014526 (0.068644) | 0.097739 / 0.176557 (-0.078817) | 0.153401 / 0.737135 (-0.583734) | 0.098628 / 0.296338 (-0.197711) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390190 / 0.215209 (0.174981) | 3.901272 / 2.077655 (1.823617) | 1.887194 / 1.504120 (0.383074) | 1.723696 / 1.541195 (0.182501) | 1.800537 / 1.468490 (0.332047) | 0.481758 / 4.584777 (-4.103019) | 3.605098 / 3.745712 (-0.140614) | 3.304482 / 5.269862 (-1.965380) | 2.053515 / 4.565676 (-2.512161) | 0.056997 / 0.424275 (-0.367278) | 0.007347 / 0.007607 (-0.000260) | 0.461367 / 0.226044 (0.235323) | 4.606152 / 2.268929 (2.337223) | 2.470048 / 55.444624 (-52.974576) | 2.060019 / 6.876477 (-4.816458) | 2.320507 / 2.142072 (0.178435) | 0.575050 / 4.805227 (-4.230178) | 0.133030 / 6.500664 (-6.367634) | 0.061508 / 0.075469 (-0.013962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275430 / 1.841788 (-0.566357) | 19.725453 / 8.074308 (11.651145) | 14.396360 / 10.191392 (4.204968) | 0.157980 / 0.680424 (-0.522443) | 0.018516 / 0.534201 (-0.515685) | 0.394717 / 0.579283 (-0.184566) | 0.404948 / 0.434364 (-0.029415) | 0.474001 / 0.540337 (-0.066336) | 0.668463 / 1.386936 (-0.718474) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006697 / 0.011353 (-0.004656) | 0.004206 / 0.011008 (-0.006802) | 0.065458 / 0.038508 (0.026950) | 0.075845 / 0.023109 (0.052735) | 0.365051 / 0.275898 (0.089153) | 0.400919 / 0.323480 (0.077439) | 0.005347 / 0.007986 (-0.002638) | 0.003386 / 0.004328 (-0.000943) | 0.065398 / 0.004250 (0.061148) | 0.057320 / 0.037052 (0.020268) | 0.379161 / 0.258489 (0.120672) | 0.406892 / 0.293841 (0.113051) | 0.031986 / 0.128546 (-0.096560) | 0.008674 / 0.075646 (-0.066972) | 0.071723 / 0.419271 (-0.347549) | 0.049897 / 0.043533 (0.006364) | 0.372034 / 0.255139 (0.116895) | 0.394293 / 0.283200 (0.111094) | 0.023681 / 0.141683 (-0.118002) | 1.479793 / 1.452155 (0.027639) | 1.553105 / 1.492716 (0.060389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233660 / 0.018006 (0.215654) | 0.454412 / 0.000490 (0.453923) | 0.004473 / 0.000200 (0.004273) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031115 / 0.037411 (-0.006296) | 0.090541 / 0.014526 (0.076015) | 0.104363 / 0.176557 (-0.072193) | 0.161022 / 0.737135 (-0.576114) | 0.105114 / 0.296338 (-0.191225) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427668 / 0.215209 (0.212459) | 4.263145 / 2.077655 (2.185490) | 2.247043 / 1.504120 (0.742923) | 2.082554 / 1.541195 (0.541360) | 2.170505 / 1.468490 (0.702015) | 0.491802 / 4.584777 (-4.092975) | 3.587295 / 3.745712 (-0.158417) | 3.344697 / 5.269862 (-1.925165) | 2.060529 / 4.565676 (-2.505148) | 0.057829 / 0.424275 (-0.366446) | 0.007780 / 0.007607 (0.000173) | 0.503374 / 0.226044 (0.277330) | 5.034742 / 2.268929 (2.765814) | 2.701957 / 55.444624 (-52.742667) | 2.479002 / 6.876477 (-4.397474) | 2.622055 / 2.142072 (0.479982) | 0.591363 / 4.805227 (-4.213864) | 0.133834 / 6.500664 (-6.366830) | 0.062276 / 0.075469 (-0.013193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338788 / 1.841788 (-0.503000) | 20.333599 / 8.074308 (12.259291) | 14.783196 / 10.191392 (4.591804) | 0.168695 / 0.680424 (-0.511729) | 0.018478 / 0.534201 (-0.515723) | 0.397398 / 0.579283 (-0.181885) | 0.409900 / 0.434364 (-0.024464) | 0.475315 / 0.540337 (-0.065023) | 0.644267 / 1.386936 (-0.742669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb0b324e0bae4c93bb5509b2f0731bc346adb21b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007315 / 0.011353 (-0.004038) | 0.004294 / 0.011008 (-0.006714) | 0.100300 / 0.038508 (0.061792) | 0.077780 / 0.023109 (0.054670) | 0.353728 / 0.275898 (0.077830) | 0.400538 / 0.323480 (0.077058) | 0.005807 / 0.007986 (-0.002178) | 0.003649 / 0.004328 (-0.000680) | 0.077548 / 0.004250 (0.073297) | 0.058834 / 0.037052 (0.021781) | 0.352064 / 0.258489 (0.093574) | 0.399951 / 0.293841 (0.106110) | 0.036472 / 0.128546 (-0.092074) | 0.008653 / 0.075646 (-0.066994) | 0.323089 / 0.419271 (-0.096182) | 0.075127 / 0.043533 (0.031594) | 0.334412 / 0.255139 (0.079273) | 0.375718 / 0.283200 (0.092519) | 0.027915 / 0.141683 (-0.113768) | 1.698795 / 1.452155 (0.246640) | 1.781447 / 1.492716 (0.288730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216111 / 0.018006 (0.198104) | 0.507706 / 0.000490 (0.507216) | 0.000851 / 0.000200 (0.000651) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030451 / 0.037411 (-0.006960) | 0.087488 / 0.014526 (0.072962) | 0.105094 / 0.176557 (-0.071462) | 0.168130 / 0.737135 (-0.569006) | 0.106791 / 0.296338 (-0.189547) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426291 / 0.215209 (0.211082) | 4.281046 / 2.077655 (2.203391) | 2.162268 / 1.504120 (0.658148) | 1.909503 / 1.541195 (0.368309) | 1.943165 / 1.468490 (0.474675) | 0.516667 / 4.584777 (-4.068110) | 4.113218 / 3.745712 (0.367506) | 5.931372 / 5.269862 (0.661510) | 3.563521 / 4.565676 (-1.002155) | 0.062415 / 0.424275 (-0.361860) | 0.007577 / 0.007607 (-0.000030) | 0.534588 / 0.226044 (0.308543) | 5.183490 / 2.268929 (2.914561) | 2.790662 / 55.444624 (-52.653962) | 2.258630 / 6.876477 (-4.617846) | 2.499930 / 2.142072 (0.357857) | 0.606154 / 4.805227 (-4.199073) | 0.136093 / 6.500664 (-6.364571) | 0.061151 / 0.075469 (-0.014318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.398392 / 1.841788 (-0.443396) | 21.482150 / 8.074308 (13.407842) | 15.477336 / 10.191392 (5.285944) | 0.192878 / 0.680424 (-0.487546) | 0.021764 / 0.534201 (-0.512437) | 0.437149 / 0.579283 (-0.142134) | 0.439976 / 0.434364 (0.005612) | 0.514498 / 0.540337 (-0.025840) | 0.762642 / 1.386936 (-0.624294) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007504 / 0.011353 (-0.003849) | 0.004526 / 0.011008 (-0.006482) | 0.071008 / 0.038508 (0.032500) | 0.078305 / 0.023109 (0.055195) | 0.436160 / 0.275898 (0.160262) | 0.439048 / 0.323480 (0.115568) | 0.006061 / 0.007986 (-0.001925) | 0.003681 / 0.004328 (-0.000648) | 0.069445 / 0.004250 (0.065195) | 0.059258 / 0.037052 (0.022206) | 0.437745 / 0.258489 (0.179256) | 0.464247 / 0.293841 (0.170406) | 0.033286 / 0.128546 (-0.095260) | 0.009846 / 0.075646 (-0.065800) | 0.076330 / 0.419271 (-0.342941) | 0.051919 / 0.043533 (0.008386) | 0.432817 / 0.255139 (0.177678) | 0.426295 / 0.283200 (0.143095) | 0.029818 / 0.141683 (-0.111865) | 1.747640 / 1.452155 (0.295485) | 1.726653 / 1.492716 (0.233937) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251253 / 0.018006 (0.233247) | 0.483394 / 0.000490 (0.482904) | 0.003992 / 0.000200 (0.003793) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032180 / 0.037411 (-0.005231) | 0.095425 / 0.014526 (0.080900) | 0.105908 / 0.176557 (-0.070648) | 0.164732 / 0.737135 (-0.572403) | 0.115903 / 0.296338 (-0.180435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469467 / 0.215209 (0.254258) | 4.633239 / 2.077655 (2.555584) | 2.517557 / 1.504120 (1.013437) | 2.352726 / 1.541195 (0.811531) | 2.314618 / 1.468490 (0.846128) | 0.548446 / 4.584777 (-4.036331) | 3.908797 / 3.745712 (0.163085) | 3.525941 / 5.269862 (-1.743921) | 2.178858 / 4.565676 (-2.386819) | 0.057614 / 0.424275 (-0.366661) | 0.008604 / 0.007607 (0.000997) | 0.554756 / 0.226044 (0.328711) | 5.325635 / 2.268929 (3.056706) | 3.014266 / 55.444624 (-52.430359) | 2.844165 / 6.876477 (-4.032312) | 2.903019 / 2.142072 (0.760947) | 0.617750 / 4.805227 (-4.187478) | 0.144259 / 6.500664 (-6.356405) | 0.065944 / 0.075469 (-0.009525) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504625 / 1.841788 (-0.337163) | 22.400787 / 8.074308 (14.326479) | 15.223702 / 10.191392 (5.032310) | 0.213357 / 0.680424 (-0.467067) | 0.019310 / 0.534201 (-0.514891) | 0.456596 / 0.579283 (-0.122687) | 0.473811 / 0.434364 (0.039447) | 0.517800 / 0.540337 (-0.022537) | 0.792468 / 1.386936 (-0.594468) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#03750f4a4c664125c7de910be004710b181dd354 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007420 / 0.011353 (-0.003933) | 0.004502 / 0.011008 (-0.006506) | 0.097882 / 0.038508 (0.059374) | 0.079084 / 0.023109 (0.055975) | 0.361797 / 0.275898 (0.085899) | 0.416563 / 0.323480 (0.093083) | 0.006106 / 0.007986 (-0.001879) | 0.003803 / 0.004328 (-0.000526) | 0.074669 / 0.004250 (0.070418) | 0.062168 / 0.037052 (0.025116) | 0.378844 / 0.258489 (0.120355) | 0.426601 / 0.293841 (0.132760) | 0.035619 / 0.128546 (-0.092927) | 0.009686 / 0.075646 (-0.065960) | 0.336481 / 0.419271 (-0.082790) | 0.065553 / 0.043533 (0.022021) | 0.362501 / 0.255139 (0.107362) | 0.399752 / 0.283200 (0.116552) | 0.028685 / 0.141683 (-0.112998) | 1.683495 / 1.452155 (0.231340) | 1.786105 / 1.492716 (0.293388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220792 / 0.018006 (0.202786) | 0.501936 / 0.000490 (0.501447) | 0.000389 / 0.000200 (0.000189) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032180 / 0.037411 (-0.005232) | 0.093079 / 0.014526 (0.078553) | 0.107967 / 0.176557 (-0.068589) | 0.171747 / 0.737135 (-0.565389) | 0.107920 / 0.296338 (-0.188418) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444431 / 0.215209 (0.229222) | 4.454934 / 2.077655 (2.377279) | 2.140265 / 1.504120 (0.636145) | 1.960126 / 1.541195 (0.418931) | 2.049649 / 1.468490 (0.581158) | 0.557861 / 4.584777 (-4.026916) | 4.046240 / 3.745712 (0.300528) | 4.513748 / 5.269862 (-0.756114) | 2.593643 / 4.565676 (-1.972034) | 0.066795 / 0.424275 (-0.357480) | 0.008302 / 0.007607 (0.000694) | 0.535643 / 0.226044 (0.309599) | 5.299429 / 2.268929 (3.030500) | 2.656019 / 55.444624 (-52.788606) | 2.281214 / 6.876477 (-4.595263) | 2.302910 / 2.142072 (0.160837) | 0.661696 / 4.805227 (-4.143532) | 0.149787 / 6.500664 (-6.350877) | 0.069609 / 0.075469 (-0.005860) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.509842 / 1.841788 (-0.331946) | 21.717504 / 8.074308 (13.643196) | 15.825102 / 10.191392 (5.633710) | 0.168115 / 0.680424 (-0.512309) | 0.021637 / 0.534201 (-0.512564) | 0.454270 / 0.579283 (-0.125013) | 0.458531 / 0.434364 (0.024167) | 0.523052 / 0.540337 (-0.017285) | 0.711219 / 1.386936 (-0.675717) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007189 / 0.011353 (-0.004164) | 0.004437 / 0.011008 (-0.006571) | 0.075111 / 0.038508 (0.036603) | 0.079245 / 0.023109 (0.056136) | 0.423169 / 0.275898 (0.147270) | 0.455007 / 0.323480 (0.131527) | 0.006076 / 0.007986 (-0.001909) | 0.003819 / 0.004328 (-0.000509) | 0.074976 / 0.004250 (0.070726) | 0.062127 / 0.037052 (0.025075) | 0.456809 / 0.258489 (0.198320) | 0.474707 / 0.293841 (0.180867) | 0.036221 / 0.128546 (-0.092325) | 0.009428 / 0.075646 (-0.066218) | 0.082842 / 0.419271 (-0.336429) | 0.057086 / 0.043533 (0.013553) | 0.436121 / 0.255139 (0.180982) | 0.453934 / 0.283200 (0.170734) | 0.026045 / 0.141683 (-0.115638) | 1.789782 / 1.452155 (0.337627) | 1.820934 / 1.492716 (0.328218) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230790 / 0.018006 (0.212784) | 0.497987 / 0.000490 (0.497497) | 0.002775 / 0.000200 (0.002575) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034418 / 0.037411 (-0.002994) | 0.105567 / 0.014526 (0.091041) | 0.113134 / 0.176557 (-0.063423) | 0.173742 / 0.737135 (-0.563394) | 0.115936 / 0.296338 (-0.180403) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502259 / 0.215209 (0.287050) | 4.969877 / 2.077655 (2.892222) | 2.684860 / 1.504120 (1.180740) | 2.484386 / 1.541195 (0.943192) | 2.543061 / 1.468490 (1.074571) | 0.545733 / 4.584777 (-4.039044) | 4.029660 / 3.745712 (0.283948) | 5.927883 / 5.269862 (0.658021) | 3.528372 / 4.565676 (-1.037305) | 0.065957 / 0.424275 (-0.358318) | 0.008933 / 0.007607 (0.001326) | 0.601630 / 0.226044 (0.375585) | 5.825872 / 2.268929 (3.556944) | 3.230721 / 55.444624 (-52.213904) | 2.891308 / 6.876477 (-3.985169) | 3.054994 / 2.142072 (0.912922) | 0.665480 / 4.805227 (-4.139747) | 0.154815 / 6.500664 (-6.345849) | 0.072997 / 0.075469 (-0.002472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549892 / 1.841788 (-0.291896) | 22.337484 / 8.074308 (14.263176) | 16.308286 / 10.191392 (6.116894) | 0.189594 / 0.680424 (-0.490830) | 0.021844 / 0.534201 (-0.512357) | 0.456958 / 0.579283 (-0.122325) | 0.459957 / 0.434364 (0.025593) | 0.529014 / 0.540337 (-0.011323) | 0.700359 / 1.386936 (-0.686577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e4df86b5fb0bc164433ce615af641ec3ba437e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009050 / 0.011353 (-0.002303) | 0.004968 / 0.011008 (-0.006040) | 0.114315 / 0.038508 (0.075807) | 0.084475 / 0.023109 (0.061366) | 0.426325 / 0.275898 (0.150427) | 0.457870 / 0.323480 (0.134390) | 0.007076 / 0.007986 (-0.000910) | 0.004635 / 0.004328 (0.000307) | 0.082950 / 0.004250 (0.078700) | 0.065414 / 0.037052 (0.028361) | 0.441936 / 0.258489 (0.183447) | 0.476983 / 0.293841 (0.183142) | 0.048575 / 0.128546 (-0.079972) | 0.013929 / 0.075646 (-0.061717) | 0.377498 / 0.419271 (-0.041774) | 0.081503 / 0.043533 (0.037970) | 0.426706 / 0.255139 (0.171567) | 0.460374 / 0.283200 (0.177175) | 0.046052 / 0.141683 (-0.095631) | 1.894896 / 1.452155 (0.442741) | 1.998639 / 1.492716 (0.505923) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313267 / 0.018006 (0.295261) | 0.607501 / 0.000490 (0.607012) | 0.003369 / 0.000200 (0.003169) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032266 / 0.037411 (-0.005145) | 0.120138 / 0.014526 (0.105613) | 0.115044 / 0.176557 (-0.061513) | 0.181374 / 0.737135 (-0.555761) | 0.114681 / 0.296338 (-0.181657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648039 / 0.215209 (0.432830) | 6.005048 / 2.077655 (3.927394) | 2.674524 / 1.504120 (1.170404) | 2.284831 / 1.541195 (0.743637) | 2.360150 / 1.468490 (0.891660) | 0.888021 / 4.584777 (-3.696756) | 5.419840 / 3.745712 (1.674128) | 4.825816 / 5.269862 (-0.444046) | 3.140876 / 4.565676 (-1.424801) | 0.099511 / 0.424275 (-0.324764) | 0.009176 / 0.007607 (0.001569) | 0.735646 / 0.226044 (0.509602) | 7.224026 / 2.268929 (4.955097) | 3.551146 / 55.444624 (-51.893478) | 2.844374 / 6.876477 (-4.032103) | 3.145307 / 2.142072 (1.003235) | 1.077636 / 4.805227 (-3.727591) | 0.217754 / 6.500664 (-6.282910) | 0.081755 / 0.075469 (0.006286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670956 / 1.841788 (-0.170831) | 25.524961 / 8.074308 (17.450653) | 23.061596 / 10.191392 (12.870204) | 0.247524 / 0.680424 (-0.432899) | 0.031712 / 0.534201 (-0.502489) | 0.513049 / 0.579283 (-0.066234) | 0.614568 / 0.434364 (0.180204) | 0.574669 / 0.540337 (0.034331) | 0.816621 / 1.386936 (-0.570315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009384 / 0.011353 (-0.001969) | 0.004959 / 0.011008 (-0.006049) | 0.084782 / 0.038508 (0.046274) | 0.098086 / 0.023109 (0.074977) | 0.544395 / 0.275898 (0.268497) | 0.585157 / 0.323480 (0.261677) | 0.006507 / 0.007986 (-0.001479) | 0.004151 / 0.004328 (-0.000178) | 0.088596 / 0.004250 (0.084345) | 0.069149 / 0.037052 (0.032097) | 0.533109 / 0.258489 (0.274620) | 0.604117 / 0.293841 (0.310276) | 0.047685 / 0.128546 (-0.080861) | 0.013651 / 0.075646 (-0.061996) | 0.096566 / 0.419271 (-0.322705) | 0.062022 / 0.043533 (0.018489) | 0.561897 / 0.255139 (0.306758) | 0.617636 / 0.283200 (0.334436) | 0.034636 / 0.141683 (-0.107047) | 1.854667 / 1.452155 (0.402512) | 1.908923 / 1.492716 (0.416207) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260633 / 0.018006 (0.242627) | 0.622268 / 0.000490 (0.621778) | 0.002116 / 0.000200 (0.001916) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035161 / 0.037411 (-0.002250) | 0.103707 / 0.014526 (0.089181) | 0.115467 / 0.176557 (-0.061090) | 0.180077 / 0.737135 (-0.557059) | 0.118871 / 0.296338 (-0.177467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.628481 / 0.215209 (0.413271) | 6.304929 / 2.077655 (4.227275) | 3.027775 / 1.504120 (1.523655) | 2.753880 / 1.541195 (1.212686) | 2.820442 / 1.468490 (1.351952) | 0.851103 / 4.584777 (-3.733674) | 5.427383 / 3.745712 (1.681670) | 7.434310 / 5.269862 (2.164449) | 4.418790 / 4.565676 (-0.146887) | 0.101733 / 0.424275 (-0.322542) | 0.009701 / 0.007607 (0.002094) | 0.763033 / 0.226044 (0.536989) | 7.497927 / 2.268929 (5.228998) | 3.735335 / 55.444624 (-51.709290) | 3.149200 / 6.876477 (-3.727277) | 3.306214 / 2.142072 (1.164141) | 1.085440 / 4.805227 (-3.719787) | 0.207562 / 6.500664 (-6.293102) | 0.078091 / 0.075469 (0.002622) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.820097 / 1.841788 (-0.021691) | 25.525539 / 8.074308 (17.451231) | 21.874219 / 10.191392 (11.682827) | 0.228391 / 0.680424 (-0.452033) | 0.029584 / 0.534201 (-0.504617) | 0.511546 / 0.579283 (-0.067737) | 0.602719 / 0.434364 (0.168355) | 0.581874 / 0.540337 (0.041537) | 0.802861 / 1.386936 (-0.584075) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6063ea2069c8b5641b983ba2c1d39b60afe7c00a \"CML watermark\")\n" ]

"2023-07-25T11:17:25"

"2023-07-25T15:29:39"

"2023-07-25T15:17:50"

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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) | 0.006186 / 0.011353 (-0.005167) | 0.003744 / 0.011008 (-0.007264) | 0.097295 / 0.038508 (0.058787) | 0.037106 / 0.023109 (0.013997) | 0.424154 / 0.275898 (0.148256) | 0.474536 / 0.323480 (0.151057) | 0.003454 / 0.007986 (-0.004532) | 0.003865 / 0.004328 (-0.000463) | 0.077348 / 0.004250 (0.073097) | 0.051728 / 0.037052 (0.014675) | 0.437120 / 0.258489 (0.178631) | 0.478379 / 0.293841 (0.184538) | 0.028939 / 0.128546 (-0.099608) | 0.008376 / 0.075646 (-0.067270) | 0.312002 / 0.419271 (-0.107270) | 0.053723 / 0.043533 (0.010190) | 0.424815 / 0.255139 (0.169676) | 0.446203 / 0.283200 (0.163004) | 0.026553 / 0.141683 (-0.115130) | 1.479983 / 1.452155 (0.027828) | 1.530613 / 1.492716 (0.037896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196627 / 0.018006 (0.178620) | 0.422361 / 0.000490 (0.421871) | 0.003442 / 0.000200 (0.003242) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.096011 / 0.014526 (0.081485) | 0.104091 / 0.176557 (-0.072466) | 0.163273 / 0.737135 (-0.573862) | 0.109142 / 0.296338 (-0.187197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431032 / 0.215209 (0.215823) | 4.314391 / 2.077655 (2.236737) | 2.003812 / 1.504120 (0.499692) | 1.799538 / 1.541195 (0.258344) | 1.830026 / 1.468490 (0.361536) | 0.560131 / 4.584777 (-4.024646) | 3.368997 / 3.745712 (-0.376715) | 1.703032 / 5.269862 (-3.566830) | 1.026949 / 4.565676 (-3.538727) | 0.067507 / 0.424275 (-0.356768) | 0.010910 / 0.007607 (0.003303) | 0.532606 / 0.226044 (0.306562) | 5.345179 / 2.268929 (3.076250) | 2.368077 / 55.444624 (-53.076548) | 2.028913 / 6.876477 (-4.847564) | 2.147621 / 2.142072 (0.005549) | 0.675696 / 4.805227 (-4.129531) | 0.134902 / 6.500664 (-6.365762) | 0.065004 / 0.075469 (-0.010465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233412 / 1.841788 (-0.608376) | 13.767465 / 8.074308 (5.693157) | 13.933653 / 10.191392 (3.742261) | 0.129010 / 0.680424 (-0.551414) | 0.016708 / 0.534201 (-0.517493) | 0.362341 / 0.579283 (-0.216942) | 0.390902 / 0.434364 (-0.043462) | 0.429156 / 0.540337 (-0.111182) | 0.521166 / 1.386936 (-0.865770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006169 / 0.011353 (-0.005184) | 0.003839 / 0.011008 (-0.007169) | 0.078784 / 0.038508 (0.040276) | 0.040218 / 0.023109 (0.017109) | 0.360439 / 0.275898 (0.084541) | 0.423957 / 0.323480 (0.100477) | 0.003456 / 0.007986 (-0.004529) | 0.002900 / 0.004328 (-0.001428) | 0.078820 / 0.004250 (0.074569) | 0.047240 / 0.037052 (0.010187) | 0.372081 / 0.258489 (0.113592) | 0.424263 / 0.293841 (0.130422) | 0.027977 / 0.128546 (-0.100569) | 0.008400 / 0.075646 (-0.067246) | 0.084399 / 0.419271 (-0.334872) | 0.043303 / 0.043533 (-0.000230) | 0.361583 / 0.255139 (0.106444) | 0.394987 / 0.283200 (0.111787) | 0.020006 / 0.141683 (-0.121677) | 1.520208 / 1.452155 (0.068053) | 1.587335 / 1.492716 (0.094619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223847 / 0.018006 (0.205840) | 0.402194 / 0.000490 (0.401704) | 0.000384 / 0.000200 (0.000184) | 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) | 0.024902 / 0.037411 (-0.012509) | 0.099076 / 0.014526 (0.084550) | 0.108041 / 0.176557 (-0.068516) | 0.159385 / 0.737135 (-0.577750) | 0.111442 / 0.296338 (-0.184896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446232 / 0.215209 (0.231023) | 4.464927 / 2.077655 (2.387272) | 2.155234 / 1.504120 (0.651114) | 1.953645 / 1.541195 (0.412450) | 1.965991 / 1.468490 (0.497501) | 0.553473 / 4.584777 (-4.031304) | 3.321397 / 3.745712 (-0.424315) | 1.693761 / 5.269862 (-3.576101) | 1.006299 / 4.565676 (-3.559378) | 0.067013 / 0.424275 (-0.357262) | 0.011116 / 0.007607 (0.003509) | 0.555014 / 0.226044 (0.328970) | 5.535694 / 2.268929 (3.266765) | 2.598339 / 55.444624 (-52.846285) | 2.249298 / 6.876477 (-4.627179) | 2.243419 / 2.142072 (0.101347) | 0.667603 / 4.805227 (-4.137624) | 0.133322 / 6.500664 (-6.367343) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293051 / 1.841788 (-0.548737) | 14.103731 / 8.074308 (6.029423) | 14.215204 / 10.191392 (4.023812) | 0.143990 / 0.680424 (-0.536434) | 0.016805 / 0.534201 (-0.517396) | 0.363264 / 0.579283 (-0.216019) | 0.392769 / 0.434364 (-0.041594) | 0.425291 / 0.540337 (-0.115046) | 0.515479 / 1.386936 (-0.871457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006346 / 0.011353 (-0.005006) | 0.004130 / 0.011008 (-0.006878) | 0.096898 / 0.038508 (0.058390) | 0.042564 / 0.023109 (0.019455) | 0.343748 / 0.275898 (0.067850) | 0.412515 / 0.323480 (0.089035) | 0.006153 / 0.007986 (-0.001833) | 0.003345 / 0.004328 (-0.000984) | 0.075314 / 0.004250 (0.071064) | 0.061478 / 0.037052 (0.024426) | 0.362948 / 0.258489 (0.104459) | 0.401533 / 0.293841 (0.107692) | 0.032363 / 0.128546 (-0.096184) | 0.008780 / 0.075646 (-0.066867) | 0.328691 / 0.419271 (-0.090580) | 0.054253 / 0.043533 (0.010721) | 0.340783 / 0.255139 (0.085644) | 0.360705 / 0.283200 (0.077505) | 0.023183 / 0.141683 (-0.118500) | 1.484078 / 1.452155 (0.031924) | 1.528581 / 1.492716 (0.035865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208732 / 0.018006 (0.190726) | 0.452572 / 0.000490 (0.452082) | 0.002936 / 0.000200 (0.002737) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024616 / 0.037411 (-0.012795) | 0.107547 / 0.014526 (0.093021) | 0.114492 / 0.176557 (-0.062065) | 0.171770 / 0.737135 (-0.565365) | 0.122538 / 0.296338 (-0.173800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406140 / 0.215209 (0.190930) | 4.062391 / 2.077655 (1.984736) | 1.865962 / 1.504120 (0.361842) | 1.682236 / 1.541195 (0.141041) | 1.738119 / 1.468490 (0.269629) | 0.532244 / 4.584777 (-4.052533) | 3.816421 / 3.745712 (0.070709) | 2.981205 / 5.269862 (-2.288656) | 1.519497 / 4.565676 (-3.046179) | 0.065904 / 0.424275 (-0.358371) | 0.011277 / 0.007607 (0.003670) | 0.512789 / 0.226044 (0.286745) | 5.107618 / 2.268929 (2.838690) | 2.419399 / 55.444624 (-53.025226) | 2.079262 / 6.876477 (-4.797214) | 2.150447 / 2.142072 (0.008375) | 0.696737 / 4.805227 (-4.108490) | 0.142497 / 6.500664 (-6.358167) | 0.063521 / 0.075469 (-0.011949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180692 / 1.841788 (-0.661095) | 14.343084 / 8.074308 (6.268776) | 13.303719 / 10.191392 (3.112327) | 0.164234 / 0.680424 (-0.516190) | 0.017439 / 0.534201 (-0.516762) | 0.399712 / 0.579283 (-0.179571) | 0.428248 / 0.434364 (-0.006115) | 0.471909 / 0.540337 (-0.068428) | 0.573853 / 1.386936 (-0.813083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006210 / 0.011353 (-0.005143) | 0.004104 / 0.011008 (-0.006905) | 0.075140 / 0.038508 (0.036632) | 0.044647 / 0.023109 (0.021538) | 0.370120 / 0.275898 (0.094222) | 0.452936 / 0.323480 (0.129457) | 0.003943 / 0.007986 (-0.004042) | 0.003285 / 0.004328 (-0.001043) | 0.075267 / 0.004250 (0.071017) | 0.055517 / 0.037052 (0.018465) | 0.396385 / 0.258489 (0.137896) | 0.447870 / 0.293841 (0.154029) | 0.031342 / 0.128546 (-0.097204) | 0.008720 / 0.075646 (-0.066926) | 0.082702 / 0.419271 (-0.336570) | 0.051010 / 0.043533 (0.007477) | 0.350546 / 0.255139 (0.095407) | 0.425395 / 0.283200 (0.142195) | 0.024483 / 0.141683 (-0.117200) | 1.467341 / 1.452155 (0.015186) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218067 / 0.018006 (0.200061) | 0.441603 / 0.000490 (0.441114) | 0.003711 / 0.000200 (0.003512) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028669 / 0.037411 (-0.008742) | 0.112941 / 0.014526 (0.098415) | 0.122584 / 0.176557 (-0.053972) | 0.176494 / 0.737135 (-0.560641) | 0.129369 / 0.296338 (-0.166970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434543 / 0.215209 (0.219334) | 4.344056 / 2.077655 (2.266401) | 2.079286 / 1.504120 (0.575166) | 1.887264 / 1.541195 (0.346069) | 1.910386 / 1.468490 (0.441896) | 0.538824 / 4.584777 (-4.045953) | 3.844786 / 3.745712 (0.099074) | 2.902091 / 5.269862 (-2.367770) | 1.270852 / 4.565676 (-3.294824) | 0.066324 / 0.424275 (-0.357951) | 0.011346 / 0.007607 (0.003739) | 0.537122 / 0.226044 (0.311078) | 5.367354 / 2.268929 (3.098426) | 2.533672 / 55.444624 (-52.910952) | 2.203260 / 6.876477 (-4.673217) | 2.224310 / 2.142072 (0.082237) | 0.663806 / 4.805227 (-4.141422) | 0.142758 / 6.500664 (-6.357906) | 0.063870 / 0.075469 (-0.011599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260487 / 1.841788 (-0.581301) | 14.800106 / 8.074308 (6.725798) | 13.993488 / 10.191392 (3.802096) | 0.165829 / 0.680424 (-0.514595) | 0.017347 / 0.534201 (-0.516854) | 0.401819 / 0.579283 (-0.177464) | 0.424577 / 0.434364 (-0.009787) | 0.475161 / 0.540337 (-0.065176) | 0.574659 / 1.386936 (-0.812277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n" ]

"2023-06-19T16:56:06"

"2023-06-19T17:29:11"

"2023-06-19T17:22:10"

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[ "https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error", "Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq", "> Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq\n\nThe problem has not been solved, I have tried this before, but the problem is the same", "> \r\n\r\n@syngokhan did u solve it? \r\nI am desperate ", "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False) # change this line to -\r\n\r\ndata[\"train\"] = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False)\r\nAfter doing this change you code should run fine.", "> > \r\n> \r\n> @syngokhan did u solve it? I am desperate\r\n\r\nrefer to my earlier comment. you will find the solution." ]

"2023-06-13T07:34:15"

"2023-07-14T12:04:48"

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[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]

"2023-07-05T06:23:41"

"2023-07-05T06:31:02"

"2023-07-05T06:31:01"

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[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008422 / 0.011353 (-0.002931) | 0.005658 / 0.011008 (-0.005350) | 0.135372 / 0.038508 (0.096864) | 0.044766 / 0.023109 (0.021657) | 0.417876 / 0.275898 (0.141978) | 0.462785 / 0.323480 (0.139305) | 0.005485 / 0.007986 (-0.002501) | 0.005640 / 0.004328 (0.001311) | 0.105020 / 0.004250 (0.100770) | 0.049114 / 0.037052 (0.012062) | 0.490450 / 0.258489 (0.231961) | 0.467693 / 0.293841 (0.173852) | 0.050929 / 0.128546 (-0.077617) | 0.014644 / 0.075646 (-0.061002) | 0.452373 / 0.419271 (0.033101) | 0.074897 / 0.043533 (0.031364) | 0.425816 / 0.255139 (0.170677) | 0.420415 / 0.283200 (0.137215) | 0.134121 / 0.141683 (-0.007561) | 1.927744 / 1.452155 (0.475589) | 2.014417 / 1.492716 (0.521701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254811 / 0.018006 (0.236805) | 0.550011 / 0.000490 (0.549521) | 0.004913 / 0.000200 (0.004714) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032644 / 0.037411 (-0.004768) | 0.135672 / 0.014526 (0.121146) | 0.158984 / 0.176557 (-0.017572) | 0.218267 / 0.737135 (-0.518869) | 0.150348 / 0.296338 (-0.145991) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.625723 / 0.215209 (0.410514) | 6.247559 / 2.077655 (4.169905) | 2.626785 / 1.504120 (1.122666) | 2.195224 / 1.541195 (0.654030) | 2.232140 / 1.468490 (0.763650) | 0.943082 / 4.584777 (-3.641695) | 5.799262 / 3.745712 (2.053550) | 2.849411 / 5.269862 (-2.420450) | 1.744160 / 4.565676 (-2.821516) | 0.119056 / 0.424275 (-0.305219) | 0.014233 / 0.007607 (0.006626) | 0.795238 / 0.226044 (0.569194) | 7.569586 / 2.268929 (5.300657) | 3.179481 / 55.444624 (-52.265143) | 2.519772 / 6.876477 (-4.356704) | 2.714570 / 2.142072 (0.572498) | 1.107197 / 4.805227 (-3.698030) | 0.229986 / 6.500664 (-6.270678) | 0.087993 / 0.075469 (0.012524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.535610 / 1.841788 (-0.306178) | 18.639369 / 8.074308 (10.565061) | 21.081844 / 10.191392 (10.890452) | 0.253247 / 0.680424 (-0.427177) | 0.026711 / 0.534201 (-0.507490) | 0.503790 / 0.579283 (-0.075493) | 0.600124 / 0.434364 (0.165760) | 0.617944 / 0.540337 (0.077607) | 0.766947 / 1.386936 (-0.619989) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007885 / 0.011353 (-0.003468) | 0.004761 / 0.011008 (-0.006248) | 0.097995 / 0.038508 (0.059487) | 0.033624 / 0.023109 (0.010515) | 0.504307 / 0.275898 (0.228409) | 0.534803 / 0.323480 (0.211323) | 0.006048 / 0.007986 (-0.001937) | 0.005042 / 0.004328 (0.000714) | 0.102288 / 0.004250 (0.098038) | 0.048695 / 0.037052 (0.011643) | 0.559086 / 0.258489 (0.300597) | 0.553233 / 0.293841 (0.259392) | 0.044596 / 0.128546 (-0.083950) | 0.013696 / 0.075646 (-0.061950) | 0.109875 / 0.419271 (-0.309397) | 0.059993 / 0.043533 (0.016460) | 0.485579 / 0.255139 (0.230440) | 0.519835 / 0.283200 (0.236635) | 0.123504 / 0.141683 (-0.018179) | 1.820506 / 1.452155 (0.368351) | 1.963448 / 1.492716 (0.470732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292663 / 0.018006 (0.274656) | 0.557783 / 0.000490 (0.557293) | 0.001330 / 0.000200 (0.001130) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036890 / 0.037411 (-0.000522) | 0.140373 / 0.014526 (0.125847) | 0.140176 / 0.176557 (-0.036381) | 0.237378 / 0.737135 (-0.499757) | 0.160186 / 0.296338 (-0.136152) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.673599 / 0.215209 (0.458390) | 6.510280 / 2.077655 (4.432625) | 2.981617 / 1.504120 (1.477497) | 2.684664 / 1.541195 (1.143469) | 2.760471 / 1.468490 (1.291981) | 0.975413 / 4.584777 (-3.609364) | 5.708933 / 3.745712 (1.963220) | 2.772069 / 5.269862 (-2.497793) | 1.763627 / 4.565676 (-2.802049) | 0.111632 / 0.424275 (-0.312643) | 0.013223 / 0.007607 (0.005616) | 0.791545 / 0.226044 (0.565500) | 8.063287 / 2.268929 (5.794359) | 3.671920 / 55.444624 (-51.772704) | 3.057248 / 6.876477 (-3.819229) | 3.083569 / 2.142072 (0.941497) | 1.118136 / 4.805227 (-3.687092) | 0.214655 / 6.500664 (-6.286009) | 0.083074 / 0.075469 (0.007605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.761731 / 1.841788 (-0.080056) | 18.874200 / 8.074308 (10.799892) | 22.383693 / 10.191392 (12.192301) | 0.240292 / 0.680424 (-0.440132) | 0.028850 / 0.534201 (-0.505351) | 0.557334 / 0.579283 (-0.021949) | 0.627732 / 0.434364 (0.193369) | 0.634484 / 0.540337 (0.094146) | 0.767372 / 1.386936 (-0.619564) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]

"2023-06-02T22:25:25"

"2023-06-14T10:25:10"

"2023-06-14T10:15:46"

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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) | 0.006722 / 0.011353 (-0.004631) | 0.004425 / 0.011008 (-0.006583) | 0.100850 / 0.038508 (0.062341) | 0.040816 / 0.023109 (0.017707) | 0.348823 / 0.275898 (0.072925) | 0.446285 / 0.323480 (0.122805) | 0.005738 / 0.007986 (-0.002247) | 0.003517 / 0.004328 (-0.000811) | 0.078824 / 0.004250 (0.074574) | 0.064695 / 0.037052 (0.027643) | 0.389894 / 0.258489 (0.131405) | 0.416107 / 0.293841 (0.122266) | 0.028850 / 0.128546 (-0.099696) | 0.009011 / 0.075646 (-0.066635) | 0.323117 / 0.419271 (-0.096154) | 0.049162 / 0.043533 (0.005629) | 0.340144 / 0.255139 (0.085005) | 0.382072 / 0.283200 (0.098872) | 0.023160 / 0.141683 (-0.118523) | 1.549218 / 1.452155 (0.097063) | 1.581266 / 1.492716 (0.088550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293360 / 0.018006 (0.275353) | 0.602189 / 0.000490 (0.601700) | 0.004608 / 0.000200 (0.004408) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.107088 / 0.014526 (0.092562) | 0.112188 / 0.176557 (-0.064369) | 0.174669 / 0.737135 (-0.562466) | 0.116359 / 0.296338 (-0.179980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422911 / 0.215209 (0.207702) | 4.231524 / 2.077655 (2.153869) | 1.906711 / 1.504120 (0.402591) | 1.706841 / 1.541195 (0.165646) | 1.792066 / 1.468490 (0.323576) | 0.559221 / 4.584777 (-4.025556) | 3.434280 / 3.745712 (-0.311433) | 1.918714 / 5.269862 (-3.351148) | 1.073070 / 4.565676 (-3.492606) | 0.067891 / 0.424275 (-0.356384) | 0.011927 / 0.007607 (0.004320) | 0.530843 / 0.226044 (0.304799) | 5.309213 / 2.268929 (3.040285) | 2.439246 / 55.444624 (-53.005378) | 2.101245 / 6.876477 (-4.775231) | 2.177436 / 2.142072 (0.035363) | 0.672150 / 4.805227 (-4.133077) | 0.137571 / 6.500664 (-6.363093) | 0.068343 / 0.075469 (-0.007126) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265262 / 1.841788 (-0.576525) | 14.988021 / 8.074308 (6.913713) | 13.611677 / 10.191392 (3.420285) | 0.171389 / 0.680424 (-0.509035) | 0.017681 / 0.534201 (-0.516520) | 0.377542 / 0.579283 (-0.201741) | 0.399475 / 0.434364 (-0.034889) | 0.469553 / 0.540337 (-0.070785) | 0.561888 / 1.386936 (-0.825048) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006782 / 0.011353 (-0.004571) | 0.004412 / 0.011008 (-0.006597) | 0.078594 / 0.038508 (0.040086) | 0.039930 / 0.023109 (0.016820) | 0.371879 / 0.275898 (0.095981) | 0.444910 / 0.323480 (0.121430) | 0.005707 / 0.007986 (-0.002279) | 0.003901 / 0.004328 (-0.000427) | 0.080125 / 0.004250 (0.075875) | 0.063977 / 0.037052 (0.026925) | 0.382781 / 0.258489 (0.124292) | 0.441791 / 0.293841 (0.147950) | 0.030428 / 0.128546 (-0.098118) | 0.009008 / 0.075646 (-0.066638) | 0.084447 / 0.419271 (-0.334824) | 0.044432 / 0.043533 (0.000899) | 0.365686 / 0.255139 (0.110547) | 0.394312 / 0.283200 (0.111113) | 0.024508 / 0.141683 (-0.117175) | 1.577020 / 1.452155 (0.124865) | 1.630259 / 1.492716 (0.137543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307960 / 0.018006 (0.289953) | 0.591473 / 0.000490 (0.590983) | 0.008098 / 0.000200 (0.007898) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029567 / 0.037411 (-0.007845) | 0.112773 / 0.014526 (0.098247) | 0.117362 / 0.176557 (-0.059194) | 0.174293 / 0.737135 (-0.562843) | 0.123156 / 0.296338 (-0.173182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457475 / 0.215209 (0.242266) | 4.599067 / 2.077655 (2.521412) | 2.262638 / 1.504120 (0.758518) | 2.124943 / 1.541195 (0.583748) | 2.339912 / 1.468490 (0.871422) | 0.566264 / 4.584777 (-4.018513) | 3.489261 / 3.745712 (-0.256451) | 1.925151 / 5.269862 (-3.344711) | 1.099389 / 4.565676 (-3.466287) | 0.068232 / 0.424275 (-0.356043) | 0.011660 / 0.007607 (0.004052) | 0.571227 / 0.226044 (0.345183) | 5.702059 / 2.268929 (3.433130) | 2.837701 / 55.444624 (-52.606924) | 2.605468 / 6.876477 (-4.271008) | 2.818396 / 2.142072 (0.676323) | 0.681856 / 4.805227 (-4.123371) | 0.141401 / 6.500664 (-6.359263) | 0.069728 / 0.075469 (-0.005741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354935 / 1.841788 (-0.486853) | 15.437404 / 8.074308 (7.363095) | 15.415193 / 10.191392 (5.223801) | 0.153459 / 0.680424 (-0.526964) | 0.017190 / 0.534201 (-0.517011) | 0.367256 / 0.579283 (-0.212027) | 0.392709 / 0.434364 (-0.041655) | 0.426125 / 0.540337 (-0.114213) | 0.522612 / 1.386936 (-0.864324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009183 / 0.011353 (-0.002170) | 0.005232 / 0.011008 (-0.005776) | 0.120349 / 0.038508 (0.081841) | 0.044715 / 0.023109 (0.021606) | 0.361519 / 0.275898 (0.085621) | 0.463702 / 0.323480 (0.140223) | 0.005842 / 0.007986 (-0.002144) | 0.004041 / 0.004328 (-0.000288) | 0.096953 / 0.004250 (0.092703) | 0.070593 / 0.037052 (0.033540) | 0.409790 / 0.258489 (0.151301) | 0.477452 / 0.293841 (0.183611) | 0.045827 / 0.128546 (-0.082719) | 0.014038 / 0.075646 (-0.061608) | 0.421317 / 0.419271 (0.002045) | 0.065276 / 0.043533 (0.021743) | 0.360074 / 0.255139 (0.104935) | 0.409147 / 0.283200 (0.125947) | 0.032444 / 0.141683 (-0.109238) | 1.739257 / 1.452155 (0.287102) | 1.831408 / 1.492716 (0.338692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274852 / 0.018006 (0.256846) | 0.596320 / 0.000490 (0.595830) | 0.006399 / 0.000200 (0.006199) | 0.000133 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031400 / 0.037411 (-0.006012) | 0.127052 / 0.014526 (0.112526) | 0.134269 / 0.176557 (-0.042288) | 0.225998 / 0.737135 (-0.511137) | 0.150019 / 0.296338 (-0.146319) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654202 / 0.215209 (0.438993) | 6.216735 / 2.077655 (4.139081) | 2.440214 / 1.504120 (0.936094) | 2.150575 / 1.541195 (0.609380) | 2.124790 / 1.468490 (0.656300) | 0.923514 / 4.584777 (-3.661263) | 5.556924 / 3.745712 (1.811212) | 2.843886 / 5.269862 (-2.425975) | 1.834232 / 4.565676 (-2.731444) | 0.111735 / 0.424275 (-0.312540) | 0.014823 / 0.007607 (0.007216) | 0.820503 / 0.226044 (0.594459) | 7.887737 / 2.268929 (5.618809) | 3.120307 / 55.444624 (-52.324317) | 2.405856 / 6.876477 (-4.470621) | 2.411239 / 2.142072 (0.269167) | 1.071283 / 4.805227 (-3.733944) | 0.227738 / 6.500664 (-6.272926) | 0.073516 / 0.075469 (-0.001953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.531806 / 1.841788 (-0.309982) | 18.547661 / 8.074308 (10.473353) | 21.083922 / 10.191392 (10.892530) | 0.241706 / 0.680424 (-0.438718) | 0.034169 / 0.534201 (-0.500032) | 0.497514 / 0.579283 (-0.081769) | 0.599801 / 0.434364 (0.165437) | 0.576465 / 0.540337 (0.036127) | 0.673509 / 1.386936 (-0.713427) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007558 / 0.011353 (-0.003795) | 0.005001 / 0.011008 (-0.006008) | 0.093809 / 0.038508 (0.055301) | 0.039792 / 0.023109 (0.016683) | 0.456869 / 0.275898 (0.180971) | 0.493370 / 0.323480 (0.169891) | 0.005561 / 0.007986 (-0.002424) | 0.003982 / 0.004328 (-0.000346) | 0.085421 / 0.004250 (0.081170) | 0.059817 / 0.037052 (0.022765) | 0.468040 / 0.258489 (0.209550) | 0.514853 / 0.293841 (0.221012) | 0.044267 / 0.128546 (-0.084279) | 0.012674 / 0.075646 (-0.062972) | 0.098324 / 0.419271 (-0.320948) | 0.056604 / 0.043533 (0.013071) | 0.432200 / 0.255139 (0.177061) | 0.459812 / 0.283200 (0.176612) | 0.033872 / 0.141683 (-0.107811) | 1.618576 / 1.452155 (0.166421) | 1.676562 / 1.492716 (0.183846) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230625 / 0.018006 (0.212619) | 0.600558 / 0.000490 (0.600068) | 0.003419 / 0.000200 (0.003219) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026916 / 0.037411 (-0.010496) | 0.103003 / 0.014526 (0.088478) | 0.117078 / 0.176557 (-0.059478) | 0.169359 / 0.737135 (-0.567776) | 0.120305 / 0.296338 (-0.176034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616877 / 0.215209 (0.401668) | 6.157232 / 2.077655 (4.079577) | 2.869219 / 1.504120 (1.365099) | 2.381410 / 1.541195 (0.840216) | 2.417357 / 1.468490 (0.948867) | 0.914947 / 4.584777 (-3.669830) | 5.718526 / 3.745712 (1.972814) | 2.757253 / 5.269862 (-2.512609) | 1.794122 / 4.565676 (-2.771554) | 0.108423 / 0.424275 (-0.315852) | 0.013378 / 0.007607 (0.005771) | 0.831067 / 0.226044 (0.605023) | 8.478946 / 2.268929 (6.210018) | 3.685937 / 55.444624 (-51.758687) | 2.867472 / 6.876477 (-4.009005) | 2.895975 / 2.142072 (0.753903) | 1.137547 / 4.805227 (-3.667681) | 0.213891 / 6.500664 (-6.286773) | 0.075825 / 0.075469 (0.000356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621193 / 1.841788 (-0.220594) | 17.322110 / 8.074308 (9.247802) | 21.804016 / 10.191392 (11.612624) | 0.243692 / 0.680424 (-0.436732) | 0.030331 / 0.534201 (-0.503870) | 0.492186 / 0.579283 (-0.087097) | 0.632583 / 0.434364 (0.198219) | 0.576265 / 0.540337 (0.035927) | 0.713165 / 1.386936 (-0.673771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) | 0.008916 / 0.011353 (-0.002437) | 0.004737 / 0.011008 (-0.006271) | 0.134271 / 0.038508 (0.095763) | 0.054472 / 0.023109 (0.031363) | 0.380942 / 0.275898 (0.105044) | 0.474138 / 0.323480 (0.150658) | 0.007917 / 0.007986 (-0.000068) | 0.003748 / 0.004328 (-0.000580) | 0.092765 / 0.004250 (0.088515) | 0.077873 / 0.037052 (0.040821) | 0.397533 / 0.258489 (0.139043) | 0.454737 / 0.293841 (0.160896) | 0.039901 / 0.128546 (-0.088645) | 0.010188 / 0.075646 (-0.065458) | 0.447312 / 0.419271 (0.028040) | 0.068684 / 0.043533 (0.025151) | 0.371554 / 0.255139 (0.116415) | 0.459655 / 0.283200 (0.176455) | 0.027157 / 0.141683 (-0.114526) | 1.874643 / 1.452155 (0.422488) | 2.014800 / 1.492716 (0.522083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227079 / 0.018006 (0.209073) | 0.483241 / 0.000490 (0.482751) | 0.012404 / 0.000200 (0.012204) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033135 / 0.037411 (-0.004277) | 0.137782 / 0.014526 (0.123257) | 0.142951 / 0.176557 (-0.033605) | 0.209825 / 0.737135 (-0.527311) | 0.152438 / 0.296338 (-0.143900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513066 / 0.215209 (0.297857) | 5.122776 / 2.077655 (3.045121) | 2.399270 / 1.504120 (0.895150) | 2.180143 / 1.541195 (0.638949) | 2.286395 / 1.468490 (0.817905) | 0.641866 / 4.584777 (-3.942911) | 4.694922 / 3.745712 (0.949210) | 2.543390 / 5.269862 (-2.726472) | 1.398592 / 4.565676 (-3.167084) | 0.088662 / 0.424275 (-0.335613) | 0.015854 / 0.007607 (0.008247) | 0.688891 / 0.226044 (0.462847) | 6.370148 / 2.268929 (4.101220) | 2.949974 / 55.444624 (-52.494650) | 2.538049 / 6.876477 (-4.338428) | 2.699380 / 2.142072 (0.557308) | 0.792670 / 4.805227 (-4.012557) | 0.169126 / 6.500664 (-6.331538) | 0.078511 / 0.075469 (0.003042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609119 / 1.841788 (-0.232669) | 18.785069 / 8.074308 (10.710761) | 16.670783 / 10.191392 (6.479391) | 0.213081 / 0.680424 (-0.467343) | 0.023904 / 0.534201 (-0.510296) | 0.567720 / 0.579283 (-0.011564) | 0.505806 / 0.434364 (0.071442) | 0.649466 / 0.540337 (0.109129) | 0.773174 / 1.386936 (-0.613762) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008036 / 0.011353 (-0.003317) | 0.004808 / 0.011008 (-0.006201) | 0.094316 / 0.038508 (0.055808) | 0.056174 / 0.023109 (0.033065) | 0.481618 / 0.275898 (0.205720) | 0.565300 / 0.323480 (0.241820) | 0.006339 / 0.007986 (-0.001646) | 0.003950 / 0.004328 (-0.000379) | 0.093389 / 0.004250 (0.089139) | 0.076163 / 0.037052 (0.039111) | 0.489013 / 0.258489 (0.230524) | 0.565451 / 0.293841 (0.271611) | 0.039392 / 0.128546 (-0.089155) | 0.010553 / 0.075646 (-0.065093) | 0.101406 / 0.419271 (-0.317865) | 0.062355 / 0.043533 (0.018822) | 0.470461 / 0.255139 (0.215322) | 0.502574 / 0.283200 (0.219375) | 0.030196 / 0.141683 (-0.111486) | 1.893926 / 1.452155 (0.441771) | 1.958902 / 1.492716 (0.466185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198074 / 0.018006 (0.180068) | 0.476828 / 0.000490 (0.476338) | 0.003457 / 0.000200 (0.003257) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037576 / 0.037411 (0.000165) | 0.146663 / 0.014526 (0.132138) | 0.152969 / 0.176557 (-0.023588) | 0.218683 / 0.737135 (-0.518452) | 0.161552 / 0.296338 (-0.134786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.525988 / 0.215209 (0.310779) | 5.234673 / 2.077655 (3.157018) | 2.571668 / 1.504120 (1.067548) | 2.339760 / 1.541195 (0.798565) | 2.422886 / 1.468490 (0.954395) | 0.651537 / 4.584777 (-3.933240) | 4.811148 / 3.745712 (1.065436) | 4.451165 / 5.269862 (-0.818697) | 2.016283 / 4.565676 (-2.549394) | 0.096393 / 0.424275 (-0.327882) | 0.015222 / 0.007607 (0.007615) | 0.739132 / 0.226044 (0.513087) | 6.813327 / 2.268929 (4.544399) | 3.169018 / 55.444624 (-52.275606) | 2.783120 / 6.876477 (-4.093356) | 2.918979 / 2.142072 (0.776907) | 0.797476 / 4.805227 (-4.007751) | 0.171038 / 6.500664 (-6.329626) | 0.079878 / 0.075469 (0.004409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595082 / 1.841788 (-0.246705) | 19.685844 / 8.074308 (11.611536) | 17.518989 / 10.191392 (7.327597) | 0.220015 / 0.680424 (-0.460409) | 0.026351 / 0.534201 (-0.507850) | 0.578977 / 0.579283 (-0.000306) | 0.549564 / 0.434364 (0.115200) | 0.667564 / 0.540337 (0.127227) | 0.802121 / 1.386936 (-0.584815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]

"2023-06-30T12:36:54"

"2023-06-30T13:17:05"

"2023-06-30T13:08:27"

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[ "cc @sanchit-gandhi @Vaibhavs10 @lhoestq - this is mainly for demos that use Common Voice datasets as done here: https://github.com/facebookresearch/fairseq/tree/main/examples/mms#-transformers\r\n", "Hi ! the error for me is\r\n\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/mozilla-foundation/common_voice_13_0/common_voice_13_0.py or any data file in the same directory. Couldn't find 'mozilla-foundation/common_voice_13_0' on the Hugging Face Hub either: FileNotFoundError: Dataset 'mozilla-foundation/common_voice_13_0' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\nAnd tbh idk how you managed to get your error. \"n_shards.json\" is not even a thing in `datasets`", "Okay, I am able to reproduce @patrickvonplaten's original error: https://github.com/Vaibhavs10/scratchpad/blob/main/cv13_datasets_test.ipynb\r\n\r\nAlso not sure why it looks for `n_shards.json`", "Ok I see, this file is downloaded from the CV dataset script - let me investigate", "Ok I see: when you log out you no longer have access to the repository.\r\n\r\nTherefore the dataset script is loaded from cache:\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_13_0/22809012aac1fc9803eaffc44122e4149043748e93933935d5ea19898587e4d7 (last modified on Wed Jun 14 10:13:17 2023) since it couldn't be found locally at mozilla-foundation/common_voice_13_0., or remotely on the Hugging Face Hub.\r\n```\r\n\r\nand the script tries to download the n_shards.json but fails", "Is this ok for you https://github.com/huggingface/datasets/pull/5954 ?\r\n\r\nI'll do a release this afternoon", "Cool! ", "this is included in the new release 2.13.0" ]

"2023-06-14T10:03:39"

"2023-06-14T16:36:51"

"2023-06-14T12:26:32"

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[ "You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize." ]

"2023-07-10T06:42:20"

"2023-07-10T15:37:52"

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

"2023-07-26T09:37:20"

"2023-07-26T11:04:35"

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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) | 0.006152 / 0.011353 (-0.005200) | 0.003916 / 0.011008 (-0.007092) | 0.097355 / 0.038508 (0.058847) | 0.037228 / 0.023109 (0.014119) | 0.315753 / 0.275898 (0.039855) | 0.387949 / 0.323480 (0.064470) | 0.004804 / 0.007986 (-0.003181) | 0.002975 / 0.004328 (-0.001353) | 0.076932 / 0.004250 (0.072682) | 0.053497 / 0.037052 (0.016445) | 0.331143 / 0.258489 (0.072654) | 0.388347 / 0.293841 (0.094506) | 0.027535 / 0.128546 (-0.101011) | 0.008509 / 0.075646 (-0.067137) | 0.312639 / 0.419271 (-0.106632) | 0.047212 / 0.043533 (0.003679) | 0.316875 / 0.255139 (0.061736) | 0.352191 / 0.283200 (0.068992) | 0.021380 / 0.141683 (-0.120303) | 1.541401 / 1.452155 (0.089247) | 1.519420 / 1.492716 (0.026704) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206332 / 0.018006 (0.188326) | 0.412252 / 0.000490 (0.411762) | 0.005119 / 0.000200 (0.004919) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023856 / 0.037411 (-0.013556) | 0.098216 / 0.014526 (0.083691) | 0.106553 / 0.176557 (-0.070003) | 0.168767 / 0.737135 (-0.568369) | 0.109244 / 0.296338 (-0.187094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457580 / 0.215209 (0.242371) | 4.583246 / 2.077655 (2.505591) | 2.296356 / 1.504120 (0.792236) | 2.096216 / 1.541195 (0.555021) | 2.159086 / 1.468490 (0.690596) | 0.557905 / 4.584777 (-4.026872) | 3.345910 / 3.745712 (-0.399802) | 1.767436 / 5.269862 (-3.502426) | 1.021583 / 4.565676 (-3.544094) | 0.067265 / 0.424275 (-0.357011) | 0.011411 / 0.007607 (0.003804) | 0.559841 / 0.226044 (0.333797) | 5.586892 / 2.268929 (3.317963) | 2.735520 / 55.444624 (-52.709104) | 2.429393 / 6.876477 (-4.447084) | 2.544901 / 2.142072 (0.402829) | 0.667603 / 4.805227 (-4.137625) | 0.136244 / 6.500664 (-6.364421) | 0.066961 / 0.075469 (-0.008508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206529 / 1.841788 (-0.635259) | 13.988306 / 8.074308 (5.913998) | 13.481813 / 10.191392 (3.290421) | 0.161901 / 0.680424 (-0.518523) | 0.016850 / 0.534201 (-0.517351) | 0.367657 / 0.579283 (-0.211626) | 0.393343 / 0.434364 (-0.041021) | 0.465288 / 0.540337 (-0.075050) | 0.559888 / 1.386936 (-0.827048) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005956 / 0.011353 (-0.005397) | 0.003734 / 0.011008 (-0.007274) | 0.077841 / 0.038508 (0.039333) | 0.036532 / 0.023109 (0.013422) | 0.438923 / 0.275898 (0.163025) | 0.490133 / 0.323480 (0.166653) | 0.004651 / 0.007986 (-0.003335) | 0.002881 / 0.004328 (-0.001448) | 0.077868 / 0.004250 (0.073618) | 0.051700 / 0.037052 (0.014647) | 0.448018 / 0.258489 (0.189529) | 0.500304 / 0.293841 (0.206464) | 0.029051 / 0.128546 (-0.099496) | 0.008498 / 0.075646 (-0.067148) | 0.082932 / 0.419271 (-0.336339) | 0.043665 / 0.043533 (0.000132) | 0.431613 / 0.255139 (0.176474) | 0.458749 / 0.283200 (0.175549) | 0.021951 / 0.141683 (-0.119731) | 1.556043 / 1.452155 (0.103888) | 1.588391 / 1.492716 (0.095675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220674 / 0.018006 (0.202667) | 0.415408 / 0.000490 (0.414918) | 0.002613 / 0.000200 (0.002413) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025548 / 0.037411 (-0.011863) | 0.103633 / 0.014526 (0.089107) | 0.115193 / 0.176557 (-0.061364) | 0.163971 / 0.737135 (-0.573164) | 0.114754 / 0.296338 (-0.181585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456823 / 0.215209 (0.241614) | 4.569950 / 2.077655 (2.492296) | 2.196339 / 1.504120 (0.692219) | 1.985822 / 1.541195 (0.444628) | 2.044083 / 1.468490 (0.575593) | 0.567919 / 4.584777 (-4.016858) | 3.397515 / 3.745712 (-0.348197) | 1.741087 / 5.269862 (-3.528775) | 1.041237 / 4.565676 (-3.524440) | 0.068963 / 0.424275 (-0.355313) | 0.011677 / 0.007607 (0.004070) | 0.565010 / 0.226044 (0.338966) | 5.625886 / 2.268929 (3.356957) | 2.670658 / 55.444624 (-52.773967) | 2.300279 / 6.876477 (-4.576198) | 2.392178 / 2.142072 (0.250106) | 0.680226 / 4.805227 (-4.125001) | 0.139119 / 6.500664 (-6.361545) | 0.067953 / 0.075469 (-0.007516) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303280 / 1.841788 (-0.538507) | 14.458686 / 8.074308 (6.384378) | 14.409369 / 10.191392 (4.217977) | 0.144581 / 0.680424 (-0.535843) | 0.016634 / 0.534201 (-0.517567) | 0.364607 / 0.579283 (-0.214676) | 0.394521 / 0.434364 (-0.039843) | 0.433417 / 0.540337 (-0.106921) | 0.527127 / 1.386936 (-0.859809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.003871 / 0.011008 (-0.007138) | 0.098823 / 0.038508 (0.060315) | 0.039853 / 0.023109 (0.016744) | 0.314989 / 0.275898 (0.039091) | 0.376733 / 0.323480 (0.053254) | 0.004754 / 0.007986 (-0.003232) | 0.002971 / 0.004328 (-0.001357) | 0.078451 / 0.004250 (0.074201) | 0.053160 / 0.037052 (0.016107) | 0.324443 / 0.258489 (0.065954) | 0.361488 / 0.293841 (0.067647) | 0.027942 / 0.128546 (-0.100604) | 0.008535 / 0.075646 (-0.067111) | 0.315526 / 0.419271 (-0.103745) | 0.045706 / 0.043533 (0.002174) | 0.329614 / 0.255139 (0.074475) | 0.336339 / 0.283200 (0.053139) | 0.021278 / 0.141683 (-0.120405) | 1.529710 / 1.452155 (0.077555) | 1.566833 / 1.492716 (0.074116) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215263 / 0.018006 (0.197257) | 0.440320 / 0.000490 (0.439830) | 0.002627 / 0.000200 (0.002427) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023971 / 0.037411 (-0.013441) | 0.100549 / 0.014526 (0.086023) | 0.106995 / 0.176557 (-0.069561) | 0.169630 / 0.737135 (-0.567505) | 0.111614 / 0.296338 (-0.184724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424911 / 0.215209 (0.209702) | 4.246920 / 2.077655 (2.169266) | 1.923321 / 1.504120 (0.419202) | 1.714795 / 1.541195 (0.173600) | 1.772906 / 1.468490 (0.304416) | 0.554676 / 4.584777 (-4.030101) | 3.478896 / 3.745712 (-0.266816) | 2.800494 / 5.269862 (-2.469368) | 1.382630 / 4.565676 (-3.183047) | 0.067271 / 0.424275 (-0.357004) | 0.010967 / 0.007607 (0.003360) | 0.526769 / 0.226044 (0.300725) | 5.288564 / 2.268929 (3.019636) | 2.337459 / 55.444624 (-53.107165) | 1.999975 / 6.876477 (-4.876502) | 2.102680 / 2.142072 (-0.039392) | 0.672181 / 4.805227 (-4.133046) | 0.135097 / 6.500664 (-6.365567) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264365 / 1.841788 (-0.577423) | 14.282440 / 8.074308 (6.208132) | 14.220200 / 10.191392 (4.028808) | 0.139055 / 0.680424 (-0.541369) | 0.016681 / 0.534201 (-0.517520) | 0.367936 / 0.579283 (-0.211348) | 0.393959 / 0.434364 (-0.040404) | 0.424438 / 0.540337 (-0.115900) | 0.508065 / 1.386936 (-0.878872) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006514 / 0.011353 (-0.004839) | 0.003890 / 0.011008 (-0.007118) | 0.078871 / 0.038508 (0.040363) | 0.038080 / 0.023109 (0.014971) | 0.358282 / 0.275898 (0.082384) | 0.430654 / 0.323480 (0.107174) | 0.005712 / 0.007986 (-0.002273) | 0.003030 / 0.004328 (-0.001299) | 0.078636 / 0.004250 (0.074386) | 0.057771 / 0.037052 (0.020719) | 0.368814 / 0.258489 (0.110325) | 0.437047 / 0.293841 (0.143206) | 0.029470 / 0.128546 (-0.099076) | 0.008523 / 0.075646 (-0.067124) | 0.083334 / 0.419271 (-0.335938) | 0.044505 / 0.043533 (0.000972) | 0.357484 / 0.255139 (0.102345) | 0.393839 / 0.283200 (0.110639) | 0.023340 / 0.141683 (-0.118343) | 1.561033 / 1.452155 (0.108878) | 1.595560 / 1.492716 (0.102844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204149 / 0.018006 (0.186143) | 0.442747 / 0.000490 (0.442257) | 0.003105 / 0.000200 (0.002905) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027002 / 0.037411 (-0.010409) | 0.105595 / 0.014526 (0.091070) | 0.108695 / 0.176557 (-0.067861) | 0.163182 / 0.737135 (-0.573953) | 0.114999 / 0.296338 (-0.181339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483713 / 0.215209 (0.268504) | 4.836063 / 2.077655 (2.758409) | 2.488072 / 1.504120 (0.983952) | 2.289556 / 1.541195 (0.748361) | 2.342912 / 1.468490 (0.874422) | 0.565937 / 4.584777 (-4.018840) | 3.479085 / 3.745712 (-0.266627) | 1.770922 / 5.269862 (-3.498940) | 1.046084 / 4.565676 (-3.519592) | 0.067857 / 0.424275 (-0.356418) | 0.011283 / 0.007607 (0.003676) | 0.592966 / 0.226044 (0.366921) | 5.932842 / 2.268929 (3.663914) | 2.956252 / 55.444624 (-52.488372) | 2.602704 / 6.876477 (-4.273772) | 2.715625 / 2.142072 (0.573552) | 0.674299 / 4.805227 (-4.130929) | 0.136039 / 6.500664 (-6.364625) | 0.067629 / 0.075469 (-0.007840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333734 / 1.841788 (-0.508054) | 14.561943 / 8.074308 (6.487634) | 14.455385 / 10.191392 (4.263993) | 0.132020 / 0.680424 (-0.548404) | 0.016893 / 0.534201 (-0.517308) | 0.367146 / 0.579283 (-0.212137) | 0.399623 / 0.434364 (-0.034741) | 0.432658 / 0.540337 (-0.107680) | 0.530475 / 1.386936 (-0.856461) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"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) | 0.006045 / 0.011353 (-0.005308) | 0.003906 / 0.011008 (-0.007103) | 0.097558 / 0.038508 (0.059050) | 0.038827 / 0.023109 (0.015718) | 0.393564 / 0.275898 (0.117666) | 0.442459 / 0.323480 (0.118980) | 0.004792 / 0.007986 (-0.003194) | 0.002984 / 0.004328 (-0.001345) | 0.076419 / 0.004250 (0.072169) | 0.053606 / 0.037052 (0.016554) | 0.409743 / 0.258489 (0.151254) | 0.445753 / 0.293841 (0.151912) | 0.027753 / 0.128546 (-0.100793) | 0.008428 / 0.075646 (-0.067219) | 0.310267 / 0.419271 (-0.109004) | 0.057582 / 0.043533 (0.014049) | 0.396624 / 0.255139 (0.141485) | 0.416288 / 0.283200 (0.133089) | 0.029048 / 0.141683 (-0.112635) | 1.495362 / 1.452155 (0.043207) | 1.546331 / 1.492716 (0.053615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203832 / 0.018006 (0.185826) | 0.423649 / 0.000490 (0.423160) | 0.004533 / 0.000200 (0.004333) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023084 / 0.037411 (-0.014328) | 0.100503 / 0.014526 (0.085977) | 0.105058 / 0.176557 (-0.071499) | 0.168506 / 0.737135 (-0.568629) | 0.112019 / 0.296338 (-0.184320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425877 / 0.215209 (0.210668) | 4.251278 / 2.077655 (2.173624) | 1.931339 / 1.504120 (0.427219) | 1.730578 / 1.541195 (0.189383) | 1.750637 / 1.468490 (0.282147) | 0.559307 / 4.584777 (-4.025470) | 3.461665 / 3.745712 (-0.284047) | 2.826959 / 5.269862 (-2.442903) | 1.418448 / 4.565676 (-3.147229) | 0.067881 / 0.424275 (-0.356394) | 0.011394 / 0.007607 (0.003787) | 0.533226 / 0.226044 (0.307181) | 5.341849 / 2.268929 (3.072921) | 2.367832 / 55.444624 (-53.076792) | 2.027240 / 6.876477 (-4.849236) | 2.095852 / 2.142072 (-0.046220) | 0.673790 / 4.805227 (-4.131437) | 0.136044 / 6.500664 (-6.364620) | 0.066350 / 0.075469 (-0.009119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203740 / 1.841788 (-0.638048) | 13.720879 / 8.074308 (5.646571) | 13.405939 / 10.191392 (3.214547) | 0.146792 / 0.680424 (-0.533632) | 0.016844 / 0.534201 (-0.517357) | 0.373455 / 0.579283 (-0.205828) | 0.394596 / 0.434364 (-0.039768) | 0.464715 / 0.540337 (-0.075623) | 0.558931 / 1.386936 (-0.828005) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006118 / 0.011353 (-0.005235) | 0.003817 / 0.011008 (-0.007191) | 0.077494 / 0.038508 (0.038985) | 0.037507 / 0.023109 (0.014398) | 0.387030 / 0.275898 (0.111132) | 0.437352 / 0.323480 (0.113872) | 0.004810 / 0.007986 (-0.003176) | 0.002935 / 0.004328 (-0.001394) | 0.077143 / 0.004250 (0.072892) | 0.053986 / 0.037052 (0.016933) | 0.393164 / 0.258489 (0.134675) | 0.449603 / 0.293841 (0.155762) | 0.029303 / 0.128546 (-0.099244) | 0.008481 / 0.075646 (-0.067165) | 0.083363 / 0.419271 (-0.335908) | 0.043877 / 0.043533 (0.000344) | 0.378175 / 0.255139 (0.123036) | 0.403996 / 0.283200 (0.120797) | 0.021688 / 0.141683 (-0.119995) | 1.541606 / 1.452155 (0.089452) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236759 / 0.018006 (0.218752) | 0.416221 / 0.000490 (0.415732) | 0.000862 / 0.000200 (0.000662) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025543 / 0.037411 (-0.011868) | 0.101731 / 0.014526 (0.087206) | 0.108482 / 0.176557 (-0.068075) | 0.160290 / 0.737135 (-0.576845) | 0.111392 / 0.296338 (-0.184946) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457767 / 0.215209 (0.242558) | 4.565976 / 2.077655 (2.488321) | 2.245413 / 1.504120 (0.741294) | 2.031458 / 1.541195 (0.490264) | 2.073193 / 1.468490 (0.604702) | 0.560461 / 4.584777 (-4.024316) | 3.422536 / 3.745712 (-0.323176) | 2.977017 / 5.269862 (-2.292845) | 1.377021 / 4.565676 (-3.188655) | 0.068444 / 0.424275 (-0.355831) | 0.011036 / 0.007607 (0.003429) | 0.571501 / 0.226044 (0.345456) | 5.702652 / 2.268929 (3.433723) | 2.727132 / 55.444624 (-52.717492) | 2.399269 / 6.876477 (-4.477208) | 2.574281 / 2.142072 (0.432208) | 0.682600 / 4.805227 (-4.122627) | 0.136943 / 6.500664 (-6.363722) | 0.067126 / 0.075469 (-0.008343) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322196 / 1.841788 (-0.519592) | 14.239509 / 8.074308 (6.165201) | 14.235779 / 10.191392 (4.044387) | 0.148262 / 0.680424 (-0.532162) | 0.016566 / 0.534201 (-0.517635) | 0.364034 / 0.579283 (-0.215249) | 0.399157 / 0.434364 (-0.035207) | 0.426348 / 0.540337 (-0.113990) | 0.520804 / 1.386936 (-0.866132) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"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) | 0.007808 / 0.011353 (-0.003545) | 0.004706 / 0.011008 (-0.006303) | 0.100530 / 0.038508 (0.062022) | 0.052052 / 0.023109 (0.028943) | 0.419300 / 0.275898 (0.143402) | 0.488451 / 0.323480 (0.164971) | 0.006350 / 0.007986 (-0.001636) | 0.003875 / 0.004328 (-0.000453) | 0.076489 / 0.004250 (0.072238) | 0.077554 / 0.037052 (0.040502) | 0.435863 / 0.258489 (0.177373) | 0.483241 / 0.293841 (0.189400) | 0.037518 / 0.128546 (-0.091028) | 0.009857 / 0.075646 (-0.065789) | 0.340933 / 0.419271 (-0.078339) | 0.087046 / 0.043533 (0.043514) | 0.410721 / 0.255139 (0.155582) | 0.428995 / 0.283200 (0.145795) | 0.041701 / 0.141683 (-0.099982) | 1.821017 / 1.452155 (0.368862) | 1.837021 / 1.492716 (0.344305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228444 / 0.018006 (0.210438) | 0.480446 / 0.000490 (0.479956) | 0.004963 / 0.000200 (0.004763) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032485 / 0.037411 (-0.004926) | 0.096500 / 0.014526 (0.081974) | 0.111547 / 0.176557 (-0.065010) | 0.178842 / 0.737135 (-0.558294) | 0.111099 / 0.296338 (-0.185240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467159 / 0.215209 (0.251950) | 4.701676 / 2.077655 (2.624021) | 2.390560 / 1.504120 (0.886440) | 2.197722 / 1.541195 (0.656528) | 2.264705 / 1.468490 (0.796215) | 0.568667 / 4.584777 (-4.016110) | 4.200724 / 3.745712 (0.455012) | 3.777625 / 5.269862 (-1.492236) | 2.372451 / 4.565676 (-2.193225) | 0.067562 / 0.424275 (-0.356714) | 0.008947 / 0.007607 (0.001340) | 0.556910 / 0.226044 (0.330865) | 5.528927 / 2.268929 (3.259998) | 2.902780 / 55.444624 (-52.541844) | 2.507933 / 6.876477 (-4.368544) | 2.734627 / 2.142072 (0.592554) | 0.683305 / 4.805227 (-4.121922) | 0.158288 / 6.500664 (-6.342376) | 0.071252 / 0.075469 (-0.004217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.487502 / 1.841788 (-0.354286) | 22.193341 / 8.074308 (14.119033) | 15.922607 / 10.191392 (5.731215) | 0.172189 / 0.680424 (-0.508235) | 0.021502 / 0.534201 (-0.512699) | 0.471198 / 0.579283 (-0.108085) | 0.475979 / 0.434364 (0.041615) | 0.544675 / 0.540337 (0.004338) | 0.756102 / 1.386936 (-0.630834) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007635 / 0.011353 (-0.003717) | 0.004614 / 0.011008 (-0.006394) | 0.075852 / 0.038508 (0.037344) | 0.049700 / 0.023109 (0.026591) | 0.425957 / 0.275898 (0.150059) | 0.512590 / 0.323480 (0.189110) | 0.006921 / 0.007986 (-0.001065) | 0.003714 / 0.004328 (-0.000615) | 0.075536 / 0.004250 (0.071286) | 0.070206 / 0.037052 (0.033153) | 0.455706 / 0.258489 (0.197217) | 0.512231 / 0.293841 (0.218390) | 0.036685 / 0.128546 (-0.091861) | 0.009793 / 0.075646 (-0.065853) | 0.084208 / 0.419271 (-0.335064) | 0.065262 / 0.043533 (0.021729) | 0.423761 / 0.255139 (0.168622) | 0.456791 / 0.283200 (0.173591) | 0.044539 / 0.141683 (-0.097144) | 1.797029 / 1.452155 (0.344874) | 1.864124 / 1.492716 (0.371408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.366840 / 0.018006 (0.348834) | 0.479254 / 0.000490 (0.478765) | 0.070383 / 0.000200 (0.070183) | 0.000762 / 0.000054 (0.000707) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034233 / 0.037411 (-0.003178) | 0.103140 / 0.014526 (0.088614) | 0.117099 / 0.176557 (-0.059457) | 0.178532 / 0.737135 (-0.558603) | 0.120092 / 0.296338 (-0.176247) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492993 / 0.215209 (0.277784) | 4.878776 / 2.077655 (2.801121) | 2.566666 / 1.504120 (1.062547) | 2.356383 / 1.541195 (0.815188) | 2.454723 / 1.468490 (0.986233) | 0.571432 / 4.584777 (-4.013345) | 4.240554 / 3.745712 (0.494842) | 7.509259 / 5.269862 (2.239398) | 4.040294 / 4.565676 (-0.525382) | 0.067409 / 0.424275 (-0.356866) | 0.008657 / 0.007607 (0.001050) | 0.585751 / 0.226044 (0.359707) | 5.967668 / 2.268929 (3.698739) | 3.195573 / 55.444624 (-52.249052) | 2.839772 / 6.876477 (-4.036704) | 2.806319 / 2.142072 (0.664246) | 0.681502 / 4.805227 (-4.123725) | 0.158673 / 6.500664 (-6.341991) | 0.073224 / 0.075469 (-0.002245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623335 / 1.841788 (-0.218453) | 22.490806 / 8.074308 (14.416498) | 16.762435 / 10.191392 (6.571043) | 0.180961 / 0.680424 (-0.499463) | 0.022716 / 0.534201 (-0.511485) | 0.472910 / 0.579283 (-0.106373) | 0.471616 / 0.434364 (0.037252) | 0.548192 / 0.540337 (0.007854) | 0.734357 / 1.386936 (-0.652579) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"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) | 0.005858 / 0.011353 (-0.005495) | 0.003512 / 0.011008 (-0.007497) | 0.079739 / 0.038508 (0.041231) | 0.057736 / 0.023109 (0.034627) | 0.317640 / 0.275898 (0.041742) | 0.354157 / 0.323480 (0.030677) | 0.004772 / 0.007986 (-0.003214) | 0.002824 / 0.004328 (-0.001504) | 0.063288 / 0.004250 (0.059037) | 0.049542 / 0.037052 (0.012489) | 0.323974 / 0.258489 (0.065485) | 0.372149 / 0.293841 (0.078308) | 0.026841 / 0.128546 (-0.101705) | 0.007846 / 0.075646 (-0.067800) | 0.262546 / 0.419271 (-0.156725) | 0.051952 / 0.043533 (0.008420) | 0.319439 / 0.255139 (0.064300) | 0.343862 / 0.283200 (0.060663) | 0.027021 / 0.141683 (-0.114662) | 1.445211 / 1.452155 (-0.006944) | 1.485006 / 1.492716 (-0.007711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183174 / 0.018006 (0.165167) | 0.422794 / 0.000490 (0.422304) | 0.004148 / 0.000200 (0.003948) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023037 / 0.037411 (-0.014374) | 0.071300 / 0.014526 (0.056775) | 0.083022 / 0.176557 (-0.093535) | 0.146215 / 0.737135 (-0.590920) | 0.082549 / 0.296338 (-0.213789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422846 / 0.215209 (0.207637) | 4.215280 / 2.077655 (2.137626) | 2.256802 / 1.504120 (0.752682) | 2.056867 / 1.541195 (0.515673) | 2.102478 / 1.468490 (0.633988) | 0.497552 / 4.584777 (-4.087225) | 3.049716 / 3.745712 (-0.695996) | 4.209227 / 5.269862 (-1.060635) | 2.599947 / 4.565676 (-1.965730) | 0.059131 / 0.424275 (-0.365144) | 0.006459 / 0.007607 (-0.001148) | 0.495047 / 0.226044 (0.269003) | 4.952332 / 2.268929 (2.683404) | 2.675260 / 55.444624 (-52.769365) | 2.333223 / 6.876477 (-4.543254) | 2.449573 / 2.142072 (0.307500) | 0.583420 / 4.805227 (-4.221807) | 0.125140 / 6.500664 (-6.375524) | 0.060209 / 0.075469 (-0.015260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215033 / 1.841788 (-0.626755) | 18.101107 / 8.074308 (10.026799) | 13.489222 / 10.191392 (3.297830) | 0.147122 / 0.680424 (-0.533302) | 0.016567 / 0.534201 (-0.517634) | 0.329909 / 0.579283 (-0.249374) | 0.340952 / 0.434364 (-0.093412) | 0.379166 / 0.540337 (-0.161172) | 0.510767 / 1.386936 (-0.876169) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005942 / 0.011353 (-0.005411) | 0.003628 / 0.011008 (-0.007380) | 0.061975 / 0.038508 (0.023467) | 0.058331 / 0.023109 (0.035221) | 0.393277 / 0.275898 (0.117379) | 0.410740 / 0.323480 (0.087261) | 0.004546 / 0.007986 (-0.003440) | 0.002826 / 0.004328 (-0.001503) | 0.062216 / 0.004250 (0.057966) | 0.049801 / 0.037052 (0.012748) | 0.394070 / 0.258489 (0.135581) | 0.414407 / 0.293841 (0.120566) | 0.027161 / 0.128546 (-0.101385) | 0.007901 / 0.075646 (-0.067746) | 0.066778 / 0.419271 (-0.352493) | 0.041354 / 0.043533 (-0.002179) | 0.379432 / 0.255139 (0.124293) | 0.402966 / 0.283200 (0.119766) | 0.020279 / 0.141683 (-0.121404) | 1.416986 / 1.452155 (-0.035169) | 1.474335 / 1.492716 (-0.018382) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226147 / 0.018006 (0.208140) | 0.404361 / 0.000490 (0.403871) | 0.000358 / 0.000200 (0.000158) | 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) | 0.025105 / 0.037411 (-0.012306) | 0.075849 / 0.014526 (0.061323) | 0.084781 / 0.176557 (-0.091775) | 0.137415 / 0.737135 (-0.599720) | 0.086288 / 0.296338 (-0.210051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445925 / 0.215209 (0.230716) | 4.453478 / 2.077655 (2.375823) | 2.419048 / 1.504120 (0.914928) | 2.246363 / 1.541195 (0.705168) | 2.304022 / 1.468490 (0.835532) | 0.499132 / 4.584777 (-4.085645) | 3.001336 / 3.745712 (-0.744376) | 2.902593 / 5.269862 (-2.367269) | 1.819843 / 4.565676 (-2.745834) | 0.057210 / 0.424275 (-0.367065) | 0.006338 / 0.007607 (-0.001269) | 0.523280 / 0.226044 (0.297236) | 5.235969 / 2.268929 (2.967040) | 2.897585 / 55.444624 (-52.547039) | 2.541586 / 6.876477 (-4.334891) | 2.564233 / 2.142072 (0.422160) | 0.584714 / 4.805227 (-4.220513) | 0.124611 / 6.500664 (-6.376053) | 0.061774 / 0.075469 (-0.013695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.349799 / 1.841788 (-0.491988) | 18.225076 / 8.074308 (10.150768) | 13.781518 / 10.191392 (3.590126) | 0.130562 / 0.680424 (-0.549862) | 0.016434 / 0.534201 (-0.517767) | 0.331607 / 0.579283 (-0.247676) | 0.343456 / 0.434364 (-0.090908) | 0.380437 / 0.540337 (-0.159900) | 0.522793 / 1.386936 (-0.864143) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"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) | 0.013721 / 0.011353 (0.002368) | 0.005715 / 0.011008 (-0.005293) | 0.090116 / 0.038508 (0.051608) | 0.087185 / 0.023109 (0.064075) | 0.427813 / 0.275898 (0.151915) | 0.390614 / 0.323480 (0.067135) | 0.006976 / 0.007986 (-0.001009) | 0.004231 / 0.004328 (-0.000098) | 0.078320 / 0.004250 (0.074070) | 0.066235 / 0.037052 (0.029183) | 0.439904 / 0.258489 (0.181415) | 0.424119 / 0.293841 (0.130278) | 0.050362 / 0.128546 (-0.078184) | 0.014992 / 0.075646 (-0.060654) | 0.293519 / 0.419271 (-0.125753) | 0.066906 / 0.043533 (0.023373) | 0.449657 / 0.255139 (0.194518) | 0.393800 / 0.283200 (0.110600) | 0.032258 / 0.141683 (-0.109425) | 1.539534 / 1.452155 (0.087379) | 1.675292 / 1.492716 (0.182576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210515 / 0.018006 (0.192508) | 0.506817 / 0.000490 (0.506327) | 0.001938 / 0.000200 (0.001738) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026019 / 0.037411 (-0.011393) | 0.080635 / 0.014526 (0.066109) | 0.103050 / 0.176557 (-0.073507) | 0.160597 / 0.737135 (-0.576538) | 0.095844 / 0.296338 (-0.200495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506359 / 0.215209 (0.291150) | 5.041586 / 2.077655 (2.963931) | 2.198288 / 1.504120 (0.694168) | 1.987544 / 1.541195 (0.446349) | 1.866790 / 1.468490 (0.398300) | 0.681642 / 4.584777 (-3.903135) | 4.719306 / 3.745712 (0.973593) | 7.669869 / 5.269862 (2.400008) | 4.466082 / 4.565676 (-0.099595) | 0.092974 / 0.424275 (-0.331301) | 0.008196 / 0.007607 (0.000589) | 0.707656 / 0.226044 (0.481612) | 6.974507 / 2.268929 (4.705579) | 3.254206 / 55.444624 (-52.190418) | 2.499019 / 6.876477 (-4.377457) | 2.509089 / 2.142072 (0.367017) | 0.915952 / 4.805227 (-3.889276) | 0.192119 / 6.500664 (-6.308545) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309078 / 1.841788 (-0.532710) | 19.660348 / 8.074308 (11.586040) | 16.659582 / 10.191392 (6.468190) | 0.194315 / 0.680424 (-0.486109) | 0.027773 / 0.534201 (-0.506428) | 0.401241 / 0.579283 (-0.178042) | 0.515799 / 0.434364 (0.081435) | 0.488772 / 0.540337 (-0.051566) | 0.604790 / 1.386936 (-0.782146) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006823 / 0.011353 (-0.004530) | 0.003940 / 0.011008 (-0.007068) | 0.061533 / 0.038508 (0.023025) | 0.065241 / 0.023109 (0.042132) | 0.411790 / 0.275898 (0.135892) | 0.475720 / 0.323480 (0.152241) | 0.005376 / 0.007986 (-0.002609) | 0.003433 / 0.004328 (-0.000895) | 0.065703 / 0.004250 (0.061452) | 0.050736 / 0.037052 (0.013683) | 0.435890 / 0.258489 (0.177401) | 0.436698 / 0.293841 (0.142857) | 0.040357 / 0.128546 (-0.088189) | 0.011578 / 0.075646 (-0.064069) | 0.072831 / 0.419271 (-0.346440) | 0.055698 / 0.043533 (0.012165) | 0.408225 / 0.255139 (0.153086) | 0.439551 / 0.283200 (0.156352) | 0.030469 / 0.141683 (-0.111214) | 1.443866 / 1.452155 (-0.008289) | 1.502022 / 1.492716 (0.009306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290338 / 0.018006 (0.272332) | 0.540726 / 0.000490 (0.540236) | 0.003244 / 0.000200 (0.003044) | 0.000170 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030865 / 0.037411 (-0.006547) | 0.090866 / 0.014526 (0.076340) | 0.106224 / 0.176557 (-0.070332) | 0.166583 / 0.737135 (-0.570553) | 0.104448 / 0.296338 (-0.191891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518025 / 0.215209 (0.302816) | 6.027065 / 2.077655 (3.949410) | 2.671840 / 1.504120 (1.167720) | 2.273949 / 1.541195 (0.732754) | 2.414892 / 1.468490 (0.946402) | 0.774318 / 4.584777 (-3.810459) | 5.020364 / 3.745712 (1.274652) | 4.146927 / 5.269862 (-1.122934) | 2.584598 / 4.565676 (-1.981078) | 0.089519 / 0.424275 (-0.334756) | 0.009181 / 0.007607 (0.001574) | 0.654467 / 0.226044 (0.428423) | 6.421595 / 2.268929 (4.152666) | 3.091589 / 55.444624 (-52.353036) | 2.554798 / 6.876477 (-4.321679) | 2.441354 / 2.142072 (0.299282) | 0.943386 / 4.805227 (-3.861841) | 0.173641 / 6.500664 (-6.327023) | 0.072209 / 0.075469 (-0.003260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.557147 / 1.841788 (-0.284641) | 19.980747 / 8.074308 (11.906439) | 17.816813 / 10.191392 (7.625421) | 0.212078 / 0.680424 (-0.468346) | 0.025435 / 0.534201 (-0.508766) | 0.396200 / 0.579283 (-0.183084) | 0.546249 / 0.434364 (0.111885) | 0.459632 / 0.540337 (-0.080705) | 0.616548 / 1.386936 (-0.770388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n" ]

"2023-07-04T15:02:37"

"2023-07-06T15:32:41"

"2023-07-06T15:22:43"

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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) | 0.006722 / 0.011353 (-0.004631) | 0.004425 / 0.011008 (-0.006583) | 0.100850 / 0.038508 (0.062341) | 0.040816 / 0.023109 (0.017707) | 0.348823 / 0.275898 (0.072925) | 0.446285 / 0.323480 (0.122805) | 0.005738 / 0.007986 (-0.002247) | 0.003517 / 0.004328 (-0.000811) | 0.078824 / 0.004250 (0.074574) | 0.064695 / 0.037052 (0.027643) | 0.389894 / 0.258489 (0.131405) | 0.416107 / 0.293841 (0.122266) | 0.028850 / 0.128546 (-0.099696) | 0.009011 / 0.075646 (-0.066635) | 0.323117 / 0.419271 (-0.096154) | 0.049162 / 0.043533 (0.005629) | 0.340144 / 0.255139 (0.085005) | 0.382072 / 0.283200 (0.098872) | 0.023160 / 0.141683 (-0.118523) | 1.549218 / 1.452155 (0.097063) | 1.581266 / 1.492716 (0.088550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293360 / 0.018006 (0.275353) | 0.602189 / 0.000490 (0.601700) | 0.004608 / 0.000200 (0.004408) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.107088 / 0.014526 (0.092562) | 0.112188 / 0.176557 (-0.064369) | 0.174669 / 0.737135 (-0.562466) | 0.116359 / 0.296338 (-0.179980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422911 / 0.215209 (0.207702) | 4.231524 / 2.077655 (2.153869) | 1.906711 / 1.504120 (0.402591) | 1.706841 / 1.541195 (0.165646) | 1.792066 / 1.468490 (0.323576) | 0.559221 / 4.584777 (-4.025556) | 3.434280 / 3.745712 (-0.311433) | 1.918714 / 5.269862 (-3.351148) | 1.073070 / 4.565676 (-3.492606) | 0.067891 / 0.424275 (-0.356384) | 0.011927 / 0.007607 (0.004320) | 0.530843 / 0.226044 (0.304799) | 5.309213 / 2.268929 (3.040285) | 2.439246 / 55.444624 (-53.005378) | 2.101245 / 6.876477 (-4.775231) | 2.177436 / 2.142072 (0.035363) | 0.672150 / 4.805227 (-4.133077) | 0.137571 / 6.500664 (-6.363093) | 0.068343 / 0.075469 (-0.007126) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265262 / 1.841788 (-0.576525) | 14.988021 / 8.074308 (6.913713) | 13.611677 / 10.191392 (3.420285) | 0.171389 / 0.680424 (-0.509035) | 0.017681 / 0.534201 (-0.516520) | 0.377542 / 0.579283 (-0.201741) | 0.399475 / 0.434364 (-0.034889) | 0.469553 / 0.540337 (-0.070785) | 0.561888 / 1.386936 (-0.825048) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006782 / 0.011353 (-0.004571) | 0.004412 / 0.011008 (-0.006597) | 0.078594 / 0.038508 (0.040086) | 0.039930 / 0.023109 (0.016820) | 0.371879 / 0.275898 (0.095981) | 0.444910 / 0.323480 (0.121430) | 0.005707 / 0.007986 (-0.002279) | 0.003901 / 0.004328 (-0.000427) | 0.080125 / 0.004250 (0.075875) | 0.063977 / 0.037052 (0.026925) | 0.382781 / 0.258489 (0.124292) | 0.441791 / 0.293841 (0.147950) | 0.030428 / 0.128546 (-0.098118) | 0.009008 / 0.075646 (-0.066638) | 0.084447 / 0.419271 (-0.334824) | 0.044432 / 0.043533 (0.000899) | 0.365686 / 0.255139 (0.110547) | 0.394312 / 0.283200 (0.111113) | 0.024508 / 0.141683 (-0.117175) | 1.577020 / 1.452155 (0.124865) | 1.630259 / 1.492716 (0.137543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307960 / 0.018006 (0.289953) | 0.591473 / 0.000490 (0.590983) | 0.008098 / 0.000200 (0.007898) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029567 / 0.037411 (-0.007845) | 0.112773 / 0.014526 (0.098247) | 0.117362 / 0.176557 (-0.059194) | 0.174293 / 0.737135 (-0.562843) | 0.123156 / 0.296338 (-0.173182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457475 / 0.215209 (0.242266) | 4.599067 / 2.077655 (2.521412) | 2.262638 / 1.504120 (0.758518) | 2.124943 / 1.541195 (0.583748) | 2.339912 / 1.468490 (0.871422) | 0.566264 / 4.584777 (-4.018513) | 3.489261 / 3.745712 (-0.256451) | 1.925151 / 5.269862 (-3.344711) | 1.099389 / 4.565676 (-3.466287) | 0.068232 / 0.424275 (-0.356043) | 0.011660 / 0.007607 (0.004052) | 0.571227 / 0.226044 (0.345183) | 5.702059 / 2.268929 (3.433130) | 2.837701 / 55.444624 (-52.606924) | 2.605468 / 6.876477 (-4.271008) | 2.818396 / 2.142072 (0.676323) | 0.681856 / 4.805227 (-4.123371) | 0.141401 / 6.500664 (-6.359263) | 0.069728 / 0.075469 (-0.005741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354935 / 1.841788 (-0.486853) | 15.437404 / 8.074308 (7.363095) | 15.415193 / 10.191392 (5.223801) | 0.153459 / 0.680424 (-0.526964) | 0.017190 / 0.534201 (-0.517011) | 0.367256 / 0.579283 (-0.212027) | 0.392709 / 0.434364 (-0.041655) | 0.426125 / 0.540337 (-0.114213) | 0.522612 / 1.386936 (-0.864324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009183 / 0.011353 (-0.002170) | 0.005232 / 0.011008 (-0.005776) | 0.120349 / 0.038508 (0.081841) | 0.044715 / 0.023109 (0.021606) | 0.361519 / 0.275898 (0.085621) | 0.463702 / 0.323480 (0.140223) | 0.005842 / 0.007986 (-0.002144) | 0.004041 / 0.004328 (-0.000288) | 0.096953 / 0.004250 (0.092703) | 0.070593 / 0.037052 (0.033540) | 0.409790 / 0.258489 (0.151301) | 0.477452 / 0.293841 (0.183611) | 0.045827 / 0.128546 (-0.082719) | 0.014038 / 0.075646 (-0.061608) | 0.421317 / 0.419271 (0.002045) | 0.065276 / 0.043533 (0.021743) | 0.360074 / 0.255139 (0.104935) | 0.409147 / 0.283200 (0.125947) | 0.032444 / 0.141683 (-0.109238) | 1.739257 / 1.452155 (0.287102) | 1.831408 / 1.492716 (0.338692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274852 / 0.018006 (0.256846) | 0.596320 / 0.000490 (0.595830) | 0.006399 / 0.000200 (0.006199) | 0.000133 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031400 / 0.037411 (-0.006012) | 0.127052 / 0.014526 (0.112526) | 0.134269 / 0.176557 (-0.042288) | 0.225998 / 0.737135 (-0.511137) | 0.150019 / 0.296338 (-0.146319) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654202 / 0.215209 (0.438993) | 6.216735 / 2.077655 (4.139081) | 2.440214 / 1.504120 (0.936094) | 2.150575 / 1.541195 (0.609380) | 2.124790 / 1.468490 (0.656300) | 0.923514 / 4.584777 (-3.661263) | 5.556924 / 3.745712 (1.811212) | 2.843886 / 5.269862 (-2.425975) | 1.834232 / 4.565676 (-2.731444) | 0.111735 / 0.424275 (-0.312540) | 0.014823 / 0.007607 (0.007216) | 0.820503 / 0.226044 (0.594459) | 7.887737 / 2.268929 (5.618809) | 3.120307 / 55.444624 (-52.324317) | 2.405856 / 6.876477 (-4.470621) | 2.411239 / 2.142072 (0.269167) | 1.071283 / 4.805227 (-3.733944) | 0.227738 / 6.500664 (-6.272926) | 0.073516 / 0.075469 (-0.001953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.531806 / 1.841788 (-0.309982) | 18.547661 / 8.074308 (10.473353) | 21.083922 / 10.191392 (10.892530) | 0.241706 / 0.680424 (-0.438718) | 0.034169 / 0.534201 (-0.500032) | 0.497514 / 0.579283 (-0.081769) | 0.599801 / 0.434364 (0.165437) | 0.576465 / 0.540337 (0.036127) | 0.673509 / 1.386936 (-0.713427) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007558 / 0.011353 (-0.003795) | 0.005001 / 0.011008 (-0.006008) | 0.093809 / 0.038508 (0.055301) | 0.039792 / 0.023109 (0.016683) | 0.456869 / 0.275898 (0.180971) | 0.493370 / 0.323480 (0.169891) | 0.005561 / 0.007986 (-0.002424) | 0.003982 / 0.004328 (-0.000346) | 0.085421 / 0.004250 (0.081170) | 0.059817 / 0.037052 (0.022765) | 0.468040 / 0.258489 (0.209550) | 0.514853 / 0.293841 (0.221012) | 0.044267 / 0.128546 (-0.084279) | 0.012674 / 0.075646 (-0.062972) | 0.098324 / 0.419271 (-0.320948) | 0.056604 / 0.043533 (0.013071) | 0.432200 / 0.255139 (0.177061) | 0.459812 / 0.283200 (0.176612) | 0.033872 / 0.141683 (-0.107811) | 1.618576 / 1.452155 (0.166421) | 1.676562 / 1.492716 (0.183846) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230625 / 0.018006 (0.212619) | 0.600558 / 0.000490 (0.600068) | 0.003419 / 0.000200 (0.003219) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026916 / 0.037411 (-0.010496) | 0.103003 / 0.014526 (0.088478) | 0.117078 / 0.176557 (-0.059478) | 0.169359 / 0.737135 (-0.567776) | 0.120305 / 0.296338 (-0.176034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616877 / 0.215209 (0.401668) | 6.157232 / 2.077655 (4.079577) | 2.869219 / 1.504120 (1.365099) | 2.381410 / 1.541195 (0.840216) | 2.417357 / 1.468490 (0.948867) | 0.914947 / 4.584777 (-3.669830) | 5.718526 / 3.745712 (1.972814) | 2.757253 / 5.269862 (-2.512609) | 1.794122 / 4.565676 (-2.771554) | 0.108423 / 0.424275 (-0.315852) | 0.013378 / 0.007607 (0.005771) | 0.831067 / 0.226044 (0.605023) | 8.478946 / 2.268929 (6.210018) | 3.685937 / 55.444624 (-51.758687) | 2.867472 / 6.876477 (-4.009005) | 2.895975 / 2.142072 (0.753903) | 1.137547 / 4.805227 (-3.667681) | 0.213891 / 6.500664 (-6.286773) | 0.075825 / 0.075469 (0.000356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621193 / 1.841788 (-0.220594) | 17.322110 / 8.074308 (9.247802) | 21.804016 / 10.191392 (11.612624) | 0.243692 / 0.680424 (-0.436732) | 0.030331 / 0.534201 (-0.503870) | 0.492186 / 0.579283 (-0.087097) | 0.632583 / 0.434364 (0.198219) | 0.576265 / 0.540337 (0.035927) | 0.713165 / 1.386936 (-0.673771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) | 0.008916 / 0.011353 (-0.002437) | 0.004737 / 0.011008 (-0.006271) | 0.134271 / 0.038508 (0.095763) | 0.054472 / 0.023109 (0.031363) | 0.380942 / 0.275898 (0.105044) | 0.474138 / 0.323480 (0.150658) | 0.007917 / 0.007986 (-0.000068) | 0.003748 / 0.004328 (-0.000580) | 0.092765 / 0.004250 (0.088515) | 0.077873 / 0.037052 (0.040821) | 0.397533 / 0.258489 (0.139043) | 0.454737 / 0.293841 (0.160896) | 0.039901 / 0.128546 (-0.088645) | 0.010188 / 0.075646 (-0.065458) | 0.447312 / 0.419271 (0.028040) | 0.068684 / 0.043533 (0.025151) | 0.371554 / 0.255139 (0.116415) | 0.459655 / 0.283200 (0.176455) | 0.027157 / 0.141683 (-0.114526) | 1.874643 / 1.452155 (0.422488) | 2.014800 / 1.492716 (0.522083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227079 / 0.018006 (0.209073) | 0.483241 / 0.000490 (0.482751) | 0.012404 / 0.000200 (0.012204) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033135 / 0.037411 (-0.004277) | 0.137782 / 0.014526 (0.123257) | 0.142951 / 0.176557 (-0.033605) | 0.209825 / 0.737135 (-0.527311) | 0.152438 / 0.296338 (-0.143900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513066 / 0.215209 (0.297857) | 5.122776 / 2.077655 (3.045121) | 2.399270 / 1.504120 (0.895150) | 2.180143 / 1.541195 (0.638949) | 2.286395 / 1.468490 (0.817905) | 0.641866 / 4.584777 (-3.942911) | 4.694922 / 3.745712 (0.949210) | 2.543390 / 5.269862 (-2.726472) | 1.398592 / 4.565676 (-3.167084) | 0.088662 / 0.424275 (-0.335613) | 0.015854 / 0.007607 (0.008247) | 0.688891 / 0.226044 (0.462847) | 6.370148 / 2.268929 (4.101220) | 2.949974 / 55.444624 (-52.494650) | 2.538049 / 6.876477 (-4.338428) | 2.699380 / 2.142072 (0.557308) | 0.792670 / 4.805227 (-4.012557) | 0.169126 / 6.500664 (-6.331538) | 0.078511 / 0.075469 (0.003042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609119 / 1.841788 (-0.232669) | 18.785069 / 8.074308 (10.710761) | 16.670783 / 10.191392 (6.479391) | 0.213081 / 0.680424 (-0.467343) | 0.023904 / 0.534201 (-0.510296) | 0.567720 / 0.579283 (-0.011564) | 0.505806 / 0.434364 (0.071442) | 0.649466 / 0.540337 (0.109129) | 0.773174 / 1.386936 (-0.613762) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008036 / 0.011353 (-0.003317) | 0.004808 / 0.011008 (-0.006201) | 0.094316 / 0.038508 (0.055808) | 0.056174 / 0.023109 (0.033065) | 0.481618 / 0.275898 (0.205720) | 0.565300 / 0.323480 (0.241820) | 0.006339 / 0.007986 (-0.001646) | 0.003950 / 0.004328 (-0.000379) | 0.093389 / 0.004250 (0.089139) | 0.076163 / 0.037052 (0.039111) | 0.489013 / 0.258489 (0.230524) | 0.565451 / 0.293841 (0.271611) | 0.039392 / 0.128546 (-0.089155) | 0.010553 / 0.075646 (-0.065093) | 0.101406 / 0.419271 (-0.317865) | 0.062355 / 0.043533 (0.018822) | 0.470461 / 0.255139 (0.215322) | 0.502574 / 0.283200 (0.219375) | 0.030196 / 0.141683 (-0.111486) | 1.893926 / 1.452155 (0.441771) | 1.958902 / 1.492716 (0.466185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198074 / 0.018006 (0.180068) | 0.476828 / 0.000490 (0.476338) | 0.003457 / 0.000200 (0.003257) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037576 / 0.037411 (0.000165) | 0.146663 / 0.014526 (0.132138) | 0.152969 / 0.176557 (-0.023588) | 0.218683 / 0.737135 (-0.518452) | 0.161552 / 0.296338 (-0.134786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.525988 / 0.215209 (0.310779) | 5.234673 / 2.077655 (3.157018) | 2.571668 / 1.504120 (1.067548) | 2.339760 / 1.541195 (0.798565) | 2.422886 / 1.468490 (0.954395) | 0.651537 / 4.584777 (-3.933240) | 4.811148 / 3.745712 (1.065436) | 4.451165 / 5.269862 (-0.818697) | 2.016283 / 4.565676 (-2.549394) | 0.096393 / 0.424275 (-0.327882) | 0.015222 / 0.007607 (0.007615) | 0.739132 / 0.226044 (0.513087) | 6.813327 / 2.268929 (4.544399) | 3.169018 / 55.444624 (-52.275606) | 2.783120 / 6.876477 (-4.093356) | 2.918979 / 2.142072 (0.776907) | 0.797476 / 4.805227 (-4.007751) | 0.171038 / 6.500664 (-6.329626) | 0.079878 / 0.075469 (0.004409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595082 / 1.841788 (-0.246705) | 19.685844 / 8.074308 (11.611536) | 17.518989 / 10.191392 (7.327597) | 0.220015 / 0.680424 (-0.460409) | 0.026351 / 0.534201 (-0.507850) | 0.578977 / 0.579283 (-0.000306) | 0.549564 / 0.434364 (0.115200) | 0.667564 / 0.540337 (0.127227) | 0.802121 / 1.386936 (-0.584815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]

"2023-06-30T12:36:54"

"2023-06-30T13:17:05"

"2023-06-30T13:08:27"

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[ "This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)\r\n```" ]

"2023-07-11T20:40:38"

"2023-07-12T15:58:24"

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{ "login": "AntreasAntoniou", "id": 10792502, "node_id": "MDQ6VXNlcjEwNzkyNTAy", "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "gravatar_id": "", "url": "https://api.github.com/users/AntreasAntoniou", "html_url": "https://github.com/AntreasAntoniou", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "type": "User", "site_admin": false }

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[ "Hi! \r\n\r\nYou want to use `push_to_hub` (creates Parquet files) instead of `save_to_disk` (creates Arrow files) when creating a Hub dataset. Parquet is designed for long-term storage and takes less space than the Arrow format, and, most importantly, `load_dataset` can parse it, which should fix the viewer. \r\n\r\nRegarding the dataset generation, `Dataset.from_generator` with the video data represented as `datasets.Value(\"binary\")` followed by `push_to_hub` should work (if the `push_to_hub` step times out, restart it to resume uploading)\r\n\r\nPS: Once the dataset is uploaded, to make working with the dataset easier, it's a good idea to add a [transform](https://huggingface.co/docs/datasets/main/en/process#format-transform) to the README that shows how to decode the binary video data into something a model can understand. Also, if you get an `ArrowInvalid` error (can happen when working with large binary data) in `Dataset.from_generator`, reduce the value of `writer_batch_size` (the default is 1000) to fix it.", "One issue here is that Dataset.from_generator can work well for the non 'infinite sampling' version of the dataset. The training set for example is often sampled dynamically given the video files that I have uploaded. I worry that storing the video data as binary means that I'll end up duplicating a lot of the data. Furthermore, storing video data as anything but .mp4 would quickly make the dataset size from 1.9TB to 1PB. ", "> storing video data as anything but .mp4\r\n\r\nWhat I mean by storing as `datasets.Value(\"binary\")` is embedding raw MP4 bytes in the Arrow table, but, indeed, this would waste a lot of space if there are duplicates.\r\n\r\nSo I see two options:\r\n* if one video is not mapped to too many samples, you can embed the video bytes and do \"group by\" on the rest of the columns (this would turn them into lists) to avoid duplicating them (then, it should be easy to define a `map` in the README that samples the video data to \"unpack\" the samples)\r\n* you can create a dataset script that downloads the video files and embeds their file paths into the Arrow file\r\n\r\nAlso, I misread MP4 as MP3. We need to add a `Video` feature to the `datasets` lib to support MP4 files in the viewer (a bit trickier to implement than the `Image` feature due to the Arrow limitations).", "I'm transferring this issue to the `datasets` repo, as it's not related to `huggingface_hub`", "@mariosasko Right. If I want my dataset to be streamable, what are the necessary requirements to achieve that within the context of .mp4 binaries like we have here? I guess your second point here would not support that right?", "The streaming would work, but the video paths would require using `fsspec.open` to get the content.", "Are there any plans to make video playable on the hub?", "Not yet. The (open source) tooling for video is not great in terms of ease of use/performance, so we are discussing internally the best way to support it (one option is creating a new library for video IO, but this will require a lot of work)", "True. I spend a good 4 months just mixing and matching existing solutions so I could get performance that would not IO bound my model training. \r\n\r\nThis is what I ended up with, in case it's useful\r\n\r\nhttps://github.com/AntreasAntoniou/TALI/blob/045cf9e5aa75b1bf2c6d5351fb910fa10e3ff32c/tali/data/data_plus.py#L85" ]

"2023-05-22T18:05:26"

"2023-06-23T03:37:16"

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

"2023-05-22T07:40:27"

"2023-05-26T12:52:08"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6076). 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) | 0.008191 / 0.011353 (-0.003162) | 0.004669 / 0.011008 (-0.006339) | 0.101315 / 0.038508 (0.062807) | 0.090235 / 0.023109 (0.067126) | 0.381265 / 0.275898 (0.105367) | 0.418266 / 0.323480 (0.094786) | 0.006292 / 0.007986 (-0.001693) | 0.003979 / 0.004328 (-0.000349) | 0.075946 / 0.004250 (0.071696) | 0.070678 / 0.037052 (0.033625) | 0.378006 / 0.258489 (0.119517) | 0.425825 / 0.293841 (0.131984) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.345687 / 0.419271 (-0.073584) | 0.063846 / 0.043533 (0.020313) | 0.386003 / 0.255139 (0.130864) | 0.400875 / 0.283200 (0.117675) | 0.027806 / 0.141683 (-0.113877) | 1.814810 / 1.452155 (0.362655) | 1.879897 / 1.492716 (0.387180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218684 / 0.018006 (0.200677) | 0.501715 / 0.000490 (0.501225) | 0.004808 / 0.000200 (0.004608) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035494 / 0.037411 (-0.001917) | 0.100949 / 0.014526 (0.086423) | 0.114639 / 0.176557 (-0.061917) | 0.188908 / 0.737135 (-0.548227) | 0.115794 / 0.296338 (-0.180545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462537 / 0.215209 (0.247328) | 4.612469 / 2.077655 (2.534814) | 2.298065 / 1.504120 (0.793945) | 2.088738 / 1.541195 (0.547543) | 2.188072 / 1.468490 (0.719582) | 0.565412 / 4.584777 (-4.019364) | 4.180394 / 3.745712 (0.434681) | 3.848696 / 5.269862 (-1.421165) | 2.391381 / 4.565676 (-2.174296) | 0.067647 / 0.424275 (-0.356628) | 0.008847 / 0.007607 (0.001240) | 0.553288 / 0.226044 (0.327243) | 5.517962 / 2.268929 (3.249033) | 2.866622 / 55.444624 (-52.578002) | 2.439025 / 6.876477 (-4.437452) | 2.740156 / 2.142072 (0.598084) | 0.694796 / 4.805227 (-4.110431) | 0.159022 / 6.500664 (-6.341642) | 0.074471 / 0.075469 (-0.000998) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534979 / 1.841788 (-0.306808) | 23.297273 / 8.074308 (15.222965) | 16.859178 / 10.191392 (6.667786) | 0.207594 / 0.680424 (-0.472830) | 0.021990 / 0.534201 (-0.512211) | 0.472059 / 0.579283 (-0.107224) | 0.497632 / 0.434364 (0.063268) | 0.565672 / 0.540337 (0.025335) | 0.772485 / 1.386936 (-0.614451) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007777 / 0.011353 (-0.003576) | 0.004679 / 0.011008 (-0.006329) | 0.077317 / 0.038508 (0.038809) | 0.087433 / 0.023109 (0.064324) | 0.437389 / 0.275898 (0.161491) | 0.479562 / 0.323480 (0.156082) | 0.006137 / 0.007986 (-0.001849) | 0.003938 / 0.004328 (-0.000390) | 0.074769 / 0.004250 (0.070518) | 0.066605 / 0.037052 (0.029553) | 0.454865 / 0.258489 (0.196376) | 0.485103 / 0.293841 (0.191262) | 0.036540 / 0.128546 (-0.092006) | 0.009983 / 0.075646 (-0.065664) | 0.083566 / 0.419271 (-0.335706) | 0.059527 / 0.043533 (0.015994) | 0.449154 / 0.255139 (0.194015) | 0.462542 / 0.283200 (0.179342) | 0.027581 / 0.141683 (-0.114102) | 1.776720 / 1.452155 (0.324565) | 1.847920 / 1.492716 (0.355204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246792 / 0.018006 (0.228786) | 0.494513 / 0.000490 (0.494024) | 0.004376 / 0.000200 (0.004176) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037837 / 0.037411 (0.000426) | 0.112752 / 0.014526 (0.098226) | 0.121742 / 0.176557 (-0.054815) | 0.189365 / 0.737135 (-0.547770) | 0.124366 / 0.296338 (-0.171973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492890 / 0.215209 (0.277681) | 4.920270 / 2.077655 (2.842615) | 2.565350 / 1.504120 (1.061230) | 2.378679 / 1.541195 (0.837484) | 2.483794 / 1.468490 (1.015304) | 0.579623 / 4.584777 (-4.005154) | 4.195924 / 3.745712 (0.450212) | 3.903382 / 5.269862 (-1.366479) | 2.466884 / 4.565676 (-2.098793) | 0.064145 / 0.424275 (-0.360130) | 0.008695 / 0.007607 (0.001088) | 0.579300 / 0.226044 (0.353256) | 5.809064 / 2.268929 (3.540136) | 3.145393 / 55.444624 (-52.299232) | 2.832760 / 6.876477 (-4.043717) | 3.020460 / 2.142072 (0.878388) | 0.700235 / 4.805227 (-4.104992) | 0.161262 / 6.500664 (-6.339402) | 0.076484 / 0.075469 (0.001015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606504 / 1.841788 (-0.235284) | 23.747863 / 8.074308 (15.673555) | 17.281712 / 10.191392 (7.090320) | 0.203874 / 0.680424 (-0.476550) | 0.021839 / 0.534201 (-0.512362) | 0.472365 / 0.579283 (-0.106918) | 0.475150 / 0.434364 (0.040786) | 0.571713 / 0.540337 (0.031376) | 0.759210 / 1.386936 (-0.627726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n" ]

"2023-07-26T12:46:07"

"2023-07-26T14:01:21"

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[ "Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.", "Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)", "We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL). " ]

"2023-07-14T09:03:10"

"2023-07-14T14:48:29"

"2023-07-14T14:48:29"

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[ "Yes, this is intended." ]

"2023-07-14T05:11:14"

"2023-07-14T14:44:15"

"2023-07-14T14:44:15"

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[ "You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ", "> You can use `with_transform` to get a new dataset object.\r\n> \r\n> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.\r\n\r\nI read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\n`with_transform` still does not chain AFAIU.", "> I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\nYou must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:\r\n```python\r\nfrom functools import partial\r\nfrom datasets import Dataset\r\n\r\ndef gen_from_iterable_dataset(iterable_ds)\r\n yield from iterable_ds\r\n\r\nds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n```\r\n\r\n> with_transform still does not chain AFAIU.\r\n\r\nYes, not supported yet - the solution is to combine the transforms into a single one.", "I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ?", "> ```python\r\n> from functools import partial\r\n> from datasets import Dataset\r\n> \r\n> def gen_from_iterable_dataset(iterable_ds)\r\n> yield from iterable_ds\r\n> \r\n> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n> ```\r\n\r\n@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?\r\n", "The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash)" ]

"2023-07-09T21:40:21"

"2023-07-14T13:12:40"

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[ "I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625", "From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration.", "This is too specific and unrelated to `datasets`, so this shouldn't be fixed here." ]

"2023-06-22T19:57:31"

"2023-07-24T11:54:52"

"2023-07-24T11:54:52"

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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." ]

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

"2023-07-04T17:03:56"

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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) | 0.007697 / 0.011353 (-0.003656) | 0.004233 / 0.011008 (-0.006776) | 0.087890 / 0.038508 (0.049382) | 0.065305 / 0.023109 (0.042196) | 0.366919 / 0.275898 (0.091020) | 0.399656 / 0.323480 (0.076176) | 0.006753 / 0.007986 (-0.001232) | 0.003428 / 0.004328 (-0.000900) | 0.070180 / 0.004250 (0.065930) | 0.054164 / 0.037052 (0.017112) | 0.377130 / 0.258489 (0.118641) | 0.403456 / 0.293841 (0.109615) | 0.042639 / 0.128546 (-0.085907) | 0.012396 / 0.075646 (-0.063250) | 0.314235 / 0.419271 (-0.105036) | 0.061976 / 0.043533 (0.018443) | 0.376959 / 0.255139 (0.121820) | 0.433313 / 0.283200 (0.150113) | 0.031253 / 0.141683 (-0.110430) | 1.555749 / 1.452155 (0.103594) | 1.643905 / 1.492716 (0.151189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208630 / 0.018006 (0.190624) | 0.519532 / 0.000490 (0.519042) | 0.003719 / 0.000200 (0.003519) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027403 / 0.037411 (-0.010008) | 0.080990 / 0.014526 (0.066464) | 0.090424 / 0.176557 (-0.086133) | 0.153922 / 0.737135 (-0.583213) | 0.098156 / 0.296338 (-0.198183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.519453 / 0.215209 (0.304244) | 5.100089 / 2.077655 (3.022434) | 2.212165 / 1.504120 (0.708045) | 1.894405 / 1.541195 (0.353210) | 1.922914 / 1.468490 (0.454424) | 0.762443 / 4.584777 (-3.822334) | 4.669214 / 3.745712 (0.923502) | 5.016066 / 5.269862 (-0.253796) | 3.128821 / 4.565676 (-1.436856) | 0.091541 / 0.424275 (-0.332734) | 0.007582 / 0.007607 (-0.000026) | 0.652753 / 0.226044 (0.426709) | 6.601375 / 2.268929 (4.332446) | 3.076948 / 55.444624 (-52.367677) | 2.250544 / 6.876477 (-4.625933) | 2.404059 / 2.142072 (0.261987) | 0.994917 / 4.805227 (-3.810311) | 0.200318 / 6.500664 (-6.300346) | 0.069354 / 0.075469 (-0.006115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.482559 / 1.841788 (-0.359229) | 20.722092 / 8.074308 (12.647784) | 17.703217 / 10.191392 (7.511825) | 0.215370 / 0.680424 (-0.465053) | 0.028208 / 0.534201 (-0.505993) | 0.425992 / 0.579283 (-0.153291) | 0.492785 / 0.434364 (0.058421) | 0.474154 / 0.540337 (-0.066183) | 0.644599 / 1.386936 (-0.742337) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008372 / 0.011353 (-0.002981) | 0.004543 / 0.011008 (-0.006465) | 0.070564 / 0.038508 (0.032056) | 0.066855 / 0.023109 (0.043746) | 0.386724 / 0.275898 (0.110826) | 0.432184 / 0.323480 (0.108704) | 0.005250 / 0.007986 (-0.002736) | 0.003630 / 0.004328 (-0.000698) | 0.069310 / 0.004250 (0.065060) | 0.055759 / 0.037052 (0.018707) | 0.375789 / 0.258489 (0.117299) | 0.417335 / 0.293841 (0.123494) | 0.043424 / 0.128546 (-0.085122) | 0.013106 / 0.075646 (-0.062541) | 0.087836 / 0.419271 (-0.331436) | 0.057770 / 0.043533 (0.014237) | 0.396694 / 0.255139 (0.141555) | 0.439350 / 0.283200 (0.156150) | 0.031660 / 0.141683 (-0.110023) | 1.571339 / 1.452155 (0.119185) | 1.667169 / 1.492716 (0.174452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180534 / 0.018006 (0.162528) | 0.540027 / 0.000490 (0.539537) | 0.003573 / 0.000200 (0.003373) | 0.000141 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031380 / 0.037411 (-0.006032) | 0.083762 / 0.014526 (0.069236) | 0.098166 / 0.176557 (-0.078390) | 0.160761 / 0.737135 (-0.576374) | 0.097683 / 0.296338 (-0.198656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.568074 / 0.215209 (0.352865) | 5.660544 / 2.077655 (3.582889) | 2.416698 / 1.504120 (0.912578) | 2.177096 / 1.541195 (0.635901) | 2.206178 / 1.468490 (0.737688) | 0.844864 / 4.584777 (-3.739912) | 4.793636 / 3.745712 (1.047923) | 7.062387 / 5.269862 (1.792525) | 4.201228 / 4.565676 (-0.364449) | 0.091997 / 0.424275 (-0.332279) | 0.007881 / 0.007607 (0.000274) | 0.679466 / 0.226044 (0.453422) | 6.580268 / 2.268929 (4.311340) | 3.229907 / 55.444624 (-52.214717) | 2.524877 / 6.876477 (-4.351600) | 2.463796 / 2.142072 (0.321723) | 0.975627 / 4.805227 (-3.829600) | 0.186670 / 6.500664 (-6.313994) | 0.065307 / 0.075469 (-0.010163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501447 / 1.841788 (-0.340340) | 21.231037 / 8.074308 (13.156729) | 17.591671 / 10.191392 (7.400279) | 0.212745 / 0.680424 (-0.467679) | 0.026100 / 0.534201 (-0.508101) | 0.428391 / 0.579283 (-0.150892) | 0.535268 / 0.434364 (0.100904) | 0.506733 / 0.540337 (-0.033604) | 0.660832 / 1.386936 (-0.726104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n" ]

"2023-07-11T21:33:32"

"2023-07-12T17:13:02"

"2023-07-12T17:03:00"

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

"2023-05-25T09:39:06"

"2023-05-29T02:32:12"

"2023-05-29T02:32:12"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ", "If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?" ]

"2023-07-21T03:13:21"

"2023-07-24T15:17:28"

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[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.", "I am afraid int32 is not the reason for this error.\r\n\r\nI have submitted a commit to use int64 for all ints in the dataset:\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/857ac00d9eab96a6708ad6a82bd9001686042a9e\r\n\r\nand I have updated my env to the latest datasets release:\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 2.13.1\r\n- Platform: macOS-13.2.1-arm64-arm-64bit\r\n- Python version: 3.11.2\r\n- Huggingface_hub version: 0.13.4\r\n- PyArrow version: 11.0.0\r\n- Pandas version: 1.5.3\r\n\r\nBut the error still exist\r\n\r\n```\r\nDownloading and preparing dataset mnbvc/news_peoples_daily to /Users/silver/.cache/huggingface/datasets/liwu___mnbvc/news_peoples_daily/0.0.1/ee380f6309fe9b8b0d1fb14d77118f132444f22c8c4b28bf5c1645312688e051...\r\nDownloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 9070.40it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 2697.16it/s]\r\n---------------------------------------------------------------------------\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1647, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1646 example = self.info.features.encode_example(record) if self.info.features is not None else record\r\n-> 1647 writer.write(example, key)\r\n 1648 num_examples_progress_update += 1\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:490, in ArrowWriter.write(self, example, key, writer_batch_size)\r\n 488 self.hkey_record = []\r\n--> 490 self.write_examples_on_file()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1656, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1655 num_shards = shard_id + 1\r\n-> 1656 num_examples, num_bytes = writer.finalize()\r\n 1657 writer.close()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)\r\n 583 self.hkey_record = []\r\n--> 584 self.write_examples_on_file()\r\n 585 # If schema is known, infer features even if no examples were written\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\nCell In[2], line 1\r\n----> 1 dataset = load_dataset(\"liwu/MNBVC\", 'news_peoples_daily', split='train')\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/load.py:1809, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1808 # Download and prepare data\r\n-> 1809 builder_instance.download_and_prepare(\r\n 1810 download_config=download_config,\r\n 1811 download_mode=download_mode,\r\n 1812 verification_mode=verification_mode,\r\n 1813 try_from_hf_gcs=try_from_hf_gcs,\r\n 1814 num_proc=num_proc,\r\n 1815 storage_options=storage_options,\r\n 1816 )\r\n 1818 # Build dataset for splits\r\n 1819 keep_in_memory = (\r\n 1820 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1821 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:909, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\r\n 907 if num_proc is not None:\r\n 908 prepare_split_kwargs[\"num_proc\"] = num_proc\r\n--> 909 self._download_and_prepare(\r\n 910 dl_manager=dl_manager,\r\n 911 verification_mode=verification_mode,\r\n 912 **prepare_split_kwargs,\r\n 913 **download_and_prepare_kwargs,\r\n 914 )\r\n 915 # Sync info\r\n 916 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1670, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)\r\n 1669 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):\r\n-> 1670 super()._download_and_prepare(\r\n 1671 dl_manager,\r\n 1672 verification_mode,\r\n 1673 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS\r\n 1674 or verification_mode == VerificationMode.ALL_CHECKS,\r\n 1675 **prepare_splits_kwargs,\r\n 1676 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1004, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)\r\n 1000 split_dict.add(split_generator.split_info)\r\n 1002 try:\r\n 1003 # Prepare split will record examples associated to the split\r\n-> 1004 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 1005 except OSError as e:\r\n 1006 raise OSError(\r\n 1007 \"Cannot find data file. \"\r\n 1008 + (self.manual_download_instructions or \"\")\r\n 1009 + \"\\nOriginal error:\\n\"\r\n 1010 + str(e)\r\n 1011 ) from None\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1508, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\r\n 1506 job_id = 0\r\n 1507 with pbar:\r\n-> 1508 for job_id, done, content in self._prepare_split_single(\r\n 1509 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args\r\n 1510 ):\r\n 1511 if done:\r\n 1512 result = content\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1665, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1663 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:\r\n 1664 e = e.__context__\r\n-> 1665 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1667 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nBesides, it works fine when I am using streamed dataset.", "`simhash` is the problematic column - it has values such as `18329103420363166823` that are out of the int64 range. You can fix this by setting the feature type to `Value(\"string\")` (it's advised to use this type for hash values in general)\r\n\r\n> Besides, it works fine when I am using streamed dataset.\r\n\r\nStreaming yields Python dictionaries from the script without converting them to the Arrow representation, as this conversion step is not that cheap performance-wise.", "i am using uint64 for simhash\r\n\r\nuint64 ranges up to about 3.69E19.\r\n\r\n18329103420363166823 is less than this value.\r\n\r\nmoreover, our simhash algorithm use 64 bits. it should fit in uint64.\r\n\r\n\r\n\r\n", "You are right. I overlooked the feature type.\r\n\r\nThis is a reproducer:\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.arrow_writer import TypedSequence\r\n\r\npa.array(TypedSequence([18329103420363166823], type=Value(\"uint64\")))\r\n```\r\n\r\n`pa.array([18329103420363166823])` also fails with the same error, so it seems PyArrow does not always infer the correct type as NumPy does (`uint64` in this case).\r\n\r\nI'll report this issue in the Arrow repo.\r\n\r\n`pa.array([18329103420363166823], pa.uint64)` works, so maybe we can implement a temporary fix (supporting complex input such as `[{\"image\": pil_image, \"num\": uint64_value}]` would be hard though).\r\n\r\nIn the meantime, you should be able to bypass this error by returning the `simhash` values as NumPy scalars in the script:\r\n```python\r\ndef _generate_examples(self, ...):\r\n ...\r\n yield {..., \"simhash\": np.uint64(simhash), ...}\r\n```", "Thank you for checking this issue in detail.\r\n\r\nHowever, it seems that using `np.uint64(simhash)` does not work. The same issue still exists.\r\n\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/1e44f1e400b7e61052647d44c99cdae3bae9c830\r\n\r\nAnyway, we decide to use string type for these simhash values. Hope pyarrow can fix their bug soon.", "Arrow issue: https://github.com/apache/arrow/issues/36520" ]

"2023-07-05T15:16:50"

"2023-07-10T19:11:17"

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"2023-07-17T16:39:26"

"2023-07-17T16:40:37"

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

"2023-05-19T07:21:10"

"2023-05-23T16:02:14"

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[ "Hi ! The huggingface/dataset-viewer code was not maintained anymore because we switched to a new dataset viewer that is deployed available for each dataset the Hugging Face website.\r\n\r\nWhat are you using this old repository for ?", "I think these parts are outdated:\r\n\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L126-L131\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L145-L150\r\n\r\nTo make the viewer work, the first one should be replaced with the following:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nconfs = builder_cls.BUILDER_CONFIGS\r\n```\r\nAnd the second one:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nif conf:\r\n builder_instance = builder_cls(name=conf, cache_dir=path if path_to_datasets is not None else None)\r\nelse:\r\n builder_instance = builder_cls(cache_dir=path if path_to_datasets is not None else None)\r\n```\r\n\r\nBut as @lhoestq suggested, it's better to use the `datasets-server` API nowadays to [fetch the rows](https://huggingface.co/docs/datasets-server/rows).", "> The dataset viewer on the Hugging Face website is incredibly useful\r\n\r\n@mariosasko i think @lilyorlilypad wants to run the new dataset-viewer, not the old one", "> wants to run the new dataset-viewer, not the old one\r\n\r\nThanks for the clarification for me. I do want to run the new dataset-viewer. ", "It should be possible to run it locally using the HF datasets-server API (docs [here](https://huggingface.co/docs/datasets-server)) but the front end part is not open source (yet ?)\r\n\r\nThe back-end is open source though if you're interested: https://github.com/huggingface/datasets-server\r\nIt automatically converts datasets on HF to Parquet, which is the format we use to power the viewer.", "the new frontend would probably be hard to open source, as is, as it's quite intertwined with the Hub's code.\r\n\r\nHowever, at some point it would be amazing to have a community-driven open source implementation of a frontend to datasets-server! " ]

"2023-07-11T06:36:09"

"2023-07-12T14:18:49"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). 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) | 0.006384 / 0.011353 (-0.004969) | 0.003872 / 0.011008 (-0.007136) | 0.083454 / 0.038508 (0.044946) | 0.069120 / 0.023109 (0.046011) | 0.312573 / 0.275898 (0.036675) | 0.345814 / 0.323480 (0.022334) | 0.005729 / 0.007986 (-0.002257) | 0.003225 / 0.004328 (-0.001103) | 0.063950 / 0.004250 (0.059700) | 0.053998 / 0.037052 (0.016946) | 0.316492 / 0.258489 (0.058003) | 0.350738 / 0.293841 (0.056897) | 0.030770 / 0.128546 (-0.097776) | 0.008474 / 0.075646 (-0.067173) | 0.286989 / 0.419271 (-0.132282) | 0.052473 / 0.043533 (0.008940) | 0.314361 / 0.255139 (0.059222) | 0.335170 / 0.283200 (0.051970) | 0.022885 / 0.141683 (-0.118798) | 1.465430 / 1.452155 (0.013275) | 1.527799 / 1.492716 (0.035083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209377 / 0.018006 (0.191371) | 0.455583 / 0.000490 (0.455094) | 0.003352 / 0.000200 (0.003152) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026284 / 0.037411 (-0.011127) | 0.080710 / 0.014526 (0.066185) | 0.091741 / 0.176557 (-0.084816) | 0.147602 / 0.737135 (-0.589534) | 0.091173 / 0.296338 (-0.205166) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386592 / 0.215209 (0.171383) | 3.856665 / 2.077655 (1.779011) | 1.835745 / 1.504120 (0.331625) | 1.671814 / 1.541195 (0.130619) | 1.711224 / 1.468490 (0.242734) | 0.484704 / 4.584777 (-4.100073) | 3.649239 / 3.745712 (-0.096473) | 3.784051 / 5.269862 (-1.485810) | 2.241195 / 4.565676 (-2.324482) | 0.056613 / 0.424275 (-0.367662) | 0.007140 / 0.007607 (-0.000467) | 0.464585 / 0.226044 (0.238540) | 4.616537 / 2.268929 (2.347609) | 2.371969 / 55.444624 (-53.072656) | 1.977754 / 6.876477 (-4.898723) | 2.083385 / 2.142072 (-0.058687) | 0.582330 / 4.805227 (-4.222897) | 0.132744 / 6.500664 (-6.367920) | 0.059822 / 0.075469 (-0.015647) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259566 / 1.841788 (-0.582221) | 18.990166 / 8.074308 (10.915858) | 13.992069 / 10.191392 (3.800677) | 0.160001 / 0.680424 (-0.520423) | 0.018622 / 0.534201 (-0.515579) | 0.392921 / 0.579283 (-0.186362) | 0.418225 / 0.434364 (-0.016139) | 0.471252 / 0.540337 (-0.069086) | 0.653227 / 1.386936 (-0.733709) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006641 / 0.011353 (-0.004712) | 0.003738 / 0.011008 (-0.007271) | 0.064053 / 0.038508 (0.025545) | 0.069467 / 0.023109 (0.046357) | 0.360625 / 0.275898 (0.084727) | 0.394291 / 0.323480 (0.070811) | 0.005236 / 0.007986 (-0.002750) | 0.003304 / 0.004328 (-0.001024) | 0.064078 / 0.004250 (0.059827) | 0.054605 / 0.037052 (0.017552) | 0.374567 / 0.258489 (0.116078) | 0.411227 / 0.293841 (0.117386) | 0.031614 / 0.128546 (-0.096933) | 0.008323 / 0.075646 (-0.067324) | 0.070616 / 0.419271 (-0.348656) | 0.050077 / 0.043533 (0.006544) | 0.362229 / 0.255139 (0.107090) | 0.388310 / 0.283200 (0.105110) | 0.024053 / 0.141683 (-0.117630) | 1.508913 / 1.452155 (0.056759) | 1.562140 / 1.492716 (0.069423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230172 / 0.018006 (0.212165) | 0.449363 / 0.000490 (0.448873) | 0.002374 / 0.000200 (0.002174) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029813 / 0.037411 (-0.007598) | 0.087298 / 0.014526 (0.072772) | 0.096712 / 0.176557 (-0.079845) | 0.152864 / 0.737135 (-0.584271) | 0.098204 / 0.296338 (-0.198135) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408664 / 0.215209 (0.193455) | 4.075068 / 2.077655 (1.997414) | 2.096365 / 1.504120 (0.592245) | 1.936096 / 1.541195 (0.394901) | 1.961872 / 1.468490 (0.493382) | 0.483383 / 4.584777 (-4.101394) | 3.686926 / 3.745712 (-0.058787) | 4.798824 / 5.269862 (-0.471037) | 2.652279 / 4.565676 (-1.913398) | 0.056695 / 0.424275 (-0.367580) | 0.007592 / 0.007607 (-0.000016) | 0.484710 / 0.226044 (0.258665) | 4.842153 / 2.268929 (2.573225) | 2.636828 / 55.444624 (-52.807796) | 2.243666 / 6.876477 (-4.632811) | 2.375972 / 2.142072 (0.233899) | 0.578544 / 4.805227 (-4.226683) | 0.132579 / 6.500664 (-6.368085) | 0.061287 / 0.075469 (-0.014182) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.360287 / 1.841788 (-0.481501) | 19.464110 / 8.074308 (11.389802) | 14.530875 / 10.191392 (4.339483) | 0.149479 / 0.680424 (-0.530944) | 0.018471 / 0.534201 (-0.515730) | 0.395399 / 0.579283 (-0.183884) | 0.412897 / 0.434364 (-0.021467) | 0.465194 / 0.540337 (-0.075144) | 0.611752 / 1.386936 (-0.775184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"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) | 0.008986 / 0.011353 (-0.002367) | 0.005104 / 0.011008 (-0.005905) | 0.108371 / 0.038508 (0.069863) | 0.091655 / 0.023109 (0.068546) | 0.430183 / 0.275898 (0.154285) | 0.481387 / 0.323480 (0.157907) | 0.006662 / 0.007986 (-0.001324) | 0.004681 / 0.004328 (0.000353) | 0.089325 / 0.004250 (0.085075) | 0.065096 / 0.037052 (0.028044) | 0.435021 / 0.258489 (0.176532) | 0.478635 / 0.293841 (0.184794) | 0.047628 / 0.128546 (-0.080918) | 0.013496 / 0.075646 (-0.062150) | 0.389661 / 0.419271 (-0.029611) | 0.082260 / 0.043533 (0.038727) | 0.474165 / 0.255139 (0.219026) | 0.464877 / 0.283200 (0.181677) | 0.039784 / 0.141683 (-0.101899) | 1.874694 / 1.452155 (0.422539) | 1.980183 / 1.492716 (0.487467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254044 / 0.018006 (0.236038) | 0.631495 / 0.000490 (0.631005) | 0.000628 / 0.000200 (0.000428) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038773 / 0.037411 (0.001362) | 0.103681 / 0.014526 (0.089156) | 0.125081 / 0.176557 (-0.051476) | 0.198345 / 0.737135 (-0.538790) | 0.122217 / 0.296338 (-0.174121) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.611677 / 0.215209 (0.396468) | 6.220790 / 2.077655 (4.143135) | 2.729858 / 1.504120 (1.225739) | 2.351944 / 1.541195 (0.810749) | 2.449137 / 1.468490 (0.980647) | 0.896842 / 4.584777 (-3.687935) | 5.537491 / 3.745712 (1.791778) | 8.480182 / 5.269862 (3.210320) | 5.251404 / 4.565676 (0.685728) | 0.100449 / 0.424275 (-0.323826) | 0.009008 / 0.007607 (0.001401) | 0.750060 / 0.226044 (0.524016) | 7.390940 / 2.268929 (5.122011) | 3.478256 / 55.444624 (-51.966369) | 2.883597 / 6.876477 (-3.992880) | 3.082256 / 2.142072 (0.940183) | 1.114339 / 4.805227 (-3.690889) | 0.225389 / 6.500664 (-6.275275) | 0.083972 / 0.075469 (0.008503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.741522 / 1.841788 (-0.100266) | 25.674700 / 8.074308 (17.600392) | 24.324412 / 10.191392 (14.133020) | 0.257878 / 0.680424 (-0.422546) | 0.038384 / 0.534201 (-0.495817) | 0.508302 / 0.579283 (-0.070981) | 0.612979 / 0.434364 (0.178615) | 0.584366 / 0.540337 (0.044029) | 0.881115 / 1.386936 (-0.505821) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009114 / 0.011353 (-0.002239) | 0.005333 / 0.011008 (-0.005675) | 0.094944 / 0.038508 (0.056436) | 0.099178 / 0.023109 (0.076068) | 0.529813 / 0.275898 (0.253915) | 0.551282 / 0.323480 (0.227802) | 0.006442 / 0.007986 (-0.001543) | 0.004283 / 0.004328 (-0.000045) | 0.084257 / 0.004250 (0.080007) | 0.067557 / 0.037052 (0.030504) | 0.514733 / 0.258489 (0.256244) | 0.568200 / 0.293841 (0.274359) | 0.050969 / 0.128546 (-0.077577) | 0.014495 / 0.075646 (-0.061151) | 0.097089 / 0.419271 (-0.322182) | 0.063142 / 0.043533 (0.019609) | 0.513327 / 0.255139 (0.258188) | 0.520593 / 0.283200 (0.237394) | 0.036824 / 0.141683 (-0.104859) | 1.954875 / 1.452155 (0.502720) | 1.976307 / 1.492716 (0.483591) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304070 / 0.018006 (0.286063) | 0.611073 / 0.000490 (0.610583) | 0.005027 / 0.000200 (0.004827) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037993 / 0.037411 (0.000582) | 0.115876 / 0.014526 (0.101350) | 0.118087 / 0.176557 (-0.058469) | 0.186437 / 0.737135 (-0.550699) | 0.129883 / 0.296338 (-0.166456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.658292 / 0.215209 (0.443083) | 6.618257 / 2.077655 (4.540602) | 3.203786 / 1.504120 (1.699667) | 2.858714 / 1.541195 (1.317519) | 2.940974 / 1.468490 (1.472484) | 0.856238 / 4.584777 (-3.728538) | 5.427708 / 3.745712 (1.681996) | 4.810048 / 5.269862 (-0.459813) | 3.120006 / 4.565676 (-1.445671) | 0.098098 / 0.424275 (-0.326177) | 0.010077 / 0.007607 (0.002470) | 0.790890 / 0.226044 (0.564845) | 7.956679 / 2.268929 (5.687750) | 3.955710 / 55.444624 (-51.488914) | 3.446419 / 6.876477 (-3.430057) | 3.541228 / 2.142072 (1.399156) | 1.013420 / 4.805227 (-3.791808) | 0.213741 / 6.500664 (-6.286923) | 0.080857 / 0.075469 (0.005388) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.813265 / 1.841788 (-0.028522) | 25.965199 / 8.074308 (17.890891) | 21.892761 / 10.191392 (11.701369) | 0.257843 / 0.680424 (-0.422580) | 0.029388 / 0.534201 (-0.504813) | 0.510609 / 0.579283 (-0.068674) | 0.626579 / 0.434364 (0.192215) | 0.576865 / 0.540337 (0.036528) | 0.826610 / 1.386936 (-0.560326) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n" ]

"2023-07-18T10:15:39"

"2023-07-18T10:28:01"

"2023-07-18T10:15:52"

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[ "We no longer host datasets in this repo. You should use the HF Hub instead." ]

"2023-07-11T17:25:49"

"2023-07-20T10:11:41"

"2023-07-20T10:11:41"

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"2023-07-11T16:24:40"

"2023-07-17T17:01:01"

"2023-07-17T17:01:01"

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[ "Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster", "Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024", "_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) | 0.005952 / 0.011353 (-0.005400) | 0.003585 / 0.011008 (-0.007424) | 0.079163 / 0.038508 (0.040655) | 0.057926 / 0.023109 (0.034817) | 0.326647 / 0.275898 (0.050749) | 0.383485 / 0.323480 (0.060005) | 0.004530 / 0.007986 (-0.003456) | 0.002821 / 0.004328 (-0.001508) | 0.062071 / 0.004250 (0.057820) | 0.048023 / 0.037052 (0.010971) | 0.329368 / 0.258489 (0.070879) | 0.390877 / 0.293841 (0.097036) | 0.026959 / 0.128546 (-0.101588) | 0.007911 / 0.075646 (-0.067735) | 0.259956 / 0.419271 (-0.159315) | 0.044582 / 0.043533 (0.001049) | 0.320537 / 0.255139 (0.065398) | 0.373814 / 0.283200 (0.090614) | 0.020275 / 0.141683 (-0.121408) | 1.532128 / 1.452155 (0.079973) | 1.595031 / 1.492716 (0.102315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186127 / 0.018006 (0.168120) | 0.428586 / 0.000490 (0.428097) | 0.005180 / 0.000200 (0.004980) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024876 / 0.037411 (-0.012536) | 0.072169 / 0.014526 (0.057643) | 0.082015 / 0.176557 (-0.094542) | 0.147467 / 0.737135 (-0.589668) | 0.082769 / 0.296338 (-0.213570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410625 / 0.215209 (0.195416) | 4.116742 / 2.077655 (2.039088) | 2.172291 / 1.504120 (0.668171) | 2.022462 / 1.541195 (0.481268) | 2.048142 / 1.468490 (0.579651) | 0.503152 / 4.584777 (-4.081625) | 3.019135 / 3.745712 (-0.726577) | 3.589451 / 5.269862 (-1.680410) | 2.206876 / 4.565676 (-2.358801) | 0.057687 / 0.424275 (-0.366588) | 0.006560 / 0.007607 (-0.001047) | 0.475585 / 0.226044 (0.249541) | 4.784344 / 2.268929 (2.515416) | 2.506322 / 55.444624 (-52.938302) | 2.168251 / 6.876477 (-4.708225) | 2.324453 / 2.142072 (0.182381) | 0.590609 / 4.805227 (-4.214618) | 0.124178 / 6.500664 (-6.376486) | 0.059197 / 0.075469 (-0.016272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212359 / 1.841788 (-0.629429) | 17.915843 / 8.074308 (9.841535) | 13.128330 / 10.191392 (2.936938) | 0.144805 / 0.680424 (-0.535618) | 0.016889 / 0.534201 (-0.517312) | 0.344056 / 0.579283 (-0.235227) | 0.359370 / 0.434364 (-0.074994) | 0.404199 / 0.540337 (-0.136138) | 0.549117 / 1.386936 (-0.837819) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005914 / 0.011353 (-0.005439) | 0.003565 / 0.011008 (-0.007443) | 0.061575 / 0.038508 (0.023067) | 0.057677 / 0.023109 (0.034568) | 0.359753 / 0.275898 (0.083855) | 0.394135 / 0.323480 (0.070655) | 0.004648 / 0.007986 (-0.003338) | 0.002795 / 0.004328 (-0.001534) | 0.061877 / 0.004250 (0.057626) | 0.049673 / 0.037052 (0.012621) | 0.363120 / 0.258489 (0.104631) | 0.402685 / 0.293841 (0.108844) | 0.027021 / 0.128546 (-0.101525) | 0.008006 / 0.075646 (-0.067641) | 0.067398 / 0.419271 (-0.351874) | 0.044442 / 0.043533 (0.000909) | 0.364851 / 0.255139 (0.109712) | 0.387219 / 0.283200 (0.104019) | 0.027267 / 0.141683 (-0.114416) | 1.466675 / 1.452155 (0.014520) | 1.512607 / 1.492716 (0.019891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206156 / 0.018006 (0.188150) | 0.410877 / 0.000490 (0.410387) | 0.003061 / 0.000200 (0.002861) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024869 / 0.037411 (-0.012542) | 0.075736 / 0.014526 (0.061210) | 0.083922 / 0.176557 (-0.092634) | 0.139510 / 0.737135 (-0.597626) | 0.087685 / 0.296338 (-0.208654) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414473 / 0.215209 (0.199264) | 4.150633 / 2.077655 (2.072979) | 2.132892 / 1.504120 (0.628773) | 1.964072 / 1.541195 (0.422878) | 2.003353 / 1.468490 (0.534863) | 0.498012 / 4.584777 (-4.086765) | 3.010135 / 3.745712 (-0.735577) | 2.841130 / 5.269862 (-2.428732) | 1.826013 / 4.565676 (-2.739664) | 0.057443 / 0.424275 (-0.366832) | 0.006374 / 0.007607 (-0.001234) | 0.490337 / 0.226044 (0.264292) | 4.889628 / 2.268929 (2.620700) | 2.575626 / 55.444624 (-52.868998) | 2.246522 / 6.876477 (-4.629955) | 2.276183 / 2.142072 (0.134110) | 0.581465 / 4.805227 (-4.223763) | 0.123877 / 6.500664 (-6.376787) | 0.060339 / 0.075469 (-0.015130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333202 / 1.841788 (-0.508585) | 18.363558 / 8.074308 (10.289250) | 14.109356 / 10.191392 (3.917964) | 0.147358 / 0.680424 (-0.533066) | 0.016813 / 0.534201 (-0.517388) | 0.334815 / 0.579283 (-0.244468) | 0.366576 / 0.434364 (-0.067788) | 0.397223 / 0.540337 (-0.143115) | 0.547893 / 1.386936 (-0.839043) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ac60bcbebe9ddac70264951b1d584c93003cdf \"CML watermark\")\n" ]

"2023-07-12T20:31:16"

"2023-07-13T16:58:32"

"2023-07-13T12:37:09"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006213 / 0.011353 (-0.005140) | 0.004230 / 0.011008 (-0.006778) | 0.098014 / 0.038508 (0.059506) | 0.028659 / 0.023109 (0.005550) | 0.303272 / 0.275898 (0.027374) | 0.337186 / 0.323480 (0.013706) | 0.005126 / 0.007986 (-0.002860) | 0.003563 / 0.004328 (-0.000765) | 0.075295 / 0.004250 (0.071045) | 0.036836 / 0.037052 (-0.000216) | 0.309612 / 0.258489 (0.051123) | 0.346484 / 0.293841 (0.052643) | 0.025714 / 0.128546 (-0.102832) | 0.008562 / 0.075646 (-0.067085) | 0.323475 / 0.419271 (-0.095796) | 0.044072 / 0.043533 (0.000539) | 0.308261 / 0.255139 (0.053122) | 0.330903 / 0.283200 (0.047703) | 0.091805 / 0.141683 (-0.049878) | 1.517011 / 1.452155 (0.064856) | 1.570815 / 1.492716 (0.078099) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211265 / 0.018006 (0.193259) | 0.438860 / 0.000490 (0.438370) | 0.001127 / 0.000200 (0.000927) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023337 / 0.037411 (-0.014074) | 0.096243 / 0.014526 (0.081717) | 0.103529 / 0.176557 (-0.073028) | 0.161171 / 0.737135 (-0.575964) | 0.105904 / 0.296338 (-0.190435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417042 / 0.215209 (0.201833) | 4.155067 / 2.077655 (2.077412) | 1.879657 / 1.504120 (0.375537) | 1.669341 / 1.541195 (0.128146) | 1.717623 / 1.468490 (0.249133) | 0.556246 / 4.584777 (-4.028531) | 3.484535 / 3.745712 (-0.261177) | 1.728845 / 5.269862 (-3.541017) | 0.997477 / 4.565676 (-3.568199) | 0.068355 / 0.424275 (-0.355920) | 0.012445 / 0.007607 (0.004837) | 0.519023 / 0.226044 (0.292978) | 5.173506 / 2.268929 (2.904577) | 2.332435 / 55.444624 (-53.112190) | 1.986348 / 6.876477 (-4.890129) | 2.076885 / 2.142072 (-0.065187) | 0.656738 / 4.805227 (-4.148489) | 0.135308 / 6.500664 (-6.365356) | 0.065486 / 0.075469 (-0.009984) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208874 / 1.841788 (-0.632914) | 13.994200 / 8.074308 (5.919892) | 14.160978 / 10.191392 (3.969586) | 0.146009 / 0.680424 (-0.534415) | 0.016573 / 0.534201 (-0.517628) | 0.356082 / 0.579283 (-0.223202) | 0.387766 / 0.434364 (-0.046598) | 0.419130 / 0.540337 (-0.121208) | 0.508634 / 1.386936 (-0.878302) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006238 / 0.011353 (-0.005115) | 0.004221 / 0.011008 (-0.006788) | 0.075155 / 0.038508 (0.036646) | 0.028491 / 0.023109 (0.005382) | 0.355606 / 0.275898 (0.079708) | 0.388986 / 0.323480 (0.065506) | 0.005941 / 0.007986 (-0.002044) | 0.003510 / 0.004328 (-0.000819) | 0.074905 / 0.004250 (0.070655) | 0.039111 / 0.037052 (0.002059) | 0.358492 / 0.258489 (0.100003) | 0.398763 / 0.293841 (0.104922) | 0.025535 / 0.128546 (-0.103012) | 0.008580 / 0.075646 (-0.067067) | 0.080461 / 0.419271 (-0.338811) | 0.041381 / 0.043533 (-0.002152) | 0.355498 / 0.255139 (0.100359) | 0.379163 / 0.283200 (0.095963) | 0.096450 / 0.141683 (-0.045233) | 1.503248 / 1.452155 (0.051093) | 1.595616 / 1.492716 (0.102900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238065 / 0.018006 (0.220058) | 0.422800 / 0.000490 (0.422311) | 0.002274 / 0.000200 (0.002074) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025746 / 0.037411 (-0.011665) | 0.103319 / 0.014526 (0.088793) | 0.112155 / 0.176557 (-0.064401) | 0.163034 / 0.737135 (-0.574101) | 0.113377 / 0.296338 (-0.182962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440522 / 0.215209 (0.225313) | 4.398123 / 2.077655 (2.320468) | 2.143538 / 1.504120 (0.639418) | 1.946084 / 1.541195 (0.404890) | 1.996556 / 1.468490 (0.528066) | 0.550108 / 4.584777 (-4.034669) | 3.455774 / 3.745712 (-0.289938) | 2.862474 / 5.269862 (-2.407387) | 1.213446 / 4.565676 (-3.352230) | 0.067987 / 0.424275 (-0.356288) | 0.012413 / 0.007607 (0.004806) | 0.543990 / 0.226044 (0.317945) | 5.454807 / 2.268929 (3.185879) | 2.669195 / 55.444624 (-52.775429) | 2.332948 / 6.876477 (-4.543528) | 2.383870 / 2.142072 (0.241797) | 0.652017 / 4.805227 (-4.153210) | 0.135508 / 6.500664 (-6.365156) | 0.068238 / 0.075469 (-0.007231) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322669 / 1.841788 (-0.519118) | 14.368136 / 8.074308 (6.293828) | 14.167431 / 10.191392 (3.976039) | 0.159371 / 0.680424 (-0.521052) | 0.016638 / 0.534201 (-0.517563) | 0.357106 / 0.579283 (-0.222177) | 0.392491 / 0.434364 (-0.041873) | 0.419458 / 0.540337 (-0.120880) | 0.504662 / 1.386936 (-0.882274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf764819ba6754cb7edf15899db517be0548676f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006296 / 0.011353 (-0.005057) | 0.004185 / 0.011008 (-0.006823) | 0.096170 / 0.038508 (0.057662) | 0.029212 / 0.023109 (0.006102) | 0.315356 / 0.275898 (0.039458) | 0.335214 / 0.323480 (0.011734) | 0.005108 / 0.007986 (-0.002877) | 0.003634 / 0.004328 (-0.000694) | 0.074186 / 0.004250 (0.069936) | 0.038716 / 0.037052 (0.001663) | 0.311041 / 0.258489 (0.052551) | 0.341202 / 0.293841 (0.047361) | 0.025584 / 0.128546 (-0.102962) | 0.008499 / 0.075646 (-0.067148) | 0.318660 / 0.419271 (-0.100611) | 0.043745 / 0.043533 (0.000212) | 0.314824 / 0.255139 (0.059685) | 0.328117 / 0.283200 (0.044917) | 0.093425 / 0.141683 (-0.048258) | 1.478732 / 1.452155 (0.026578) | 1.531743 / 1.492716 (0.039027) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203484 / 0.018006 (0.185478) | 0.416131 / 0.000490 (0.415641) | 0.007352 / 0.000200 (0.007152) | 0.000211 / 0.000054 (0.000156) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022908 / 0.037411 (-0.014503) | 0.098641 / 0.014526 (0.084115) | 0.103426 / 0.176557 (-0.073131) | 0.161658 / 0.737135 (-0.575477) | 0.106506 / 0.296338 (-0.189832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430781 / 0.215209 (0.215572) | 4.315677 / 2.077655 (2.238022) | 2.022302 / 1.504120 (0.518182) | 1.832043 / 1.541195 (0.290849) | 1.789302 / 1.468490 (0.320812) | 0.560484 / 4.584777 (-4.024293) | 3.448204 / 3.745712 (-0.297508) | 1.725016 / 5.269862 (-3.544846) | 1.002649 / 4.565676 (-3.563027) | 0.068480 / 0.424275 (-0.355795) | 0.012617 / 0.007607 (0.005010) | 0.532291 / 0.226044 (0.306246) | 5.319352 / 2.268929 (3.050423) | 2.520730 / 55.444624 (-52.923894) | 2.213881 / 6.876477 (-4.662596) | 2.352477 / 2.142072 (0.210404) | 0.662516 / 4.805227 (-4.142711) | 0.136481 / 6.500664 (-6.364183) | 0.066597 / 0.075469 (-0.008872) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224537 / 1.841788 (-0.617251) | 13.849920 / 8.074308 (5.775612) | 14.026358 / 10.191392 (3.834966) | 0.131018 / 0.680424 (-0.549405) | 0.016756 / 0.534201 (-0.517445) | 0.358091 / 0.579283 (-0.221192) | 0.397709 / 0.434364 (-0.036655) | 0.450024 / 0.540337 (-0.090314) | 0.542609 / 1.386936 (-0.844327) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006179 / 0.011353 (-0.005174) | 0.004145 / 0.011008 (-0.006863) | 0.077482 / 0.038508 (0.038974) | 0.028005 / 0.023109 (0.004896) | 0.400010 / 0.275898 (0.124112) | 0.408206 / 0.323480 (0.084726) | 0.005049 / 0.007986 (-0.002937) | 0.003608 / 0.004328 (-0.000721) | 0.076841 / 0.004250 (0.072590) | 0.036714 / 0.037052 (-0.000338) | 0.406020 / 0.258489 (0.147531) | 0.412392 / 0.293841 (0.118551) | 0.025626 / 0.128546 (-0.102920) | 0.008560 / 0.075646 (-0.067087) | 0.084088 / 0.419271 (-0.335183) | 0.039707 / 0.043533 (-0.003826) | 0.396909 / 0.255139 (0.141770) | 0.403623 / 0.283200 (0.120424) | 0.095137 / 0.141683 (-0.046546) | 1.515670 / 1.452155 (0.063515) | 1.568379 / 1.492716 (0.075662) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.181802 / 0.018006 (0.163795) | 0.408778 / 0.000490 (0.408289) | 0.000393 / 0.000200 (0.000193) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025940 / 0.037411 (-0.011471) | 0.099992 / 0.014526 (0.085466) | 0.106280 / 0.176557 (-0.070276) | 0.161729 / 0.737135 (-0.575406) | 0.108625 / 0.296338 (-0.187713) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459802 / 0.215209 (0.244593) | 4.603002 / 2.077655 (2.525347) | 2.406851 / 1.504120 (0.902732) | 2.265422 / 1.541195 (0.724227) | 2.306305 / 1.468490 (0.837815) | 0.553903 / 4.584777 (-4.030874) | 3.482052 / 3.745712 (-0.263660) | 2.969855 / 5.269862 (-2.300007) | 1.309285 / 4.565676 (-3.256391) | 0.068130 / 0.424275 (-0.356145) | 0.012189 / 0.007607 (0.004582) | 0.571299 / 0.226044 (0.345254) | 5.711420 / 2.268929 (3.442492) | 2.716748 / 55.444624 (-52.727876) | 2.369869 / 6.876477 (-4.506608) | 2.544240 / 2.142072 (0.402167) | 0.659955 / 4.805227 (-4.145272) | 0.136684 / 6.500664 (-6.363980) | 0.068962 / 0.075469 (-0.006507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.297659 / 1.841788 (-0.544129) | 14.012758 / 8.074308 (5.938449) | 14.324644 / 10.191392 (4.133252) | 0.144894 / 0.680424 (-0.535530) | 0.016751 / 0.534201 (-0.517450) | 0.361547 / 0.579283 (-0.217736) | 0.396595 / 0.434364 (-0.037769) | 0.422375 / 0.540337 (-0.117962) | 0.508209 / 1.386936 (-0.878727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba5f81357b53099b1bedfbb277211dba3952257b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006303 / 0.011353 (-0.005050) | 0.004043 / 0.011008 (-0.006965) | 0.096239 / 0.038508 (0.057731) | 0.029608 / 0.023109 (0.006498) | 0.321058 / 0.275898 (0.045160) | 0.367066 / 0.323480 (0.043587) | 0.005236 / 0.007986 (-0.002749) | 0.003342 / 0.004328 (-0.000987) | 0.074407 / 0.004250 (0.070157) | 0.038810 / 0.037052 (0.001757) | 0.332597 / 0.258489 (0.074108) | 0.363562 / 0.293841 (0.069721) | 0.025460 / 0.128546 (-0.103086) | 0.008426 / 0.075646 (-0.067221) | 0.316998 / 0.419271 (-0.102273) | 0.043621 / 0.043533 (0.000088) | 0.338043 / 0.255139 (0.082904) | 0.366441 / 0.283200 (0.083241) | 0.092061 / 0.141683 (-0.049622) | 1.461531 / 1.452155 (0.009376) | 1.538047 / 1.492716 (0.045331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206796 / 0.018006 (0.188790) | 0.517959 / 0.000490 (0.517469) | 0.002745 / 0.000200 (0.002545) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022902 / 0.037411 (-0.014510) | 0.097901 / 0.014526 (0.083375) | 0.103664 / 0.176557 (-0.072893) | 0.163516 / 0.737135 (-0.573619) | 0.108561 / 0.296338 (-0.187778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418964 / 0.215209 (0.203755) | 4.159113 / 2.077655 (2.081458) | 1.843946 / 1.504120 (0.339827) | 1.641083 / 1.541195 (0.099888) | 1.686848 / 1.468490 (0.218358) | 0.554583 / 4.584777 (-4.030194) | 3.409862 / 3.745712 (-0.335850) | 2.647904 / 5.269862 (-2.621958) | 1.355424 / 4.565676 (-3.210253) | 0.068229 / 0.424275 (-0.356046) | 0.012217 / 0.007607 (0.004610) | 0.515895 / 0.226044 (0.289851) | 5.144920 / 2.268929 (2.875991) | 2.298046 / 55.444624 (-53.146579) | 1.964735 / 6.876477 (-4.911741) | 2.075580 / 2.142072 (-0.066492) | 0.657104 / 4.805227 (-4.148123) | 0.134759 / 6.500664 (-6.365905) | 0.067545 / 0.075469 (-0.007924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233075 / 1.841788 (-0.608713) | 13.896762 / 8.074308 (5.822454) | 14.055143 / 10.191392 (3.863751) | 0.145507 / 0.680424 (-0.534917) | 0.016702 / 0.534201 (-0.517499) | 0.365157 / 0.579283 (-0.214126) | 0.385842 / 0.434364 (-0.048522) | 0.459993 / 0.540337 (-0.080344) | 0.547115 / 1.386936 (-0.839821) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006174 / 0.011353 (-0.005179) | 0.004191 / 0.011008 (-0.006817) | 0.078311 / 0.038508 (0.039803) | 0.028038 / 0.023109 (0.004928) | 0.360056 / 0.275898 (0.084158) | 0.398081 / 0.323480 (0.074602) | 0.005069 / 0.007986 (-0.002916) | 0.003464 / 0.004328 (-0.000864) | 0.077858 / 0.004250 (0.073608) | 0.039420 / 0.037052 (0.002367) | 0.361743 / 0.258489 (0.103254) | 0.404829 / 0.293841 (0.110988) | 0.025604 / 0.128546 (-0.102943) | 0.008573 / 0.075646 (-0.067074) | 0.084944 / 0.419271 (-0.334328) | 0.042652 / 0.043533 (-0.000881) | 0.368549 / 0.255139 (0.113410) | 0.385682 / 0.283200 (0.102482) | 0.099085 / 0.141683 (-0.042598) | 1.495815 / 1.452155 (0.043661) | 1.548168 / 1.492716 (0.055452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193737 / 0.018006 (0.175730) | 0.421871 / 0.000490 (0.421381) | 0.002306 / 0.000200 (0.002106) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025928 / 0.037411 (-0.011483) | 0.103410 / 0.014526 (0.088885) | 0.107931 / 0.176557 (-0.068626) | 0.157127 / 0.737135 (-0.580008) | 0.111892 / 0.296338 (-0.184446) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477562 / 0.215209 (0.262353) | 4.772711 / 2.077655 (2.695056) | 2.458725 / 1.504120 (0.954605) | 2.269871 / 1.541195 (0.728676) | 2.365502 / 1.468490 (0.897012) | 0.556182 / 4.584777 (-4.028595) | 3.408016 / 3.745712 (-0.337697) | 1.730639 / 5.269862 (-3.539222) | 1.000973 / 4.565676 (-3.564704) | 0.068293 / 0.424275 (-0.355982) | 0.012119 / 0.007607 (0.004512) | 0.581281 / 0.226044 (0.355236) | 5.811930 / 2.268929 (3.543001) | 2.890337 / 55.444624 (-52.554288) | 2.592156 / 6.876477 (-4.284321) | 2.687764 / 2.142072 (0.545691) | 0.664282 / 4.805227 (-4.140946) | 0.136029 / 6.500664 (-6.364635) | 0.067493 / 0.075469 (-0.007976) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330723 / 1.841788 (-0.511064) | 14.379172 / 8.074308 (6.304864) | 14.153286 / 10.191392 (3.961894) | 0.142942 / 0.680424 (-0.537482) | 0.016698 / 0.534201 (-0.517503) | 0.361044 / 0.579283 (-0.218239) | 0.393174 / 0.434364 (-0.041190) | 0.423107 / 0.540337 (-0.117231) | 0.514299 / 1.386936 (-0.872637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb02285358ab4be6386e0a2aae40d267ff561fc \"CML watermark\")\n" ]

"2023-05-25T16:26:33"

"2023-05-26T12:22:04"

"2023-05-26T11:57:16"

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[ "Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5" ]

"2023-06-30T04:13:54"

"2023-06-30T05:57:23"

"2023-06-30T05:57:22"

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"2023-07-22T02:04:19"

"2023-07-22T02:04:19"

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

"2023-05-20T15:25:40"

"2023-06-01T17:40:00"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint." ]

"2023-07-18T17:13:50"

"2023-07-20T12:09:16"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6072). 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) | 0.007617 / 0.011353 (-0.003736) | 0.004580 / 0.011008 (-0.006428) | 0.100913 / 0.038508 (0.062405) | 0.087703 / 0.023109 (0.064594) | 0.424159 / 0.275898 (0.148261) | 0.467195 / 0.323480 (0.143715) | 0.006890 / 0.007986 (-0.001096) | 0.003765 / 0.004328 (-0.000564) | 0.077513 / 0.004250 (0.073262) | 0.064889 / 0.037052 (0.027837) | 0.422349 / 0.258489 (0.163860) | 0.477391 / 0.293841 (0.183550) | 0.036025 / 0.128546 (-0.092522) | 0.009939 / 0.075646 (-0.065707) | 0.342409 / 0.419271 (-0.076862) | 0.061568 / 0.043533 (0.018035) | 0.431070 / 0.255139 (0.175931) | 0.462008 / 0.283200 (0.178809) | 0.027480 / 0.141683 (-0.114203) | 1.802271 / 1.452155 (0.350116) | 1.861336 / 1.492716 (0.368620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255806 / 0.018006 (0.237800) | 0.507969 / 0.000490 (0.507479) | 0.010060 / 0.000200 (0.009860) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032286 / 0.037411 (-0.005125) | 0.104468 / 0.014526 (0.089942) | 0.112707 / 0.176557 (-0.063850) | 0.181285 / 0.737135 (-0.555850) | 0.113180 / 0.296338 (-0.183158) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449265 / 0.215209 (0.234056) | 4.465941 / 2.077655 (2.388287) | 2.177889 / 1.504120 (0.673769) | 1.969864 / 1.541195 (0.428669) | 2.077502 / 1.468490 (0.609011) | 0.561607 / 4.584777 (-4.023170) | 4.281873 / 3.745712 (0.536161) | 4.975352 / 5.269862 (-0.294510) | 2.907121 / 4.565676 (-1.658555) | 0.070205 / 0.424275 (-0.354070) | 0.009164 / 0.007607 (0.001557) | 0.581921 / 0.226044 (0.355876) | 5.538667 / 2.268929 (3.269739) | 2.798853 / 55.444624 (-52.645771) | 2.314015 / 6.876477 (-4.562462) | 2.584836 / 2.142072 (0.442763) | 0.672333 / 4.805227 (-4.132894) | 0.153828 / 6.500664 (-6.346836) | 0.069757 / 0.075469 (-0.005712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559670 / 1.841788 (-0.282118) | 23.994639 / 8.074308 (15.920331) | 16.856160 / 10.191392 (6.664768) | 0.195555 / 0.680424 (-0.484869) | 0.021586 / 0.534201 (-0.512615) | 0.469295 / 0.579283 (-0.109989) | 0.481582 / 0.434364 (0.047218) | 0.588667 / 0.540337 (0.048329) | 0.734347 / 1.386936 (-0.652589) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009614 / 0.011353 (-0.001739) | 0.004616 / 0.011008 (-0.006392) | 0.077223 / 0.038508 (0.038715) | 0.103074 / 0.023109 (0.079965) | 0.447834 / 0.275898 (0.171936) | 0.524696 / 0.323480 (0.201216) | 0.007120 / 0.007986 (-0.000866) | 0.003890 / 0.004328 (-0.000438) | 0.076406 / 0.004250 (0.072156) | 0.073488 / 0.037052 (0.036436) | 0.466221 / 0.258489 (0.207732) | 0.532206 / 0.293841 (0.238365) | 0.037596 / 0.128546 (-0.090950) | 0.010029 / 0.075646 (-0.065617) | 0.084313 / 0.419271 (-0.334959) | 0.060088 / 0.043533 (0.016555) | 0.437792 / 0.255139 (0.182653) | 0.512850 / 0.283200 (0.229650) | 0.032424 / 0.141683 (-0.109259) | 1.762130 / 1.452155 (0.309975) | 1.946097 / 1.492716 (0.453381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250774 / 0.018006 (0.232768) | 0.506869 / 0.000490 (0.506379) | 0.008232 / 0.000200 (0.008032) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037779 / 0.037411 (0.000368) | 0.111933 / 0.014526 (0.097407) | 0.122385 / 0.176557 (-0.054172) | 0.190372 / 0.737135 (-0.546763) | 0.122472 / 0.296338 (-0.173866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488502 / 0.215209 (0.273293) | 4.878114 / 2.077655 (2.800459) | 2.504144 / 1.504120 (1.000024) | 2.321077 / 1.541195 (0.779883) | 2.416797 / 1.468490 (0.948307) | 0.583582 / 4.584777 (-4.001195) | 4.277896 / 3.745712 (0.532184) | 3.874780 / 5.269862 (-1.395082) | 2.540099 / 4.565676 (-2.025577) | 0.068734 / 0.424275 (-0.355541) | 0.009158 / 0.007607 (0.001550) | 0.578401 / 0.226044 (0.352357) | 5.763354 / 2.268929 (3.494426) | 3.167771 / 55.444624 (-52.276853) | 2.675220 / 6.876477 (-4.201257) | 2.920927 / 2.142072 (0.778855) | 0.673948 / 4.805227 (-4.131280) | 0.157908 / 6.500664 (-6.342756) | 0.071672 / 0.075469 (-0.003797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.635120 / 1.841788 (-0.206668) | 24.853480 / 8.074308 (16.779172) | 17.162978 / 10.191392 (6.971586) | 0.209577 / 0.680424 (-0.470847) | 0.030110 / 0.534201 (-0.504091) | 0.546970 / 0.579283 (-0.032313) | 0.581912 / 0.434364 (0.147548) | 0.571460 / 0.540337 (0.031123) | 0.823411 / 1.386936 (-0.563525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006674 / 0.011353 (-0.004679) | 0.004198 / 0.011008 (-0.006810) | 0.084859 / 0.038508 (0.046351) | 0.076065 / 0.023109 (0.052955) | 0.316065 / 0.275898 (0.040167) | 0.352097 / 0.323480 (0.028617) | 0.005610 / 0.007986 (-0.002376) | 0.003600 / 0.004328 (-0.000729) | 0.064921 / 0.004250 (0.060671) | 0.054493 / 0.037052 (0.017441) | 0.318125 / 0.258489 (0.059636) | 0.370183 / 0.293841 (0.076342) | 0.031141 / 0.128546 (-0.097405) | 0.008755 / 0.075646 (-0.066891) | 0.288241 / 0.419271 (-0.131030) | 0.052379 / 0.043533 (0.008846) | 0.328147 / 0.255139 (0.073008) | 0.347548 / 0.283200 (0.064348) | 0.024393 / 0.141683 (-0.117290) | 1.480646 / 1.452155 (0.028492) | 1.575867 / 1.492716 (0.083151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268978 / 0.018006 (0.250971) | 0.586470 / 0.000490 (0.585980) | 0.003190 / 0.000200 (0.002990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030595 / 0.037411 (-0.006816) | 0.083037 / 0.014526 (0.068511) | 0.103706 / 0.176557 (-0.072850) | 0.164104 / 0.737135 (-0.573031) | 0.104536 / 0.296338 (-0.191802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382274 / 0.215209 (0.167065) | 3.811878 / 2.077655 (1.734223) | 1.840098 / 1.504120 (0.335978) | 1.670949 / 1.541195 (0.129754) | 1.763755 / 1.468490 (0.295264) | 0.479526 / 4.584777 (-4.105251) | 3.544443 / 3.745712 (-0.201269) | 3.263004 / 5.269862 (-2.006858) | 2.092801 / 4.565676 (-2.472875) | 0.057167 / 0.424275 (-0.367108) | 0.007450 / 0.007607 (-0.000157) | 0.463731 / 0.226044 (0.237686) | 4.624630 / 2.268929 (2.355701) | 2.327078 / 55.444624 (-53.117546) | 1.977734 / 6.876477 (-4.898743) | 2.237152 / 2.142072 (0.095079) | 0.573210 / 4.805227 (-4.232018) | 0.132095 / 6.500664 (-6.368569) | 0.060283 / 0.075469 (-0.015186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243404 / 1.841788 (-0.598384) | 20.306778 / 8.074308 (12.232470) | 14.561660 / 10.191392 (4.370268) | 0.170826 / 0.680424 (-0.509598) | 0.018574 / 0.534201 (-0.515627) | 0.392367 / 0.579283 (-0.186916) | 0.402918 / 0.434364 (-0.031446) | 0.476629 / 0.540337 (-0.063708) | 0.653709 / 1.386936 (-0.733227) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006562 / 0.011353 (-0.004791) | 0.004092 / 0.011008 (-0.006916) | 0.065951 / 0.038508 (0.027443) | 0.078090 / 0.023109 (0.054981) | 0.369679 / 0.275898 (0.093781) | 0.411442 / 0.323480 (0.087962) | 0.005646 / 0.007986 (-0.002339) | 0.003537 / 0.004328 (-0.000791) | 0.066024 / 0.004250 (0.061773) | 0.058947 / 0.037052 (0.021895) | 0.389219 / 0.258489 (0.130730) | 0.414200 / 0.293841 (0.120359) | 0.030372 / 0.128546 (-0.098174) | 0.008631 / 0.075646 (-0.067015) | 0.071692 / 0.419271 (-0.347580) | 0.048035 / 0.043533 (0.004502) | 0.376960 / 0.255139 (0.121821) | 0.389847 / 0.283200 (0.106648) | 0.023940 / 0.141683 (-0.117743) | 1.487633 / 1.452155 (0.035479) | 1.561680 / 1.492716 (0.068964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301467 / 0.018006 (0.283461) | 0.544159 / 0.000490 (0.543669) | 0.000408 / 0.000200 (0.000208) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030939 / 0.037411 (-0.006472) | 0.087432 / 0.014526 (0.072906) | 0.103263 / 0.176557 (-0.073293) | 0.154551 / 0.737135 (-0.582585) | 0.104631 / 0.296338 (-0.191707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422348 / 0.215209 (0.207139) | 4.206003 / 2.077655 (2.128348) | 2.212619 / 1.504120 (0.708499) | 2.049616 / 1.541195 (0.508421) | 2.139093 / 1.468490 (0.670603) | 0.489647 / 4.584777 (-4.095130) | 3.523291 / 3.745712 (-0.222422) | 3.277657 / 5.269862 (-1.992205) | 2.111353 / 4.565676 (-2.454324) | 0.057597 / 0.424275 (-0.366679) | 0.007675 / 0.007607 (0.000068) | 0.493068 / 0.226044 (0.267023) | 4.939493 / 2.268929 (2.670565) | 2.695995 / 55.444624 (-52.748630) | 2.374904 / 6.876477 (-4.501573) | 2.600110 / 2.142072 (0.458038) | 0.586306 / 4.805227 (-4.218921) | 0.134137 / 6.500664 (-6.366527) | 0.061897 / 0.075469 (-0.013572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330628 / 1.841788 (-0.511160) | 20.557964 / 8.074308 (12.483656) | 14.251632 / 10.191392 (4.060240) | 0.148772 / 0.680424 (-0.531652) | 0.018383 / 0.534201 (-0.515817) | 0.392552 / 0.579283 (-0.186731) | 0.403959 / 0.434364 (-0.030405) | 0.462154 / 0.540337 (-0.078184) | 0.608832 / 1.386936 (-0.778104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"CML watermark\")\n" ]

"2023-07-26T10:44:23"

"2023-07-26T13:01:27"

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[]

[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]

"2023-05-26T12:33:02"

"2023-06-15T13:34:18"

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[]

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[ "Thanks for reporting, @wangyuxinwhy. " ]

"2023-06-30T03:16:57"

"2023-06-30T14:21:03"

"2023-06-30T14:11:25"

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[]

[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas." ]

"2023-07-07T16:38:03"

"2023-07-16T13:00:18"

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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) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"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) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"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) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]

"2023-06-20T14:28:35"

"2023-06-21T13:39:50"

"2023-06-21T13:32:22"

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[ "Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster", "Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024", "_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) | 0.005952 / 0.011353 (-0.005400) | 0.003585 / 0.011008 (-0.007424) | 0.079163 / 0.038508 (0.040655) | 0.057926 / 0.023109 (0.034817) | 0.326647 / 0.275898 (0.050749) | 0.383485 / 0.323480 (0.060005) | 0.004530 / 0.007986 (-0.003456) | 0.002821 / 0.004328 (-0.001508) | 0.062071 / 0.004250 (0.057820) | 0.048023 / 0.037052 (0.010971) | 0.329368 / 0.258489 (0.070879) | 0.390877 / 0.293841 (0.097036) | 0.026959 / 0.128546 (-0.101588) | 0.007911 / 0.075646 (-0.067735) | 0.259956 / 0.419271 (-0.159315) | 0.044582 / 0.043533 (0.001049) | 0.320537 / 0.255139 (0.065398) | 0.373814 / 0.283200 (0.090614) | 0.020275 / 0.141683 (-0.121408) | 1.532128 / 1.452155 (0.079973) | 1.595031 / 1.492716 (0.102315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186127 / 0.018006 (0.168120) | 0.428586 / 0.000490 (0.428097) | 0.005180 / 0.000200 (0.004980) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024876 / 0.037411 (-0.012536) | 0.072169 / 0.014526 (0.057643) | 0.082015 / 0.176557 (-0.094542) | 0.147467 / 0.737135 (-0.589668) | 0.082769 / 0.296338 (-0.213570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410625 / 0.215209 (0.195416) | 4.116742 / 2.077655 (2.039088) | 2.172291 / 1.504120 (0.668171) | 2.022462 / 1.541195 (0.481268) | 2.048142 / 1.468490 (0.579651) | 0.503152 / 4.584777 (-4.081625) | 3.019135 / 3.745712 (-0.726577) | 3.589451 / 5.269862 (-1.680410) | 2.206876 / 4.565676 (-2.358801) | 0.057687 / 0.424275 (-0.366588) | 0.006560 / 0.007607 (-0.001047) | 0.475585 / 0.226044 (0.249541) | 4.784344 / 2.268929 (2.515416) | 2.506322 / 55.444624 (-52.938302) | 2.168251 / 6.876477 (-4.708225) | 2.324453 / 2.142072 (0.182381) | 0.590609 / 4.805227 (-4.214618) | 0.124178 / 6.500664 (-6.376486) | 0.059197 / 0.075469 (-0.016272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212359 / 1.841788 (-0.629429) | 17.915843 / 8.074308 (9.841535) | 13.128330 / 10.191392 (2.936938) | 0.144805 / 0.680424 (-0.535618) | 0.016889 / 0.534201 (-0.517312) | 0.344056 / 0.579283 (-0.235227) | 0.359370 / 0.434364 (-0.074994) | 0.404199 / 0.540337 (-0.136138) | 0.549117 / 1.386936 (-0.837819) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005914 / 0.011353 (-0.005439) | 0.003565 / 0.011008 (-0.007443) | 0.061575 / 0.038508 (0.023067) | 0.057677 / 0.023109 (0.034568) | 0.359753 / 0.275898 (0.083855) | 0.394135 / 0.323480 (0.070655) | 0.004648 / 0.007986 (-0.003338) | 0.002795 / 0.004328 (-0.001534) | 0.061877 / 0.004250 (0.057626) | 0.049673 / 0.037052 (0.012621) | 0.363120 / 0.258489 (0.104631) | 0.402685 / 0.293841 (0.108844) | 0.027021 / 0.128546 (-0.101525) | 0.008006 / 0.075646 (-0.067641) | 0.067398 / 0.419271 (-0.351874) | 0.044442 / 0.043533 (0.000909) | 0.364851 / 0.255139 (0.109712) | 0.387219 / 0.283200 (0.104019) | 0.027267 / 0.141683 (-0.114416) | 1.466675 / 1.452155 (0.014520) | 1.512607 / 1.492716 (0.019891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206156 / 0.018006 (0.188150) | 0.410877 / 0.000490 (0.410387) | 0.003061 / 0.000200 (0.002861) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024869 / 0.037411 (-0.012542) | 0.075736 / 0.014526 (0.061210) | 0.083922 / 0.176557 (-0.092634) | 0.139510 / 0.737135 (-0.597626) | 0.087685 / 0.296338 (-0.208654) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414473 / 0.215209 (0.199264) | 4.150633 / 2.077655 (2.072979) | 2.132892 / 1.504120 (0.628773) | 1.964072 / 1.541195 (0.422878) | 2.003353 / 1.468490 (0.534863) | 0.498012 / 4.584777 (-4.086765) | 3.010135 / 3.745712 (-0.735577) | 2.841130 / 5.269862 (-2.428732) | 1.826013 / 4.565676 (-2.739664) | 0.057443 / 0.424275 (-0.366832) | 0.006374 / 0.007607 (-0.001234) | 0.490337 / 0.226044 (0.264292) | 4.889628 / 2.268929 (2.620700) | 2.575626 / 55.444624 (-52.868998) | 2.246522 / 6.876477 (-4.629955) | 2.276183 / 2.142072 (0.134110) | 0.581465 / 4.805227 (-4.223763) | 0.123877 / 6.500664 (-6.376787) | 0.060339 / 0.075469 (-0.015130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333202 / 1.841788 (-0.508585) | 18.363558 / 8.074308 (10.289250) | 14.109356 / 10.191392 (3.917964) | 0.147358 / 0.680424 (-0.533066) | 0.016813 / 0.534201 (-0.517388) | 0.334815 / 0.579283 (-0.244468) | 0.366576 / 0.434364 (-0.067788) | 0.397223 / 0.540337 (-0.143115) | 0.547893 / 1.386936 (-0.839043) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ac60bcbebe9ddac70264951b1d584c93003cdf \"CML watermark\")\n" ]

"2023-07-12T20:31:16"

"2023-07-13T16:58:32"

"2023-07-13T12:37:09"

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[ "Thanks for reporting, @wangyuxinwhy. " ]

"2023-06-30T03:16:57"

"2023-06-30T14:21:03"

"2023-06-30T14:11:25"

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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) | 0.008698 / 0.011353 (-0.002655) | 0.005250 / 0.011008 (-0.005758) | 0.104101 / 0.038508 (0.065593) | 0.085021 / 0.023109 (0.061912) | 0.426653 / 0.275898 (0.150755) | 0.460449 / 0.323480 (0.136969) | 0.005222 / 0.007986 (-0.002763) | 0.006280 / 0.004328 (0.001951) | 0.083458 / 0.004250 (0.079207) | 0.066132 / 0.037052 (0.029079) | 0.433416 / 0.258489 (0.174927) | 0.482718 / 0.293841 (0.188877) | 0.048872 / 0.128546 (-0.079675) | 0.013699 / 0.075646 (-0.061948) | 0.365660 / 0.419271 (-0.053611) | 0.071008 / 0.043533 (0.027475) | 0.428688 / 0.255139 (0.173549) | 0.443554 / 0.283200 (0.160354) | 0.035901 / 0.141683 (-0.105782) | 1.829296 / 1.452155 (0.377141) | 1.862351 / 1.492716 (0.369635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236284 / 0.018006 (0.218278) | 0.584075 / 0.000490 (0.583585) | 0.004634 / 0.000200 (0.004434) | 0.000125 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034723 / 0.037411 (-0.002688) | 0.100989 / 0.014526 (0.086464) | 0.113722 / 0.176557 (-0.062834) | 0.187659 / 0.737135 (-0.549477) | 0.113937 / 0.296338 (-0.182401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587500 / 0.215209 (0.372291) | 5.847371 / 2.077655 (3.769716) | 2.599691 / 1.504120 (1.095571) | 2.246187 / 1.541195 (0.704992) | 2.419126 / 1.468490 (0.950636) | 0.847327 / 4.584777 (-3.737450) | 5.230438 / 3.745712 (1.484726) | 7.539021 / 5.269862 (2.269160) | 4.617473 / 4.565676 (0.051797) | 0.103620 / 0.424275 (-0.320655) | 0.009195 / 0.007607 (0.001588) | 0.714247 / 0.226044 (0.488203) | 7.331621 / 2.268929 (5.062693) | 3.416575 / 55.444624 (-52.028049) | 2.649467 / 6.876477 (-4.227009) | 2.928091 / 2.142072 (0.786018) | 1.002155 / 4.805227 (-3.803072) | 0.210790 / 6.500664 (-6.289874) | 0.081303 / 0.075469 (0.005834) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655431 / 1.841788 (-0.186357) | 24.069595 / 8.074308 (15.995287) | 20.923766 / 10.191392 (10.732374) | 0.232021 / 0.680424 (-0.448403) | 0.026355 / 0.534201 (-0.507846) | 0.496830 / 0.579283 (-0.082453) | 0.582620 / 0.434364 (0.148257) | 0.551227 / 0.540337 (0.010890) | 0.756389 / 1.386936 (-0.630547) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009329 / 0.011353 (-0.002024) | 0.005045 / 0.011008 (-0.005964) | 0.082116 / 0.038508 (0.043608) | 0.082420 / 0.023109 (0.059311) | 0.502513 / 0.275898 (0.226615) | 0.526098 / 0.323480 (0.202618) | 0.007468 / 0.007986 (-0.000517) | 0.005477 / 0.004328 (0.001148) | 0.082617 / 0.004250 (0.078367) | 0.070292 / 0.037052 (0.033239) | 0.503290 / 0.258489 (0.244801) | 0.541631 / 0.293841 (0.247790) | 0.050826 / 0.128546 (-0.077721) | 0.014699 / 0.075646 (-0.060948) | 0.094441 / 0.419271 (-0.324830) | 0.065034 / 0.043533 (0.021501) | 0.486778 / 0.255139 (0.231639) | 0.516907 / 0.283200 (0.233707) | 0.045140 / 0.141683 (-0.096543) | 1.831676 / 1.452155 (0.379521) | 1.910865 / 1.492716 (0.418149) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286818 / 0.018006 (0.268812) | 0.558621 / 0.000490 (0.558131) | 0.002830 / 0.000200 (0.002630) | 0.000148 / 0.000054 (0.000094) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036716 / 0.037411 (-0.000696) | 0.107830 / 0.014526 (0.093305) | 0.116368 / 0.176557 (-0.060188) | 0.178401 / 0.737135 (-0.558734) | 0.124729 / 0.296338 (-0.171609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633557 / 0.215209 (0.418348) | 6.423135 / 2.077655 (4.345480) | 2.981883 / 1.504120 (1.477763) | 2.755592 / 1.541195 (1.214398) | 2.769337 / 1.468490 (1.300847) | 0.836219 / 4.584777 (-3.748558) | 5.302030 / 3.745712 (1.556318) | 7.463960 / 5.269862 (2.194098) | 4.427254 / 4.565676 (-0.138422) | 0.095990 / 0.424275 (-0.328285) | 0.009264 / 0.007607 (0.001657) | 0.770642 / 0.226044 (0.544597) | 7.779667 / 2.268929 (5.510739) | 3.799115 / 55.444624 (-51.645509) | 3.212560 / 6.876477 (-3.663917) | 3.281657 / 2.142072 (1.139584) | 1.044981 / 4.805227 (-3.760246) | 0.210693 / 6.500664 (-6.289971) | 0.079466 / 0.075469 (0.003997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793155 / 1.841788 (-0.048632) | 24.691127 / 8.074308 (16.616819) | 22.083150 / 10.191392 (11.891758) | 0.242246 / 0.680424 (-0.438178) | 0.028001 / 0.534201 (-0.506200) | 0.494061 / 0.579283 (-0.085222) | 0.599288 / 0.434364 (0.164924) | 0.552101 / 0.540337 (0.011764) | 0.784093 / 1.386936 (-0.602843) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd429c39604af34bc3a3ba1f463329b23fcbc1e3 \"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) | 0.006658 / 0.011353 (-0.004695) | 0.004044 / 0.011008 (-0.006965) | 0.085844 / 0.038508 (0.047336) | 0.077147 / 0.023109 (0.054038) | 0.344387 / 0.275898 (0.068489) | 0.376718 / 0.323480 (0.053238) | 0.005537 / 0.007986 (-0.002448) | 0.003452 / 0.004328 (-0.000876) | 0.065326 / 0.004250 (0.061076) | 0.057639 / 0.037052 (0.020587) | 0.352363 / 0.258489 (0.093873) | 0.378939 / 0.293841 (0.085098) | 0.031259 / 0.128546 (-0.097287) | 0.008464 / 0.075646 (-0.067183) | 0.289076 / 0.419271 (-0.130195) | 0.052991 / 0.043533 (0.009459) | 0.346053 / 0.255139 (0.090914) | 0.362761 / 0.283200 (0.079561) | 0.023501 / 0.141683 (-0.118182) | 1.478312 / 1.452155 (0.026157) | 1.545437 / 1.492716 (0.052721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202964 / 0.018006 (0.184957) | 0.534793 / 0.000490 (0.534303) | 0.006025 / 0.000200 (0.005825) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029418 / 0.037411 (-0.007993) | 0.084297 / 0.014526 (0.069771) | 0.096702 / 0.176557 (-0.079855) | 0.157355 / 0.737135 (-0.579781) | 0.097858 / 0.296338 (-0.198480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.380728 / 0.215209 (0.165519) | 3.787712 / 2.077655 (1.710057) | 1.836393 / 1.504120 (0.332273) | 1.678415 / 1.541195 (0.137220) | 1.781800 / 1.468490 (0.313310) | 0.478677 / 4.584777 (-4.106100) | 3.614080 / 3.745712 (-0.131632) | 3.255637 / 5.269862 (-2.014225) | 2.063642 / 4.565676 (-2.502035) | 0.056470 / 0.424275 (-0.367805) | 0.007408 / 0.007607 (-0.000199) | 0.459155 / 0.226044 (0.233111) | 4.586679 / 2.268929 (2.317750) | 2.305737 / 55.444624 (-53.138888) | 1.954755 / 6.876477 (-4.921721) | 2.190809 / 2.142072 (0.048737) | 0.572426 / 4.805227 (-4.232802) | 0.130349 / 6.500664 (-6.370315) | 0.059346 / 0.075469 (-0.016124) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253671 / 1.841788 (-0.588117) | 19.509015 / 8.074308 (11.434706) | 13.951349 / 10.191392 (3.759957) | 0.171038 / 0.680424 (-0.509386) | 0.018826 / 0.534201 (-0.515375) | 0.394642 / 0.579283 (-0.184642) | 0.419614 / 0.434364 (-0.014750) | 0.470931 / 0.540337 (-0.069406) | 0.643858 / 1.386936 (-0.743078) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006765 / 0.011353 (-0.004587) | 0.003955 / 0.011008 (-0.007053) | 0.064377 / 0.038508 (0.025869) | 0.076980 / 0.023109 (0.053871) | 0.368675 / 0.275898 (0.092777) | 0.403746 / 0.323480 (0.080267) | 0.005303 / 0.007986 (-0.002683) | 0.003257 / 0.004328 (-0.001072) | 0.064154 / 0.004250 (0.059903) | 0.056975 / 0.037052 (0.019923) | 0.376718 / 0.258489 (0.118229) | 0.416291 / 0.293841 (0.122450) | 0.031444 / 0.128546 (-0.097102) | 0.008532 / 0.075646 (-0.067115) | 0.070455 / 0.419271 (-0.348816) | 0.049032 / 0.043533 (0.005499) | 0.361413 / 0.255139 (0.106274) | 0.384648 / 0.283200 (0.101448) | 0.024050 / 0.141683 (-0.117633) | 1.514330 / 1.452155 (0.062176) | 1.585424 / 1.492716 (0.092708) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214701 / 0.018006 (0.196695) | 0.447706 / 0.000490 (0.447216) | 0.000373 / 0.000200 (0.000173) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031007 / 0.037411 (-0.006404) | 0.090545 / 0.014526 (0.076019) | 0.100611 / 0.176557 (-0.075945) | 0.154847 / 0.737135 (-0.582289) | 0.102864 / 0.296338 (-0.193475) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427740 / 0.215209 (0.212531) | 4.273143 / 2.077655 (2.195488) | 2.294906 / 1.504120 (0.790786) | 2.138460 / 1.541195 (0.597265) | 2.274126 / 1.468490 (0.805636) | 0.486559 / 4.584777 (-4.098218) | 3.565554 / 3.745712 (-0.180158) | 3.377659 / 5.269862 (-1.892202) | 2.029883 / 4.565676 (-2.535793) | 0.057303 / 0.424275 (-0.366972) | 0.007314 / 0.007607 (-0.000293) | 0.504263 / 0.226044 (0.278219) | 5.041196 / 2.268929 (2.772268) | 2.819273 / 55.444624 (-52.625351) | 2.421479 / 6.876477 (-4.454998) | 2.503063 / 2.142072 (0.360991) | 0.581467 / 4.805227 (-4.223760) | 0.133532 / 6.500664 (-6.367132) | 0.062504 / 0.075469 (-0.012965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.328765 / 1.841788 (-0.513022) | 20.131672 / 8.074308 (12.057363) | 14.312895 / 10.191392 (4.121503) | 0.191199 / 0.680424 (-0.489225) | 0.018522 / 0.534201 (-0.515679) | 0.393121 / 0.579283 (-0.186162) | 0.413122 / 0.434364 (-0.021242) | 0.469312 / 0.540337 (-0.071026) | 0.633140 / 1.386936 (-0.753796) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dbf6c103f5844de40431478e7e4a64fbf2c2c067 \"CML watermark\")\n" ]

"2023-07-13T13:16:17"

"2023-07-13T14:06:20"

"2023-07-13T13:57:35"

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[ "Hi! The times match when I run this code locally or on Colab.\r\n\r\nAlso, we use `multiprocess`, not `multiprocessing`, for parallelization, and torch's `__init__.py` (executed on `import torch` ) slightly modifies the latter.", "Hey Mariosasko,\r\n\r\nThanks for looking into it. We further did some investigations after your comment and figured out it's only affecting some hardware/software configurations with the `pytorch` installation of `conda-forge`. Based on this we found the following issue in PyTorch: https://github.com/pytorch/pytorch/issues/102269 with a quick fix for now.\r\n\r\nSince it seems to be a deeper issue with forking processes, the difference between`multiprocess` and `multiprocessing` didn't make a difference.\r\n\r\nClosing this, since the issue comes from `pytorch` not `dataset`. \r\n" ]

"2023-06-06T19:42:25"

"2023-06-16T13:09:12"

"2023-06-16T13:09:12"

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[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]

"2023-06-27T10:54:07"

"2023-06-27T15:36:42"

"2023-06-27T15:32:44"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006658 / 0.011353 (-0.004695) | 0.004347 / 0.011008 (-0.006661) | 0.084179 / 0.038508 (0.045671) | 0.080842 / 0.023109 (0.057733) | 0.321642 / 0.275898 (0.045744) | 0.348758 / 0.323480 (0.025278) | 0.005624 / 0.007986 (-0.002362) | 0.003479 / 0.004328 (-0.000850) | 0.065125 / 0.004250 (0.060875) | 0.057624 / 0.037052 (0.020572) | 0.323643 / 0.258489 (0.065154) | 0.360939 / 0.293841 (0.067098) | 0.031005 / 0.128546 (-0.097541) | 0.008618 / 0.075646 (-0.067028) | 0.287443 / 0.419271 (-0.131828) | 0.052640 / 0.043533 (0.009107) | 0.316947 / 0.255139 (0.061808) | 0.330292 / 0.283200 (0.047093) | 0.024393 / 0.141683 (-0.117289) | 1.476734 / 1.452155 (0.024579) | 1.534505 / 1.492716 (0.041789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273808 / 0.018006 (0.255802) | 0.591146 / 0.000490 (0.590656) | 0.000322 / 0.000200 (0.000122) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029992 / 0.037411 (-0.007419) | 0.086654 / 0.014526 (0.072129) | 0.098590 / 0.176557 (-0.077967) | 0.157225 / 0.737135 (-0.579910) | 0.101816 / 0.296338 (-0.194522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382578 / 0.215209 (0.167368) | 3.803576 / 2.077655 (1.725922) | 1.875136 / 1.504120 (0.371016) | 1.704207 / 1.541195 (0.163012) | 1.765146 / 1.468490 (0.296656) | 0.482802 / 4.584777 (-4.101975) | 3.571772 / 3.745712 (-0.173940) | 3.245626 / 5.269862 (-2.024235) | 2.051612 / 4.565676 (-2.514064) | 0.056539 / 0.424275 (-0.367736) | 0.007199 / 0.007607 (-0.000408) | 0.462445 / 0.226044 (0.236401) | 4.623800 / 2.268929 (2.354872) | 2.318948 / 55.444624 (-53.125677) | 1.971442 / 6.876477 (-4.905035) | 2.225444 / 2.142072 (0.083371) | 0.575205 / 4.805227 (-4.230022) | 0.129243 / 6.500664 (-6.371421) | 0.059036 / 0.075469 (-0.016433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266827 / 1.841788 (-0.574960) | 20.323419 / 8.074308 (12.249110) | 14.577603 / 10.191392 (4.386210) | 0.162131 / 0.680424 (-0.518293) | 0.018529 / 0.534201 (-0.515672) | 0.395046 / 0.579283 (-0.184237) | 0.410870 / 0.434364 (-0.023494) | 0.455782 / 0.540337 (-0.084556) | 0.662851 / 1.386936 (-0.724085) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006867 / 0.011353 (-0.004486) | 0.004197 / 0.011008 (-0.006811) | 0.066060 / 0.038508 (0.027552) | 0.084145 / 0.023109 (0.061036) | 0.366740 / 0.275898 (0.090842) | 0.402362 / 0.323480 (0.078882) | 0.005785 / 0.007986 (-0.002200) | 0.003551 / 0.004328 (-0.000778) | 0.066177 / 0.004250 (0.061926) | 0.061521 / 0.037052 (0.024468) | 0.377807 / 0.258489 (0.119318) | 0.413490 / 0.293841 (0.119649) | 0.031918 / 0.128546 (-0.096628) | 0.008767 / 0.075646 (-0.066879) | 0.071437 / 0.419271 (-0.347835) | 0.049237 / 0.043533 (0.005704) | 0.365929 / 0.255139 (0.110790) | 0.393545 / 0.283200 (0.110346) | 0.024054 / 0.141683 (-0.117628) | 1.524599 / 1.452155 (0.072445) | 1.576592 / 1.492716 (0.083876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315181 / 0.018006 (0.297174) | 0.535501 / 0.000490 (0.535011) | 0.000410 / 0.000200 (0.000210) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032915 / 0.037411 (-0.004497) | 0.089310 / 0.014526 (0.074784) | 0.105136 / 0.176557 (-0.071421) | 0.158572 / 0.737135 (-0.578563) | 0.106850 / 0.296338 (-0.189489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419343 / 0.215209 (0.204134) | 4.200166 / 2.077655 (2.122511) | 2.180234 / 1.504120 (0.676114) | 2.016885 / 1.541195 (0.475690) | 2.131480 / 1.468490 (0.662990) | 0.484681 / 4.584777 (-4.100096) | 3.613535 / 3.745712 (-0.132177) | 5.762111 / 5.269862 (0.492249) | 3.190590 / 4.565676 (-1.375086) | 0.057403 / 0.424275 (-0.366872) | 0.007862 / 0.007607 (0.000255) | 0.490857 / 0.226044 (0.264813) | 4.911241 / 2.268929 (2.642313) | 2.650787 / 55.444624 (-52.793838) | 2.317060 / 6.876477 (-4.559416) | 2.579677 / 2.142072 (0.437605) | 0.587388 / 4.805227 (-4.217840) | 0.148109 / 6.500664 (-6.352555) | 0.061435 / 0.075469 (-0.014034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322181 / 1.841788 (-0.519606) | 20.647184 / 8.074308 (12.572875) | 14.907635 / 10.191392 (4.716243) | 0.156330 / 0.680424 (-0.524094) | 0.018719 / 0.534201 (-0.515482) | 0.397636 / 0.579283 (-0.181647) | 0.414107 / 0.434364 (-0.020257) | 0.460812 / 0.540337 (-0.079526) | 0.609568 / 1.386936 (-0.777368) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n", "This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ", "what would be the change i need to make? ", "@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n", "The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged", "I just merged the other PR so you should be good now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006303 / 0.011353 (-0.005049) | 0.003746 / 0.011008 (-0.007262) | 0.081083 / 0.038508 (0.042575) | 0.067973 / 0.023109 (0.044864) | 0.322221 / 0.275898 (0.046323) | 0.359432 / 0.323480 (0.035952) | 0.004891 / 0.007986 (-0.003095) | 0.002988 / 0.004328 (-0.001341) | 0.064068 / 0.004250 (0.059818) | 0.052042 / 0.037052 (0.014990) | 0.323387 / 0.258489 (0.064898) | 0.390416 / 0.293841 (0.096575) | 0.028090 / 0.128546 (-0.100457) | 0.008009 / 0.075646 (-0.067638) | 0.262288 / 0.419271 (-0.156984) | 0.044986 / 0.043533 (0.001453) | 0.322319 / 0.255139 (0.067180) | 0.345323 / 0.283200 (0.062123) | 0.021798 / 0.141683 (-0.119885) | 1.417259 / 1.452155 (-0.034895) | 1.490050 / 1.492716 (-0.002667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195902 / 0.018006 (0.177896) | 0.490808 / 0.000490 (0.490318) | 0.002969 / 0.000200 (0.002770) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025221 / 0.037411 (-0.012190) | 0.075341 / 0.014526 (0.060815) | 0.086703 / 0.176557 (-0.089853) | 0.146953 / 0.737135 (-0.590182) | 0.086610 / 0.296338 (-0.209728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434890 / 0.215209 (0.219681) | 4.352283 / 2.077655 (2.274629) | 2.293098 / 1.504120 (0.788979) | 2.123023 / 1.541195 (0.581829) | 2.179722 / 1.468490 (0.711232) | 0.503851 / 4.584777 (-4.080926) | 3.087991 / 3.745712 (-0.657721) | 2.898689 / 5.269862 (-2.371173) | 1.902813 / 4.565676 (-2.662864) | 0.058079 / 0.424275 (-0.366196) | 0.006600 / 0.007607 (-0.001007) | 0.509243 / 0.226044 (0.283199) | 5.080204 / 2.268929 (2.811275) | 2.753594 / 55.444624 (-52.691030) | 2.417385 / 6.876477 (-4.459091) | 2.635470 / 2.142072 (0.493398) | 0.591059 / 4.805227 (-4.214168) | 0.126360 / 6.500664 (-6.374304) | 0.062108 / 0.075469 (-0.013361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254398 / 1.841788 (-0.587390) | 18.866729 / 8.074308 (10.792420) | 14.120008 / 10.191392 (3.928616) | 0.152388 / 0.680424 (-0.528035) | 0.016997 / 0.534201 (-0.517204) | 0.336435 / 0.579283 (-0.242848) | 0.364612 / 0.434364 (-0.069752) | 0.391434 / 0.540337 (-0.148903) | 0.567180 / 1.386936 (-0.819756) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006477 / 0.011353 (-0.004876) | 0.003723 / 0.011008 (-0.007285) | 0.062712 / 0.038508 (0.024204) | 0.069380 / 0.023109 (0.046271) | 0.393394 / 0.275898 (0.117496) | 0.446903 / 0.323480 (0.123423) | 0.004833 / 0.007986 (-0.003153) | 0.002946 / 0.004328 (-0.001382) | 0.062076 / 0.004250 (0.057826) | 0.051589 / 0.037052 (0.014537) | 0.388536 / 0.258489 (0.130047) | 0.451406 / 0.293841 (0.157565) | 0.027824 / 0.128546 (-0.100722) | 0.008040 / 0.075646 (-0.067606) | 0.067085 / 0.419271 (-0.352187) | 0.042269 / 0.043533 (-0.001264) | 0.363419 / 0.255139 (0.108280) | 0.435201 / 0.283200 (0.152001) | 0.021275 / 0.141683 (-0.120408) | 1.439838 / 1.452155 (-0.012316) | 1.477279 / 1.492716 (-0.015437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229667 / 0.018006 (0.211661) | 0.434101 / 0.000490 (0.433611) | 0.000652 / 0.000200 (0.000452) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026141 / 0.037411 (-0.011271) | 0.078950 / 0.014526 (0.064424) | 0.090143 / 0.176557 (-0.086413) | 0.143941 / 0.737135 (-0.593195) | 0.090465 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432042 / 0.215209 (0.216833) | 4.322134 / 2.077655 (2.244479) | 2.242897 / 1.504120 (0.738777) | 2.076351 / 1.541195 (0.535157) | 2.166739 / 1.468490 (0.698249) | 0.500833 / 4.584777 (-4.083944) | 3.140117 / 3.745712 (-0.605595) | 4.383050 / 5.269862 (-0.886812) | 2.548245 / 4.565676 (-2.017432) | 0.057521 / 0.424275 (-0.366754) | 0.006946 / 0.007607 (-0.000662) | 0.509613 / 0.226044 (0.283569) | 5.114052 / 2.268929 (2.845123) | 2.682112 / 55.444624 (-52.762512) | 2.362385 / 6.876477 (-4.514092) | 2.531787 / 2.142072 (0.389715) | 0.595085 / 4.805227 (-4.210142) | 0.130198 / 6.500664 (-6.370466) | 0.064057 / 0.075469 (-0.011412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346254 / 1.841788 (-0.495534) | 19.036911 / 8.074308 (10.962603) | 14.478689 / 10.191392 (4.287297) | 0.147541 / 0.680424 (-0.532883) | 0.016851 / 0.534201 (-0.517350) | 0.333901 / 0.579283 (-0.245382) | 0.380012 / 0.434364 (-0.054352) | 0.396021 / 0.540337 (-0.144317) | 0.540612 / 1.386936 (-0.846324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n", "CI failure is unrelated. Merging.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009498 / 0.011353 (-0.001855) | 0.005639 / 0.011008 (-0.005369) | 0.108731 / 0.038508 (0.070223) | 0.094052 / 0.023109 (0.070943) | 0.454375 / 0.275898 (0.178477) | 0.486852 / 0.323480 (0.163372) | 0.006627 / 0.007986 (-0.001359) | 0.004712 / 0.004328 (0.000383) | 0.082006 / 0.004250 (0.077756) | 0.079394 / 0.037052 (0.042342) | 0.450982 / 0.258489 (0.192493) | 0.502659 / 0.293841 (0.208818) | 0.049741 / 0.128546 (-0.078806) | 0.014482 / 0.075646 (-0.061164) | 0.362661 / 0.419271 (-0.056611) | 0.068225 / 0.043533 (0.024692) | 0.456219 / 0.255139 (0.201080) | 0.483919 / 0.283200 (0.200719) | 0.044490 / 0.141683 (-0.097193) | 1.809420 / 1.452155 (0.357265) | 1.908859 / 1.492716 (0.416143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267350 / 0.018006 (0.249344) | 0.600403 / 0.000490 (0.599913) | 0.003665 / 0.000200 (0.003465) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032499 / 0.037411 (-0.004912) | 0.104829 / 0.014526 (0.090303) | 0.115809 / 0.176557 (-0.060747) | 0.191561 / 0.737135 (-0.545574) | 0.113454 / 0.296338 (-0.182885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599165 / 0.215209 (0.383956) | 5.802947 / 2.077655 (3.725292) | 2.477330 / 1.504120 (0.973210) | 2.231147 / 1.541195 (0.689952) | 2.365688 / 1.468490 (0.897197) | 0.853912 / 4.584777 (-3.730865) | 5.529472 / 3.745712 (1.783760) | 6.145286 / 5.269862 (0.875424) | 3.415871 / 4.565676 (-1.149805) | 0.099889 / 0.424275 (-0.324386) | 0.008933 / 0.007607 (0.001325) | 0.704643 / 0.226044 (0.478598) | 7.178101 / 2.268929 (4.909173) | 3.367120 / 55.444624 (-52.077504) | 2.795177 / 6.876477 (-4.081300) | 2.796798 / 2.142072 (0.654726) | 1.039097 / 4.805227 (-3.766130) | 0.232784 / 6.500664 (-6.267881) | 0.083608 / 0.075469 (0.008138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646827 / 1.841788 (-0.194961) | 25.003419 / 8.074308 (16.929111) | 22.165746 / 10.191392 (11.974354) | 0.246179 / 0.680424 (-0.434245) | 0.029304 / 0.534201 (-0.504897) | 0.500767 / 0.579283 (-0.078516) | 0.606501 / 0.434364 (0.172137) | 0.564092 / 0.540337 (0.023755) | 0.857568 / 1.386936 (-0.529368) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009206 / 0.011353 (-0.002146) | 0.005084 / 0.011008 (-0.005925) | 0.081402 / 0.038508 (0.042894) | 0.088028 / 0.023109 (0.064919) | 0.539509 / 0.275898 (0.263611) | 0.590759 / 0.323480 (0.267280) | 0.006527 / 0.007986 (-0.001459) | 0.004375 / 0.004328 (0.000047) | 0.082327 / 0.004250 (0.078076) | 0.065442 / 0.037052 (0.028390) | 0.548254 / 0.258489 (0.289765) | 0.598388 / 0.293841 (0.304547) | 0.049409 / 0.128546 (-0.079137) | 0.014366 / 0.075646 (-0.061280) | 0.094568 / 0.419271 (-0.324703) | 0.063685 / 0.043533 (0.020152) | 0.545359 / 0.255139 (0.290220) | 0.573358 / 0.283200 (0.290159) | 0.036864 / 0.141683 (-0.104819) | 1.817985 / 1.452155 (0.365830) | 1.925188 / 1.492716 (0.432472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303205 / 0.018006 (0.285199) | 0.620981 / 0.000490 (0.620491) | 0.004910 / 0.000200 (0.004710) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033791 / 0.037411 (-0.003620) | 0.114974 / 0.014526 (0.100448) | 0.117682 / 0.176557 (-0.058875) | 0.177188 / 0.737135 (-0.559947) | 0.126425 / 0.296338 (-0.169914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636932 / 0.215209 (0.421723) | 6.289054 / 2.077655 (4.211399) | 2.920772 / 1.504120 (1.416652) | 2.672080 / 1.541195 (1.130885) | 2.712271 / 1.468490 (1.243781) | 0.889305 / 4.584777 (-3.695472) | 5.536018 / 3.745712 (1.790306) | 4.687564 / 5.269862 (-0.582298) | 3.204239 / 4.565676 (-1.361437) | 0.095546 / 0.424275 (-0.328729) | 0.008838 / 0.007607 (0.001231) | 0.714584 / 0.226044 (0.488540) | 7.482663 / 2.268929 (5.213735) | 3.621392 / 55.444624 (-51.823232) | 2.987777 / 6.876477 (-3.888700) | 3.312636 / 2.142072 (1.170564) | 1.033721 / 4.805227 (-3.771506) | 0.206292 / 6.500664 (-6.294372) | 0.079423 / 0.075469 (0.003953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798645 / 1.841788 (-0.043143) | 25.544329 / 8.074308 (17.470021) | 23.041318 / 10.191392 (12.849926) | 0.259067 / 0.680424 (-0.421357) | 0.029839 / 0.534201 (-0.504362) | 0.495583 / 0.579283 (-0.083700) | 0.598755 / 0.434364 (0.164391) | 0.574864 / 0.540337 (0.034527) | 0.831160 / 1.386936 (-0.555776) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n" ]

"2023-07-19T15:00:01"

"2023-07-19T17:39:11"

"2023-07-19T17:27:17"

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[ "You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n", "This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.", "The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.", "The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed" ]

"2023-07-25T17:45:50"

"2023-07-26T15:18:51"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006616 / 0.011353 (-0.004737) | 0.003915 / 0.011008 (-0.007093) | 0.083271 / 0.038508 (0.044763) | 0.072595 / 0.023109 (0.049485) | 0.307224 / 0.275898 (0.031326) | 0.337244 / 0.323480 (0.013764) | 0.005296 / 0.007986 (-0.002690) | 0.003325 / 0.004328 (-0.001003) | 0.064589 / 0.004250 (0.060339) | 0.056369 / 0.037052 (0.019316) | 0.310829 / 0.258489 (0.052340) | 0.345563 / 0.293841 (0.051722) | 0.030551 / 0.128546 (-0.097995) | 0.008519 / 0.075646 (-0.067127) | 0.286368 / 0.419271 (-0.132903) | 0.052498 / 0.043533 (0.008966) | 0.308735 / 0.255139 (0.053596) | 0.329234 / 0.283200 (0.046034) | 0.022588 / 0.141683 (-0.119095) | 1.453135 / 1.452155 (0.000981) | 1.525956 / 1.492716 (0.033239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199417 / 0.018006 (0.181410) | 0.454621 / 0.000490 (0.454131) | 0.004928 / 0.000200 (0.004728) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028436 / 0.037411 (-0.008975) | 0.083722 / 0.014526 (0.069196) | 0.095162 / 0.176557 (-0.081395) | 0.153434 / 0.737135 (-0.583702) | 0.099480 / 0.296338 (-0.196859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384647 / 0.215209 (0.169438) | 3.838406 / 2.077655 (1.760751) | 1.891267 / 1.504120 (0.387148) | 1.751432 / 1.541195 (0.210238) | 1.737443 / 1.468490 (0.268953) | 0.487758 / 4.584777 (-4.097019) | 3.635925 / 3.745712 (-0.109787) | 5.208718 / 5.269862 (-0.061144) | 3.029374 / 4.565676 (-1.536302) | 0.057613 / 0.424275 (-0.366662) | 0.007177 / 0.007607 (-0.000430) | 0.455596 / 0.226044 (0.229552) | 4.559969 / 2.268929 (2.291040) | 2.325321 / 55.444624 (-53.119303) | 2.034924 / 6.876477 (-4.841552) | 2.163869 / 2.142072 (0.021796) | 0.583477 / 4.805227 (-4.221750) | 0.132870 / 6.500664 (-6.367795) | 0.059618 / 0.075469 (-0.015851) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263751 / 1.841788 (-0.578037) | 19.740004 / 8.074308 (11.665696) | 14.410980 / 10.191392 (4.219588) | 0.170367 / 0.680424 (-0.510057) | 0.018225 / 0.534201 (-0.515976) | 0.390101 / 0.579283 (-0.189182) | 0.404298 / 0.434364 (-0.030066) | 0.455295 / 0.540337 (-0.085043) | 0.621179 / 1.386936 (-0.765757) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006580 / 0.011353 (-0.004773) | 0.004078 / 0.011008 (-0.006930) | 0.065842 / 0.038508 (0.027334) | 0.074494 / 0.023109 (0.051385) | 0.403644 / 0.275898 (0.127746) | 0.430204 / 0.323480 (0.106724) | 0.005343 / 0.007986 (-0.002643) | 0.003366 / 0.004328 (-0.000963) | 0.064858 / 0.004250 (0.060607) | 0.056252 / 0.037052 (0.019200) | 0.412556 / 0.258489 (0.154067) | 0.434099 / 0.293841 (0.140258) | 0.031518 / 0.128546 (-0.097028) | 0.008543 / 0.075646 (-0.067104) | 0.071658 / 0.419271 (-0.347613) | 0.049962 / 0.043533 (0.006430) | 0.398511 / 0.255139 (0.143372) | 0.415908 / 0.283200 (0.132708) | 0.025011 / 0.141683 (-0.116672) | 1.492350 / 1.452155 (0.040195) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204971 / 0.018006 (0.186964) | 0.439965 / 0.000490 (0.439475) | 0.002071 / 0.000200 (0.001872) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031673 / 0.037411 (-0.005738) | 0.087529 / 0.014526 (0.073004) | 0.099882 / 0.176557 (-0.076675) | 0.156994 / 0.737135 (-0.580141) | 0.101421 / 0.296338 (-0.194918) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407480 / 0.215209 (0.192271) | 4.069123 / 2.077655 (1.991468) | 2.081288 / 1.504120 (0.577169) | 1.920367 / 1.541195 (0.379172) | 1.981053 / 1.468490 (0.512563) | 0.481995 / 4.584777 (-4.102782) | 3.546486 / 3.745712 (-0.199226) | 5.133150 / 5.269862 (-0.136712) | 3.056444 / 4.565676 (-1.509232) | 0.056650 / 0.424275 (-0.367625) | 0.007746 / 0.007607 (0.000139) | 0.490891 / 0.226044 (0.264847) | 4.902160 / 2.268929 (2.633232) | 2.564726 / 55.444624 (-52.879899) | 2.234988 / 6.876477 (-4.641489) | 2.387656 / 2.142072 (0.245583) | 0.576315 / 4.805227 (-4.228912) | 0.132065 / 6.500664 (-6.368599) | 0.060728 / 0.075469 (-0.014741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.370568 / 1.841788 (-0.471220) | 19.883159 / 8.074308 (11.808851) | 14.442066 / 10.191392 (4.250674) | 0.150119 / 0.680424 (-0.530305) | 0.018359 / 0.534201 (-0.515842) | 0.394128 / 0.579283 (-0.185155) | 0.411697 / 0.434364 (-0.022667) | 0.460580 / 0.540337 (-0.079757) | 0.608490 / 1.386936 (-0.778446) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#035d0cf842b82b14059999baa78e8d158dfbed12 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6074). All of your documentation changes will be reflected on that endpoint." ]

"2023-07-26T12:20:54"

"2023-07-26T14:42:56"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.005673 / 0.011008 (-0.005335) | 0.124034 / 0.038508 (0.085526) | 0.037550 / 0.023109 (0.014441) | 0.331301 / 0.275898 (0.055403) | 0.383542 / 0.323480 (0.060062) | 0.006940 / 0.007986 (-0.001046) | 0.005959 / 0.004328 (0.001631) | 0.084670 / 0.004250 (0.080419) | 0.054214 / 0.037052 (0.017162) | 0.359897 / 0.258489 (0.101408) | 0.383260 / 0.293841 (0.089419) | 0.047642 / 0.128546 (-0.080904) | 0.013902 / 0.075646 (-0.061744) | 0.380232 / 0.419271 (-0.039040) | 0.077790 / 0.043533 (0.034257) | 0.376648 / 0.255139 (0.121509) | 0.387536 / 0.283200 (0.104336) | 0.104644 / 0.141683 (-0.037038) | 1.618560 / 1.452155 (0.166406) | 1.742569 / 1.492716 (0.249853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257218 / 0.018006 (0.239212) | 0.636801 / 0.000490 (0.636311) | 0.000634 / 0.000200 (0.000434) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037874 / 0.037411 (0.000462) | 0.107454 / 0.014526 (0.092928) | 0.117855 / 0.176557 (-0.058702) | 0.204067 / 0.737135 (-0.533068) | 0.134029 / 0.296338 (-0.162310) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583657 / 0.215209 (0.368447) | 5.761289 / 2.077655 (3.683635) | 2.280201 / 1.504120 (0.776081) | 2.033442 / 1.541195 (0.492247) | 2.035343 / 1.468490 (0.566853) | 0.868122 / 4.584777 (-3.716655) | 5.352591 / 3.745712 (1.606879) | 2.432814 / 5.269862 (-2.837047) | 1.560765 / 4.565676 (-3.004911) | 0.098793 / 0.424275 (-0.325482) | 0.017327 / 0.007607 (0.009720) | 0.734676 / 0.226044 (0.508631) | 7.070318 / 2.268929 (4.801390) | 2.972701 / 55.444624 (-52.471924) | 2.442189 / 6.876477 (-4.434288) | 2.604379 / 2.142072 (0.462307) | 1.028853 / 4.805227 (-3.776374) | 0.210390 / 6.500664 (-6.290274) | 0.069329 / 0.075469 (-0.006140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.469586 / 1.841788 (-0.372202) | 16.570305 / 8.074308 (8.495997) | 19.187845 / 10.191392 (8.996453) | 0.219162 / 0.680424 (-0.461262) | 0.026356 / 0.534201 (-0.507845) | 0.447370 / 0.579283 (-0.131913) | 0.555893 / 0.434364 (0.121529) | 0.574958 / 0.540337 (0.034621) | 0.639166 / 1.386936 (-0.747770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008166 / 0.011353 (-0.003187) | 0.005577 / 0.011008 (-0.005431) | 0.103578 / 0.038508 (0.065070) | 0.040563 / 0.023109 (0.017454) | 0.441996 / 0.275898 (0.166098) | 0.483594 / 0.323480 (0.160114) | 0.007329 / 0.007986 (-0.000657) | 0.004546 / 0.004328 (0.000218) | 0.090471 / 0.004250 (0.086220) | 0.052740 / 0.037052 (0.015688) | 0.442197 / 0.258489 (0.183708) | 0.524310 / 0.293841 (0.230469) | 0.042487 / 0.128546 (-0.086060) | 0.012917 / 0.075646 (-0.062730) | 0.103992 / 0.419271 (-0.315280) | 0.060570 / 0.043533 (0.017037) | 0.441956 / 0.255139 (0.186817) | 0.477084 / 0.283200 (0.193885) | 0.103815 / 0.141683 (-0.037868) | 1.696963 / 1.452155 (0.244809) | 1.747849 / 1.492716 (0.255132) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292465 / 0.018006 (0.274458) | 0.571518 / 0.000490 (0.571028) | 0.000476 / 0.000200 (0.000276) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028697 / 0.037411 (-0.008714) | 0.111671 / 0.014526 (0.097145) | 0.138826 / 0.176557 (-0.037731) | 0.189697 / 0.737135 (-0.547439) | 0.125454 / 0.296338 (-0.170884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.619273 / 0.215209 (0.404064) | 6.138669 / 2.077655 (4.061015) | 2.558622 / 1.504120 (1.054502) | 2.201550 / 1.541195 (0.660356) | 2.279034 / 1.468490 (0.810544) | 0.850752 / 4.584777 (-3.734025) | 5.438185 / 3.745712 (1.692473) | 2.529343 / 5.269862 (-2.740518) | 1.572178 / 4.565676 (-2.993499) | 0.100768 / 0.424275 (-0.323507) | 0.013902 / 0.007607 (0.006295) | 0.726660 / 0.226044 (0.500616) | 7.794918 / 2.268929 (5.525990) | 3.311695 / 55.444624 (-52.132930) | 2.729167 / 6.876477 (-4.147310) | 2.630984 / 2.142072 (0.488911) | 1.018534 / 4.805227 (-3.786693) | 0.194602 / 6.500664 (-6.306062) | 0.070876 / 0.075469 (-0.004593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573005 / 1.841788 (-0.268783) | 17.042710 / 8.074308 (8.968401) | 19.615320 / 10.191392 (9.423928) | 0.229405 / 0.680424 (-0.451019) | 0.027560 / 0.534201 (-0.506641) | 0.447984 / 0.579283 (-0.131299) | 0.598392 / 0.434364 (0.164028) | 0.571769 / 0.540337 (0.031431) | 0.653025 / 1.386936 (-0.733911) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"CML watermark\")\n" ]

"2023-05-31T08:33:02"

"2023-05-31T13:34:35"

"2023-05-31T13:25:57"

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

"2023-07-22T05:06:43"

"2023-07-24T19:29:55"

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"2023-07-16T09:13:07"

"2023-07-24T14:35:04"

"2023-07-24T14:35:04"

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

"2023-05-20T01:39:11"

"2023-05-25T06:42:34"

"2023-05-25T06:42:34"

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[ "Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```" ]

"2023-06-13T15:49:00"

"2023-06-14T12:13:38"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006658 / 0.011353 (-0.004695) | 0.004347 / 0.011008 (-0.006661) | 0.084179 / 0.038508 (0.045671) | 0.080842 / 0.023109 (0.057733) | 0.321642 / 0.275898 (0.045744) | 0.348758 / 0.323480 (0.025278) | 0.005624 / 0.007986 (-0.002362) | 0.003479 / 0.004328 (-0.000850) | 0.065125 / 0.004250 (0.060875) | 0.057624 / 0.037052 (0.020572) | 0.323643 / 0.258489 (0.065154) | 0.360939 / 0.293841 (0.067098) | 0.031005 / 0.128546 (-0.097541) | 0.008618 / 0.075646 (-0.067028) | 0.287443 / 0.419271 (-0.131828) | 0.052640 / 0.043533 (0.009107) | 0.316947 / 0.255139 (0.061808) | 0.330292 / 0.283200 (0.047093) | 0.024393 / 0.141683 (-0.117289) | 1.476734 / 1.452155 (0.024579) | 1.534505 / 1.492716 (0.041789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273808 / 0.018006 (0.255802) | 0.591146 / 0.000490 (0.590656) | 0.000322 / 0.000200 (0.000122) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029992 / 0.037411 (-0.007419) | 0.086654 / 0.014526 (0.072129) | 0.098590 / 0.176557 (-0.077967) | 0.157225 / 0.737135 (-0.579910) | 0.101816 / 0.296338 (-0.194522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382578 / 0.215209 (0.167368) | 3.803576 / 2.077655 (1.725922) | 1.875136 / 1.504120 (0.371016) | 1.704207 / 1.541195 (0.163012) | 1.765146 / 1.468490 (0.296656) | 0.482802 / 4.584777 (-4.101975) | 3.571772 / 3.745712 (-0.173940) | 3.245626 / 5.269862 (-2.024235) | 2.051612 / 4.565676 (-2.514064) | 0.056539 / 0.424275 (-0.367736) | 0.007199 / 0.007607 (-0.000408) | 0.462445 / 0.226044 (0.236401) | 4.623800 / 2.268929 (2.354872) | 2.318948 / 55.444624 (-53.125677) | 1.971442 / 6.876477 (-4.905035) | 2.225444 / 2.142072 (0.083371) | 0.575205 / 4.805227 (-4.230022) | 0.129243 / 6.500664 (-6.371421) | 0.059036 / 0.075469 (-0.016433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266827 / 1.841788 (-0.574960) | 20.323419 / 8.074308 (12.249110) | 14.577603 / 10.191392 (4.386210) | 0.162131 / 0.680424 (-0.518293) | 0.018529 / 0.534201 (-0.515672) | 0.395046 / 0.579283 (-0.184237) | 0.410870 / 0.434364 (-0.023494) | 0.455782 / 0.540337 (-0.084556) | 0.662851 / 1.386936 (-0.724085) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006867 / 0.011353 (-0.004486) | 0.004197 / 0.011008 (-0.006811) | 0.066060 / 0.038508 (0.027552) | 0.084145 / 0.023109 (0.061036) | 0.366740 / 0.275898 (0.090842) | 0.402362 / 0.323480 (0.078882) | 0.005785 / 0.007986 (-0.002200) | 0.003551 / 0.004328 (-0.000778) | 0.066177 / 0.004250 (0.061926) | 0.061521 / 0.037052 (0.024468) | 0.377807 / 0.258489 (0.119318) | 0.413490 / 0.293841 (0.119649) | 0.031918 / 0.128546 (-0.096628) | 0.008767 / 0.075646 (-0.066879) | 0.071437 / 0.419271 (-0.347835) | 0.049237 / 0.043533 (0.005704) | 0.365929 / 0.255139 (0.110790) | 0.393545 / 0.283200 (0.110346) | 0.024054 / 0.141683 (-0.117628) | 1.524599 / 1.452155 (0.072445) | 1.576592 / 1.492716 (0.083876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315181 / 0.018006 (0.297174) | 0.535501 / 0.000490 (0.535011) | 0.000410 / 0.000200 (0.000210) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032915 / 0.037411 (-0.004497) | 0.089310 / 0.014526 (0.074784) | 0.105136 / 0.176557 (-0.071421) | 0.158572 / 0.737135 (-0.578563) | 0.106850 / 0.296338 (-0.189489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419343 / 0.215209 (0.204134) | 4.200166 / 2.077655 (2.122511) | 2.180234 / 1.504120 (0.676114) | 2.016885 / 1.541195 (0.475690) | 2.131480 / 1.468490 (0.662990) | 0.484681 / 4.584777 (-4.100096) | 3.613535 / 3.745712 (-0.132177) | 5.762111 / 5.269862 (0.492249) | 3.190590 / 4.565676 (-1.375086) | 0.057403 / 0.424275 (-0.366872) | 0.007862 / 0.007607 (0.000255) | 0.490857 / 0.226044 (0.264813) | 4.911241 / 2.268929 (2.642313) | 2.650787 / 55.444624 (-52.793838) | 2.317060 / 6.876477 (-4.559416) | 2.579677 / 2.142072 (0.437605) | 0.587388 / 4.805227 (-4.217840) | 0.148109 / 6.500664 (-6.352555) | 0.061435 / 0.075469 (-0.014034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322181 / 1.841788 (-0.519606) | 20.647184 / 8.074308 (12.572875) | 14.907635 / 10.191392 (4.716243) | 0.156330 / 0.680424 (-0.524094) | 0.018719 / 0.534201 (-0.515482) | 0.397636 / 0.579283 (-0.181647) | 0.414107 / 0.434364 (-0.020257) | 0.460812 / 0.540337 (-0.079526) | 0.609568 / 1.386936 (-0.777368) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n", "This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ", "what would be the change i need to make? ", "@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n", "The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged", "I just merged the other PR so you should be good now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006303 / 0.011353 (-0.005049) | 0.003746 / 0.011008 (-0.007262) | 0.081083 / 0.038508 (0.042575) | 0.067973 / 0.023109 (0.044864) | 0.322221 / 0.275898 (0.046323) | 0.359432 / 0.323480 (0.035952) | 0.004891 / 0.007986 (-0.003095) | 0.002988 / 0.004328 (-0.001341) | 0.064068 / 0.004250 (0.059818) | 0.052042 / 0.037052 (0.014990) | 0.323387 / 0.258489 (0.064898) | 0.390416 / 0.293841 (0.096575) | 0.028090 / 0.128546 (-0.100457) | 0.008009 / 0.075646 (-0.067638) | 0.262288 / 0.419271 (-0.156984) | 0.044986 / 0.043533 (0.001453) | 0.322319 / 0.255139 (0.067180) | 0.345323 / 0.283200 (0.062123) | 0.021798 / 0.141683 (-0.119885) | 1.417259 / 1.452155 (-0.034895) | 1.490050 / 1.492716 (-0.002667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195902 / 0.018006 (0.177896) | 0.490808 / 0.000490 (0.490318) | 0.002969 / 0.000200 (0.002770) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025221 / 0.037411 (-0.012190) | 0.075341 / 0.014526 (0.060815) | 0.086703 / 0.176557 (-0.089853) | 0.146953 / 0.737135 (-0.590182) | 0.086610 / 0.296338 (-0.209728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434890 / 0.215209 (0.219681) | 4.352283 / 2.077655 (2.274629) | 2.293098 / 1.504120 (0.788979) | 2.123023 / 1.541195 (0.581829) | 2.179722 / 1.468490 (0.711232) | 0.503851 / 4.584777 (-4.080926) | 3.087991 / 3.745712 (-0.657721) | 2.898689 / 5.269862 (-2.371173) | 1.902813 / 4.565676 (-2.662864) | 0.058079 / 0.424275 (-0.366196) | 0.006600 / 0.007607 (-0.001007) | 0.509243 / 0.226044 (0.283199) | 5.080204 / 2.268929 (2.811275) | 2.753594 / 55.444624 (-52.691030) | 2.417385 / 6.876477 (-4.459091) | 2.635470 / 2.142072 (0.493398) | 0.591059 / 4.805227 (-4.214168) | 0.126360 / 6.500664 (-6.374304) | 0.062108 / 0.075469 (-0.013361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254398 / 1.841788 (-0.587390) | 18.866729 / 8.074308 (10.792420) | 14.120008 / 10.191392 (3.928616) | 0.152388 / 0.680424 (-0.528035) | 0.016997 / 0.534201 (-0.517204) | 0.336435 / 0.579283 (-0.242848) | 0.364612 / 0.434364 (-0.069752) | 0.391434 / 0.540337 (-0.148903) | 0.567180 / 1.386936 (-0.819756) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006477 / 0.011353 (-0.004876) | 0.003723 / 0.011008 (-0.007285) | 0.062712 / 0.038508 (0.024204) | 0.069380 / 0.023109 (0.046271) | 0.393394 / 0.275898 (0.117496) | 0.446903 / 0.323480 (0.123423) | 0.004833 / 0.007986 (-0.003153) | 0.002946 / 0.004328 (-0.001382) | 0.062076 / 0.004250 (0.057826) | 0.051589 / 0.037052 (0.014537) | 0.388536 / 0.258489 (0.130047) | 0.451406 / 0.293841 (0.157565) | 0.027824 / 0.128546 (-0.100722) | 0.008040 / 0.075646 (-0.067606) | 0.067085 / 0.419271 (-0.352187) | 0.042269 / 0.043533 (-0.001264) | 0.363419 / 0.255139 (0.108280) | 0.435201 / 0.283200 (0.152001) | 0.021275 / 0.141683 (-0.120408) | 1.439838 / 1.452155 (-0.012316) | 1.477279 / 1.492716 (-0.015437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229667 / 0.018006 (0.211661) | 0.434101 / 0.000490 (0.433611) | 0.000652 / 0.000200 (0.000452) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026141 / 0.037411 (-0.011271) | 0.078950 / 0.014526 (0.064424) | 0.090143 / 0.176557 (-0.086413) | 0.143941 / 0.737135 (-0.593195) | 0.090465 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432042 / 0.215209 (0.216833) | 4.322134 / 2.077655 (2.244479) | 2.242897 / 1.504120 (0.738777) | 2.076351 / 1.541195 (0.535157) | 2.166739 / 1.468490 (0.698249) | 0.500833 / 4.584777 (-4.083944) | 3.140117 / 3.745712 (-0.605595) | 4.383050 / 5.269862 (-0.886812) | 2.548245 / 4.565676 (-2.017432) | 0.057521 / 0.424275 (-0.366754) | 0.006946 / 0.007607 (-0.000662) | 0.509613 / 0.226044 (0.283569) | 5.114052 / 2.268929 (2.845123) | 2.682112 / 55.444624 (-52.762512) | 2.362385 / 6.876477 (-4.514092) | 2.531787 / 2.142072 (0.389715) | 0.595085 / 4.805227 (-4.210142) | 0.130198 / 6.500664 (-6.370466) | 0.064057 / 0.075469 (-0.011412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346254 / 1.841788 (-0.495534) | 19.036911 / 8.074308 (10.962603) | 14.478689 / 10.191392 (4.287297) | 0.147541 / 0.680424 (-0.532883) | 0.016851 / 0.534201 (-0.517350) | 0.333901 / 0.579283 (-0.245382) | 0.380012 / 0.434364 (-0.054352) | 0.396021 / 0.540337 (-0.144317) | 0.540612 / 1.386936 (-0.846324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n", "CI failure is unrelated. Merging.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009498 / 0.011353 (-0.001855) | 0.005639 / 0.011008 (-0.005369) | 0.108731 / 0.038508 (0.070223) | 0.094052 / 0.023109 (0.070943) | 0.454375 / 0.275898 (0.178477) | 0.486852 / 0.323480 (0.163372) | 0.006627 / 0.007986 (-0.001359) | 0.004712 / 0.004328 (0.000383) | 0.082006 / 0.004250 (0.077756) | 0.079394 / 0.037052 (0.042342) | 0.450982 / 0.258489 (0.192493) | 0.502659 / 0.293841 (0.208818) | 0.049741 / 0.128546 (-0.078806) | 0.014482 / 0.075646 (-0.061164) | 0.362661 / 0.419271 (-0.056611) | 0.068225 / 0.043533 (0.024692) | 0.456219 / 0.255139 (0.201080) | 0.483919 / 0.283200 (0.200719) | 0.044490 / 0.141683 (-0.097193) | 1.809420 / 1.452155 (0.357265) | 1.908859 / 1.492716 (0.416143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267350 / 0.018006 (0.249344) | 0.600403 / 0.000490 (0.599913) | 0.003665 / 0.000200 (0.003465) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032499 / 0.037411 (-0.004912) | 0.104829 / 0.014526 (0.090303) | 0.115809 / 0.176557 (-0.060747) | 0.191561 / 0.737135 (-0.545574) | 0.113454 / 0.296338 (-0.182885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599165 / 0.215209 (0.383956) | 5.802947 / 2.077655 (3.725292) | 2.477330 / 1.504120 (0.973210) | 2.231147 / 1.541195 (0.689952) | 2.365688 / 1.468490 (0.897197) | 0.853912 / 4.584777 (-3.730865) | 5.529472 / 3.745712 (1.783760) | 6.145286 / 5.269862 (0.875424) | 3.415871 / 4.565676 (-1.149805) | 0.099889 / 0.424275 (-0.324386) | 0.008933 / 0.007607 (0.001325) | 0.704643 / 0.226044 (0.478598) | 7.178101 / 2.268929 (4.909173) | 3.367120 / 55.444624 (-52.077504) | 2.795177 / 6.876477 (-4.081300) | 2.796798 / 2.142072 (0.654726) | 1.039097 / 4.805227 (-3.766130) | 0.232784 / 6.500664 (-6.267881) | 0.083608 / 0.075469 (0.008138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646827 / 1.841788 (-0.194961) | 25.003419 / 8.074308 (16.929111) | 22.165746 / 10.191392 (11.974354) | 0.246179 / 0.680424 (-0.434245) | 0.029304 / 0.534201 (-0.504897) | 0.500767 / 0.579283 (-0.078516) | 0.606501 / 0.434364 (0.172137) | 0.564092 / 0.540337 (0.023755) | 0.857568 / 1.386936 (-0.529368) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009206 / 0.011353 (-0.002146) | 0.005084 / 0.011008 (-0.005925) | 0.081402 / 0.038508 (0.042894) | 0.088028 / 0.023109 (0.064919) | 0.539509 / 0.275898 (0.263611) | 0.590759 / 0.323480 (0.267280) | 0.006527 / 0.007986 (-0.001459) | 0.004375 / 0.004328 (0.000047) | 0.082327 / 0.004250 (0.078076) | 0.065442 / 0.037052 (0.028390) | 0.548254 / 0.258489 (0.289765) | 0.598388 / 0.293841 (0.304547) | 0.049409 / 0.128546 (-0.079137) | 0.014366 / 0.075646 (-0.061280) | 0.094568 / 0.419271 (-0.324703) | 0.063685 / 0.043533 (0.020152) | 0.545359 / 0.255139 (0.290220) | 0.573358 / 0.283200 (0.290159) | 0.036864 / 0.141683 (-0.104819) | 1.817985 / 1.452155 (0.365830) | 1.925188 / 1.492716 (0.432472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303205 / 0.018006 (0.285199) | 0.620981 / 0.000490 (0.620491) | 0.004910 / 0.000200 (0.004710) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033791 / 0.037411 (-0.003620) | 0.114974 / 0.014526 (0.100448) | 0.117682 / 0.176557 (-0.058875) | 0.177188 / 0.737135 (-0.559947) | 0.126425 / 0.296338 (-0.169914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636932 / 0.215209 (0.421723) | 6.289054 / 2.077655 (4.211399) | 2.920772 / 1.504120 (1.416652) | 2.672080 / 1.541195 (1.130885) | 2.712271 / 1.468490 (1.243781) | 0.889305 / 4.584777 (-3.695472) | 5.536018 / 3.745712 (1.790306) | 4.687564 / 5.269862 (-0.582298) | 3.204239 / 4.565676 (-1.361437) | 0.095546 / 0.424275 (-0.328729) | 0.008838 / 0.007607 (0.001231) | 0.714584 / 0.226044 (0.488540) | 7.482663 / 2.268929 (5.213735) | 3.621392 / 55.444624 (-51.823232) | 2.987777 / 6.876477 (-3.888700) | 3.312636 / 2.142072 (1.170564) | 1.033721 / 4.805227 (-3.771506) | 0.206292 / 6.500664 (-6.294372) | 0.079423 / 0.075469 (0.003953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798645 / 1.841788 (-0.043143) | 25.544329 / 8.074308 (17.470021) | 23.041318 / 10.191392 (12.849926) | 0.259067 / 0.680424 (-0.421357) | 0.029839 / 0.534201 (-0.504362) | 0.495583 / 0.579283 (-0.083700) | 0.598755 / 0.434364 (0.164391) | 0.574864 / 0.540337 (0.034527) | 0.831160 / 1.386936 (-0.555776) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n" ]

"2023-07-19T15:00:01"

"2023-07-19T17:39:11"

"2023-07-19T17:27:17"

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