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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) | 0.007241 / 0.011353 (-0.004112) | 0.004574 / 0.011008 (-0.006434) | 0.120481 / 0.038508 (0.081973) | 0.040492 / 0.023109 (0.017383) | 0.391399 / 0.275898 (0.115501) | 0.422844 / 0.323480 (0.099365) | 0.004441 / 0.007986 (-0.003545) | 0.004544 / 0.004328 (0.000216) | 0.089482 / 0.004250 (0.085231) | 0.052939 / 0.037052 (0.015887) | 0.393649 / 0.258489 (0.135160) | 0.433852 / 0.293841 (0.140011) | 0.035882 / 0.128546 (-0.092664) | 0.010172 / 0.075646 (-0.065474) | 0.410331 / 0.419271 (-0.008940) | 0.061481 / 0.043533 (0.017948) | 0.405066 / 0.255139 (0.149927) | 0.417732 / 0.283200 (0.134532) | 0.121647 / 0.141683 (-0.020035) | 1.790624 / 1.452155 (0.338469) | 1.863398 / 1.492716 (0.370681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250650 / 0.018006 (0.232644) | 0.489044 / 0.000490 (0.488554) | 0.010421 / 0.000200 (0.010222) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030340 / 0.037411 (-0.007071) | 0.128318 / 0.014526 (0.113792) | 0.140463 / 0.176557 (-0.036093) | 0.205762 / 0.737135 (-0.531373) | 0.147996 / 0.296338 (-0.148342) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493158 / 0.215209 (0.277949) | 4.858346 / 2.077655 (2.780691) | 2.242942 / 1.504120 (0.738822) | 2.010092 / 1.541195 (0.468897) | 2.076765 / 1.468490 (0.608275) | 0.636669 / 4.584777 (-3.948108) | 4.478027 / 3.745712 (0.732314) | 2.157843 / 5.269862 (-3.112019) | 1.305133 / 4.565676 (-3.260543) | 0.079220 / 0.424275 (-0.345055) | 0.013858 / 0.007607 (0.006251) | 0.604501 / 0.226044 (0.378457) | 5.950071 / 2.268929 (3.681143) | 2.738373 / 55.444624 (-52.706251) | 2.380275 / 6.876477 (-4.496201) | 2.517108 / 2.142072 (0.375035) | 0.772249 / 4.805227 (-4.032979) | 0.169874 / 6.500664 (-6.330790) | 0.078026 / 0.075469 (0.002557) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.450200 / 1.841788 (-0.391588) | 17.810965 / 8.074308 (9.736657) | 15.518998 / 10.191392 (5.327606) | 0.200469 / 0.680424 (-0.479954) | 0.020777 / 0.534201 (-0.513424) | 0.504556 / 0.579283 (-0.074727) | 0.518493 / 0.434364 (0.084129) | 0.615335 / 0.540337 (0.074998) | 0.754065 / 1.386936 (-0.632871) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007224 / 0.011353 (-0.004129) | 0.004663 / 0.011008 (-0.006345) | 0.092151 / 0.038508 (0.053643) | 0.038359 / 0.023109 (0.015250) | 0.486413 / 0.275898 (0.210515) | 0.521596 / 0.323480 (0.198116) | 0.004207 / 0.007986 (-0.003778) | 0.003745 / 0.004328 (-0.000583) | 0.089840 / 0.004250 (0.085589) | 0.050996 / 0.037052 (0.013943) | 0.498090 / 0.258489 (0.239601) | 0.533647 / 0.293841 (0.239806) | 0.035151 / 0.128546 (-0.093395) | 0.010293 / 0.075646 (-0.065354) | 0.099056 / 0.419271 (-0.320215) | 0.057365 / 0.043533 (0.013833) | 0.470652 / 0.255139 (0.215513) | 0.509801 / 0.283200 (0.226602) | 0.115650 / 0.141683 (-0.026033) | 1.810860 / 1.452155 (0.358705) | 1.896775 / 1.492716 (0.404059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261887 / 0.018006 (0.243880) | 0.489919 / 0.000490 (0.489430) | 0.006117 / 0.000200 (0.005917) | 0.000134 / 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) | 0.035033 / 0.037411 (-0.002378) | 0.141093 / 0.014526 (0.126567) | 0.152613 / 0.176557 (-0.023943) | 0.218351 / 0.737135 (-0.518785) | 0.158366 / 0.296338 (-0.137972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.542219 / 0.215209 (0.327010) | 5.479358 / 2.077655 (3.401703) | 2.749586 / 1.504120 (1.245466) | 2.537686 / 1.541195 (0.996491) | 2.582351 / 1.468490 (1.113861) | 0.636750 / 4.584777 (-3.948027) | 4.537501 / 3.745712 (0.791789) | 2.141392 / 5.269862 (-3.128469) | 1.279711 / 4.565676 (-3.285965) | 0.079227 / 0.424275 (-0.345048) | 0.014141 / 0.007607 (0.006534) | 0.662070 / 0.226044 (0.436025) | 6.572144 / 2.268929 (4.303215) | 3.321349 / 55.444624 (-52.123275) | 2.928219 / 6.876477 (-3.948258) | 3.002732 / 2.142072 (0.860659) | 0.773808 / 4.805227 (-4.031419) | 0.166017 / 6.500664 (-6.334647) | 0.076424 / 0.075469 (0.000955) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584325 / 1.841788 (-0.257463) | 18.359247 / 8.074308 (10.284938) | 16.977875 / 10.191392 (6.786483) | 0.195381 / 0.680424 (-0.485043) | 0.021048 / 0.534201 (-0.513153) | 0.512237 / 0.579283 (-0.067047) | 0.511435 / 0.434364 (0.077071) | 0.592856 / 0.540337 (0.052518) | 0.711905 / 1.386936 (-0.675031) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d536e37b21a6dd5c122b6d8113994ec50846c5b5 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006157 / 0.011353 (-0.005196) | 0.003790 / 0.011008 (-0.007219) | 0.097889 / 0.038508 (0.059381) | 0.029038 / 0.023109 (0.005929) | 0.306918 / 0.275898 (0.031020) | 0.339637 / 0.323480 (0.016157) | 0.003526 / 0.007986 (-0.004460) | 0.003102 / 0.004328 (-0.001227) | 0.076908 / 0.004250 (0.072658) | 0.039254 / 0.037052 (0.002201) | 0.309197 / 0.258489 (0.050708) | 0.345635 / 0.293841 (0.051794) | 0.027954 / 0.128546 (-0.100593) | 0.008510 / 0.075646 (-0.067136) | 0.314674 / 0.419271 (-0.104598) | 0.057102 / 0.043533 (0.013569) | 0.307495 / 0.255139 (0.052356) | 0.329501 / 0.283200 (0.046302) | 0.098450 / 0.141683 (-0.043233) | 1.480102 / 1.452155 (0.027948) | 1.550554 / 1.492716 (0.057838) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207440 / 0.018006 (0.189434) | 0.426560 / 0.000490 (0.426071) | 0.003250 / 0.000200 (0.003050) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023777 / 0.037411 (-0.013634) | 0.103905 / 0.014526 (0.089379) | 0.108324 / 0.176557 (-0.068233) | 0.167223 / 0.737135 (-0.569913) | 0.113529 / 0.296338 (-0.182810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426770 / 0.215209 (0.211561) | 4.251806 / 2.077655 (2.174151) | 2.010426 / 1.504120 (0.506306) | 1.858630 / 1.541195 (0.317435) | 1.941318 / 1.468490 (0.472828) | 0.558056 / 4.584777 (-4.026721) | 3.399107 / 3.745712 (-0.346606) | 1.758386 / 5.269862 (-3.511476) | 1.036305 / 4.565676 (-3.529372) | 0.067094 / 0.424275 (-0.357182) | 0.011167 / 0.007607 (0.003560) | 0.526705 / 0.226044 (0.300661) | 5.250319 / 2.268929 (2.981390) | 2.496723 / 55.444624 (-52.947902) | 2.154013 / 6.876477 (-4.722464) | 2.394724 / 2.142072 (0.252652) | 0.669723 / 4.805227 (-4.135504) | 0.136367 / 6.500664 (-6.364297) | 0.067080 / 0.075469 (-0.008389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269700 / 1.841788 (-0.572088) | 14.099775 / 8.074308 (6.025467) | 14.422936 / 10.191392 (4.231544) | 0.132344 / 0.680424 (-0.548080) | 0.016744 / 0.534201 (-0.517457) | 0.378286 / 0.579283 (-0.200997) | 0.392282 / 0.434364 (-0.042082) | 0.437648 / 0.540337 (-0.102689) | 0.528554 / 1.386936 (-0.858382) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006086 / 0.011353 (-0.005267) | 0.003769 / 0.011008 (-0.007239) | 0.077414 / 0.038508 (0.038906) | 0.027806 / 0.023109 (0.004697) | 0.360333 / 0.275898 (0.084434) | 0.404725 / 0.323480 (0.081245) | 0.003443 / 0.007986 (-0.004543) | 0.004434 / 0.004328 (0.000106) | 0.077309 / 0.004250 (0.073059) | 0.040441 / 0.037052 (0.003388) | 0.358627 / 0.258489 (0.100138) | 0.415246 / 0.293841 (0.121405) | 0.027718 / 0.128546 (-0.100829) | 0.008495 / 0.075646 (-0.067151) | 0.082874 / 0.419271 (-0.336397) | 0.042323 / 0.043533 (-0.001210) | 0.354895 / 0.255139 (0.099756) | 0.390032 / 0.283200 (0.106832) | 0.092377 / 0.141683 (-0.049306) | 1.492817 / 1.452155 (0.040662) | 1.551859 / 1.492716 (0.059143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198921 / 0.018006 (0.180915) | 0.417699 / 0.000490 (0.417209) | 0.001349 / 0.000200 (0.001149) | 0.000071 / 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) | 0.026349 / 0.037411 (-0.011062) | 0.105712 / 0.014526 (0.091186) | 0.111792 / 0.176557 (-0.064765) | 0.163677 / 0.737135 (-0.573459) | 0.116864 / 0.296338 (-0.179474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447532 / 0.215209 (0.232323) | 4.468770 / 2.077655 (2.391116) | 2.403820 / 1.504120 (0.899700) | 2.273640 / 1.541195 (0.732445) | 2.337505 / 1.468490 (0.869015) | 0.560729 / 4.584777 (-4.024048) | 3.389165 / 3.745712 (-0.356547) | 2.697614 / 5.269862 (-2.572247) | 1.351909 / 4.565676 (-3.213768) | 0.068089 / 0.424275 (-0.356186) | 0.011639 / 0.007607 (0.004032) | 0.555277 / 0.226044 (0.329233) | 5.559291 / 2.268929 (3.290363) | 2.657609 / 55.444624 (-52.787015) | 2.346667 / 6.876477 (-4.529809) | 2.615823 / 2.142072 (0.473751) | 0.668662 / 4.805227 (-4.136566) | 0.136593 / 6.500664 (-6.364071) | 0.068384 / 0.075469 (-0.007085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312089 / 1.841788 (-0.529699) | 14.477510 / 8.074308 (6.403202) | 14.231432 / 10.191392 (4.040040) | 0.132015 / 0.680424 (-0.548409) | 0.016908 / 0.534201 (-0.517293) | 0.368315 / 0.579283 (-0.210968) | 0.397964 / 0.434364 (-0.036400) | 0.432446 / 0.540337 (-0.107891) | 0.526349 / 1.386936 (-0.860587) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78b4d55c3cfc60e309eb033d3ed0aba5e796b6ce \"CML watermark\")\n" ]

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[ "Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```", "Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).", "Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.", "Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?" ]

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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) | 0.007489 / 0.011353 (-0.003864) | 0.004914 / 0.011008 (-0.006095) | 0.111626 / 0.038508 (0.073117) | 0.037920 / 0.023109 (0.014811) | 0.350571 / 0.275898 (0.074673) | 0.389667 / 0.323480 (0.066187) | 0.006309 / 0.007986 (-0.001676) | 0.005488 / 0.004328 (0.001160) | 0.083962 / 0.004250 (0.079712) | 0.050728 / 0.037052 (0.013675) | 0.360997 / 0.258489 (0.102508) | 0.392736 / 0.293841 (0.098895) | 0.031975 / 0.128546 (-0.096571) | 0.009941 / 0.075646 (-0.065705) | 0.379840 / 0.419271 (-0.039432) | 0.056522 / 0.043533 (0.012989) | 0.359379 / 0.255139 (0.104240) | 0.384487 / 0.283200 (0.101287) | 0.117523 / 0.141683 (-0.024160) | 1.683639 / 1.452155 (0.231485) | 1.791645 / 1.492716 (0.298929) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236862 / 0.018006 (0.218856) | 0.481208 / 0.000490 (0.480719) | 0.007455 / 0.000200 (0.007255) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030854 / 0.037411 (-0.006557) | 0.126892 / 0.014526 (0.112367) | 0.139207 / 0.176557 (-0.037350) | 0.206447 / 0.737135 (-0.530689) | 0.143095 / 0.296338 (-0.153244) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474677 / 0.215209 (0.259468) | 4.699534 / 2.077655 (2.621879) | 2.152102 / 1.504120 (0.647983) | 1.934815 / 1.541195 (0.393620) | 1.986448 / 1.468490 (0.517958) | 0.607184 / 4.584777 (-3.977593) | 4.480385 / 3.745712 (0.734673) | 2.074729 / 5.269862 (-3.195132) | 1.182383 / 4.565676 (-3.383294) | 0.075624 / 0.424275 (-0.348651) | 0.014046 / 0.007607 (0.006439) | 0.598859 / 0.226044 (0.372814) | 5.959551 / 2.268929 (3.690622) | 2.700851 / 55.444624 (-52.743773) | 2.303775 / 6.876477 (-4.572702) | 2.456441 / 2.142072 (0.314369) | 0.747185 / 4.805227 (-4.058042) | 0.165787 / 6.500664 (-6.334878) | 0.075817 / 0.075469 (0.000348) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.411859 / 1.841788 (-0.429928) | 17.375495 / 8.074308 (9.301187) | 15.187098 / 10.191392 (4.995706) | 0.169953 / 0.680424 (-0.510471) | 0.020204 / 0.534201 (-0.513997) | 0.461424 / 0.579283 (-0.117859) | 0.494443 / 0.434364 (0.060080) | 0.544583 / 0.540337 (0.004246) | 0.648231 / 1.386936 (-0.738705) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007785 / 0.011353 (-0.003568) | 0.005314 / 0.011008 (-0.005694) | 0.087273 / 0.038508 (0.048765) | 0.037810 / 0.023109 (0.014701) | 0.425473 / 0.275898 (0.149575) | 0.459976 / 0.323480 (0.136497) | 0.007270 / 0.007986 (-0.000716) | 0.004631 / 0.004328 (0.000303) | 0.087063 / 0.004250 (0.082812) | 0.052630 / 0.037052 (0.015578) | 0.432384 / 0.258489 (0.173895) | 0.500291 / 0.293841 (0.206450) | 0.033144 / 0.128546 (-0.095402) | 0.010101 / 0.075646 (-0.065545) | 0.096068 / 0.419271 (-0.323204) | 0.062750 / 0.043533 (0.019217) | 0.419308 / 0.255139 (0.164169) | 0.437099 / 0.283200 (0.153900) | 0.122289 / 0.141683 (-0.019394) | 1.737829 / 1.452155 (0.285674) | 1.851481 / 1.492716 (0.358765) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014277 / 0.018006 (-0.003729) | 0.489835 / 0.000490 (0.489345) | 0.008423 / 0.000200 (0.008223) | 0.000188 / 0.000054 (0.000134) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032966 / 0.037411 (-0.004445) | 0.130069 / 0.014526 (0.115544) | 0.144372 / 0.176557 (-0.032185) | 0.200400 / 0.737135 (-0.536735) | 0.149384 / 0.296338 (-0.146954) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.511542 / 0.215209 (0.296333) | 5.093879 / 2.077655 (3.016225) | 2.572088 / 1.504120 (1.067968) | 2.339118 / 1.541195 (0.797923) | 2.441637 / 1.468490 (0.973147) | 0.614818 / 4.584777 (-3.969959) | 4.724441 / 3.745712 (0.978729) | 5.431978 / 5.269862 (0.162116) | 2.257794 / 4.565676 (-2.307883) | 0.078109 / 0.424275 (-0.346166) | 0.013821 / 0.007607 (0.006214) | 0.639232 / 0.226044 (0.413188) | 6.424623 / 2.268929 (4.155694) | 3.163018 / 55.444624 (-52.281606) | 2.756786 / 6.876477 (-4.119690) | 2.808655 / 2.142072 (0.666583) | 0.745843 / 4.805227 (-4.059385) | 0.165562 / 6.500664 (-6.335102) | 0.076610 / 0.075469 (0.001141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.738630 / 1.841788 (-0.103158) | 18.073573 / 8.074308 (9.999265) | 16.482820 / 10.191392 (6.291428) | 0.213233 / 0.680424 (-0.467191) | 0.022839 / 0.534201 (-0.511362) | 0.487043 / 0.579283 (-0.092240) | 0.512518 / 0.434364 (0.078154) | 0.549365 / 0.540337 (0.009028) | 0.656612 / 1.386936 (-0.730324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#288e92b03bd4ec91c10c8a529b32631cfaba9fb7 \"CML watermark\")\n", "Good idea!\r\n\r\nI was wondering: if we want to optimize the balance between the size of downloading a row group, and the number of rows in the group, would it make sense to compute the row group size by checking the average size of the rows?\r\n\r\neg. 32x32 images could have a larger row group size than full HD images, no? Relying on the size would even remove the need to check the column types.\r\n\r\n(in this proposal, we could use the computed row group size, eg 837, or use the nearest row group size in a list of values: 10, 100, 1000, 10000)", "Probably, but I would go for a simpler solution first :p", "Sure! I wanted to understand if the idea made sense or not, but it's not for this PR.", "I think it will be more useful for people who use the viewer and won't impact sequential io that much.", "DuckDB [paragraph](https://duckdb.org/docs/data/parquet/tips.html#selecting-a-row_group_size) that explains how to choose the `row_group_size`. Our default shard size is 500 MB in `push_to_hub`, so, ideally, we should aim for 64 MB row groups (and make this part configurable for power users 🙂).\r\n\r\nSo, before merging this PR, let's add a TODO or open an issue as a reminder that this can be improved.", "I moved the config values, improved the features check and mentioned the improvements we could do in the docstring :)", "_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) | 0.006211 / 0.011353 (-0.005141) | 0.004244 / 0.011008 (-0.006764) | 0.097941 / 0.038508 (0.059433) | 0.028564 / 0.023109 (0.005455) | 0.299651 / 0.275898 (0.023753) | 0.340694 / 0.323480 (0.017214) | 0.005161 / 0.007986 (-0.002824) | 0.004764 / 0.004328 (0.000435) | 0.075505 / 0.004250 (0.071255) | 0.039656 / 0.037052 (0.002603) | 0.309242 / 0.258489 (0.050753) | 0.350783 / 0.293841 (0.056942) | 0.025145 / 0.128546 (-0.103401) | 0.008498 / 0.075646 (-0.067148) | 0.317657 / 0.419271 (-0.101615) | 0.043926 / 0.043533 (0.000394) | 0.305915 / 0.255139 (0.050776) | 0.331630 / 0.283200 (0.048430) | 0.088564 / 0.141683 (-0.053119) | 1.533175 / 1.452155 (0.081021) | 1.581017 / 1.492716 (0.088301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206032 / 0.018006 (0.188025) | 0.433446 / 0.000490 (0.432956) | 0.003955 / 0.000200 (0.003755) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023468 / 0.037411 (-0.013943) | 0.103292 / 0.014526 (0.088766) | 0.107234 / 0.176557 (-0.069322) | 0.168525 / 0.737135 (-0.568610) | 0.113218 / 0.296338 (-0.183120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431085 / 0.215209 (0.215875) | 4.302082 / 2.077655 (2.224427) | 2.068290 / 1.504120 (0.564171) | 1.850718 / 1.541195 (0.309523) | 1.964261 / 1.468490 (0.495771) | 0.547562 / 4.584777 (-4.037215) | 3.410739 / 3.745712 (-0.334974) | 1.779640 / 5.269862 (-3.490221) | 1.005466 / 4.565676 (-3.560210) | 0.066250 / 0.424275 (-0.358025) | 0.011877 / 0.007607 (0.004270) | 0.525185 / 0.226044 (0.299141) | 5.234786 / 2.268929 (2.965857) | 2.398045 / 55.444624 (-53.046580) | 2.073020 / 6.876477 (-4.803457) | 2.210753 / 2.142072 (0.068680) | 0.654897 / 4.805227 (-4.150331) | 0.134639 / 6.500664 (-6.366025) | 0.067050 / 0.075469 (-0.008419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180210 / 1.841788 (-0.661577) | 13.613091 / 8.074308 (5.538783) | 13.441837 / 10.191392 (3.250445) | 0.146048 / 0.680424 (-0.534376) | 0.016505 / 0.534201 (-0.517696) | 0.363210 / 0.579283 (-0.216073) | 0.405484 / 0.434364 (-0.028880) | 0.428712 / 0.540337 (-0.111625) | 0.522300 / 1.386936 (-0.864636) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006147 / 0.011353 (-0.005206) | 0.004161 / 0.011008 (-0.006847) | 0.075861 / 0.038508 (0.037353) | 0.027948 / 0.023109 (0.004839) | 0.362466 / 0.275898 (0.086568) | 0.398227 / 0.323480 (0.074747) | 0.005014 / 0.007986 (-0.002972) | 0.004772 / 0.004328 (0.000444) | 0.075674 / 0.004250 (0.071423) | 0.039158 / 0.037052 (0.002106) | 0.363567 / 0.258489 (0.105078) | 0.410378 / 0.293841 (0.116537) | 0.025510 / 0.128546 (-0.103036) | 0.008528 / 0.075646 (-0.067118) | 0.081803 / 0.419271 (-0.337468) | 0.040954 / 0.043533 (-0.002579) | 0.358492 / 0.255139 (0.103353) | 0.381345 / 0.283200 (0.098145) | 0.092347 / 0.141683 (-0.049336) | 1.567695 / 1.452155 (0.115540) | 1.668412 / 1.492716 (0.175696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203367 / 0.018006 (0.185360) | 0.424642 / 0.000490 (0.424152) | 0.002451 / 0.000200 (0.002251) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026129 / 0.037411 (-0.011282) | 0.102564 / 0.014526 (0.088039) | 0.110583 / 0.176557 (-0.065973) | 0.164332 / 0.737135 (-0.572804) | 0.115706 / 0.296338 (-0.180632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468925 / 0.215209 (0.253716) | 4.657266 / 2.077655 (2.579612) | 2.423280 / 1.504120 (0.919160) | 2.236284 / 1.541195 (0.695089) | 2.323019 / 1.468490 (0.854529) | 0.548120 / 4.584777 (-4.036657) | 3.455602 / 3.745712 (-0.290110) | 1.730421 / 5.269862 (-3.539441) | 1.006089 / 4.565676 (-3.559588) | 0.067478 / 0.424275 (-0.356797) | 0.011465 / 0.007607 (0.003857) | 0.574235 / 0.226044 (0.348190) | 5.744404 / 2.268929 (3.475475) | 2.882225 / 55.444624 (-52.562400) | 2.618246 / 6.876477 (-4.258231) | 2.642920 / 2.142072 (0.500847) | 0.661441 / 4.805227 (-4.143787) | 0.137358 / 6.500664 (-6.363306) | 0.070372 / 0.075469 (-0.005097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333815 / 1.841788 (-0.507973) | 14.689667 / 8.074308 (6.615359) | 14.362294 / 10.191392 (4.170902) | 0.152011 / 0.680424 (-0.528413) | 0.016869 / 0.534201 (-0.517332) | 0.370433 / 0.579283 (-0.208851) | 0.399642 / 0.434364 (-0.034722) | 0.433759 / 0.540337 (-0.106578) | 0.525443 / 1.386936 (-0.861493) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e9f9a88edd9055b5c540e3d83b5a11d48f8ba8 \"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) | 0.006564 / 0.011353 (-0.004789) | 0.004350 / 0.011008 (-0.006658) | 0.096277 / 0.038508 (0.057769) | 0.032956 / 0.023109 (0.009847) | 0.303675 / 0.275898 (0.027777) | 0.336384 / 0.323480 (0.012904) | 0.005789 / 0.007986 (-0.002197) | 0.003957 / 0.004328 (-0.000371) | 0.073990 / 0.004250 (0.069740) | 0.050974 / 0.037052 (0.013922) | 0.321754 / 0.258489 (0.063265) | 0.349489 / 0.293841 (0.055648) | 0.031138 / 0.128546 (-0.097409) | 0.009000 / 0.075646 (-0.066646) | 0.325445 / 0.419271 (-0.093826) | 0.070173 / 0.043533 (0.026640) | 0.304706 / 0.255139 (0.049567) | 0.321803 / 0.283200 (0.038603) | 0.109405 / 0.141683 (-0.032278) | 1.489812 / 1.452155 (0.037657) | 1.577729 / 1.492716 (0.085013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287187 / 0.018006 (0.269181) | 0.527625 / 0.000490 (0.527135) | 0.006533 / 0.000200 (0.006333) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026659 / 0.037411 (-0.010752) | 0.106236 / 0.014526 (0.091710) | 0.118615 / 0.176557 (-0.057941) | 0.173156 / 0.737135 (-0.563979) | 0.122883 / 0.296338 (-0.173456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407189 / 0.215209 (0.191980) | 4.055732 / 2.077655 (1.978078) | 1.865594 / 1.504120 (0.361474) | 1.664325 / 1.541195 (0.123130) | 1.668961 / 1.468490 (0.200471) | 0.521207 / 4.584777 (-4.063570) | 3.740424 / 3.745712 (-0.005288) | 3.431973 / 5.269862 (-1.837889) | 1.636669 / 4.565676 (-2.929008) | 0.065271 / 0.424275 (-0.359005) | 0.012151 / 0.007607 (0.004544) | 0.514233 / 0.226044 (0.288189) | 5.110150 / 2.268929 (2.841222) | 2.264340 / 55.444624 (-53.180284) | 1.940428 / 6.876477 (-4.936049) | 2.042286 / 2.142072 (-0.099787) | 0.639200 / 4.805227 (-4.166028) | 0.139537 / 6.500664 (-6.361127) | 0.063195 / 0.075469 (-0.012274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.179501 / 1.841788 (-0.662286) | 14.600133 / 8.074308 (6.525825) | 14.902137 / 10.191392 (4.710745) | 0.144509 / 0.680424 (-0.535915) | 0.017449 / 0.534201 (-0.516752) | 0.393135 / 0.579283 (-0.186148) | 0.413103 / 0.434364 (-0.021261) | 0.459897 / 0.540337 (-0.080440) | 0.552602 / 1.386936 (-0.834334) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006891 / 0.011353 (-0.004462) | 0.004633 / 0.011008 (-0.006375) | 0.073093 / 0.038508 (0.034585) | 0.032509 / 0.023109 (0.009399) | 0.348332 / 0.275898 (0.072434) | 0.381920 / 0.323480 (0.058440) | 0.005978 / 0.007986 (-0.002007) | 0.005360 / 0.004328 (0.001032) | 0.074307 / 0.004250 (0.070056) | 0.049668 / 0.037052 (0.012615) | 0.354713 / 0.258489 (0.096224) | 0.398521 / 0.293841 (0.104681) | 0.032013 / 0.128546 (-0.096534) | 0.008890 / 0.075646 (-0.066756) | 0.080013 / 0.419271 (-0.339259) | 0.051820 / 0.043533 (0.008288) | 0.349730 / 0.255139 (0.094591) | 0.369267 / 0.283200 (0.086067) | 0.103874 / 0.141683 (-0.037809) | 1.484148 / 1.452155 (0.031993) | 1.573927 / 1.492716 (0.081211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009699 / 0.018006 (-0.008307) | 0.511176 / 0.000490 (0.510686) | 0.002938 / 0.000200 (0.002738) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027847 / 0.037411 (-0.009564) | 0.111565 / 0.014526 (0.097039) | 0.120625 / 0.176557 (-0.055932) | 0.172130 / 0.737135 (-0.565006) | 0.125949 / 0.296338 (-0.170389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430634 / 0.215209 (0.215424) | 4.315377 / 2.077655 (2.237722) | 2.070764 / 1.504120 (0.566644) | 1.881962 / 1.541195 (0.340767) | 1.904053 / 1.468490 (0.435563) | 0.524973 / 4.584777 (-4.059804) | 3.718359 / 3.745712 (-0.027353) | 3.415344 / 5.269862 (-1.854518) | 1.224568 / 4.565676 (-3.341108) | 0.065593 / 0.424275 (-0.358682) | 0.011643 / 0.007607 (0.004036) | 0.537050 / 0.226044 (0.311006) | 5.352155 / 2.268929 (3.083226) | 2.557361 / 55.444624 (-52.887263) | 2.217770 / 6.876477 (-4.658707) | 2.194975 / 2.142072 (0.052902) | 0.635142 / 4.805227 (-4.170085) | 0.140642 / 6.500664 (-6.360022) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266125 / 1.841788 (-0.575663) | 14.836413 / 8.074308 (6.762105) | 14.446870 / 10.191392 (4.255478) | 0.191545 / 0.680424 (-0.488878) | 0.017433 / 0.534201 (-0.516768) | 0.392296 / 0.579283 (-0.186987) | 0.420698 / 0.434364 (-0.013666) | 0.463225 / 0.540337 (-0.077112) | 0.556127 / 1.386936 (-0.830809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fcbe5b1575c8d162b65b9397b3dfda995a4e048 \"CML watermark\")\n" ]

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[ "I've addressed this as part of #6019, so feel free to close this PR. ", "Thanks !" ]

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[ "I just added the same test with dynamic shape", "_The documentation is not available anymore as the PR was closed or merged._", "Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009980 / 0.011353 (-0.001373) | 0.005709 / 0.011008 (-0.005300) | 0.132185 / 0.038508 (0.093677) | 0.039299 / 0.023109 (0.016190) | 0.400168 / 0.275898 (0.124270) | 0.470582 / 0.323480 (0.147102) | 0.007753 / 0.007986 (-0.000233) | 0.005196 / 0.004328 (0.000868) | 0.093698 / 0.004250 (0.089448) | 0.052631 / 0.037052 (0.015579) | 0.430347 / 0.258489 (0.171858) | 0.460162 / 0.293841 (0.166321) | 0.057511 / 0.128546 (-0.071035) | 0.013944 / 0.075646 (-0.061702) | 0.459008 / 0.419271 (0.039737) | 0.075532 / 0.043533 (0.031999) | 0.405165 / 0.255139 (0.150026) | 0.456142 / 0.283200 (0.172942) | 0.117309 / 0.141683 (-0.024374) | 1.945787 / 1.452155 (0.493633) | 2.067162 / 1.492716 (0.574446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285755 / 0.018006 (0.267749) | 0.619965 / 0.000490 (0.619476) | 0.005071 / 0.000200 (0.004871) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031112 / 0.037411 (-0.006299) | 0.128514 / 0.014526 (0.113988) | 0.137161 / 0.176557 (-0.039396) | 0.211363 / 0.737135 (-0.525772) | 0.151045 / 0.296338 (-0.145293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.609361 / 0.215209 (0.394152) | 6.124844 / 2.077655 (4.047189) | 2.440757 / 1.504120 (0.936637) | 2.034495 / 1.541195 (0.493300) | 2.047192 / 1.468490 (0.578702) | 0.883171 / 4.584777 (-3.701606) | 5.470552 / 3.745712 (1.724840) | 4.401696 / 5.269862 (-0.868165) | 2.378674 / 4.565676 (-2.187003) | 0.108065 / 0.424275 (-0.316210) | 0.013239 / 0.007607 (0.005632) | 0.830957 / 0.226044 (0.604913) | 8.090659 / 2.268929 (5.821731) | 3.289203 / 55.444624 (-52.155422) | 2.500777 / 6.876477 (-4.375700) | 2.561440 / 2.142072 (0.419367) | 1.064893 / 4.805227 (-3.740334) | 0.220486 / 6.500664 (-6.280178) | 0.079507 / 0.075469 (0.004038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544334 / 1.841788 (-0.297454) | 17.878997 / 8.074308 (9.804689) | 18.952191 / 10.191392 (8.760799) | 0.245166 / 0.680424 (-0.435258) | 0.028022 / 0.534201 (-0.506179) | 0.517828 / 0.579283 (-0.061455) | 0.618988 / 0.434364 (0.184624) | 0.589742 / 0.540337 (0.049405) | 0.670902 / 1.386936 (-0.716034) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009616 / 0.011353 (-0.001737) | 0.006098 / 0.011008 (-0.004911) | 0.100301 / 0.038508 (0.061793) | 0.037792 / 0.023109 (0.014683) | 0.484667 / 0.275898 (0.208769) | 0.519286 / 0.323480 (0.195806) | 0.007427 / 0.007986 (-0.000558) | 0.007172 / 0.004328 (0.002844) | 0.104429 / 0.004250 (0.100179) | 0.056567 / 0.037052 (0.019515) | 0.502641 / 0.258489 (0.244152) | 0.549629 / 0.293841 (0.255788) | 0.049574 / 0.128546 (-0.078972) | 0.015223 / 0.075646 (-0.060424) | 0.113947 / 0.419271 (-0.305324) | 0.064585 / 0.043533 (0.021053) | 0.512962 / 0.255139 (0.257823) | 0.507218 / 0.283200 (0.224019) | 0.122194 / 0.141683 (-0.019488) | 1.927821 / 1.452155 (0.475667) | 2.051161 / 1.492716 (0.558445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291350 / 0.018006 (0.273344) | 0.588099 / 0.000490 (0.587610) | 0.001368 / 0.000200 (0.001168) | 0.000153 / 0.000054 (0.000099) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030604 / 0.037411 (-0.006807) | 0.126810 / 0.014526 (0.112285) | 0.139309 / 0.176557 (-0.037248) | 0.208030 / 0.737135 (-0.529105) | 0.138985 / 0.296338 (-0.157353) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.681254 / 0.215209 (0.466045) | 6.753856 / 2.077655 (4.676201) | 2.780704 / 1.504120 (1.276585) | 2.475205 / 1.541195 (0.934010) | 2.486784 / 1.468490 (1.018294) | 0.879223 / 4.584777 (-3.705554) | 5.662294 / 3.745712 (1.916582) | 2.698705 / 5.269862 (-2.571156) | 1.660620 / 4.565676 (-2.905057) | 0.112218 / 0.424275 (-0.312057) | 0.014211 / 0.007607 (0.006604) | 0.796957 / 0.226044 (0.570913) | 8.180897 / 2.268929 (5.911969) | 3.540419 / 55.444624 (-51.904205) | 2.899467 / 6.876477 (-3.977010) | 2.870306 / 2.142072 (0.728233) | 1.069537 / 4.805227 (-3.735690) | 0.211281 / 6.500664 (-6.289383) | 0.078898 / 0.075469 (0.003429) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.666790 / 1.841788 (-0.174998) | 18.302127 / 8.074308 (10.227819) | 21.317546 / 10.191392 (11.126153) | 0.242795 / 0.680424 (-0.437629) | 0.026754 / 0.534201 (-0.507447) | 0.493375 / 0.579283 (-0.085908) | 0.605400 / 0.434364 (0.171036) | 0.586888 / 0.540337 (0.046550) | 0.722809 / 1.386936 (-0.664127) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce2328e7b1d62998b22510492530af55d4493b73 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 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" ]

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[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]

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[ "This issue seems to have been resolved, so I'm closing it." ]

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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" ]

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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) | 0.006693 / 0.011353 (-0.004660) | 0.004331 / 0.011008 (-0.006677) | 0.098022 / 0.038508 (0.059514) | 0.032764 / 0.023109 (0.009654) | 0.295812 / 0.275898 (0.019914) | 0.325029 / 0.323480 (0.001550) | 0.005779 / 0.007986 (-0.002206) | 0.005381 / 0.004328 (0.001052) | 0.075785 / 0.004250 (0.071535) | 0.048759 / 0.037052 (0.011707) | 0.308986 / 0.258489 (0.050497) | 0.348000 / 0.293841 (0.054159) | 0.027686 / 0.128546 (-0.100860) | 0.008839 / 0.075646 (-0.066807) | 0.328389 / 0.419271 (-0.090883) | 0.062173 / 0.043533 (0.018640) | 0.312257 / 0.255139 (0.057119) | 0.325024 / 0.283200 (0.041824) | 0.103886 / 0.141683 (-0.037797) | 1.440215 / 1.452155 (-0.011940) | 1.528665 / 1.492716 (0.035948) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210082 / 0.018006 (0.192076) | 0.442480 / 0.000490 (0.441990) | 0.006559 / 0.000200 (0.006359) | 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) | 0.026774 / 0.037411 (-0.010637) | 0.108362 / 0.014526 (0.093837) | 0.117631 / 0.176557 (-0.058926) | 0.176657 / 0.737135 (-0.560478) | 0.124154 / 0.296338 (-0.172184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428136 / 0.215209 (0.212927) | 4.270287 / 2.077655 (2.192632) | 2.014728 / 1.504120 (0.510608) | 1.806772 / 1.541195 (0.265577) | 1.946284 / 1.468490 (0.477794) | 0.525542 / 4.584777 (-4.059235) | 3.667025 / 3.745712 (-0.078687) | 1.878751 / 5.269862 (-3.391111) | 1.048321 / 4.565676 (-3.517356) | 0.065550 / 0.424275 (-0.358725) | 0.011881 / 0.007607 (0.004274) | 0.529873 / 0.226044 (0.303829) | 5.289641 / 2.268929 (3.020712) | 2.489403 / 55.444624 (-52.955221) | 2.141037 / 6.876477 (-4.735440) | 2.230735 / 2.142072 (0.088662) | 0.639781 / 4.805227 (-4.165447) | 0.141410 / 6.500664 (-6.359254) | 0.064374 / 0.075469 (-0.011095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.159462 / 1.841788 (-0.682325) | 14.524730 / 8.074308 (6.450422) | 13.578070 / 10.191392 (3.386678) | 0.152138 / 0.680424 (-0.528286) | 0.017255 / 0.534201 (-0.516946) | 0.387607 / 0.579283 (-0.191676) | 0.413652 / 0.434364 (-0.020712) | 0.453644 / 0.540337 (-0.086693) | 0.550051 / 1.386936 (-0.836885) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006668 / 0.011353 (-0.004685) | 0.004677 / 0.011008 (-0.006331) | 0.075950 / 0.038508 (0.037442) | 0.032439 / 0.023109 (0.009329) | 0.381839 / 0.275898 (0.105941) | 0.419411 / 0.323480 (0.095931) | 0.005813 / 0.007986 (-0.002172) | 0.004090 / 0.004328 (-0.000238) | 0.075052 / 0.004250 (0.070802) | 0.048453 / 0.037052 (0.011401) | 0.388076 / 0.258489 (0.129587) | 0.431793 / 0.293841 (0.137952) | 0.028408 / 0.128546 (-0.100138) | 0.009028 / 0.075646 (-0.066618) | 0.082569 / 0.419271 (-0.336702) | 0.046772 / 0.043533 (0.003239) | 0.380182 / 0.255139 (0.125043) | 0.401828 / 0.283200 (0.118629) | 0.105388 / 0.141683 (-0.036294) | 1.453356 / 1.452155 (0.001201) | 1.561483 / 1.492716 (0.068767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008922 / 0.018006 (-0.009084) | 0.444112 / 0.000490 (0.443623) | 0.002756 / 0.000200 (0.002556) | 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) | 0.030408 / 0.037411 (-0.007003) | 0.112924 / 0.014526 (0.098399) | 0.124625 / 0.176557 (-0.051932) | 0.176915 / 0.737135 (-0.560220) | 0.129141 / 0.296338 (-0.167198) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448197 / 0.215209 (0.232987) | 4.476548 / 2.077655 (2.398893) | 2.243977 / 1.504120 (0.739857) | 2.054060 / 1.541195 (0.512865) | 2.130680 / 1.468490 (0.662190) | 0.526815 / 4.584777 (-4.057962) | 3.759312 / 3.745712 (0.013600) | 3.333618 / 5.269862 (-1.936244) | 1.579611 / 4.565676 (-2.986065) | 0.065714 / 0.424275 (-0.358561) | 0.011939 / 0.007607 (0.004332) | 0.550313 / 0.226044 (0.324269) | 5.476946 / 2.268929 (3.208018) | 2.726521 / 55.444624 (-52.718104) | 2.364977 / 6.876477 (-4.511499) | 2.450624 / 2.142072 (0.308551) | 0.647174 / 4.805227 (-4.158053) | 0.141265 / 6.500664 (-6.359399) | 0.065493 / 0.075469 (-0.009976) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249702 / 1.841788 (-0.592085) | 15.205647 / 8.074308 (7.131338) | 14.678310 / 10.191392 (4.486918) | 0.141539 / 0.680424 (-0.538884) | 0.017323 / 0.534201 (-0.516878) | 0.387602 / 0.579283 (-0.191681) | 0.415106 / 0.434364 (-0.019258) | 0.458146 / 0.540337 (-0.082192) | 0.553318 / 1.386936 (-0.833618) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#55127d7bf399fd2f3a8713db9822e8cb47cdbbed \"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) | 0.008567 / 0.011353 (-0.002786) | 0.005245 / 0.011008 (-0.005763) | 0.115074 / 0.038508 (0.076566) | 0.032567 / 0.023109 (0.009458) | 0.352297 / 0.275898 (0.076399) | 0.393403 / 0.323480 (0.069923) | 0.006402 / 0.007986 (-0.001583) | 0.004353 / 0.004328 (0.000025) | 0.087903 / 0.004250 (0.083653) | 0.048424 / 0.037052 (0.011372) | 0.370078 / 0.258489 (0.111588) | 0.410192 / 0.293841 (0.116351) | 0.042396 / 0.128546 (-0.086150) | 0.014426 / 0.075646 (-0.061220) | 0.411358 / 0.419271 (-0.007914) | 0.059546 / 0.043533 (0.016013) | 0.364721 / 0.255139 (0.109582) | 0.385100 / 0.283200 (0.101901) | 0.100572 / 0.141683 (-0.041111) | 1.741457 / 1.452155 (0.289302) | 1.933134 / 1.492716 (0.440418) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217177 / 0.018006 (0.199171) | 0.510399 / 0.000490 (0.509909) | 0.005542 / 0.000200 (0.005342) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026852 / 0.037411 (-0.010559) | 0.125580 / 0.014526 (0.111054) | 0.132164 / 0.176557 (-0.044392) | 0.189073 / 0.737135 (-0.548063) | 0.135980 / 0.296338 (-0.160358) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.601924 / 0.215209 (0.386715) | 5.891397 / 2.077655 (3.813743) | 2.389494 / 1.504120 (0.885375) | 2.044013 / 1.541195 (0.502818) | 2.019367 / 1.468490 (0.550877) | 0.883807 / 4.584777 (-3.700970) | 5.141349 / 3.745712 (1.395636) | 2.607415 / 5.269862 (-2.662446) | 1.567268 / 4.565676 (-2.998409) | 0.102738 / 0.424275 (-0.321537) | 0.013480 / 0.007607 (0.005873) | 0.744979 / 0.226044 (0.518934) | 7.404182 / 2.268929 (5.135254) | 2.983406 / 55.444624 (-52.461219) | 2.331847 / 6.876477 (-4.544630) | 2.465119 / 2.142072 (0.323047) | 1.106725 / 4.805227 (-3.698502) | 0.205779 / 6.500664 (-6.294885) | 0.081019 / 0.075469 (0.005550) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.527840 / 1.841788 (-0.313947) | 16.989487 / 8.074308 (8.915179) | 18.016123 / 10.191392 (7.824731) | 0.216157 / 0.680424 (-0.464266) | 0.025393 / 0.534201 (-0.508808) | 0.496743 / 0.579283 (-0.082540) | 0.575365 / 0.434364 (0.141002) | 0.559978 / 0.540337 (0.019641) | 0.677474 / 1.386936 (-0.709462) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008913 / 0.011353 (-0.002440) | 0.005540 / 0.011008 (-0.005469) | 0.100001 / 0.038508 (0.061493) | 0.034432 / 0.023109 (0.011323) | 0.419824 / 0.275898 (0.143926) | 0.443566 / 0.323480 (0.120086) | 0.006372 / 0.007986 (-0.001614) | 0.004405 / 0.004328 (0.000077) | 0.094927 / 0.004250 (0.090677) | 0.050300 / 0.037052 (0.013248) | 0.424806 / 0.258489 (0.166317) | 0.480793 / 0.293841 (0.186952) | 0.050869 / 0.128546 (-0.077677) | 0.015899 / 0.075646 (-0.059747) | 0.111413 / 0.419271 (-0.307859) | 0.058093 / 0.043533 (0.014560) | 0.430575 / 0.255139 (0.175436) | 0.483786 / 0.283200 (0.200586) | 0.106878 / 0.141683 (-0.034805) | 1.763576 / 1.452155 (0.311422) | 1.837750 / 1.492716 (0.345033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011565 / 0.018006 (-0.006441) | 0.484411 / 0.000490 (0.483922) | 0.004869 / 0.000200 (0.004669) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030706 / 0.037411 (-0.006706) | 0.126901 / 0.014526 (0.112375) | 0.130367 / 0.176557 (-0.046190) | 0.206568 / 0.737135 (-0.530567) | 0.146505 / 0.296338 (-0.149834) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627266 / 0.215209 (0.412057) | 6.314049 / 2.077655 (4.236394) | 2.582920 / 1.504120 (1.078800) | 2.249401 / 1.541195 (0.708206) | 2.244960 / 1.468490 (0.776470) | 0.907770 / 4.584777 (-3.677007) | 5.349622 / 3.745712 (1.603910) | 4.591244 / 5.269862 (-0.678618) | 2.301612 / 4.565676 (-2.264064) | 0.108813 / 0.424275 (-0.315462) | 0.013187 / 0.007607 (0.005580) | 0.806071 / 0.226044 (0.580027) | 7.843903 / 2.268929 (5.574974) | 3.405968 / 55.444624 (-52.038656) | 2.564301 / 6.876477 (-4.312176) | 2.652208 / 2.142072 (0.510135) | 1.168142 / 4.805227 (-3.637086) | 0.218551 / 6.500664 (-6.282113) | 0.078120 / 0.075469 (0.002651) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.562517 / 1.841788 (-0.279271) | 17.519325 / 8.074308 (9.445017) | 20.727083 / 10.191392 (10.535691) | 0.207135 / 0.680424 (-0.473288) | 0.028208 / 0.534201 (-0.505993) | 0.496157 / 0.579283 (-0.083126) | 0.569239 / 0.434364 (0.134875) | 0.566137 / 0.540337 (0.025799) | 0.704208 / 1.386936 (-0.682728) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8eb3f34d876da98e722d866be90d7f26135ea9e3 \"CML watermark\")\n" ]

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[ "Easy fix would be to add:\r\n\r\n```python\r\nnull_indices -= np.arange(len(null_indices))\r\n```\r\n\r\nbefore L279, but I'm not sure it's the most intuitive way to fix it.", "Same issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/features/features.py#L1398\r\n\r\nFixed in #5948 " ]

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[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]

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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" ]

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[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list | grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list | grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n", "Got the same error with:\r\n\r\n```\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n\r\npython 3.10.11 h7a1cb2a_2 \r\n\r\ndatasets 2.13.0 pyhd8ed1ab_0 conda-forge\r\n```", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nThis solved the issue for me as well.", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nSolved it for me also", "> 基于 [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187)，这可能意味着您的安装与 不兼容。`pyarrow``datasets`\r\n> \r\n> 您能否在终端中执行以下命令并将输出粘贴到此处？\r\n> \r\n> ```\r\n> conda list | grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\n/root/miniconda3/lib/python3.10/site-packages/pyarrow/__init__.py", "Got the same problem with\r\n\r\narrow-cpp 11.0.0 py310h1fc3239_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\nminiforge3/envs/mlp/lib/python3.10/site-packages/pyarrow/__init__.py\r\n\r\nReverting back to pyarrow 11 solved the problem.\r\n", "Solved with `pip install pyarrow==11.0.0`" ]

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

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007088 / 0.011353 (-0.004265) | 0.005216 / 0.011008 (-0.005793) | 0.097572 / 0.038508 (0.059064) | 0.036510 / 0.023109 (0.013401) | 0.316885 / 0.275898 (0.040987) | 0.348541 / 0.323480 (0.025061) | 0.006513 / 0.007986 (-0.001473) | 0.004579 / 0.004328 (0.000251) | 0.073779 / 0.004250 (0.069529) | 0.057500 / 0.037052 (0.020448) | 0.329840 / 0.258489 (0.071351) | 0.357530 / 0.293841 (0.063690) | 0.028515 / 0.128546 (-0.100031) | 0.009156 / 0.075646 (-0.066491) | 0.328340 / 0.419271 (-0.090932) | 0.068400 / 0.043533 (0.024867) | 0.313692 / 0.255139 (0.058553) | 0.329170 / 0.283200 (0.045971) | 0.111969 / 0.141683 (-0.029714) | 1.422096 / 1.452155 (-0.030059) | 1.550042 / 1.492716 (0.057326) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285113 / 0.018006 (0.267107) | 0.546788 / 0.000490 (0.546298) | 0.006992 / 0.000200 (0.006792) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026841 / 0.037411 (-0.010570) | 0.108413 / 0.014526 (0.093887) | 0.118375 / 0.176557 (-0.058181) | 0.174889 / 0.737135 (-0.562246) | 0.122781 / 0.296338 (-0.173558) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404187 / 0.215209 (0.188978) | 4.039673 / 2.077655 (1.962019) | 1.894616 / 1.504120 (0.390496) | 1.729182 / 1.541195 (0.187987) | 1.772917 / 1.468490 (0.304427) | 0.524046 / 4.584777 (-4.060731) | 3.628111 / 3.745712 (-0.117601) | 1.866075 / 5.269862 (-3.403787) | 1.026435 / 4.565676 (-3.539242) | 0.065328 / 0.424275 (-0.358947) | 0.012717 / 0.007607 (0.005110) | 0.505821 / 0.226044 (0.279777) | 5.049518 / 2.268929 (2.780589) | 2.338486 / 55.444624 (-53.106139) | 2.002874 / 6.876477 (-4.873602) | 2.193049 / 2.142072 (0.050976) | 0.664638 / 4.805227 (-4.140589) | 0.151323 / 6.500664 (-6.349341) | 0.063774 / 0.075469 (-0.011695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168168 / 1.841788 (-0.673620) | 15.289200 / 8.074308 (7.214891) | 13.614249 / 10.191392 (3.422857) | 0.167950 / 0.680424 (-0.512474) | 0.017522 / 0.534201 (-0.516679) | 0.393480 / 0.579283 (-0.185803) | 0.420549 / 0.434364 (-0.013815) | 0.461425 / 0.540337 (-0.078912) | 0.563583 / 1.386936 (-0.823353) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004493) | 0.004864 / 0.011008 (-0.006144) | 0.075084 / 0.038508 (0.036576) | 0.033989 / 0.023109 (0.010880) | 0.372512 / 0.275898 (0.096614) | 0.394725 / 0.323480 (0.071246) | 0.006382 / 0.007986 (-0.001604) | 0.004521 / 0.004328 (0.000193) | 0.076422 / 0.004250 (0.072172) | 0.055383 / 0.037052 (0.018331) | 0.400974 / 0.258489 (0.142485) | 0.411570 / 0.293841 (0.117729) | 0.028264 / 0.128546 (-0.100282) | 0.009123 / 0.075646 (-0.066523) | 0.081257 / 0.419271 (-0.338015) | 0.048147 / 0.043533 (0.004614) | 0.390735 / 0.255139 (0.135596) | 0.376426 / 0.283200 (0.093226) | 0.108164 / 0.141683 (-0.033518) | 1.429667 / 1.452155 (-0.022488) | 1.556291 / 1.492716 (0.063575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289514 / 0.018006 (0.271508) | 0.532860 / 0.000490 (0.532370) | 0.003810 / 0.000200 (0.003611) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031292 / 0.037411 (-0.006119) | 0.116530 / 0.014526 (0.102005) | 0.127624 / 0.176557 (-0.048932) | 0.178276 / 0.737135 (-0.558859) | 0.133742 / 0.296338 (-0.162597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431505 / 0.215209 (0.216296) | 4.309206 / 2.077655 (2.231551) | 2.174779 / 1.504120 (0.670659) | 1.998122 / 1.541195 (0.456927) | 2.126478 / 1.468490 (0.657988) | 0.528971 / 4.584777 (-4.055806) | 3.797608 / 3.745712 (0.051895) | 1.876275 / 5.269862 (-3.393586) | 1.087458 / 4.565676 (-3.478218) | 0.066940 / 0.424275 (-0.357335) | 0.012432 / 0.007607 (0.004825) | 0.538346 / 0.226044 (0.312301) | 5.370968 / 2.268929 (3.102039) | 2.613718 / 55.444624 (-52.830906) | 2.246585 / 6.876477 (-4.629892) | 2.375695 / 2.142072 (0.233622) | 0.652227 / 4.805227 (-4.153001) | 0.143246 / 6.500664 (-6.357418) | 0.066163 / 0.075469 (-0.009306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291263 / 1.841788 (-0.550524) | 16.532281 / 8.074308 (8.457973) | 15.038471 / 10.191392 (4.847079) | 0.168139 / 0.680424 (-0.512285) | 0.017724 / 0.534201 (-0.516477) | 0.391636 / 0.579283 (-0.187648) | 0.429690 / 0.434364 (-0.004674) | 0.474941 / 0.540337 (-0.065396) | 0.579461 / 1.386936 (-0.807475) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006083 / 0.011353 (-0.005269) | 0.004085 / 0.011008 (-0.006923) | 0.098337 / 0.038508 (0.059829) | 0.027573 / 0.023109 (0.004464) | 0.305688 / 0.275898 (0.029790) | 0.341767 / 0.323480 (0.018287) | 0.005143 / 0.007986 (-0.002842) | 0.003396 / 0.004328 (-0.000932) | 0.076925 / 0.004250 (0.072674) | 0.041027 / 0.037052 (0.003975) | 0.307877 / 0.258489 (0.049388) | 0.346559 / 0.293841 (0.052718) | 0.025183 / 0.128546 (-0.103363) | 0.008575 / 0.075646 (-0.067071) | 0.319449 / 0.419271 (-0.099823) | 0.043378 / 0.043533 (-0.000154) | 0.304563 / 0.255139 (0.049424) | 0.332019 / 0.283200 (0.048819) | 0.087725 / 0.141683 (-0.053958) | 1.484904 / 1.452155 (0.032749) | 1.582780 / 1.492716 (0.090064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197503 / 0.018006 (0.179497) | 0.410370 / 0.000490 (0.409880) | 0.003840 / 0.000200 (0.003640) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024179 / 0.037411 (-0.013232) | 0.098876 / 0.014526 (0.084350) | 0.106189 / 0.176557 (-0.070367) | 0.168964 / 0.737135 (-0.568171) | 0.109723 / 0.296338 (-0.186616) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429453 / 0.215209 (0.214244) | 4.295584 / 2.077655 (2.217929) | 2.014330 / 1.504120 (0.510210) | 1.841119 / 1.541195 (0.299924) | 1.928378 / 1.468490 (0.459888) | 0.554571 / 4.584777 (-4.030206) | 3.431769 / 3.745712 (-0.313943) | 1.716204 / 5.269862 (-3.553658) | 0.995054 / 4.565676 (-3.570622) | 0.067374 / 0.424275 (-0.356902) | 0.012557 / 0.007607 (0.004950) | 0.533785 / 0.226044 (0.307740) | 5.363360 / 2.268929 (3.094431) | 2.535190 / 55.444624 (-52.909434) | 2.191646 / 6.876477 (-4.684831) | 2.400799 / 2.142072 (0.258727) | 0.663961 / 4.805227 (-4.141266) | 0.135992 / 6.500664 (-6.364672) | 0.067378 / 0.075469 (-0.008092) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235110 / 1.841788 (-0.606678) | 13.820695 / 8.074308 (5.746387) | 13.667202 / 10.191392 (3.475810) | 0.143025 / 0.680424 (-0.537399) | 0.016757 / 0.534201 (-0.517444) | 0.356262 / 0.579283 (-0.223021) | 0.401871 / 0.434364 (-0.032493) | 0.423928 / 0.540337 (-0.116410) | 0.514598 / 1.386936 (-0.872338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006260 / 0.011353 (-0.005093) | 0.004159 / 0.011008 (-0.006850) | 0.076780 / 0.038508 (0.038272) | 0.027899 / 0.023109 (0.004789) | 0.412756 / 0.275898 (0.136858) | 0.455145 / 0.323480 (0.131665) | 0.005029 / 0.007986 (-0.002956) | 0.003482 / 0.004328 (-0.000847) | 0.076148 / 0.004250 (0.071898) | 0.038969 / 0.037052 (0.001917) | 0.429975 / 0.258489 (0.171486) | 0.465880 / 0.293841 (0.172039) | 0.025555 / 0.128546 (-0.102991) | 0.008612 / 0.075646 (-0.067034) | 0.082604 / 0.419271 (-0.336667) | 0.039690 / 0.043533 (-0.003842) | 0.403644 / 0.255139 (0.148505) | 0.440438 / 0.283200 (0.157238) | 0.090984 / 0.141683 (-0.050699) | 1.465915 / 1.452155 (0.013760) | 1.564227 / 1.492716 (0.071511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010502 / 0.018006 (-0.007504) | 0.410573 / 0.000490 (0.410083) | 0.000384 / 0.000200 (0.000184) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025726 / 0.037411 (-0.011686) | 0.101760 / 0.014526 (0.087235) | 0.110102 / 0.176557 (-0.066454) | 0.161321 / 0.737135 (-0.575815) | 0.112507 / 0.296338 (-0.183832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469925 / 0.215209 (0.254716) | 4.718740 / 2.077655 (2.641085) | 2.466272 / 1.504120 (0.962152) | 2.267357 / 1.541195 (0.726162) | 2.331343 / 1.468490 (0.862853) | 0.553448 / 4.584777 (-4.031329) | 3.464228 / 3.745712 (-0.281484) | 3.060957 / 5.269862 (-2.208905) | 1.387261 / 4.565676 (-3.178415) | 0.067989 / 0.424275 (-0.356286) | 0.012349 / 0.007607 (0.004741) | 0.575046 / 0.226044 (0.349001) | 5.740322 / 2.268929 (3.471394) | 2.925666 / 55.444624 (-52.518958) | 2.606535 / 6.876477 (-4.269942) | 2.658144 / 2.142072 (0.516072) | 0.655157 / 4.805227 (-4.150071) | 0.138520 / 6.500664 (-6.362144) | 0.069442 / 0.075469 (-0.006027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306523 / 1.841788 (-0.535265) | 14.400380 / 8.074308 (6.326072) | 14.231519 / 10.191392 (4.040127) | 0.146194 / 0.680424 (-0.534230) | 0.016632 / 0.534201 (-0.517569) | 0.361151 / 0.579283 (-0.218132) | 0.388838 / 0.434364 (-0.045526) | 0.419337 / 0.540337 (-0.121001) | 0.500483 / 1.386936 (-0.886453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009430 / 0.011353 (-0.001923) | 0.006673 / 0.011008 (-0.004335) | 0.125151 / 0.038508 (0.086643) | 0.038258 / 0.023109 (0.015149) | 0.426383 / 0.275898 (0.150485) | 0.432327 / 0.323480 (0.108847) | 0.006964 / 0.007986 (-0.001022) | 0.005140 / 0.004328 (0.000811) | 0.100767 / 0.004250 (0.096517) | 0.058663 / 0.037052 (0.021610) | 0.424709 / 0.258489 (0.166220) | 0.453049 / 0.293841 (0.159208) | 0.051042 / 0.128546 (-0.077505) | 0.015291 / 0.075646 (-0.060355) | 0.456549 / 0.419271 (0.037278) | 0.067106 / 0.043533 (0.023573) | 0.408959 / 0.255139 (0.153820) | 0.445067 / 0.283200 (0.161867) | 0.115590 / 0.141683 (-0.026092) | 1.929439 / 1.452155 (0.477284) | 2.045709 / 1.492716 (0.552992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250726 / 0.018006 (0.232720) | 0.598976 / 0.000490 (0.598486) | 0.007542 / 0.000200 (0.007342) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030317 / 0.037411 (-0.007094) | 0.133177 / 0.014526 (0.118651) | 0.152761 / 0.176557 (-0.023795) | 0.233708 / 0.737135 (-0.503428) | 0.147303 / 0.296338 (-0.149036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633562 / 0.215209 (0.418353) | 6.235021 / 2.077655 (4.157366) | 2.652573 / 1.504120 (1.148454) | 2.223363 / 1.541195 (0.682168) | 2.231022 / 1.468490 (0.762531) | 0.942218 / 4.584777 (-3.642559) | 6.068661 / 3.745712 (2.322949) | 2.778604 / 5.269862 (-2.491257) | 1.787939 / 4.565676 (-2.777737) | 0.117749 / 0.424275 (-0.306526) | 0.015613 / 0.007607 (0.008006) | 0.810222 / 0.226044 (0.584177) | 7.931509 / 2.268929 (5.662581) | 3.260679 / 55.444624 (-52.183945) | 2.609085 / 6.876477 (-4.267391) | 2.867838 / 2.142072 (0.725766) | 1.144672 / 4.805227 (-3.660555) | 0.224379 / 6.500664 (-6.276285) | 0.084490 / 0.075469 (0.009021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.650608 / 1.841788 (-0.191179) | 18.919748 / 8.074308 (10.845440) | 20.163162 / 10.191392 (9.971770) | 0.229427 / 0.680424 (-0.450997) | 0.033090 / 0.534201 (-0.501111) | 0.535549 / 0.579283 (-0.043734) | 0.658629 / 0.434364 (0.224265) | 0.631526 / 0.540337 (0.091189) | 0.748701 / 1.386936 (-0.638235) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009157 / 0.011353 (-0.002196) | 0.006153 / 0.011008 (-0.004856) | 0.106294 / 0.038508 (0.067786) | 0.040947 / 0.023109 (0.017837) | 0.493242 / 0.275898 (0.217344) | 0.563525 / 0.323480 (0.240045) | 0.007256 / 0.007986 (-0.000730) | 0.006757 / 0.004328 (0.002429) | 0.105151 / 0.004250 (0.100901) | 0.056262 / 0.037052 (0.019209) | 0.573341 / 0.258489 (0.314852) | 0.591125 / 0.293841 (0.297284) | 0.047935 / 0.128546 (-0.080611) | 0.015385 / 0.075646 (-0.060262) | 0.119457 / 0.419271 (-0.299814) | 0.066510 / 0.043533 (0.022977) | 0.485622 / 0.255139 (0.230483) | 0.540929 / 0.283200 (0.257730) | 0.132619 / 0.141683 (-0.009064) | 1.916905 / 1.452155 (0.464750) | 2.152722 / 1.492716 (0.660006) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294823 / 0.018006 (0.276817) | 0.569371 / 0.000490 (0.568882) | 0.000642 / 0.000200 (0.000442) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034321 / 0.037411 (-0.003090) | 0.134165 / 0.014526 (0.119639) | 0.157871 / 0.176557 (-0.018685) | 0.210753 / 0.737135 (-0.526382) | 0.152961 / 0.296338 (-0.143377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686810 / 0.215209 (0.471601) | 6.890432 / 2.077655 (4.812778) | 3.182875 / 1.504120 (1.678755) | 2.770836 / 1.541195 (1.229641) | 2.790785 / 1.468490 (1.322295) | 0.938145 / 4.584777 (-3.646632) | 5.861093 / 3.745712 (2.115381) | 2.719862 / 5.269862 (-2.550000) | 1.760834 / 4.565676 (-2.804842) | 0.111317 / 0.424275 (-0.312958) | 0.015722 / 0.007607 (0.008115) | 0.863032 / 0.226044 (0.636988) | 8.482433 / 2.268929 (6.213504) | 3.892621 / 55.444624 (-51.552003) | 3.207370 / 6.876477 (-3.669106) | 3.344412 / 2.142072 (1.202339) | 1.133903 / 4.805227 (-3.671324) | 0.223456 / 6.500664 (-6.277209) | 0.084335 / 0.075469 (0.008866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.794116 / 1.841788 (-0.047672) | 19.077447 / 8.074308 (11.003139) | 23.102309 / 10.191392 (12.910917) | 0.268806 / 0.680424 (-0.411617) | 0.027709 / 0.534201 (-0.506492) | 0.540488 / 0.579283 (-0.038796) | 0.658478 / 0.434364 (0.224114) | 0.604769 / 0.540337 (0.064431) | 0.722768 / 1.386936 (-0.664168) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007439 / 0.011353 (-0.003914) | 0.004884 / 0.011008 (-0.006124) | 0.098750 / 0.038508 (0.060242) | 0.040723 / 0.023109 (0.017613) | 0.347242 / 0.275898 (0.071344) | 0.381202 / 0.323480 (0.057722) | 0.006814 / 0.007986 (-0.001171) | 0.004543 / 0.004328 (0.000215) | 0.075338 / 0.004250 (0.071088) | 0.058976 / 0.037052 (0.021924) | 0.344746 / 0.258489 (0.086257) | 0.406761 / 0.293841 (0.112920) | 0.028961 / 0.128546 (-0.099585) | 0.009531 / 0.075646 (-0.066115) | 0.337324 / 0.419271 (-0.081947) | 0.051071 / 0.043533 (0.007538) | 0.341251 / 0.255139 (0.086112) | 0.362773 / 0.283200 (0.079573) | 0.109423 / 0.141683 (-0.032260) | 1.457420 / 1.452155 (0.005266) | 1.588824 / 1.492716 (0.096108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288620 / 0.018006 (0.270614) | 0.568975 / 0.000490 (0.568485) | 0.003350 / 0.000200 (0.003150) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028732 / 0.037411 (-0.008680) | 0.117820 / 0.014526 (0.103294) | 0.120180 / 0.176557 (-0.056376) | 0.178736 / 0.737135 (-0.558399) | 0.126399 / 0.296338 (-0.169939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428357 / 0.215209 (0.213148) | 4.251989 / 2.077655 (2.174334) | 2.005239 / 1.504120 (0.501119) | 1.784009 / 1.541195 (0.242815) | 1.883763 / 1.468490 (0.415272) | 0.555429 / 4.584777 (-4.029348) | 3.868146 / 3.745712 (0.122434) | 2.081896 / 5.269862 (-3.187965) | 1.126047 / 4.565676 (-3.439629) | 0.069496 / 0.424275 (-0.354779) | 0.012926 / 0.007607 (0.005318) | 0.536989 / 0.226044 (0.310944) | 5.256052 / 2.268929 (2.987124) | 2.526802 / 55.444624 (-52.917822) | 2.233346 / 6.876477 (-4.643131) | 2.389063 / 2.142072 (0.246990) | 0.677107 / 4.805227 (-4.128120) | 0.147212 / 6.500664 (-6.353452) | 0.067061 / 0.075469 (-0.008408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210651 / 1.841788 (-0.631137) | 17.236898 / 8.074308 (9.162589) | 14.427301 / 10.191392 (4.235909) | 0.207194 / 0.680424 (-0.473229) | 0.018079 / 0.534201 (-0.516122) | 0.398355 / 0.579283 (-0.180929) | 0.462453 / 0.434364 (0.028089) | 0.484544 / 0.540337 (-0.055794) | 0.590119 / 1.386936 (-0.796817) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007392 / 0.011353 (-0.003961) | 0.005614 / 0.011008 (-0.005394) | 0.075587 / 0.038508 (0.037079) | 0.040429 / 0.023109 (0.017320) | 0.389901 / 0.275898 (0.114003) | 0.429466 / 0.323480 (0.105986) | 0.006790 / 0.007986 (-0.001196) | 0.006627 / 0.004328 (0.002299) | 0.075227 / 0.004250 (0.070976) | 0.060298 / 0.037052 (0.023246) | 0.391905 / 0.258489 (0.133416) | 0.449385 / 0.293841 (0.155544) | 0.028794 / 0.128546 (-0.099753) | 0.009461 / 0.075646 (-0.066185) | 0.083386 / 0.419271 (-0.335886) | 0.057968 / 0.043533 (0.014435) | 0.377327 / 0.255139 (0.122188) | 0.402825 / 0.283200 (0.119626) | 0.125477 / 0.141683 (-0.016206) | 1.462986 / 1.452155 (0.010832) | 1.595959 / 1.492716 (0.103243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304179 / 0.018006 (0.286173) | 0.543113 / 0.000490 (0.542623) | 0.004136 / 0.000200 (0.003936) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032617 / 0.037411 (-0.004794) | 0.123596 / 0.014526 (0.109070) | 0.128714 / 0.176557 (-0.047842) | 0.176344 / 0.737135 (-0.560792) | 0.132525 / 0.296338 (-0.163813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446041 / 0.215209 (0.230832) | 4.438799 / 2.077655 (2.361144) | 2.210815 / 1.504120 (0.706695) | 2.052025 / 1.541195 (0.510830) | 2.204687 / 1.468490 (0.736197) | 0.535219 / 4.584777 (-4.049558) | 3.858407 / 3.745712 (0.112695) | 3.826043 / 5.269862 (-1.443819) | 1.334149 / 4.565676 (-3.231527) | 0.067454 / 0.424275 (-0.356821) | 0.012566 / 0.007607 (0.004958) | 0.551597 / 0.226044 (0.325553) | 5.520054 / 2.268929 (3.251126) | 2.817976 / 55.444624 (-52.626649) | 2.528074 / 6.876477 (-4.348403) | 2.622391 / 2.142072 (0.480319) | 0.657632 / 4.805227 (-4.147595) | 0.147039 / 6.500664 (-6.353625) | 0.069603 / 0.075469 (-0.005866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300140 / 1.841788 (-0.541648) | 17.303907 / 8.074308 (9.229599) | 15.657887 / 10.191392 (5.466495) | 0.168991 / 0.680424 (-0.511433) | 0.021332 / 0.534201 (-0.512869) | 0.487261 / 0.579283 (-0.092022) | 0.450073 / 0.434364 (0.015709) | 0.465865 / 0.540337 (-0.074473) | 0.565501 / 1.386936 (-0.821435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006536 / 0.011353 (-0.004817) | 0.004254 / 0.011008 (-0.006755) | 0.095387 / 0.038508 (0.056878) | 0.032885 / 0.023109 (0.009776) | 0.298580 / 0.275898 (0.022682) | 0.319771 / 0.323480 (-0.003709) | 0.005510 / 0.007986 (-0.002476) | 0.003891 / 0.004328 (-0.000437) | 0.073763 / 0.004250 (0.069513) | 0.041625 / 0.037052 (0.004573) | 0.294896 / 0.258489 (0.036407) | 0.341308 / 0.293841 (0.047467) | 0.027898 / 0.128546 (-0.100648) | 0.008837 / 0.075646 (-0.066809) | 0.325055 / 0.419271 (-0.094216) | 0.050652 / 0.043533 (0.007119) | 0.298756 / 0.255139 (0.043617) | 0.318261 / 0.283200 (0.035061) | 0.098927 / 0.141683 (-0.042756) | 1.450356 / 1.452155 (-0.001798) | 1.508034 / 1.492716 (0.015318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209009 / 0.018006 (0.191003) | 0.439154 / 0.000490 (0.438665) | 0.004299 / 0.000200 (0.004099) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025938 / 0.037411 (-0.011473) | 0.105954 / 0.014526 (0.091429) | 0.113858 / 0.176557 (-0.062698) | 0.168887 / 0.737135 (-0.568249) | 0.121292 / 0.296338 (-0.175046) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402050 / 0.215209 (0.186841) | 4.002310 / 2.077655 (1.924655) | 1.816190 / 1.504120 (0.312070) | 1.634404 / 1.541195 (0.093209) | 1.713632 / 1.468490 (0.245142) | 0.519633 / 4.584777 (-4.065144) | 3.740291 / 3.745712 (-0.005421) | 1.787602 / 5.269862 (-3.482260) | 1.038844 / 4.565676 (-3.526833) | 0.064973 / 0.424275 (-0.359302) | 0.012475 / 0.007607 (0.004868) | 0.498152 / 0.226044 (0.272108) | 4.970941 / 2.268929 (2.702013) | 2.287429 / 55.444624 (-53.157195) | 1.998050 / 6.876477 (-4.878427) | 2.091903 / 2.142072 (-0.050169) | 0.630363 / 4.805227 (-4.174864) | 0.138623 / 6.500664 (-6.362041) | 0.063293 / 0.075469 (-0.012176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201802 / 1.841788 (-0.639986) | 14.073836 / 8.074308 (5.999528) | 12.968665 / 10.191392 (2.777273) | 0.144653 / 0.680424 (-0.535771) | 0.017613 / 0.534201 (-0.516588) | 0.392067 / 0.579283 (-0.187216) | 0.416955 / 0.434364 (-0.017409) | 0.471492 / 0.540337 (-0.068845) | 0.554576 / 1.386936 (-0.832360) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006408 / 0.011353 (-0.004945) | 0.004452 / 0.011008 (-0.006556) | 0.073648 / 0.038508 (0.035140) | 0.032536 / 0.023109 (0.009427) | 0.358546 / 0.275898 (0.082648) | 0.387330 / 0.323480 (0.063850) | 0.005542 / 0.007986 (-0.002444) | 0.003882 / 0.004328 (-0.000447) | 0.073867 / 0.004250 (0.069617) | 0.044798 / 0.037052 (0.007746) | 0.362303 / 0.258489 (0.103814) | 0.400496 / 0.293841 (0.106655) | 0.028244 / 0.128546 (-0.100302) | 0.008931 / 0.075646 (-0.066715) | 0.080617 / 0.419271 (-0.338654) | 0.046575 / 0.043533 (0.003043) | 0.364283 / 0.255139 (0.109145) | 0.373215 / 0.283200 (0.090015) | 0.100080 / 0.141683 (-0.041603) | 1.430047 / 1.452155 (-0.022108) | 1.530957 / 1.492716 (0.038240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221061 / 0.018006 (0.203055) | 0.441753 / 0.000490 (0.441263) | 0.003626 / 0.000200 (0.003426) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029509 / 0.037411 (-0.007902) | 0.109578 / 0.014526 (0.095053) | 0.121009 / 0.176557 (-0.055548) | 0.168950 / 0.737135 (-0.568185) | 0.124475 / 0.296338 (-0.171864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431355 / 0.215209 (0.216146) | 4.295507 / 2.077655 (2.217852) | 2.167514 / 1.504120 (0.663394) | 2.013073 / 1.541195 (0.471879) | 1.973730 / 1.468490 (0.505240) | 0.529778 / 4.584777 (-4.054999) | 3.794702 / 3.745712 (0.048989) | 3.062940 / 5.269862 (-2.206922) | 1.503426 / 4.565676 (-3.062251) | 0.066692 / 0.424275 (-0.357583) | 0.011682 / 0.007607 (0.004075) | 0.539311 / 0.226044 (0.313266) | 5.406342 / 2.268929 (3.137414) | 2.652709 / 55.444624 (-52.791916) | 2.260066 / 6.876477 (-4.616410) | 2.295752 / 2.142072 (0.153680) | 0.647199 / 4.805227 (-4.158029) | 0.142981 / 6.500664 (-6.357683) | 0.065082 / 0.075469 (-0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279788 / 1.841788 (-0.562000) | 14.982845 / 8.074308 (6.908536) | 14.277166 / 10.191392 (4.085774) | 0.145082 / 0.680424 (-0.535342) | 0.017885 / 0.534201 (-0.516316) | 0.392071 / 0.579283 (-0.187212) | 0.420425 / 0.434364 (-0.013939) | 0.461244 / 0.540337 (-0.079093) | 0.559956 / 1.386936 (-0.826980) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"CML watermark\")\n" ]

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[ "hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`", "I just re-ran the CI, hopefully it's fixed", "_The documentation is not available anymore as the PR was closed or merged._", "> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it", "I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Hi sorry to bother you, the CI check_code_quality failed and it said `would reformat /home/runner/work/datasets/datasets/src/datasets/download/streaming_download_manager.py` but I cant see any changes when I run `python3 -m black --check tests src benchmarks metrics` and `python3 -m ruff tests src benchmarks metrics` on my own computer, is there any version requirements on the tools? I didn't specific the version.", "I just ran `make style` and pushed the changes.\r\nYou can install the right versions of black and ruff using `pip install -e .[quality]` ;)", "I am working on this issue right now https://github.com/huggingface/datasets/issues/6017 which is strongly connected to your PR, and I might end up cherry-picking some of your commits (keeping attribution of course !). Would you be ok with that ?", "it's totally ok for me, I just wish the S3 File system could support streaming too.\r\n", "\r\nI already adjust the code and test on my local Mac, you can check it now, and you can make any changes to it.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6028 which includes your contribution :)" ]

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[ "load_dataset () did not work for loading local files either " ]

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

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006113 / 0.011353 (-0.005239) | 0.004195 / 0.011008 (-0.006813) | 0.098103 / 0.038508 (0.059595) | 0.027970 / 0.023109 (0.004860) | 0.300992 / 0.275898 (0.025094) | 0.335402 / 0.323480 (0.011922) | 0.005079 / 0.007986 (-0.002906) | 0.003516 / 0.004328 (-0.000813) | 0.077311 / 0.004250 (0.073061) | 0.037863 / 0.037052 (0.000810) | 0.302638 / 0.258489 (0.044149) | 0.346554 / 0.293841 (0.052713) | 0.025218 / 0.128546 (-0.103328) | 0.008630 / 0.075646 (-0.067017) | 0.319748 / 0.419271 (-0.099523) | 0.049182 / 0.043533 (0.005650) | 0.306233 / 0.255139 (0.051094) | 0.331040 / 0.283200 (0.047840) | 0.089203 / 0.141683 (-0.052480) | 1.496104 / 1.452155 (0.043949) | 1.567878 / 1.492716 (0.075162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215774 / 0.018006 (0.197768) | 0.436810 / 0.000490 (0.436320) | 0.000307 / 0.000200 (0.000107) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024102 / 0.037411 (-0.013310) | 0.095459 / 0.014526 (0.080933) | 0.106564 / 0.176557 (-0.069992) | 0.169894 / 0.737135 (-0.567241) | 0.109152 / 0.296338 (-0.187186) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429066 / 0.215209 (0.213857) | 4.297385 / 2.077655 (2.219730) | 2.054854 / 1.504120 (0.550734) | 1.846844 / 1.541195 (0.305649) | 1.840807 / 1.468490 (0.372317) | 0.553193 / 4.584777 (-4.031584) | 3.366788 / 3.745712 (-0.378924) | 1.727337 / 5.269862 (-3.542525) | 0.994357 / 4.565676 (-3.571319) | 0.067790 / 0.424275 (-0.356485) | 0.012002 / 0.007607 (0.004395) | 0.533335 / 0.226044 (0.307291) | 5.341341 / 2.268929 (3.072412) | 2.543581 / 55.444624 (-52.901043) | 2.220374 / 6.876477 (-4.656103) | 2.321656 / 2.142072 (0.179583) | 0.654408 / 4.805227 (-4.150819) | 0.134693 / 6.500664 (-6.365971) | 0.066926 / 0.075469 (-0.008544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209463 / 1.841788 (-0.632325) | 13.568221 / 8.074308 (5.493913) | 13.965418 / 10.191392 (3.774026) | 0.145049 / 0.680424 (-0.535375) | 0.016936 / 0.534201 (-0.517265) | 0.371587 / 0.579283 (-0.207696) | 0.386363 / 0.434364 (-0.048001) | 0.437137 / 0.540337 (-0.103201) | 0.514779 / 1.386936 (-0.872157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.004232 / 0.011008 (-0.006776) | 0.075682 / 0.038508 (0.037174) | 0.027858 / 0.023109 (0.004749) | 0.425325 / 0.275898 (0.149427) | 0.466732 / 0.323480 (0.143253) | 0.005240 / 0.007986 (-0.002745) | 0.003506 / 0.004328 (-0.000823) | 0.075294 / 0.004250 (0.071044) | 0.041677 / 0.037052 (0.004624) | 0.426552 / 0.258489 (0.168063) | 0.469452 / 0.293841 (0.175611) | 0.025443 / 0.128546 (-0.103104) | 0.008526 / 0.075646 (-0.067120) | 0.082190 / 0.419271 (-0.337081) | 0.040906 / 0.043533 (-0.002626) | 0.428406 / 0.255139 (0.173267) | 0.446795 / 0.283200 (0.163595) | 0.093837 / 0.141683 (-0.047846) | 1.518639 / 1.452155 (0.066484) | 1.620214 / 1.492716 (0.127498) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223259 / 0.018006 (0.205253) | 0.425077 / 0.000490 (0.424588) | 0.001980 / 0.000200 (0.001780) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025813 / 0.037411 (-0.011599) | 0.103062 / 0.014526 (0.088536) | 0.108958 / 0.176557 (-0.067598) | 0.161591 / 0.737135 (-0.575544) | 0.112130 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472843 / 0.215209 (0.257634) | 4.713281 / 2.077655 (2.635626) | 2.458216 / 1.504120 (0.954096) | 2.272467 / 1.541195 (0.731273) | 2.324456 / 1.468490 (0.855965) | 0.554686 / 4.584777 (-4.030091) | 3.445079 / 3.745712 (-0.300634) | 3.451896 / 5.269862 (-1.817966) | 1.431065 / 4.565676 (-3.134612) | 0.067868 / 0.424275 (-0.356407) | 0.012093 / 0.007607 (0.004486) | 0.573571 / 0.226044 (0.347526) | 5.820452 / 2.268929 (3.551523) | 2.934858 / 55.444624 (-52.509767) | 2.602719 / 6.876477 (-4.273758) | 2.645999 / 2.142072 (0.503927) | 0.660688 / 4.805227 (-4.144540) | 0.137490 / 6.500664 (-6.363174) | 0.068311 / 0.075469 (-0.007158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.321709 / 1.841788 (-0.520079) | 14.592346 / 8.074308 (6.518038) | 14.520748 / 10.191392 (4.329356) | 0.132689 / 0.680424 (-0.547735) | 0.016422 / 0.534201 (-0.517779) | 0.370071 / 0.579283 (-0.209212) | 0.397091 / 0.434364 (-0.037273) | 0.431979 / 0.540337 (-0.108358) | 0.509965 / 1.386936 (-0.876971) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006182 / 0.011353 (-0.005171) | 0.004153 / 0.011008 (-0.006855) | 0.095715 / 0.038508 (0.057207) | 0.032457 / 0.023109 (0.009347) | 0.314961 / 0.275898 (0.039063) | 0.353696 / 0.323480 (0.030216) | 0.005256 / 0.007986 (-0.002729) | 0.004870 / 0.004328 (0.000541) | 0.072442 / 0.004250 (0.068192) | 0.046102 / 0.037052 (0.009050) | 0.324410 / 0.258489 (0.065921) | 0.366861 / 0.293841 (0.073020) | 0.027088 / 0.128546 (-0.101458) | 0.008572 / 0.075646 (-0.067075) | 0.325988 / 0.419271 (-0.093284) | 0.049494 / 0.043533 (0.005961) | 0.311221 / 0.255139 (0.056082) | 0.359720 / 0.283200 (0.076521) | 0.095101 / 0.141683 (-0.046581) | 1.472821 / 1.452155 (0.020667) | 1.516157 / 1.492716 (0.023441) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210456 / 0.018006 (0.192450) | 0.439440 / 0.000490 (0.438950) | 0.003764 / 0.000200 (0.003564) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024076 / 0.037411 (-0.013335) | 0.104886 / 0.014526 (0.090360) | 0.114164 / 0.176557 (-0.062393) | 0.167289 / 0.737135 (-0.569847) | 0.116457 / 0.296338 (-0.179882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400039 / 0.215209 (0.184830) | 3.973243 / 2.077655 (1.895588) | 1.801991 / 1.504120 (0.297871) | 1.592017 / 1.541195 (0.050822) | 1.612564 / 1.468490 (0.144074) | 0.527475 / 4.584777 (-4.057302) | 3.676246 / 3.745712 (-0.069466) | 1.806423 / 5.269862 (-3.463438) | 1.176921 / 4.565676 (-3.388756) | 0.065902 / 0.424275 (-0.358373) | 0.012245 / 0.007607 (0.004638) | 0.490883 / 0.226044 (0.264838) | 4.905270 / 2.268929 (2.636341) | 2.218694 / 55.444624 (-53.225930) | 1.903074 / 6.876477 (-4.973403) | 1.979505 / 2.142072 (-0.162567) | 0.644415 / 4.805227 (-4.160812) | 0.142433 / 6.500664 (-6.358231) | 0.063564 / 0.075469 (-0.011905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193756 / 1.841788 (-0.648032) | 14.673103 / 8.074308 (6.598795) | 13.410951 / 10.191392 (3.219559) | 0.159175 / 0.680424 (-0.521249) | 0.017076 / 0.534201 (-0.517125) | 0.388880 / 0.579283 (-0.190403) | 0.409974 / 0.434364 (-0.024390) | 0.454494 / 0.540337 (-0.085844) | 0.556873 / 1.386936 (-0.830063) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006107 / 0.011353 (-0.005246) | 0.004433 / 0.011008 (-0.006575) | 0.073892 / 0.038508 (0.035384) | 0.032386 / 0.023109 (0.009277) | 0.370339 / 0.275898 (0.094441) | 0.388996 / 0.323480 (0.065516) | 0.005438 / 0.007986 (-0.002548) | 0.003875 / 0.004328 (-0.000454) | 0.073867 / 0.004250 (0.069617) | 0.048350 / 0.037052 (0.011298) | 0.380328 / 0.258489 (0.121839) | 0.411373 / 0.293841 (0.117532) | 0.028183 / 0.128546 (-0.100363) | 0.008924 / 0.075646 (-0.066723) | 0.082484 / 0.419271 (-0.336787) | 0.047321 / 0.043533 (0.003788) | 0.371702 / 0.255139 (0.116563) | 0.380535 / 0.283200 (0.097335) | 0.100772 / 0.141683 (-0.040911) | 1.475038 / 1.452155 (0.022883) | 1.564293 / 1.492716 (0.071577) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214589 / 0.018006 (0.196583) | 0.437193 / 0.000490 (0.436703) | 0.003676 / 0.000200 (0.003476) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027991 / 0.037411 (-0.009421) | 0.111154 / 0.014526 (0.096628) | 0.120365 / 0.176557 (-0.056191) | 0.173601 / 0.737135 (-0.563535) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442848 / 0.215209 (0.227639) | 4.398336 / 2.077655 (2.320681) | 2.217058 / 1.504120 (0.712938) | 2.011155 / 1.541195 (0.469960) | 2.123086 / 1.468490 (0.654596) | 0.525857 / 4.584777 (-4.058920) | 3.730191 / 3.745712 (-0.015521) | 3.517680 / 5.269862 (-1.752181) | 1.557940 / 4.565676 (-3.007736) | 0.066309 / 0.424275 (-0.357967) | 0.011788 / 0.007607 (0.004181) | 0.548506 / 0.226044 (0.322462) | 5.483615 / 2.268929 (3.214687) | 2.663784 / 55.444624 (-52.780840) | 2.325744 / 6.876477 (-4.550732) | 2.344179 / 2.142072 (0.202106) | 0.644217 / 4.805227 (-4.161010) | 0.141546 / 6.500664 (-6.359118) | 0.063730 / 0.075469 (-0.011739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296032 / 1.841788 (-0.545756) | 14.903729 / 8.074308 (6.829421) | 14.505409 / 10.191392 (4.314017) | 0.170478 / 0.680424 (-0.509946) | 0.017876 / 0.534201 (-0.516325) | 0.401047 / 0.579283 (-0.178236) | 0.417855 / 0.434364 (-0.016509) | 0.472138 / 0.540337 (-0.068200) | 0.570859 / 1.386936 (-0.816077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008495 / 0.011353 (-0.002858) | 0.005322 / 0.011008 (-0.005686) | 0.125471 / 0.038508 (0.086962) | 0.034604 / 0.023109 (0.011495) | 0.419831 / 0.275898 (0.143933) | 0.415707 / 0.323480 (0.092227) | 0.007471 / 0.007986 (-0.000515) | 0.005441 / 0.004328 (0.001112) | 0.095412 / 0.004250 (0.091162) | 0.053865 / 0.037052 (0.016812) | 0.375257 / 0.258489 (0.116768) | 0.438114 / 0.293841 (0.144273) | 0.046183 / 0.128546 (-0.082363) | 0.013663 / 0.075646 (-0.061984) | 0.438317 / 0.419271 (0.019045) | 0.065665 / 0.043533 (0.022133) | 0.387640 / 0.255139 (0.132501) | 0.431350 / 0.283200 (0.148150) | 0.112841 / 0.141683 (-0.028842) | 1.778639 / 1.452155 (0.326484) | 1.891948 / 1.492716 (0.399232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284371 / 0.018006 (0.266365) | 0.598247 / 0.000490 (0.597758) | 0.013674 / 0.000200 (0.013474) | 0.000483 / 0.000054 (0.000428) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032437 / 0.037411 (-0.004974) | 0.120547 / 0.014526 (0.106021) | 0.129845 / 0.176557 (-0.046711) | 0.203455 / 0.737135 (-0.533680) | 0.140039 / 0.296338 (-0.156300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596549 / 0.215209 (0.381340) | 6.138766 / 2.077655 (4.061111) | 2.515506 / 1.504120 (1.011386) | 2.124472 / 1.541195 (0.583277) | 2.160812 / 1.468490 (0.692322) | 0.898965 / 4.584777 (-3.685812) | 5.588152 / 3.745712 (1.842440) | 2.717580 / 5.269862 (-2.552282) | 1.683641 / 4.565676 (-2.882036) | 0.108045 / 0.424275 (-0.316230) | 0.014089 / 0.007607 (0.006481) | 0.749567 / 0.226044 (0.523523) | 7.518051 / 2.268929 (5.249123) | 3.198238 / 55.444624 (-52.246386) | 2.575156 / 6.876477 (-4.301321) | 2.725818 / 2.142072 (0.583745) | 1.149338 / 4.805227 (-3.655889) | 0.220443 / 6.500664 (-6.280221) | 0.081452 / 0.075469 (0.005983) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624462 / 1.841788 (-0.217325) | 18.204963 / 8.074308 (10.130655) | 21.379169 / 10.191392 (11.187777) | 0.248520 / 0.680424 (-0.431903) | 0.030121 / 0.534201 (-0.504080) | 0.499542 / 0.579283 (-0.079741) | 0.599783 / 0.434364 (0.165419) | 0.597642 / 0.540337 (0.057305) | 0.681948 / 1.386936 (-0.704988) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008431 / 0.011353 (-0.002921) | 0.006143 / 0.011008 (-0.004865) | 0.107531 / 0.038508 (0.069023) | 0.036308 / 0.023109 (0.013199) | 0.480555 / 0.275898 (0.204657) | 0.556407 / 0.323480 (0.232927) | 0.007614 / 0.007986 (-0.000372) | 0.004749 / 0.004328 (0.000421) | 0.105734 / 0.004250 (0.101484) | 0.051619 / 0.037052 (0.014567) | 0.514821 / 0.258489 (0.256332) | 0.562143 / 0.293841 (0.268302) | 0.042957 / 0.128546 (-0.085589) | 0.015142 / 0.075646 (-0.060505) | 0.143161 / 0.419271 (-0.276111) | 0.061910 / 0.043533 (0.018377) | 0.496923 / 0.255139 (0.241784) | 0.556302 / 0.283200 (0.273102) | 0.136700 / 0.141683 (-0.004983) | 1.886184 / 1.452155 (0.434029) | 2.004087 / 1.492716 (0.511371) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235530 / 0.018006 (0.217523) | 0.600796 / 0.000490 (0.600306) | 0.009074 / 0.000200 (0.008874) | 0.000203 / 0.000054 (0.000149) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036345 / 0.037411 (-0.001066) | 0.126112 / 0.014526 (0.111586) | 0.143369 / 0.176557 (-0.033188) | 0.211381 / 0.737135 (-0.525755) | 0.151095 / 0.296338 (-0.145243) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.695022 / 0.215209 (0.479813) | 6.685981 / 2.077655 (4.608326) | 3.104521 / 1.504120 (1.600401) | 2.758323 / 1.541195 (1.217128) | 2.706286 / 1.468490 (1.237796) | 0.941182 / 4.584777 (-3.643595) | 5.715839 / 3.745712 (1.970127) | 5.089636 / 5.269862 (-0.180226) | 2.594739 / 4.565676 (-1.970937) | 0.112621 / 0.424275 (-0.311655) | 0.014001 / 0.007607 (0.006394) | 0.812990 / 0.226044 (0.586945) | 8.060890 / 2.268929 (5.791961) | 3.832506 / 55.444624 (-51.612119) | 3.148051 / 6.876477 (-3.728425) | 3.110096 / 2.142072 (0.968023) | 1.105050 / 4.805227 (-3.700178) | 0.219835 / 6.500664 (-6.280829) | 0.078600 / 0.075469 (0.003131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.707551 / 1.841788 (-0.134237) | 19.238194 / 8.074308 (11.163885) | 22.167076 / 10.191392 (11.975684) | 0.233458 / 0.680424 (-0.446966) | 0.025131 / 0.534201 (-0.509070) | 0.525241 / 0.579283 (-0.054042) | 0.649666 / 0.434364 (0.215303) | 0.602941 / 0.540337 (0.062603) | 0.718472 / 1.386936 (-0.668464) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006416 / 0.011353 (-0.004937) | 0.004278 / 0.011008 (-0.006731) | 0.097562 / 0.038508 (0.059054) | 0.029488 / 0.023109 (0.006379) | 0.308648 / 0.275898 (0.032750) | 0.339879 / 0.323480 (0.016399) | 0.005288 / 0.007986 (-0.002697) | 0.005033 / 0.004328 (0.000704) | 0.074666 / 0.004250 (0.070416) | 0.034888 / 0.037052 (-0.002164) | 0.309960 / 0.258489 (0.051471) | 0.344276 / 0.293841 (0.050435) | 0.025564 / 0.128546 (-0.102982) | 0.008579 / 0.075646 (-0.067067) | 0.319796 / 0.419271 (-0.099476) | 0.044786 / 0.043533 (0.001253) | 0.308888 / 0.255139 (0.053749) | 0.334001 / 0.283200 (0.050802) | 0.089917 / 0.141683 (-0.051766) | 1.456696 / 1.452155 (0.004541) | 1.542273 / 1.492716 (0.049557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213236 / 0.018006 (0.195230) | 0.425139 / 0.000490 (0.424650) | 0.008831 / 0.000200 (0.008631) | 0.000209 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023990 / 0.037411 (-0.013421) | 0.096787 / 0.014526 (0.082261) | 0.105783 / 0.176557 (-0.070774) | 0.167182 / 0.737135 (-0.569954) | 0.108896 / 0.296338 (-0.187442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419844 / 0.215209 (0.204635) | 4.201909 / 2.077655 (2.124254) | 1.910784 / 1.504120 (0.406664) | 1.685183 / 1.541195 (0.143988) | 1.716927 / 1.468490 (0.248437) | 0.548261 / 4.584777 (-4.036516) | 3.414168 / 3.745712 (-0.331544) | 1.695446 / 5.269862 (-3.574415) | 0.989668 / 4.565676 (-3.576008) | 0.067328 / 0.424275 (-0.356948) | 0.012084 / 0.007607 (0.004477) | 0.523799 / 0.226044 (0.297754) | 5.240589 / 2.268929 (2.971661) | 2.331618 / 55.444624 (-53.113007) | 1.996094 / 6.876477 (-4.880383) | 2.105450 / 2.142072 (-0.036623) | 0.654614 / 4.805227 (-4.150613) | 0.134721 / 6.500664 (-6.365943) | 0.066227 / 0.075469 (-0.009242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196266 / 1.841788 (-0.645521) | 13.990045 / 8.074308 (5.915737) | 13.928126 / 10.191392 (3.736734) | 0.142600 / 0.680424 (-0.537824) | 0.016462 / 0.534201 (-0.517739) | 0.363113 / 0.579283 (-0.216170) | 0.428590 / 0.434364 (-0.005773) | 0.452594 / 0.540337 (-0.087743) | 0.551678 / 1.386936 (-0.835258) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005992 / 0.011353 (-0.005361) | 0.004161 / 0.011008 (-0.006847) | 0.076098 / 0.038508 (0.037589) | 0.028559 / 0.023109 (0.005450) | 0.411696 / 0.275898 (0.135798) | 0.444519 / 0.323480 (0.121040) | 0.004965 / 0.007986 (-0.003021) | 0.003452 / 0.004328 (-0.000876) | 0.075107 / 0.004250 (0.070857) | 0.037305 / 0.037052 (0.000252) | 0.429728 / 0.258489 (0.171239) | 0.444313 / 0.293841 (0.150472) | 0.025278 / 0.128546 (-0.103268) | 0.008527 / 0.075646 (-0.067120) | 0.081502 / 0.419271 (-0.337770) | 0.041237 / 0.043533 (-0.002296) | 0.417848 / 0.255139 (0.162709) | 0.426615 / 0.283200 (0.143415) | 0.094641 / 0.141683 (-0.047041) | 1.525141 / 1.452155 (0.072987) | 1.615608 / 1.492716 (0.122892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192867 / 0.018006 (0.174861) | 0.414979 / 0.000490 (0.414490) | 0.000815 / 0.000200 (0.000615) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025354 / 0.037411 (-0.012058) | 0.102085 / 0.014526 (0.087559) | 0.107930 / 0.176557 (-0.068626) | 0.160483 / 0.737135 (-0.576652) | 0.112341 / 0.296338 (-0.183997) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446938 / 0.215209 (0.231728) | 4.480057 / 2.077655 (2.402402) | 2.154825 / 1.504120 (0.650705) | 1.942774 / 1.541195 (0.401580) | 1.996418 / 1.468490 (0.527928) | 0.556728 / 4.584777 (-4.028049) | 3.441228 / 3.745712 (-0.304484) | 3.004179 / 5.269862 (-2.265683) | 1.314104 / 4.565676 (-3.251573) | 0.068670 / 0.424275 (-0.355606) | 0.011972 / 0.007607 (0.004365) | 0.556604 / 0.226044 (0.330560) | 5.561783 / 2.268929 (3.292855) | 2.631262 / 55.444624 (-52.813363) | 2.262143 / 6.876477 (-4.614333) | 2.364243 / 2.142072 (0.222170) | 0.660621 / 4.805227 (-4.144607) | 0.137371 / 6.500664 (-6.363293) | 0.069104 / 0.075469 (-0.006365) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305706 / 1.841788 (-0.536081) | 14.015932 / 8.074308 (5.941624) | 14.353580 / 10.191392 (4.162187) | 0.146172 / 0.680424 (-0.534251) | 0.016699 / 0.534201 (-0.517502) | 0.357970 / 0.579283 (-0.221313) | 0.389067 / 0.434364 (-0.045297) | 0.415470 / 0.540337 (-0.124867) | 0.501359 / 1.386936 (-0.885577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006800 / 0.011353 (-0.004552) | 0.004721 / 0.011008 (-0.006287) | 0.097760 / 0.038508 (0.059252) | 0.034192 / 0.023109 (0.011083) | 0.298240 / 0.275898 (0.022342) | 0.331119 / 0.323480 (0.007639) | 0.005826 / 0.007986 (-0.002160) | 0.003968 / 0.004328 (-0.000360) | 0.073833 / 0.004250 (0.069582) | 0.046288 / 0.037052 (0.009236) | 0.303018 / 0.258489 (0.044529) | 0.342163 / 0.293841 (0.048322) | 0.028504 / 0.128546 (-0.100042) | 0.009031 / 0.075646 (-0.066615) | 0.331617 / 0.419271 (-0.087655) | 0.060911 / 0.043533 (0.017379) | 0.304044 / 0.255139 (0.048905) | 0.328959 / 0.283200 (0.045759) | 0.113174 / 0.141683 (-0.028509) | 1.424652 / 1.452155 (-0.027502) | 1.531392 / 1.492716 (0.038676) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206175 / 0.018006 (0.188169) | 0.435916 / 0.000490 (0.435426) | 0.002587 / 0.000200 (0.002387) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026996 / 0.037411 (-0.010415) | 0.106722 / 0.014526 (0.092196) | 0.117655 / 0.176557 (-0.058902) | 0.176969 / 0.737135 (-0.560166) | 0.122577 / 0.296338 (-0.173762) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396086 / 0.215209 (0.180877) | 3.972465 / 2.077655 (1.894811) | 1.800798 / 1.504120 (0.296678) | 1.616747 / 1.541195 (0.075552) | 1.680711 / 1.468490 (0.212221) | 0.526479 / 4.584777 (-4.058298) | 3.791528 / 3.745712 (0.045816) | 2.989518 / 5.269862 (-2.280344) | 1.463221 / 4.565676 (-3.102455) | 0.065649 / 0.424275 (-0.358626) | 0.012155 / 0.007607 (0.004548) | 0.500241 / 0.226044 (0.274197) | 5.008895 / 2.268929 (2.739966) | 2.315288 / 55.444624 (-53.129336) | 1.959409 / 6.876477 (-4.917067) | 2.102371 / 2.142072 (-0.039701) | 0.639611 / 4.805227 (-4.165617) | 0.140101 / 6.500664 (-6.360563) | 0.063599 / 0.075469 (-0.011870) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206729 / 1.841788 (-0.635059) | 15.127250 / 8.074308 (7.052942) | 14.397228 / 10.191392 (4.205836) | 0.148802 / 0.680424 (-0.531622) | 0.017628 / 0.534201 (-0.516573) | 0.396150 / 0.579283 (-0.183133) | 0.435826 / 0.434364 (0.001462) | 0.471215 / 0.540337 (-0.069122) | 0.559413 / 1.386936 (-0.827523) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006479 / 0.011353 (-0.004874) | 0.004520 / 0.011008 (-0.006488) | 0.074395 / 0.038508 (0.035887) | 0.033400 / 0.023109 (0.010291) | 0.388411 / 0.275898 (0.112513) | 0.396714 / 0.323480 (0.073234) | 0.005736 / 0.007986 (-0.002250) | 0.004038 / 0.004328 (-0.000291) | 0.073595 / 0.004250 (0.069345) | 0.045207 / 0.037052 (0.008155) | 0.378096 / 0.258489 (0.119607) | 0.417830 / 0.293841 (0.123989) | 0.028365 / 0.128546 (-0.100181) | 0.008887 / 0.075646 (-0.066760) | 0.080766 / 0.419271 (-0.338505) | 0.046923 / 0.043533 (0.003390) | 0.376190 / 0.255139 (0.121051) | 0.385875 / 0.283200 (0.102675) | 0.107542 / 0.141683 (-0.034141) | 1.409257 / 1.452155 (-0.042898) | 1.518475 / 1.492716 (0.025759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223299 / 0.018006 (0.205292) | 0.440640 / 0.000490 (0.440150) | 0.000397 / 0.000200 (0.000197) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031388 / 0.037411 (-0.006024) | 0.113078 / 0.014526 (0.098552) | 0.124398 / 0.176557 (-0.052159) | 0.173802 / 0.737135 (-0.563333) | 0.129555 / 0.296338 (-0.166783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440220 / 0.215209 (0.225011) | 4.398052 / 2.077655 (2.320398) | 2.188396 / 1.504120 (0.684276) | 1.997811 / 1.541195 (0.456616) | 2.093338 / 1.468490 (0.624847) | 0.519597 / 4.584777 (-4.065180) | 3.885795 / 3.745712 (0.140083) | 2.896327 / 5.269862 (-2.373534) | 1.245785 / 4.565676 (-3.319891) | 0.065675 / 0.424275 (-0.358600) | 0.011729 / 0.007607 (0.004121) | 0.541526 / 0.226044 (0.315482) | 5.406763 / 2.268929 (3.137834) | 2.722914 / 55.444624 (-52.721711) | 2.471111 / 6.876477 (-4.405366) | 2.541488 / 2.142072 (0.399415) | 0.633566 / 4.805227 (-4.171661) | 0.139622 / 6.500664 (-6.361042) | 0.064220 / 0.075469 (-0.011249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296097 / 1.841788 (-0.545690) | 15.095320 / 8.074308 (7.021012) | 14.300821 / 10.191392 (4.109429) | 0.145470 / 0.680424 (-0.534954) | 0.017496 / 0.534201 (-0.516705) | 0.400589 / 0.579283 (-0.178694) | 0.423091 / 0.434364 (-0.011273) | 0.468258 / 0.540337 (-0.072079) | 0.570873 / 1.386936 (-0.816063) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005918 / 0.011353 (-0.005435) | 0.004393 / 0.011008 (-0.006615) | 0.091677 / 0.038508 (0.053169) | 0.033546 / 0.023109 (0.010437) | 0.344682 / 0.275898 (0.068784) | 0.388906 / 0.323480 (0.065426) | 0.005412 / 0.007986 (-0.002574) | 0.004909 / 0.004328 (0.000580) | 0.082589 / 0.004250 (0.078339) | 0.045242 / 0.037052 (0.008190) | 0.339191 / 0.258489 (0.080702) | 0.349673 / 0.293841 (0.055832) | 0.026805 / 0.128546 (-0.101742) | 0.007529 / 0.075646 (-0.068117) | 0.319108 / 0.419271 (-0.100164) | 0.049482 / 0.043533 (0.005949) | 0.320013 / 0.255139 (0.064874) | 0.342059 / 0.283200 (0.058859) | 0.096623 / 0.141683 (-0.045060) | 1.458204 / 1.452155 (0.006049) | 1.571172 / 1.492716 (0.078455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235171 / 0.018006 (0.217165) | 0.479678 / 0.000490 (0.479188) | 0.006627 / 0.000200 (0.006427) | 0.000257 / 0.000054 (0.000202) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025716 / 0.037411 (-0.011696) | 0.107730 / 0.014526 (0.093204) | 0.111595 / 0.176557 (-0.064962) | 0.171316 / 0.737135 (-0.565819) | 0.118962 / 0.296338 (-0.177377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376318 / 0.215209 (0.161109) | 4.039484 / 2.077655 (1.961829) | 1.811548 / 1.504120 (0.307428) | 1.646728 / 1.541195 (0.105533) | 1.688071 / 1.468490 (0.219581) | 0.551256 / 4.584777 (-4.033520) | 4.153931 / 3.745712 (0.408218) | 3.424154 / 5.269862 (-1.845707) | 1.734860 / 4.565676 (-2.830816) | 0.067753 / 0.424275 (-0.356522) | 0.012699 / 0.007607 (0.005092) | 0.505722 / 0.226044 (0.279677) | 4.997321 / 2.268929 (2.728392) | 2.258755 / 55.444624 (-53.185869) | 1.954382 / 6.876477 (-4.922095) | 1.967545 / 2.142072 (-0.174527) | 0.630489 / 4.805227 (-4.174738) | 0.138738 / 6.500664 (-6.361926) | 0.064907 / 0.075469 (-0.010562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209634 / 1.841788 (-0.632154) | 15.055062 / 8.074308 (6.980754) | 12.721606 / 10.191392 (2.530214) | 0.164908 / 0.680424 (-0.515516) | 0.019528 / 0.534201 (-0.514673) | 0.400136 / 0.579283 (-0.179147) | 0.451640 / 0.434364 (0.017276) | 0.466272 / 0.540337 (-0.074065) | 0.553258 / 1.386936 (-0.833679) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006341 / 0.011353 (-0.005011) | 0.004617 / 0.011008 (-0.006391) | 0.077953 / 0.038508 (0.039445) | 0.031104 / 0.023109 (0.007995) | 0.360328 / 0.275898 (0.084430) | 0.408403 / 0.323480 (0.084923) | 0.005704 / 0.007986 (-0.002282) | 0.003588 / 0.004328 (-0.000741) | 0.071441 / 0.004250 (0.067190) | 0.043520 / 0.037052 (0.006468) | 0.375798 / 0.258489 (0.117309) | 0.400955 / 0.293841 (0.107114) | 0.028166 / 0.128546 (-0.100381) | 0.008578 / 0.075646 (-0.067068) | 0.086673 / 0.419271 (-0.332598) | 0.046424 / 0.043533 (0.002891) | 0.367276 / 0.255139 (0.112137) | 0.414550 / 0.283200 (0.131351) | 0.097355 / 0.141683 (-0.044328) | 1.465191 / 1.452155 (0.013036) | 1.555028 / 1.492716 (0.062312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196642 / 0.018006 (0.178636) | 0.464221 / 0.000490 (0.463731) | 0.002726 / 0.000200 (0.002526) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028078 / 0.037411 (-0.009333) | 0.110762 / 0.014526 (0.096236) | 0.122212 / 0.176557 (-0.054344) | 0.164758 / 0.737135 (-0.572377) | 0.133969 / 0.296338 (-0.162370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448134 / 0.215209 (0.232925) | 4.339335 / 2.077655 (2.261680) | 2.129209 / 1.504120 (0.625089) | 1.957805 / 1.541195 (0.416611) | 1.994038 / 1.468490 (0.525548) | 0.497101 / 4.584777 (-4.087676) | 4.114432 / 3.745712 (0.368720) | 3.437305 / 5.269862 (-1.832556) | 1.692810 / 4.565676 (-2.872866) | 0.071077 / 0.424275 (-0.353198) | 0.012735 / 0.007607 (0.005128) | 0.534393 / 0.226044 (0.308348) | 5.217445 / 2.268929 (2.948517) | 2.594858 / 55.444624 (-52.849766) | 2.317464 / 6.876477 (-4.559012) | 2.337974 / 2.142072 (0.195902) | 0.622291 / 4.805227 (-4.182936) | 0.144934 / 6.500664 (-6.355730) | 0.068524 / 0.075469 (-0.006945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310601 / 1.841788 (-0.531187) | 15.771527 / 8.074308 (7.697219) | 13.952032 / 10.191392 (3.760640) | 0.212473 / 0.680424 (-0.467951) | 0.017963 / 0.534201 (-0.516238) | 0.400755 / 0.579283 (-0.178528) | 0.439817 / 0.434364 (0.005453) | 0.472614 / 0.540337 (-0.067724) | 0.558410 / 1.386936 (-0.828526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.\r\n\r\nThanks! I have encountered similar problems. I modify the json format from list to line and works!" ]

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

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[ "Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```", "Hey Mario,\r\nThanks, for getting back to me. the toDouble was just an example my real life case requires many more transforms.\r\n\r\nWhat do you mean by:\r\n> using set_format/set_transform for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n\r\nHow would that go, I thought you can't chain them?\r\n\r\nAs for the custom formatter, is it possible to reference an existing formatter, in my case `torch_formatter` inside of my custom formatter?\r\n\r\nmaybe I can inherit from it and just call `super.recursive_tensorize()`?", "> How would that go, I thought you can't chain them?\r\n\r\nYes, they cannot be chained. This is what I meant:\r\n```python\r\nds.set_transform(first_transform)\r\n# calling the 2nd transform on each accessed batch\r\nsecond_transform(ds[2:3])\r\n```\r\n\r\n> As for the custom formatter, is it possible to reference an existing formatter, in my case torch_formatter inside of my custom formatter?\r\n>\r\n>maybe I can inherit from it and just call super.recursive_tensorize()?\r\n\r\nYes, subclassing makes the most sense.", "Great, thank you for the details.", "https://github.com/huggingface/datasets/issues/6012" ]

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

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[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well", "@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)", "Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though", "> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ", "Yes indeed !", "I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006192 / 0.011353 (-0.005161) | 0.004410 / 0.011008 (-0.006598) | 0.095990 / 0.038508 (0.057482) | 0.032662 / 0.023109 (0.009553) | 0.322827 / 0.275898 (0.046929) | 0.352542 / 0.323480 (0.029062) | 0.005398 / 0.007986 (-0.002588) | 0.003926 / 0.004328 (-0.000403) | 0.075131 / 0.004250 (0.070880) | 0.046205 / 0.037052 (0.009153) | 0.330957 / 0.258489 (0.072468) | 0.360166 / 0.293841 (0.066325) | 0.027880 / 0.128546 (-0.100666) | 0.008813 / 0.075646 (-0.066833) | 0.327316 / 0.419271 (-0.091955) | 0.050071 / 0.043533 (0.006539) | 0.319939 / 0.255139 (0.064800) | 0.331593 / 0.283200 (0.048393) | 0.096745 / 0.141683 (-0.044938) | 1.445165 / 1.452155 (-0.006990) | 1.515538 / 1.492716 (0.022821) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209365 / 0.018006 (0.191358) | 0.437007 / 0.000490 (0.436518) | 0.003207 / 0.000200 (0.003007) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027261 / 0.037411 (-0.010151) | 0.105101 / 0.014526 (0.090575) | 0.117163 / 0.176557 (-0.059394) | 0.176237 / 0.737135 (-0.560898) | 0.122559 / 0.296338 (-0.173779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406792 / 0.215209 (0.191583) | 4.060831 / 2.077655 (1.983176) | 1.829691 / 1.504120 (0.325571) | 1.633155 / 1.541195 (0.091960) | 1.704817 / 1.468490 (0.236327) | 0.525325 / 4.584777 (-4.059452) | 3.752907 / 3.745712 (0.007194) | 1.857513 / 5.269862 (-3.412349) | 1.222237 / 4.565676 (-3.343439) | 0.065941 / 0.424275 (-0.358334) | 0.012498 / 0.007607 (0.004891) | 0.495009 / 0.226044 (0.268965) | 4.968074 / 2.268929 (2.699145) | 2.277898 / 55.444624 (-53.166727) | 1.936656 / 6.876477 (-4.939821) | 1.970698 / 2.142072 (-0.171374) | 0.635221 / 4.805227 (-4.170006) | 0.140539 / 6.500664 (-6.360125) | 0.064111 / 0.075469 (-0.011358) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238151 / 1.841788 (-0.603637) | 14.681262 / 8.074308 (6.606954) | 13.405525 / 10.191392 (3.214133) | 0.163225 / 0.680424 (-0.517199) | 0.017282 / 0.534201 (-0.516918) | 0.395526 / 0.579283 (-0.183757) | 0.429156 / 0.434364 (-0.005208) | 0.470806 / 0.540337 (-0.069531) | 0.571290 / 1.386936 (-0.815646) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006444 / 0.011353 (-0.004909) | 0.004388 / 0.011008 (-0.006621) | 0.075004 / 0.038508 (0.036496) | 0.032904 / 0.023109 (0.009795) | 0.375360 / 0.275898 (0.099462) | 0.413684 / 0.323480 (0.090204) | 0.005854 / 0.007986 (-0.002132) | 0.005504 / 0.004328 (0.001175) | 0.075049 / 0.004250 (0.070799) | 0.047973 / 0.037052 (0.010920) | 0.377943 / 0.258489 (0.119454) | 0.427039 / 0.293841 (0.133198) | 0.028248 / 0.128546 (-0.100298) | 0.008972 / 0.075646 (-0.066674) | 0.081848 / 0.419271 (-0.337424) | 0.047935 / 0.043533 (0.004402) | 0.377980 / 0.255139 (0.122841) | 0.407856 / 0.283200 (0.124656) | 0.103454 / 0.141683 (-0.038229) | 1.469051 / 1.452155 (0.016896) | 1.590657 / 1.492716 (0.097941) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192380 / 0.018006 (0.174374) | 0.440995 / 0.000490 (0.440505) | 0.004082 / 0.000200 (0.003882) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029584 / 0.037411 (-0.007828) | 0.110051 / 0.014526 (0.095525) | 0.121196 / 0.176557 (-0.055361) | 0.172249 / 0.737135 (-0.564886) | 0.125380 / 0.296338 (-0.170958) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435218 / 0.215209 (0.220009) | 4.354811 / 2.077655 (2.277156) | 2.102050 / 1.504120 (0.597930) | 1.913454 / 1.541195 (0.372260) | 1.974624 / 1.468490 (0.506134) | 0.529975 / 4.584777 (-4.054802) | 3.801605 / 3.745712 (0.055893) | 3.162408 / 5.269862 (-2.107454) | 1.599576 / 4.565676 (-2.966101) | 0.066710 / 0.424275 (-0.357565) | 0.012158 / 0.007607 (0.004551) | 0.549187 / 0.226044 (0.323142) | 5.489930 / 2.268929 (3.221002) | 2.646787 / 55.444624 (-52.797837) | 2.311915 / 6.876477 (-4.564562) | 2.335645 / 2.142072 (0.193572) | 0.641067 / 4.805227 (-4.164160) | 0.142227 / 6.500664 (-6.358437) | 0.065303 / 0.075469 (-0.010166) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283209 / 1.841788 (-0.558579) | 15.241809 / 8.074308 (7.167501) | 14.131471 / 10.191392 (3.940079) | 0.143921 / 0.680424 (-0.536503) | 0.017497 / 0.534201 (-0.516704) | 0.402236 / 0.579283 (-0.177047) | 0.418917 / 0.434364 (-0.015447) | 0.461745 / 0.540337 (-0.078593) | 0.560212 / 1.386936 (-0.826724) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]

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[ "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```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007401 / 0.011353 (-0.003952) | 0.005198 / 0.011008 (-0.005810) | 0.112317 / 0.038508 (0.073809) | 0.038406 / 0.023109 (0.015297) | 0.358008 / 0.275898 (0.082110) | 0.395350 / 0.323480 (0.071870) | 0.006201 / 0.007986 (-0.001785) | 0.004368 / 0.004328 (0.000039) | 0.087718 / 0.004250 (0.083467) | 0.055299 / 0.037052 (0.018247) | 0.350481 / 0.258489 (0.091992) | 0.419876 / 0.293841 (0.126035) | 0.032459 / 0.128546 (-0.096087) | 0.010635 / 0.075646 (-0.065011) | 0.383282 / 0.419271 (-0.035989) | 0.059241 / 0.043533 (0.015708) | 0.365101 / 0.255139 (0.109962) | 0.378144 / 0.283200 (0.094944) | 0.114287 / 0.141683 (-0.027396) | 1.680870 / 1.452155 (0.228715) | 1.788183 / 1.492716 (0.295467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242919 / 0.018006 (0.224913) | 0.489850 / 0.000490 (0.489360) | 0.011408 / 0.000200 (0.011208) | 0.000444 / 0.000054 (0.000389) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030742 / 0.037411 (-0.006669) | 0.123092 / 0.014526 (0.108566) | 0.138246 / 0.176557 (-0.038311) | 0.207299 / 0.737135 (-0.529836) | 0.142647 / 0.296338 (-0.153691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472553 / 0.215209 (0.257344) | 4.671763 / 2.077655 (2.594108) | 2.119986 / 1.504120 (0.615866) | 1.891851 / 1.541195 (0.350656) | 1.979094 / 1.468490 (0.510604) | 0.617956 / 4.584777 (-3.966821) | 4.969418 / 3.745712 (1.223706) | 4.672083 / 5.269862 (-0.597779) | 2.119049 / 4.565676 (-2.446627) | 0.077466 / 0.424275 (-0.346809) | 0.014434 / 0.007607 (0.006827) | 0.580746 / 0.226044 (0.354701) | 5.805458 / 2.268929 (3.536530) | 2.622498 / 55.444624 (-52.822126) | 2.259499 / 6.876477 (-4.616978) | 2.362078 / 2.142072 (0.220006) | 0.719911 / 4.805227 (-4.085317) | 0.164939 / 6.500664 (-6.335725) | 0.074762 / 0.075469 (-0.000707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.496709 / 1.841788 (-0.345079) | 18.247499 / 8.074308 (10.173191) | 15.397075 / 10.191392 (5.205683) | 0.181163 / 0.680424 (-0.499261) | 0.022604 / 0.534201 (-0.511597) | 0.462791 / 0.579283 (-0.116492) | 0.504473 / 0.434364 (0.070109) | 0.582254 / 0.540337 (0.041917) | 0.673849 / 1.386936 (-0.713087) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007633 / 0.011353 (-0.003720) | 0.004859 / 0.011008 (-0.006149) | 0.091194 / 0.038508 (0.052686) | 0.038255 / 0.023109 (0.015146) | 0.460972 / 0.275898 (0.185074) | 0.470441 / 0.323480 (0.146961) | 0.006482 / 0.007986 (-0.001504) | 0.004500 / 0.004328 (0.000172) | 0.089998 / 0.004250 (0.085748) | 0.055470 / 0.037052 (0.018418) | 0.459188 / 0.258489 (0.200699) | 0.491255 / 0.293841 (0.197414) | 0.032200 / 0.128546 (-0.096346) | 0.010372 / 0.075646 (-0.065274) | 0.097429 / 0.419271 (-0.321843) | 0.052469 / 0.043533 (0.008936) | 0.452492 / 0.255139 (0.197353) | 0.475210 / 0.283200 (0.192010) | 0.116976 / 0.141683 (-0.024707) | 1.752742 / 1.452155 (0.300587) | 1.849535 / 1.492716 (0.356819) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.472259 / 0.000490 (0.471770) | 0.000455 / 0.000200 (0.000255) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033796 / 0.037411 (-0.003615) | 0.136151 / 0.014526 (0.121625) | 0.144015 / 0.176557 (-0.032542) | 0.199337 / 0.737135 (-0.537798) | 0.150024 / 0.296338 (-0.146315) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522737 / 0.215209 (0.307528) | 5.165223 / 2.077655 (3.087568) | 2.630334 / 1.504120 (1.126214) | 2.392383 / 1.541195 (0.851188) | 2.488966 / 1.468490 (1.020476) | 0.608981 / 4.584777 (-3.975796) | 4.711545 / 3.745712 (0.965833) | 2.121537 / 5.269862 (-3.148325) | 1.205477 / 4.565676 (-3.360199) | 0.078277 / 0.424275 (-0.345998) | 0.014175 / 0.007607 (0.006568) | 0.640720 / 0.226044 (0.414675) | 6.391173 / 2.268929 (4.122245) | 3.265131 / 55.444624 (-52.179493) | 2.939188 / 6.876477 (-3.937289) | 2.919217 / 2.142072 (0.777145) | 0.745095 / 4.805227 (-4.060132) | 0.164065 / 6.500664 (-6.336599) | 0.076993 / 0.075469 (0.001524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.539971 / 1.841788 (-0.301817) | 18.597296 / 8.074308 (10.522988) | 16.899330 / 10.191392 (6.707938) | 0.169005 / 0.680424 (-0.511419) | 0.020447 / 0.534201 (-0.513754) | 0.465862 / 0.579283 (-0.113421) | 0.522819 / 0.434364 (0.088455) | 0.547111 / 0.540337 (0.006773) | 0.657777 / 1.386936 (-0.729159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]

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

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[ "Random fact: previous run was showing that the Hub was hosting 13336 datasets, while the most recent run shows 36662 👀🎉", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks! \r\n\r\nHowever, I think we should stop linking this notebook and use the notebook version of the Quickstart doc page instead of it for easier maintenance (we would have the \"Open in Colab\" button in the Quickstart doc as Transformers [does](https://huggingface.co/docs/transformers/quicktour)). \r\n\r\n@stevhliu should be able to help with this. If I'm not mistaken, this can be done by adding the `[[open in colab]]` marker to the doc page.\r\n\r\nAlso, if some useful info from the Overview notebook is not in the docs, feel free to add it so we don't lose it 🙂.", "Cool, makes sense @mariosasko, then I'll check both notebooks and see whether there's something in `Overview.ipynb` worth including in the `docs/source/quickstart.mdx` and remove `Overview.ipynb` and update references in favour of `docs/source/quickstart.mdx`\r\n\r\nAre you OK if I do that @stevhliu @mariosasko? Thanks 🤗 ", "For the moment I've just updated the `quickstart.mdx` to be more similar to [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx), but regarding the `Overview.ipynb` notebook I was planning to create a PR in https://github.com/huggingface/notebooks to add it there, does that make sense @stevhliu? And then to create a `README.md` in this repository in `notebooks/` as `transformers` does to point to the related notebooks hosted in https://github.com/huggingface/notebooks, WDYT? 🤗 ", "Hi @stevhliu thanks for the feedback! Already applied your suggestions, I'll also add the pointers to both audio and image datasets in the \"What's next\" section.\r\n\r\nBesides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!", "Thanks a lot for the detailed feedback @mariosasko, I'll apply the changes today!", "> Besides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!\r\n\r\nAwesome! If you're up for it, I think you can go ahead and open a PR with the changes I've outlined [here](https://github.com/huggingface/datasets/pull/5902#pullrequestreview-1475236887) to add the notebook building workflow. ", "Hi @stevhliu @mariosasko, sorry for the delay I had a busy week, I'll tackle this either today or tomorrow to ideally close it before the weekend, thanks again for the help and guidance 😄 ", "Hi guys @stevhliu @mariosasko sorry for the delay! I've resolved all the comments and applied your reviews 👍🏻 Let me know if this works and we can finally close this PR, thanks for the help in the meantime!", "> Thanks for iterating on this and wrapping it up! 🤗\r\n\r\nNo need to! Always a pleasure to collaborate with you guys 🤗 ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009814 / 0.011353 (-0.001539) | 0.004632 / 0.011008 (-0.006376) | 0.103059 / 0.038508 (0.064551) | 0.090277 / 0.023109 (0.067167) | 0.389344 / 0.275898 (0.113446) | 0.464536 / 0.323480 (0.141056) | 0.008196 / 0.007986 (0.000210) | 0.003872 / 0.004328 (-0.000457) | 0.081912 / 0.004250 (0.077662) | 0.073197 / 0.037052 (0.036145) | 0.407545 / 0.258489 (0.149056) | 0.458035 / 0.293841 (0.164194) | 0.037485 / 0.128546 (-0.091061) | 0.010141 / 0.075646 (-0.065505) | 0.365998 / 0.419271 (-0.053273) | 0.065218 / 0.043533 (0.021685) | 0.414091 / 0.255139 (0.158952) | 0.435617 / 0.283200 (0.152417) | 0.028850 / 0.141683 (-0.112833) | 1.883510 / 1.452155 (0.431355) | 1.979986 / 1.492716 (0.487269) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236623 / 0.018006 (0.218616) | 0.467128 / 0.000490 (0.466638) | 0.008273 / 0.000200 (0.008074) | 0.000699 / 0.000054 (0.000645) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033061 / 0.037411 (-0.004350) | 0.101381 / 0.014526 (0.086856) | 0.110862 / 0.176557 (-0.065695) | 0.180982 / 0.737135 (-0.556154) | 0.113791 / 0.296338 (-0.182548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450805 / 0.215209 (0.235596) | 4.478374 / 2.077655 (2.400719) | 2.190814 / 1.504120 (0.686694) | 1.976726 / 1.541195 (0.435532) | 2.078527 / 1.468490 (0.610037) | 0.569150 / 4.584777 (-4.015627) | 4.557790 / 3.745712 (0.812078) | 3.794964 / 5.269862 (-1.474898) | 2.555689 / 4.565676 (-2.009987) | 0.067380 / 0.424275 (-0.356896) | 0.008741 / 0.007607 (0.001134) | 0.536913 / 0.226044 (0.310868) | 5.364588 / 2.268929 (3.095659) | 2.725602 / 55.444624 (-52.719022) | 2.332012 / 6.876477 (-4.544465) | 2.560550 / 2.142072 (0.418477) | 0.672490 / 4.805227 (-4.132738) | 0.153629 / 6.500664 (-6.347035) | 0.070583 / 0.075469 (-0.004886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620083 / 1.841788 (-0.221704) | 23.094248 / 8.074308 (15.019939) | 17.797625 / 10.191392 (7.606233) | 0.167993 / 0.680424 (-0.512430) | 0.021151 / 0.534201 (-0.513050) | 0.470216 / 0.579283 (-0.109067) | 0.515492 / 0.434364 (0.081128) | 0.666359 / 0.540337 (0.126021) | 0.772928 / 1.386936 (-0.614008) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007853 / 0.011353 (-0.003500) | 0.004627 / 0.011008 (-0.006381) | 0.079803 / 0.038508 (0.041295) | 0.091562 / 0.023109 (0.068453) | 0.488537 / 0.275898 (0.212639) | 0.579207 / 0.323480 (0.255728) | 0.006579 / 0.007986 (-0.001406) | 0.003946 / 0.004328 (-0.000382) | 0.080224 / 0.004250 (0.075973) | 0.074499 / 0.037052 (0.037446) | 0.488292 / 0.258489 (0.229803) | 0.569246 / 0.293841 (0.275405) | 0.039994 / 0.128546 (-0.088553) | 0.012867 / 0.075646 (-0.062780) | 0.092563 / 0.419271 (-0.326709) | 0.061656 / 0.043533 (0.018124) | 0.488271 / 0.255139 (0.233132) | 0.550651 / 0.283200 (0.267451) | 0.032078 / 0.141683 (-0.109605) | 1.874440 / 1.452155 (0.422286) | 1.973480 / 1.492716 (0.480763) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238789 / 0.018006 (0.220782) | 0.460237 / 0.000490 (0.459748) | 0.000500 / 0.000200 (0.000300) | 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) | 0.034961 / 0.037411 (-0.002450) | 0.102696 / 0.014526 (0.088170) | 0.117772 / 0.176557 (-0.058784) | 0.183865 / 0.737135 (-0.553270) | 0.119216 / 0.296338 (-0.177122) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528894 / 0.215209 (0.313685) | 5.303954 / 2.077655 (3.226300) | 2.897505 / 1.504120 (1.393385) | 2.475898 / 1.541195 (0.934703) | 2.553479 / 1.468490 (1.084988) | 0.625847 / 4.584777 (-3.958930) | 4.656595 / 3.745712 (0.910882) | 3.745170 / 5.269862 (-1.524691) | 2.470922 / 4.565676 (-2.094755) | 0.066908 / 0.424275 (-0.357367) | 0.009172 / 0.007607 (0.001565) | 0.572695 / 0.226044 (0.346650) | 5.753428 / 2.268929 (3.484499) | 3.033226 / 55.444624 (-52.411398) | 2.677280 / 6.876477 (-4.199197) | 2.908857 / 2.142072 (0.766785) | 0.681595 / 4.805227 (-4.123632) | 0.154602 / 6.500664 (-6.346062) | 0.072608 / 0.075469 (-0.002861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.738550 / 1.841788 (-0.103237) | 25.090637 / 8.074308 (17.016329) | 18.371478 / 10.191392 (8.180086) | 0.207357 / 0.680424 (-0.473067) | 0.023396 / 0.534201 (-0.510805) | 0.505663 / 0.579283 (-0.073620) | 0.503137 / 0.434364 (0.068773) | 0.598015 / 0.540337 (0.057678) | 0.714122 / 1.386936 (-0.672814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#971e33ec81b1013654e845b1c2e33cb43cda5558 \"CML watermark\")\n", "Just as a heads up @mariosasko, the `quickstart.ipynb` Jupyter Notebook has been built at https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb, while the URLs in here point to https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb instead, should we update that?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008809 / 0.011353 (-0.002544) | 0.005641 / 0.011008 (-0.005367) | 0.124986 / 0.038508 (0.086477) | 0.037311 / 0.023109 (0.014202) | 0.388915 / 0.275898 (0.113017) | 0.430123 / 0.323480 (0.106643) | 0.007447 / 0.007986 (-0.000538) | 0.009593 / 0.004328 (0.005264) | 0.099148 / 0.004250 (0.094898) | 0.052393 / 0.037052 (0.015341) | 0.399779 / 0.258489 (0.141290) | 0.439109 / 0.293841 (0.145268) | 0.043409 / 0.128546 (-0.085137) | 0.016286 / 0.075646 (-0.059360) | 0.431198 / 0.419271 (0.011927) | 0.064932 / 0.043533 (0.021400) | 0.390650 / 0.255139 (0.135511) | 0.432883 / 0.283200 (0.149684) | 0.110978 / 0.141683 (-0.030705) | 1.796121 / 1.452155 (0.343967) | 1.960097 / 1.492716 (0.467381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286292 / 0.018006 (0.268286) | 0.659495 / 0.000490 (0.659005) | 0.008294 / 0.000200 (0.008094) | 0.000485 / 0.000054 (0.000431) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029325 / 0.037411 (-0.008086) | 0.125454 / 0.014526 (0.110928) | 0.136459 / 0.176557 (-0.040097) | 0.221075 / 0.737135 (-0.516060) | 0.140281 / 0.296338 (-0.156058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602401 / 0.215209 (0.387192) | 6.124553 / 2.077655 (4.046898) | 2.453141 / 1.504120 (0.949021) | 2.038611 / 1.541195 (0.497416) | 2.073611 / 1.468490 (0.605121) | 0.938040 / 4.584777 (-3.646737) | 5.755972 / 3.745712 (2.010260) | 4.450935 / 5.269862 (-0.818926) | 2.337219 / 4.565676 (-2.228457) | 0.107118 / 0.424275 (-0.317157) | 0.015201 / 0.007607 (0.007594) | 0.785833 / 0.226044 (0.559788) | 7.732984 / 2.268929 (5.464055) | 3.236892 / 55.444624 (-52.207733) | 2.696402 / 6.876477 (-4.180074) | 2.805036 / 2.142072 (0.662964) | 1.108612 / 4.805227 (-3.696616) | 0.221067 / 6.500664 (-6.279597) | 0.085538 / 0.075469 (0.010068) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.600311 / 1.841788 (-0.241476) | 18.528118 / 8.074308 (10.453810) | 21.107199 / 10.191392 (10.915807) | 0.219489 / 0.680424 (-0.460934) | 0.028927 / 0.534201 (-0.505274) | 0.503446 / 0.579283 (-0.075837) | 0.619833 / 0.434364 (0.185469) | 0.582454 / 0.540337 (0.042117) | 0.709154 / 1.386936 (-0.677782) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008516 / 0.011353 (-0.002837) | 0.006090 / 0.011008 (-0.004918) | 0.104574 / 0.038508 (0.066066) | 0.042676 / 0.023109 (0.019566) | 0.458623 / 0.275898 (0.182725) | 0.568479 / 0.323480 (0.244999) | 0.008374 / 0.007986 (0.000389) | 0.004677 / 0.004328 (0.000349) | 0.105946 / 0.004250 (0.101695) | 0.055256 / 0.037052 (0.018204) | 0.511036 / 0.258489 (0.252547) | 0.598383 / 0.293841 (0.304542) | 0.043612 / 0.128546 (-0.084934) | 0.014707 / 0.075646 (-0.060940) | 0.116350 / 0.419271 (-0.302921) | 0.061413 / 0.043533 (0.017880) | 0.477785 / 0.255139 (0.222646) | 0.542643 / 0.283200 (0.259443) | 0.120431 / 0.141683 (-0.021252) | 1.994083 / 1.452155 (0.541928) | 2.100600 / 1.492716 (0.607883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298480 / 0.018006 (0.280474) | 0.601921 / 0.000490 (0.601432) | 0.000445 / 0.000200 (0.000245) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034784 / 0.037411 (-0.002627) | 0.133555 / 0.014526 (0.119029) | 0.138541 / 0.176557 (-0.038015) | 0.203114 / 0.737135 (-0.534021) | 0.153477 / 0.296338 (-0.142861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.780484 / 0.215209 (0.565275) | 7.150876 / 2.077655 (5.073222) | 3.168590 / 1.504120 (1.664470) | 2.698746 / 1.541195 (1.157552) | 2.695678 / 1.468490 (1.227188) | 1.037706 / 4.584777 (-3.547071) | 5.672631 / 3.745712 (1.926918) | 2.798137 / 5.269862 (-2.471725) | 1.738588 / 4.565676 (-2.827088) | 0.111160 / 0.424275 (-0.313115) | 0.013878 / 0.007607 (0.006271) | 0.800191 / 0.226044 (0.574146) | 8.546676 / 2.268929 (6.277748) | 4.116852 / 55.444624 (-51.327773) | 3.331271 / 6.876477 (-3.545206) | 3.307410 / 2.142072 (1.165337) | 1.191019 / 4.805227 (-3.614208) | 0.248953 / 6.500664 (-6.251711) | 0.086632 / 0.075469 (0.011162) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.795057 / 1.841788 (-0.046730) | 18.038785 / 8.074308 (9.964476) | 21.865566 / 10.191392 (11.674174) | 0.211058 / 0.680424 (-0.469366) | 0.026956 / 0.534201 (-0.507245) | 0.518855 / 0.579283 (-0.060428) | 0.618105 / 0.434364 (0.183741) | 0.569227 / 0.540337 (0.028889) | 0.705431 / 1.386936 (-0.681505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008900 / 0.011353 (-0.002453) | 0.005726 / 0.011008 (-0.005283) | 0.131747 / 0.038508 (0.093239) | 0.040585 / 0.023109 (0.017476) | 0.420531 / 0.275898 (0.144633) | 0.459430 / 0.323480 (0.135950) | 0.007642 / 0.007986 (-0.000344) | 0.006750 / 0.004328 (0.002421) | 0.099147 / 0.004250 (0.094897) | 0.055852 / 0.037052 (0.018799) | 0.423653 / 0.258489 (0.165164) | 0.453304 / 0.293841 (0.159463) | 0.045247 / 0.128546 (-0.083300) | 0.016034 / 0.075646 (-0.059612) | 0.443115 / 0.419271 (0.023843) | 0.078853 / 0.043533 (0.035320) | 0.417508 / 0.255139 (0.162369) | 0.440936 / 0.283200 (0.157736) | 0.115603 / 0.141683 (-0.026080) | 1.844610 / 1.452155 (0.392456) | 1.998497 / 1.492716 (0.505781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272622 / 0.018006 (0.254616) | 0.598045 / 0.000490 (0.597556) | 0.007088 / 0.000200 (0.006888) | 0.000159 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032976 / 0.037411 (-0.004436) | 0.143970 / 0.014526 (0.129444) | 0.142172 / 0.176557 (-0.034384) | 0.216747 / 0.737135 (-0.520389) | 0.146004 / 0.296338 (-0.150334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.687507 / 0.215209 (0.472298) | 6.549524 / 2.077655 (4.471870) | 2.924142 / 1.504120 (1.420022) | 2.504471 / 1.541195 (0.963277) | 2.496280 / 1.468490 (1.027790) | 0.959054 / 4.584777 (-3.625723) | 5.851742 / 3.745712 (2.106030) | 4.983357 / 5.269862 (-0.286504) | 2.627403 / 4.565676 (-1.938274) | 0.112955 / 0.424275 (-0.311320) | 0.016206 / 0.007607 (0.008599) | 0.819158 / 0.226044 (0.593114) | 8.416949 / 2.268929 (6.148020) | 3.776765 / 55.444624 (-51.667859) | 3.002397 / 6.876477 (-3.874080) | 3.158852 / 2.142072 (1.016779) | 1.197099 / 4.805227 (-3.608129) | 0.280654 / 6.500664 (-6.220010) | 0.099471 / 0.075469 (0.024002) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687007 / 1.841788 (-0.154781) | 19.411976 / 8.074308 (11.337668) | 22.053482 / 10.191392 (11.862090) | 0.228038 / 0.680424 (-0.452386) | 0.028226 / 0.534201 (-0.505975) | 0.527695 / 0.579283 (-0.051588) | 0.635911 / 0.434364 (0.201547) | 0.618205 / 0.540337 (0.077868) | 0.735164 / 1.386936 (-0.651772) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009450 / 0.011353 (-0.001903) | 0.006566 / 0.011008 (-0.004442) | 0.108919 / 0.038508 (0.070411) | 0.050010 / 0.023109 (0.026900) | 0.505168 / 0.275898 (0.229270) | 0.552190 / 0.323480 (0.228710) | 0.007569 / 0.007986 (-0.000417) | 0.006807 / 0.004328 (0.002478) | 0.116621 / 0.004250 (0.112371) | 0.060374 / 0.037052 (0.023321) | 0.515165 / 0.258489 (0.256676) | 0.572125 / 0.293841 (0.278284) | 0.046561 / 0.128546 (-0.081986) | 0.016159 / 0.075646 (-0.059487) | 0.114568 / 0.419271 (-0.304704) | 0.064689 / 0.043533 (0.021157) | 0.497870 / 0.255139 (0.242731) | 0.567332 / 0.283200 (0.284132) | 0.126254 / 0.141683 (-0.015429) | 1.954074 / 1.452155 (0.501919) | 2.057682 / 1.492716 (0.564966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.013857 / 0.018006 (-0.004149) | 0.601561 / 0.000490 (0.601071) | 0.002897 / 0.000200 (0.002697) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038480 / 0.037411 (0.001069) | 0.142480 / 0.014526 (0.127954) | 0.160479 / 0.176557 (-0.016077) | 0.217942 / 0.737135 (-0.519194) | 0.159908 / 0.296338 (-0.136431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.697926 / 0.215209 (0.482717) | 6.869754 / 2.077655 (4.792100) | 3.125463 / 1.504120 (1.621343) | 2.729123 / 1.541195 (1.187928) | 2.855747 / 1.468490 (1.387257) | 1.015345 / 4.584777 (-3.569432) | 5.839176 / 3.745712 (2.093463) | 5.019678 / 5.269862 (-0.250184) | 2.080489 / 4.565676 (-2.485187) | 0.118884 / 0.424275 (-0.305391) | 0.021381 / 0.007607 (0.013774) | 0.877847 / 0.226044 (0.651803) | 8.714561 / 2.268929 (6.445633) | 3.933399 / 55.444624 (-51.511226) | 3.281809 / 6.876477 (-3.594668) | 3.330342 / 2.142072 (1.188269) | 1.235005 / 4.805227 (-3.570222) | 0.239686 / 6.500664 (-6.260978) | 0.093546 / 0.075469 (0.018077) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.787916 / 1.841788 (-0.053872) | 20.094828 / 8.074308 (12.020520) | 22.902101 / 10.191392 (12.710709) | 0.249315 / 0.680424 (-0.431109) | 0.028058 / 0.534201 (-0.506143) | 0.524960 / 0.579283 (-0.054323) | 0.643881 / 0.434364 (0.209517) | 0.621203 / 0.540337 (0.080866) | 0.723337 / 1.386936 (-0.663599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006763 / 0.011353 (-0.004589) | 0.004548 / 0.011008 (-0.006460) | 0.095631 / 0.038508 (0.057123) | 0.034046 / 0.023109 (0.010936) | 0.298064 / 0.275898 (0.022166) | 0.330391 / 0.323480 (0.006911) | 0.006058 / 0.007986 (-0.001928) | 0.004163 / 0.004328 (-0.000165) | 0.073260 / 0.004250 (0.069010) | 0.048885 / 0.037052 (0.011832) | 0.304651 / 0.258489 (0.046162) | 0.345882 / 0.293841 (0.052042) | 0.028061 / 0.128546 (-0.100485) | 0.008823 / 0.075646 (-0.066823) | 0.325620 / 0.419271 (-0.093651) | 0.064480 / 0.043533 (0.020948) | 0.303373 / 0.255139 (0.048234) | 0.321672 / 0.283200 (0.038472) | 0.116353 / 0.141683 (-0.025330) | 1.442327 / 1.452155 (-0.009827) | 1.567553 / 1.492716 (0.074837) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213042 / 0.018006 (0.195035) | 0.457646 / 0.000490 (0.457156) | 0.003989 / 0.000200 (0.003789) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028068 / 0.037411 (-0.009344) | 0.114791 / 0.014526 (0.100265) | 0.120870 / 0.176557 (-0.055686) | 0.183006 / 0.737135 (-0.554130) | 0.126772 / 0.296338 (-0.169567) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406438 / 0.215209 (0.191229) | 4.041890 / 2.077655 (1.964235) | 1.839967 / 1.504120 (0.335847) | 1.646857 / 1.541195 (0.105662) | 1.729372 / 1.468490 (0.260882) | 0.525540 / 4.584777 (-4.059237) | 3.809996 / 3.745712 (0.064284) | 1.842598 / 5.269862 (-3.427263) | 1.062815 / 4.565676 (-3.502862) | 0.065301 / 0.424275 (-0.358974) | 0.012027 / 0.007607 (0.004420) | 0.505459 / 0.226044 (0.279415) | 5.051177 / 2.268929 (2.782248) | 2.354368 / 55.444624 (-53.090256) | 2.035482 / 6.876477 (-4.840995) | 2.120493 / 2.142072 (-0.021579) | 0.642233 / 4.805227 (-4.162994) | 0.141690 / 6.500664 (-6.358974) | 0.063933 / 0.075469 (-0.011536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186261 / 1.841788 (-0.655527) | 14.919653 / 8.074308 (6.845345) | 14.534003 / 10.191392 (4.342611) | 0.183165 / 0.680424 (-0.497259) | 0.017581 / 0.534201 (-0.516620) | 0.397284 / 0.579283 (-0.181999) | 0.431363 / 0.434364 (-0.003001) | 0.510774 / 0.540337 (-0.029564) | 0.614421 / 1.386936 (-0.772516) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006682 / 0.011353 (-0.004671) | 0.004558 / 0.011008 (-0.006450) | 0.076272 / 0.038508 (0.037764) | 0.034285 / 0.023109 (0.011176) | 0.395594 / 0.275898 (0.119696) | 0.402702 / 0.323480 (0.079222) | 0.006093 / 0.007986 (-0.001893) | 0.005538 / 0.004328 (0.001209) | 0.075797 / 0.004250 (0.071547) | 0.051638 / 0.037052 (0.014585) | 0.396071 / 0.258489 (0.137582) | 0.409282 / 0.293841 (0.115441) | 0.028193 / 0.128546 (-0.100354) | 0.008827 / 0.075646 (-0.066819) | 0.083182 / 0.419271 (-0.336089) | 0.047605 / 0.043533 (0.004072) | 0.391148 / 0.255139 (0.136009) | 0.386784 / 0.283200 (0.103584) | 0.115303 / 0.141683 (-0.026380) | 1.463666 / 1.452155 (0.011512) | 1.566147 / 1.492716 (0.073431) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213846 / 0.018006 (0.195839) | 0.454769 / 0.000490 (0.454279) | 0.004767 / 0.000200 (0.004567) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030369 / 0.037411 (-0.007042) | 0.115585 / 0.014526 (0.101059) | 0.125181 / 0.176557 (-0.051376) | 0.179247 / 0.737135 (-0.557888) | 0.129336 / 0.296338 (-0.167003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446040 / 0.215209 (0.230831) | 4.462644 / 2.077655 (2.384989) | 2.254511 / 1.504120 (0.750392) | 2.062679 / 1.541195 (0.521484) | 2.180766 / 1.468490 (0.712276) | 0.530928 / 4.584777 (-4.053849) | 3.781392 / 3.745712 (0.035680) | 3.522539 / 5.269862 (-1.747322) | 1.506960 / 4.565676 (-3.058717) | 0.067101 / 0.424275 (-0.357174) | 0.012011 / 0.007607 (0.004404) | 0.546407 / 0.226044 (0.320362) | 5.429894 / 2.268929 (3.160965) | 2.702244 / 55.444624 (-52.742381) | 2.367559 / 6.876477 (-4.508917) | 2.556032 / 2.142072 (0.413960) | 0.639690 / 4.805227 (-4.165538) | 0.144538 / 6.500664 (-6.356126) | 0.067822 / 0.075469 (-0.007647) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284977 / 1.841788 (-0.556811) | 15.546489 / 8.074308 (7.472181) | 14.747519 / 10.191392 (4.556127) | 0.160044 / 0.680424 (-0.520380) | 0.017746 / 0.534201 (-0.516454) | 0.390140 / 0.579283 (-0.189143) | 0.420342 / 0.434364 (-0.014021) | 0.459788 / 0.540337 (-0.080549) | 0.556360 / 1.386936 (-0.830576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d646afbac7ea3dc0996fa2cb6ffd8a98e158e742 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006493 / 0.011353 (-0.004860) | 0.004532 / 0.011008 (-0.006476) | 0.096509 / 0.038508 (0.058001) | 0.033084 / 0.023109 (0.009974) | 0.297802 / 0.275898 (0.021904) | 0.345880 / 0.323480 (0.022400) | 0.005461 / 0.007986 (-0.002525) | 0.005282 / 0.004328 (0.000954) | 0.073719 / 0.004250 (0.069469) | 0.045035 / 0.037052 (0.007983) | 0.295504 / 0.258489 (0.037015) | 0.345400 / 0.293841 (0.051559) | 0.027880 / 0.128546 (-0.100666) | 0.008804 / 0.075646 (-0.066842) | 0.328017 / 0.419271 (-0.091255) | 0.050169 / 0.043533 (0.006637) | 0.299642 / 0.255139 (0.044503) | 0.313573 / 0.283200 (0.030374) | 0.103359 / 0.141683 (-0.038323) | 1.482145 / 1.452155 (0.029990) | 1.554584 / 1.492716 (0.061867) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212860 / 0.018006 (0.194853) | 0.444823 / 0.000490 (0.444334) | 0.003014 / 0.000200 (0.002815) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026906 / 0.037411 (-0.010506) | 0.108056 / 0.014526 (0.093530) | 0.118721 / 0.176557 (-0.057835) | 0.176646 / 0.737135 (-0.560489) | 0.123285 / 0.296338 (-0.173053) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430157 / 0.215209 (0.214948) | 4.279362 / 2.077655 (2.201707) | 1.999732 / 1.504120 (0.495612) | 1.803787 / 1.541195 (0.262592) | 1.868322 / 1.468490 (0.399832) | 0.529314 / 4.584777 (-4.055463) | 3.785101 / 3.745712 (0.039389) | 2.812608 / 5.269862 (-2.457254) | 1.373460 / 4.565676 (-3.192216) | 0.066208 / 0.424275 (-0.358067) | 0.012173 / 0.007607 (0.004566) | 0.528716 / 0.226044 (0.302672) | 5.295003 / 2.268929 (3.026074) | 2.450188 / 55.444624 (-52.994437) | 2.114560 / 6.876477 (-4.761917) | 2.268468 / 2.142072 (0.126395) | 0.651706 / 4.805227 (-4.153521) | 0.142185 / 6.500664 (-6.358479) | 0.064862 / 0.075469 (-0.010607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.184933 / 1.841788 (-0.656854) | 14.503903 / 8.074308 (6.429595) | 13.928965 / 10.191392 (3.737573) | 0.156788 / 0.680424 (-0.523636) | 0.017320 / 0.534201 (-0.516881) | 0.391366 / 0.579283 (-0.187918) | 0.416261 / 0.434364 (-0.018103) | 0.461951 / 0.540337 (-0.078387) | 0.553496 / 1.386936 (-0.833440) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006623 / 0.011353 (-0.004730) | 0.004617 / 0.011008 (-0.006392) | 0.075579 / 0.038508 (0.037071) | 0.033863 / 0.023109 (0.010754) | 0.357097 / 0.275898 (0.081199) | 0.396177 / 0.323480 (0.072697) | 0.005712 / 0.007986 (-0.002274) | 0.004232 / 0.004328 (-0.000097) | 0.074669 / 0.004250 (0.070418) | 0.048253 / 0.037052 (0.011201) | 0.362453 / 0.258489 (0.103964) | 0.405423 / 0.293841 (0.111582) | 0.028709 / 0.128546 (-0.099837) | 0.008884 / 0.075646 (-0.066763) | 0.083042 / 0.419271 (-0.336230) | 0.048074 / 0.043533 (0.004541) | 0.355314 / 0.255139 (0.100175) | 0.372536 / 0.283200 (0.089336) | 0.111548 / 0.141683 (-0.030135) | 1.466353 / 1.452155 (0.014198) | 1.555077 / 1.492716 (0.062361) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217016 / 0.018006 (0.199010) | 0.450145 / 0.000490 (0.449655) | 0.001910 / 0.000200 (0.001711) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029787 / 0.037411 (-0.007624) | 0.115282 / 0.014526 (0.100756) | 0.121962 / 0.176557 (-0.054595) | 0.173424 / 0.737135 (-0.563711) | 0.127519 / 0.296338 (-0.168819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438211 / 0.215209 (0.223002) | 4.346352 / 2.077655 (2.268697) | 2.140197 / 1.504120 (0.636077) | 1.957890 / 1.541195 (0.416696) | 2.044300 / 1.468490 (0.575810) | 0.527958 / 4.584777 (-4.056819) | 3.805079 / 3.745712 (0.059367) | 2.601763 / 5.269862 (-2.668098) | 1.359469 / 4.565676 (-3.206208) | 0.065358 / 0.424275 (-0.358917) | 0.011571 / 0.007607 (0.003964) | 0.538513 / 0.226044 (0.312469) | 5.363508 / 2.268929 (3.094580) | 2.640495 / 55.444624 (-52.804129) | 2.335930 / 6.876477 (-4.540547) | 2.407782 / 2.142072 (0.265710) | 0.641637 / 4.805227 (-4.163590) | 0.142196 / 6.500664 (-6.358468) | 0.065041 / 0.075469 (-0.010428) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296031 / 1.841788 (-0.545757) | 14.950424 / 8.074308 (6.876115) | 14.371304 / 10.191392 (4.179912) | 0.148157 / 0.680424 (-0.532267) | 0.017506 / 0.534201 (-0.516695) | 0.392037 / 0.579283 (-0.187246) | 0.423238 / 0.434364 (-0.011126) | 0.464608 / 0.540337 (-0.075730) | 0.563876 / 1.386936 (-0.823060) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04b1d0371408beb0c7bc587a69c382bd8d0bec36 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009137 / 0.011353 (-0.002216) | 0.006119 / 0.011008 (-0.004889) | 0.136530 / 0.038508 (0.098022) | 0.038434 / 0.023109 (0.015325) | 0.427900 / 0.275898 (0.152002) | 0.449757 / 0.323480 (0.126277) | 0.007673 / 0.007986 (-0.000313) | 0.007147 / 0.004328 (0.002818) | 0.108029 / 0.004250 (0.103778) | 0.055072 / 0.037052 (0.018020) | 0.439245 / 0.258489 (0.180756) | 0.477285 / 0.293841 (0.183444) | 0.044838 / 0.128546 (-0.083708) | 0.020814 / 0.075646 (-0.054832) | 0.436098 / 0.419271 (0.016826) | 0.067459 / 0.043533 (0.023926) | 0.427470 / 0.255139 (0.172331) | 0.443260 / 0.283200 (0.160060) | 0.125466 / 0.141683 (-0.016216) | 1.996756 / 1.452155 (0.544601) | 2.100679 / 1.492716 (0.607962) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278407 / 0.018006 (0.260401) | 0.625855 / 0.000490 (0.625365) | 0.005544 / 0.000200 (0.005344) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033495 / 0.037411 (-0.003916) | 0.134718 / 0.014526 (0.120192) | 0.150151 / 0.176557 (-0.026406) | 0.221385 / 0.737135 (-0.515751) | 0.150932 / 0.296338 (-0.145406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668845 / 0.215209 (0.453636) | 6.678436 / 2.077655 (4.600781) | 2.714074 / 1.504120 (1.209954) | 2.275784 / 1.541195 (0.734589) | 2.332852 / 1.468490 (0.864361) | 1.014877 / 4.584777 (-3.569900) | 6.086455 / 3.745712 (2.340743) | 2.990029 / 5.269862 (-2.279832) | 1.862236 / 4.565676 (-2.703441) | 0.122179 / 0.424275 (-0.302096) | 0.015706 / 0.007607 (0.008099) | 0.873473 / 0.226044 (0.647429) | 8.580109 / 2.268929 (6.311180) | 3.458360 / 55.444624 (-51.986264) | 2.738801 / 6.876477 (-4.137676) | 2.918428 / 2.142072 (0.776356) | 1.224910 / 4.805227 (-3.580317) | 0.243006 / 6.500664 (-6.257658) | 0.087121 / 0.075469 (0.011652) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.757802 / 1.841788 (-0.083986) | 19.447999 / 8.074308 (11.373691) | 24.518157 / 10.191392 (14.326765) | 0.245013 / 0.680424 (-0.435411) | 0.032290 / 0.534201 (-0.501911) | 0.542043 / 0.579283 (-0.037240) | 0.708154 / 0.434364 (0.273790) | 0.660584 / 0.540337 (0.120247) | 0.794868 / 1.386936 (-0.592068) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009496 / 0.011353 (-0.001857) | 0.005842 / 0.011008 (-0.005166) | 0.112813 / 0.038508 (0.074305) | 0.039120 / 0.023109 (0.016011) | 0.489717 / 0.275898 (0.213819) | 0.532586 / 0.323480 (0.209107) | 0.007681 / 0.007986 (-0.000304) | 0.005337 / 0.004328 (0.001009) | 0.107244 / 0.004250 (0.102994) | 0.056847 / 0.037052 (0.019794) | 0.499447 / 0.258489 (0.240958) | 0.548995 / 0.293841 (0.255154) | 0.058047 / 0.128546 (-0.070499) | 0.015468 / 0.075646 (-0.060179) | 0.124600 / 0.419271 (-0.294671) | 0.060940 / 0.043533 (0.017407) | 0.488370 / 0.255139 (0.233231) | 0.518540 / 0.283200 (0.235341) | 0.124147 / 0.141683 (-0.017536) | 1.902922 / 1.452155 (0.450767) | 2.033519 / 1.492716 (0.540803) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319527 / 0.018006 (0.301521) | 0.629641 / 0.000490 (0.629152) | 0.000721 / 0.000200 (0.000521) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033150 / 0.037411 (-0.004262) | 0.134250 / 0.014526 (0.119724) | 0.161273 / 0.176557 (-0.015283) | 0.211471 / 0.737135 (-0.525664) | 0.155326 / 0.296338 (-0.141012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.705244 / 0.215209 (0.490035) | 7.043040 / 2.077655 (4.965386) | 3.308948 / 1.504120 (1.804828) | 2.885050 / 1.541195 (1.343855) | 2.810260 / 1.468490 (1.341770) | 1.027095 / 4.584777 (-3.557682) | 6.111398 / 3.745712 (2.365686) | 5.385545 / 5.269862 (0.115684) | 2.521668 / 4.565676 (-2.044009) | 0.122419 / 0.424275 (-0.301856) | 0.016376 / 0.007607 (0.008768) | 0.830856 / 0.226044 (0.604811) | 8.952199 / 2.268929 (6.683271) | 4.207875 / 55.444624 (-51.236749) | 3.346624 / 6.876477 (-3.529853) | 3.395316 / 2.142072 (1.253244) | 1.351816 / 4.805227 (-3.453411) | 0.303056 / 6.500664 (-6.197608) | 0.098713 / 0.075469 (0.023244) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.841903 / 1.841788 (0.000116) | 20.472125 / 8.074308 (12.397817) | 23.433200 / 10.191392 (13.241808) | 0.242599 / 0.680424 (-0.437825) | 0.030701 / 0.534201 (-0.503500) | 0.541614 / 0.579283 (-0.037669) | 0.657827 / 0.434364 (0.223463) | 0.652448 / 0.540337 (0.112111) | 0.773743 / 1.386936 (-0.613193) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ee418831aba68d0be93227bce8b3f42ef8980f \"CML watermark\")\n" ]

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

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

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

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009139 / 0.011353 (-0.002214) | 0.005634 / 0.011008 (-0.005374) | 0.129587 / 0.038508 (0.091079) | 0.038298 / 0.023109 (0.015189) | 0.428149 / 0.275898 (0.152251) | 0.443744 / 0.323480 (0.120264) | 0.007501 / 0.007986 (-0.000485) | 0.005999 / 0.004328 (0.001671) | 0.100796 / 0.004250 (0.096546) | 0.053236 / 0.037052 (0.016184) | 0.423868 / 0.258489 (0.165379) | 0.460110 / 0.293841 (0.166269) | 0.041255 / 0.128546 (-0.087291) | 0.013790 / 0.075646 (-0.061856) | 0.438398 / 0.419271 (0.019127) | 0.063086 / 0.043533 (0.019553) | 0.414826 / 0.255139 (0.159687) | 0.460652 / 0.283200 (0.177453) | 0.121223 / 0.141683 (-0.020460) | 1.754430 / 1.452155 (0.302275) | 1.900037 / 1.492716 (0.407320) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.027222 / 0.018006 (0.009216) | 0.617666 / 0.000490 (0.617176) | 0.022443 / 0.000200 (0.022243) | 0.000820 / 0.000054 (0.000766) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030397 / 0.037411 (-0.007014) | 0.125732 / 0.014526 (0.111206) | 0.149805 / 0.176557 (-0.026752) | 0.234048 / 0.737135 (-0.503087) | 0.143108 / 0.296338 (-0.153231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.631189 / 0.215209 (0.415980) | 6.182871 / 2.077655 (4.105216) | 2.635730 / 1.504120 (1.131610) | 2.231429 / 1.541195 (0.690235) | 2.438360 / 1.468490 (0.969870) | 0.861170 / 4.584777 (-3.723607) | 5.785984 / 3.745712 (2.040272) | 2.758358 / 5.269862 (-2.511504) | 1.678095 / 4.565676 (-2.887582) | 0.105961 / 0.424275 (-0.318314) | 0.013659 / 0.007607 (0.006052) | 0.762943 / 0.226044 (0.536898) | 7.774399 / 2.268929 (5.505471) | 3.319027 / 55.444624 (-52.125598) | 2.700248 / 6.876477 (-4.176229) | 3.008581 / 2.142072 (0.866509) | 1.122522 / 4.805227 (-3.682705) | 0.214832 / 6.500664 (-6.285832) | 0.085281 / 0.075469 (0.009811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.647610 / 1.841788 (-0.194177) | 18.178316 / 8.074308 (10.104008) | 21.199177 / 10.191392 (11.007785) | 0.247063 / 0.680424 (-0.433361) | 0.030443 / 0.534201 (-0.503758) | 0.512527 / 0.579283 (-0.066757) | 0.640758 / 0.434364 (0.206394) | 0.639986 / 0.540337 (0.099649) | 0.760113 / 1.386936 (-0.626823) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008293 / 0.011353 (-0.003060) | 0.005360 / 0.011008 (-0.005648) | 0.102932 / 0.038508 (0.064424) | 0.037457 / 0.023109 (0.014347) | 0.444114 / 0.275898 (0.168216) | 0.512855 / 0.323480 (0.189375) | 0.007030 / 0.007986 (-0.000956) | 0.004954 / 0.004328 (0.000625) | 0.095757 / 0.004250 (0.091507) | 0.051239 / 0.037052 (0.014187) | 0.471118 / 0.258489 (0.212629) | 0.517764 / 0.293841 (0.223923) | 0.041953 / 0.128546 (-0.086593) | 0.013748 / 0.075646 (-0.061898) | 0.118089 / 0.419271 (-0.301182) | 0.060159 / 0.043533 (0.016626) | 0.466011 / 0.255139 (0.210872) | 0.489180 / 0.283200 (0.205980) | 0.123250 / 0.141683 (-0.018433) | 1.714738 / 1.452155 (0.262584) | 1.838571 / 1.492716 (0.345855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267792 / 0.018006 (0.249785) | 0.624313 / 0.000490 (0.623824) | 0.007315 / 0.000200 (0.007115) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033751 / 0.037411 (-0.003661) | 0.122819 / 0.014526 (0.108293) | 0.148270 / 0.176557 (-0.028286) | 0.198581 / 0.737135 (-0.538554) | 0.144845 / 0.296338 (-0.151494) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620631 / 0.215209 (0.405422) | 6.224665 / 2.077655 (4.147010) | 2.856592 / 1.504120 (1.352473) | 2.525089 / 1.541195 (0.983894) | 2.600198 / 1.468490 (1.131708) | 0.872038 / 4.584777 (-3.712739) | 5.571650 / 3.745712 (1.825937) | 5.907643 / 5.269862 (0.637782) | 2.348770 / 4.565676 (-2.216906) | 0.111665 / 0.424275 (-0.312610) | 0.013886 / 0.007607 (0.006278) | 0.762154 / 0.226044 (0.536109) | 7.792686 / 2.268929 (5.523758) | 3.601122 / 55.444624 (-51.843503) | 2.939412 / 6.876477 (-3.937064) | 2.973430 / 2.142072 (0.831358) | 1.065016 / 4.805227 (-3.740211) | 0.221701 / 6.500664 (-6.278963) | 0.088157 / 0.075469 (0.012688) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.771061 / 1.841788 (-0.070727) | 18.826926 / 8.074308 (10.752618) | 21.283830 / 10.191392 (11.092438) | 0.239233 / 0.680424 (-0.441191) | 0.026159 / 0.534201 (-0.508042) | 0.487074 / 0.579283 (-0.092209) | 0.623241 / 0.434364 (0.188877) | 0.600506 / 0.540337 (0.060169) | 0.691271 / 1.386936 (-0.695665) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bbe2c3496498a6415765b517ac4bc600a02ad06 \"CML watermark\")\n" ]

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[ "@lhoestq Could you please look into that and review?", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq I refactored the code. Could you please check is it what you requested?", "@lhoestq Thanks for a review. Excellent tips. All tips applied. ", "I think there is just PythonFormatter that needs to be imported in the test file and we should be good to merge", "@lhoestq that is weird. I have linter error when I do it.", "@lhoestq Now it should work properly.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006152 / 0.011353 (-0.005201) | 0.004169 / 0.011008 (-0.006839) | 0.097968 / 0.038508 (0.059460) | 0.028325 / 0.023109 (0.005216) | 0.308958 / 0.275898 (0.033060) | 0.341832 / 0.323480 (0.018352) | 0.005098 / 0.007986 (-0.002887) | 0.004721 / 0.004328 (0.000393) | 0.075067 / 0.004250 (0.070817) | 0.040514 / 0.037052 (0.003462) | 0.308355 / 0.258489 (0.049866) | 0.351063 / 0.293841 (0.057222) | 0.025261 / 0.128546 (-0.103285) | 0.008483 / 0.075646 (-0.067163) | 0.321219 / 0.419271 (-0.098052) | 0.058258 / 0.043533 (0.014725) | 0.312572 / 0.255139 (0.057433) | 0.330667 / 0.283200 (0.047467) | 0.091047 / 0.141683 (-0.050635) | 1.536541 / 1.452155 (0.084387) | 1.606566 / 1.492716 (0.113850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213234 / 0.018006 (0.195228) | 0.494801 / 0.000490 (0.494311) | 0.003764 / 0.000200 (0.003564) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023653 / 0.037411 (-0.013758) | 0.097176 / 0.014526 (0.082650) | 0.102961 / 0.176557 (-0.073595) | 0.164285 / 0.737135 (-0.572851) | 0.107586 / 0.296338 (-0.188753) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421402 / 0.215209 (0.206193) | 4.195828 / 2.077655 (2.118174) | 1.884664 / 1.504120 (0.380544) | 1.679750 / 1.541195 (0.138556) | 1.719725 / 1.468490 (0.251235) | 0.552290 / 4.584777 (-4.032486) | 3.386337 / 3.745712 (-0.359375) | 1.771527 / 5.269862 (-3.498334) | 1.133327 / 4.565676 (-3.432349) | 0.067911 / 0.424275 (-0.356364) | 0.012572 / 0.007607 (0.004965) | 0.518004 / 0.226044 (0.291960) | 5.192381 / 2.268929 (2.923453) | 2.316032 / 55.444624 (-53.128592) | 1.993264 / 6.876477 (-4.883212) | 2.071009 / 2.142072 (-0.071063) | 0.655062 / 4.805227 (-4.150165) | 0.135488 / 6.500664 (-6.365177) | 0.067273 / 0.075469 (-0.008196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217731 / 1.841788 (-0.624056) | 13.812927 / 8.074308 (5.738619) | 13.137886 / 10.191392 (2.946494) | 0.143102 / 0.680424 (-0.537322) | 0.016884 / 0.534201 (-0.517317) | 0.370106 / 0.579283 (-0.209178) | 0.392349 / 0.434364 (-0.042015) | 0.424501 / 0.540337 (-0.115837) | 0.509830 / 1.386936 (-0.877106) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006210 / 0.011353 (-0.005142) | 0.004215 / 0.011008 (-0.006793) | 0.076129 / 0.038508 (0.037621) | 0.027825 / 0.023109 (0.004716) | 0.403973 / 0.275898 (0.128075) | 0.441089 / 0.323480 (0.117609) | 0.005420 / 0.007986 (-0.002566) | 0.004870 / 0.004328 (0.000542) | 0.075558 / 0.004250 (0.071308) | 0.039464 / 0.037052 (0.002411) | 0.404329 / 0.258489 (0.145840) | 0.447213 / 0.293841 (0.153372) | 0.025877 / 0.128546 (-0.102669) | 0.008660 / 0.075646 (-0.066987) | 0.081849 / 0.419271 (-0.337422) | 0.044551 / 0.043533 (0.001018) | 0.379102 / 0.255139 (0.123963) | 0.403104 / 0.283200 (0.119905) | 0.094754 / 0.141683 (-0.046929) | 1.460772 / 1.452155 (0.008617) | 1.569531 / 1.492716 (0.076815) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183923 / 0.018006 (0.165917) | 0.420708 / 0.000490 (0.420219) | 0.002091 / 0.000200 (0.001891) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026180 / 0.037411 (-0.011231) | 0.101529 / 0.014526 (0.087003) | 0.108739 / 0.176557 (-0.067818) | 0.160702 / 0.737135 (-0.576433) | 0.111739 / 0.296338 (-0.184600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448671 / 0.215209 (0.233462) | 4.469287 / 2.077655 (2.391632) | 2.244335 / 1.504120 (0.740215) | 2.107495 / 1.541195 (0.566301) | 2.224763 / 1.468490 (0.756272) | 0.554006 / 4.584777 (-4.030771) | 3.390109 / 3.745712 (-0.355603) | 1.744189 / 5.269862 (-3.525673) | 1.008515 / 4.565676 (-3.557161) | 0.067904 / 0.424275 (-0.356371) | 0.012243 / 0.007607 (0.004636) | 0.557635 / 0.226044 (0.331590) | 5.610383 / 2.268929 (3.341454) | 2.687326 / 55.444624 (-52.757298) | 2.405262 / 6.876477 (-4.471214) | 2.527300 / 2.142072 (0.385227) | 0.662282 / 4.805227 (-4.142945) | 0.136225 / 6.500664 (-6.364439) | 0.068136 / 0.075469 (-0.007334) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310791 / 1.841788 (-0.530997) | 14.370381 / 8.074308 (6.296072) | 14.122675 / 10.191392 (3.931283) | 0.152302 / 0.680424 (-0.528122) | 0.016624 / 0.534201 (-0.517577) | 0.359395 / 0.579283 (-0.219888) | 0.392131 / 0.434364 (-0.042233) | 0.423796 / 0.540337 (-0.116542) | 0.511387 / 1.386936 (-0.875549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6a61a1af1502677a6f2333896a6ffeede9ca21b \"CML watermark\")\n" ]

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[ "cc @SBrandeis", "I think this has been addressed.\r\n\r\nPlease open a new issue if you are still not getting notified." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006916 / 0.011353 (-0.004437) | 0.004749 / 0.011008 (-0.006259) | 0.096086 / 0.038508 (0.057578) | 0.035448 / 0.023109 (0.012338) | 0.299645 / 0.275898 (0.023747) | 0.331279 / 0.323480 (0.007799) | 0.006018 / 0.007986 (-0.001968) | 0.004210 / 0.004328 (-0.000118) | 0.072998 / 0.004250 (0.068747) | 0.050082 / 0.037052 (0.013030) | 0.297714 / 0.258489 (0.039225) | 0.365523 / 0.293841 (0.071682) | 0.028081 / 0.128546 (-0.100465) | 0.009072 / 0.075646 (-0.066574) | 0.327628 / 0.419271 (-0.091643) | 0.051165 / 0.043533 (0.007633) | 0.295091 / 0.255139 (0.039952) | 0.320052 / 0.283200 (0.036852) | 0.109841 / 0.141683 (-0.031842) | 1.467867 / 1.452155 (0.015712) | 1.572600 / 1.492716 (0.079884) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281490 / 0.018006 (0.263484) | 0.499259 / 0.000490 (0.498770) | 0.000691 / 0.000200 (0.000491) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027548 / 0.037411 (-0.009863) | 0.106592 / 0.014526 (0.092066) | 0.118654 / 0.176557 (-0.057902) | 0.174313 / 0.737135 (-0.562822) | 0.124491 / 0.296338 (-0.171848) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399674 / 0.215209 (0.184465) | 3.984092 / 2.077655 (1.906437) | 1.790935 / 1.504120 (0.286815) | 1.593612 / 1.541195 (0.052417) | 1.694595 / 1.468490 (0.226105) | 0.517588 / 4.584777 (-4.067189) | 3.724353 / 3.745712 (-0.021359) | 3.244807 / 5.269862 (-2.025054) | 1.602929 / 4.565676 (-2.962748) | 0.065334 / 0.424275 (-0.358941) | 0.012259 / 0.007607 (0.004652) | 0.501355 / 0.226044 (0.275311) | 4.996546 / 2.268929 (2.727618) | 2.279333 / 55.444624 (-53.165291) | 1.940126 / 6.876477 (-4.936351) | 2.122945 / 2.142072 (-0.019128) | 0.626104 / 4.805227 (-4.179123) | 0.141278 / 6.500664 (-6.359386) | 0.064522 / 0.075469 (-0.010947) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195351 / 1.841788 (-0.646436) | 15.258932 / 8.074308 (7.184624) | 14.627623 / 10.191392 (4.436231) | 0.266897 / 0.680424 (-0.413527) | 0.017557 / 0.534201 (-0.516644) | 0.392932 / 0.579283 (-0.186351) | 0.416409 / 0.434364 (-0.017955) | 0.469100 / 0.540337 (-0.071237) | 0.556247 / 1.386936 (-0.830689) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006880 / 0.011353 (-0.004473) | 0.004837 / 0.011008 (-0.006171) | 0.074518 / 0.038508 (0.036010) | 0.034204 / 0.023109 (0.011095) | 0.365100 / 0.275898 (0.089202) | 0.394976 / 0.323480 (0.071496) | 0.006364 / 0.007986 (-0.001621) | 0.004269 / 0.004328 (-0.000060) | 0.073531 / 0.004250 (0.069281) | 0.051334 / 0.037052 (0.014281) | 0.373904 / 0.258489 (0.115415) | 0.413662 / 0.293841 (0.119821) | 0.028779 / 0.128546 (-0.099767) | 0.009292 / 0.075646 (-0.066354) | 0.081574 / 0.419271 (-0.337698) | 0.046531 / 0.043533 (0.002998) | 0.368995 / 0.255139 (0.113856) | 0.376938 / 0.283200 (0.093739) | 0.112576 / 0.141683 (-0.029107) | 1.458880 / 1.452155 (0.006725) | 1.550918 / 1.492716 (0.058202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319521 / 0.018006 (0.301515) | 0.510146 / 0.000490 (0.509656) | 0.000438 / 0.000200 (0.000238) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033082 / 0.037411 (-0.004329) | 0.118009 / 0.014526 (0.103483) | 0.127108 / 0.176557 (-0.049448) | 0.176600 / 0.737135 (-0.560535) | 0.133790 / 0.296338 (-0.162549) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437360 / 0.215209 (0.222151) | 4.367426 / 2.077655 (2.289771) | 2.193646 / 1.504120 (0.689526) | 2.025002 / 1.541195 (0.483808) | 2.142347 / 1.468490 (0.673856) | 0.525497 / 4.584777 (-4.059280) | 3.751275 / 3.745712 (0.005563) | 1.912271 / 5.269862 (-3.357590) | 1.087286 / 4.565676 (-3.478390) | 0.066328 / 0.424275 (-0.357947) | 0.011904 / 0.007607 (0.004297) | 0.545870 / 0.226044 (0.319825) | 5.434481 / 2.268929 (3.165552) | 2.719745 / 55.444624 (-52.724880) | 2.445001 / 6.876477 (-4.431476) | 2.500205 / 2.142072 (0.358133) | 0.645735 / 4.805227 (-4.159492) | 0.144210 / 6.500664 (-6.356455) | 0.065688 / 0.075469 (-0.009781) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273522 / 1.841788 (-0.568265) | 15.771778 / 8.074308 (7.697470) | 14.685261 / 10.191392 (4.493869) | 0.176523 / 0.680424 (-0.503900) | 0.017877 / 0.534201 (-0.516324) | 0.392687 / 0.579283 (-0.186596) | 0.449992 / 0.434364 (0.015628) | 0.462851 / 0.540337 (-0.077487) | 0.560178 / 1.386936 (-0.826758) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fa3ef6eba906ee1214e0596d15a78fc358909f4 \"CML watermark\")\n" ]

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

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

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

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

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

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[ "_The documentation is not available anymore as the PR was closed or merged._", "To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r\nColab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nAlso, here's a quick sample of what's happening:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"imdb\", split=\"train\")\r\ntfds = ds.to_tf_dataset(batch_size=16)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nA more detailed version of it:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"a\": [1],\r\n \"b\": [\"é\"],\r\n }\r\n)\r\ntfds = ds.to_tf_dataset(batch_size=1)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nThe original issue comes from https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#LL234C4-L234C4, which could easily be solved by replacing that line with `return result.astype(np.unicode_)` but they are mentioning that it may lead to issues.\r\n\r\nEven the following fails in `numpy`:\r\n\r\n```python\r\nimport numpy as np\r\n\r\nx = np.array([\"é\"]).astype(np.bytes_)\r\n```", "cc. @lhoestq :hugs:", "cc @Rocketknight1 ", "> Nice ! Could you add some tests to make sure that batch_size=None works as expected ?\r\n\r\nSure, I'll add the tests for everything, including the string-encoding issue to make sure it's solved!", "Thanks for the review @lhoestq and @Rocketknight1! I do understand that processing it in batches is always more efficient than processing it one-by-one, it was just to make `batch_size` optional. What we can do is default it to a certain batch size e.g. 16 as before, and that's it, but I think it can still remain optional.", "@Rocketknight1 then I'll add the integration tests for the optional `batch_size` as well as for the encoding of non-ASCII compatible characters 😄 Do we set the default `batch_size` to 16 instead of `None`?", "@alvarobartt I think 16 is a reasonable default, yep!", "I think default should be None, not 16.\r\nUsers won't expect to have it batched by default.", "Then I'll leave it as is, and add the unit/integration tests, thanks @Rocketknight1 and @lhoestq ", "Hi @Rocketknight1 @lhoestq! So the string-encoding issue is already solved, but I've got one doubt about the `batch_size` being optional in the multiprocessing approach, since in that case I assume the `batch_size` should be mandatory, for the moment I'm assuming it is/should be mandatory, but let me know if you want me to add a check to disallow `batch_size=None` when `num_workers>1`. Thanks!", "> To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n> \r\n> Colab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nI've used the Colab shared above for testing purposes, and it works fine, plus the unit/integration tests are passing. I've also trained a `KerasNLP` model with incoming data from 🤗`datasets` with no issue at all!", "> in the multiprocessing approach, since in that case I assume the batch_size should be mandatory,\r\n\r\nNo I think they're quite orthogonal, no need to have it mandatory", "> No I think they're quite orthogonal, no need to have it mandatory\r\n\r\nBut it will break if `batch_size=None` as the multiprocessing approach will aim to prepare batches and distribute those to every worker, and assuming `batch_size=1` when `batch_size=None` I guess is not a good assumption, right?", "Ah I see. Multiprocessing should support batch_size=None indeed. If you have ideas you can do it in this PR, or raise a NotImplementedError and we can see later", "Sure @lhoestq, I can add a `NotImplementedError` for the moment, and prepare the next PR straight-away to tackle the multiprocessing approach with `batch_size=None`, but not sure if that may eventually collide with @Rocketknight1 PR at https://github.com/huggingface/datasets/pull/5863", "Yes, let me merge the PR at #5863 after this one, and then we can open another to improve the behaviour with multiprocessing and `batch_size=None`!", "Sure @Rocketknight1 makes complete sense to me! Do you want me to add the `raise NotImplementedError` and then we merge this PR? Or you prefer to directly merge the current?", "`raise NotImplementedError` for now with an error telling the user that multiprocessing needs them to specify a batch size, I think!", "Since you recently approved @Rocketknight1, are we ready to merge? Thanks 🤗", "Ah actually it looks like `minimal_tf_collate_fn` doesn't support batch_size=None", "Hi @lhoestq so I didn't include the call to `collate_fn`, as we won't need to collate the incoming data e.g. \"str\" should remain a \"str\" not a [\"str\"], and the `minimal_collate_fn` was indeed putting everything into a list, so the output was not un-batched, but batched with size 1", "What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n\r\nDoes my last change look of to you ? If so I think we can merge", "> What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n> \r\n> Does my last change look of to you ? If so I think we can merge\r\n\r\nI think we're good, since it won't batch it under the scenario of `str` being provided instead of `List[str]`, and the unit/integration tests are passing, so I'm OK to merge. Maybe we can double check with Matt? cc @Rocketknight1 ", "Yes, and sorry for the delay! I'm happy to merge.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006555 / 0.011353 (-0.004798) | 0.004521 / 0.011008 (-0.006487) | 0.096633 / 0.038508 (0.058125) | 0.032859 / 0.023109 (0.009750) | 0.294632 / 0.275898 (0.018734) | 0.325140 / 0.323480 (0.001660) | 0.005676 / 0.007986 (-0.002310) | 0.005252 / 0.004328 (0.000924) | 0.074349 / 0.004250 (0.070099) | 0.045836 / 0.037052 (0.008784) | 0.302919 / 0.258489 (0.044430) | 0.340686 / 0.293841 (0.046845) | 0.028398 / 0.128546 (-0.100148) | 0.008942 / 0.075646 (-0.066704) | 0.326994 / 0.419271 (-0.092278) | 0.049556 / 0.043533 (0.006023) | 0.293883 / 0.255139 (0.038744) | 0.316522 / 0.283200 (0.033322) | 0.097385 / 0.141683 (-0.044298) | 1.405334 / 1.452155 (-0.046821) | 1.521529 / 1.492716 (0.028812) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212269 / 0.018006 (0.194263) | 0.445692 / 0.000490 (0.445203) | 0.004930 / 0.000200 (0.004730) | 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) | 0.026907 / 0.037411 (-0.010504) | 0.108607 / 0.014526 (0.094081) | 0.116806 / 0.176557 (-0.059751) | 0.178428 / 0.737135 (-0.558707) | 0.122326 / 0.296338 (-0.174012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404211 / 0.215209 (0.189002) | 4.045374 / 2.077655 (1.967719) | 1.877237 / 1.504120 (0.373117) | 1.706276 / 1.541195 (0.165081) | 1.750610 / 1.468490 (0.282120) | 0.522331 / 4.584777 (-4.062446) | 3.742286 / 3.745712 (-0.003426) | 1.791285 / 5.269862 (-3.478577) | 1.043872 / 4.565676 (-3.521805) | 0.065176 / 0.424275 (-0.359099) | 0.011821 / 0.007607 (0.004214) | 0.507374 / 0.226044 (0.281329) | 5.088803 / 2.268929 (2.819875) | 2.282742 / 55.444624 (-53.161882) | 1.950737 / 6.876477 (-4.925740) | 2.042262 / 2.142072 (-0.099810) | 0.636525 / 4.805227 (-4.168702) | 0.140837 / 6.500664 (-6.359827) | 0.063223 / 0.075469 (-0.012246) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188070 / 1.841788 (-0.653718) | 14.622681 / 8.074308 (6.548372) | 13.247988 / 10.191392 (3.056596) | 0.165858 / 0.680424 (-0.514566) | 0.017476 / 0.534201 (-0.516725) | 0.391973 / 0.579283 (-0.187310) | 0.433326 / 0.434364 (-0.001038) | 0.467163 / 0.540337 (-0.073175) | 0.568359 / 1.386936 (-0.818577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006076 / 0.011353 (-0.005276) | 0.004439 / 0.011008 (-0.006570) | 0.074496 / 0.038508 (0.035988) | 0.031396 / 0.023109 (0.008287) | 0.372237 / 0.275898 (0.096339) | 0.403412 / 0.323480 (0.079932) | 0.005430 / 0.007986 (-0.002555) | 0.003846 / 0.004328 (-0.000483) | 0.074403 / 0.004250 (0.070153) | 0.045398 / 0.037052 (0.008346) | 0.394133 / 0.258489 (0.135644) | 0.421769 / 0.293841 (0.127928) | 0.027936 / 0.128546 (-0.100610) | 0.008962 / 0.075646 (-0.066685) | 0.083158 / 0.419271 (-0.336113) | 0.044863 / 0.043533 (0.001331) | 0.393834 / 0.255139 (0.138695) | 0.391537 / 0.283200 (0.108337) | 0.097971 / 0.141683 (-0.043712) | 1.496632 / 1.452155 (0.044477) | 1.585511 / 1.492716 (0.092795) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010094 / 0.018006 (-0.007913) | 0.437811 / 0.000490 (0.437321) | 0.000963 / 0.000200 (0.000763) | 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) | 0.028864 / 0.037411 (-0.008547) | 0.112480 / 0.014526 (0.097954) | 0.120938 / 0.176557 (-0.055619) | 0.170888 / 0.737135 (-0.566247) | 0.125903 / 0.296338 (-0.170435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426716 / 0.215209 (0.211507) | 4.238380 / 2.077655 (2.160725) | 2.052889 / 1.504120 (0.548769) | 1.871043 / 1.541195 (0.329848) | 1.890405 / 1.468490 (0.421915) | 0.522059 / 4.584777 (-4.062718) | 3.813331 / 3.745712 (0.067619) | 2.891651 / 5.269862 (-2.378210) | 1.323836 / 4.565676 (-3.241841) | 0.065124 / 0.424275 (-0.359151) | 0.011498 / 0.007607 (0.003891) | 0.525102 / 0.226044 (0.299057) | 5.245190 / 2.268929 (2.976261) | 2.531149 / 55.444624 (-52.913476) | 2.197323 / 6.876477 (-4.679153) | 2.197314 / 2.142072 (0.055241) | 0.633423 / 4.805227 (-4.171804) | 0.140248 / 6.500664 (-6.360416) | 0.064432 / 0.075469 (-0.011037) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270639 / 1.841788 (-0.571149) | 14.856678 / 8.074308 (6.782369) | 14.337631 / 10.191392 (4.146239) | 0.195319 / 0.680424 (-0.485105) | 0.017628 / 0.534201 (-0.516573) | 0.393984 / 0.579283 (-0.185299) | 0.421987 / 0.434364 (-0.012376) | 0.459245 / 0.540337 (-0.081092) | 0.557786 / 1.386936 (-0.829150) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "Will you eventually need help with your PR @Rocketknight1? I'll be happy to help if needed 😄 ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007577 / 0.011353 (-0.003776) | 0.004960 / 0.011008 (-0.006048) | 0.113622 / 0.038508 (0.075114) | 0.037981 / 0.023109 (0.014872) | 0.355312 / 0.275898 (0.079414) | 0.393384 / 0.323480 (0.069904) | 0.006575 / 0.007986 (-0.001411) | 0.005941 / 0.004328 (0.001612) | 0.085976 / 0.004250 (0.081726) | 0.053784 / 0.037052 (0.016732) | 0.369358 / 0.258489 (0.110869) | 0.399402 / 0.293841 (0.105561) | 0.032155 / 0.128546 (-0.096391) | 0.010448 / 0.075646 (-0.065199) | 0.389009 / 0.419271 (-0.030263) | 0.057377 / 0.043533 (0.013844) | 0.354968 / 0.255139 (0.099829) | 0.382404 / 0.283200 (0.099204) | 0.111056 / 0.141683 (-0.030627) | 1.807986 / 1.452155 (0.355832) | 1.866070 / 1.492716 (0.373354) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244439 / 0.018006 (0.226432) | 0.491942 / 0.000490 (0.491452) | 0.001910 / 0.000200 (0.001710) | 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) | 0.031024 / 0.037411 (-0.006387) | 0.129674 / 0.014526 (0.115148) | 0.142974 / 0.176557 (-0.033583) | 0.213568 / 0.737135 (-0.523568) | 0.147794 / 0.296338 (-0.148545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480333 / 0.215209 (0.265124) | 4.792901 / 2.077655 (2.715246) | 2.233145 / 1.504120 (0.729025) | 2.036291 / 1.541195 (0.495096) | 2.109631 / 1.468490 (0.641140) | 0.624546 / 4.584777 (-3.960231) | 4.543511 / 3.745712 (0.797799) | 3.961345 / 5.269862 (-1.308517) | 1.903634 / 4.565676 (-2.662042) | 0.076584 / 0.424275 (-0.347691) | 0.014590 / 0.007607 (0.006983) | 0.593195 / 0.226044 (0.367151) | 5.928740 / 2.268929 (3.659811) | 2.781164 / 55.444624 (-52.663460) | 2.364303 / 6.876477 (-4.512173) | 2.510139 / 2.142072 (0.368067) | 0.770886 / 4.805227 (-4.034341) | 0.167995 / 6.500664 (-6.332669) | 0.076622 / 0.075469 (0.001153) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.402398 / 1.841788 (-0.439390) | 17.921233 / 8.074308 (9.846925) | 17.036738 / 10.191392 (6.845346) | 0.168997 / 0.680424 (-0.511427) | 0.020259 / 0.534201 (-0.513941) | 0.465322 / 0.579283 (-0.113962) | 0.500435 / 0.434364 (0.066071) | 0.546846 / 0.540337 (0.006509) | 0.658130 / 1.386936 (-0.728806) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007624 / 0.011353 (-0.003729) | 0.005265 / 0.011008 (-0.005744) | 0.086886 / 0.038508 (0.048377) | 0.038235 / 0.023109 (0.015126) | 0.463969 / 0.275898 (0.188071) | 0.502451 / 0.323480 (0.178971) | 0.006285 / 0.007986 (-0.001701) | 0.004525 / 0.004328 (0.000197) | 0.086557 / 0.004250 (0.082307) | 0.052414 / 0.037052 (0.015362) | 0.482167 / 0.258489 (0.223678) | 0.513684 / 0.293841 (0.219843) | 0.032929 / 0.128546 (-0.095618) | 0.010249 / 0.075646 (-0.065397) | 0.093377 / 0.419271 (-0.325895) | 0.054114 / 0.043533 (0.010582) | 0.466116 / 0.255139 (0.210977) | 0.488977 / 0.283200 (0.205777) | 0.115446 / 0.141683 (-0.026237) | 1.762912 / 1.452155 (0.310757) | 1.874191 / 1.492716 (0.381475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012666 / 0.018006 (-0.005341) | 0.485962 / 0.000490 (0.485473) | 0.002621 / 0.000200 (0.002421) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033661 / 0.037411 (-0.003751) | 0.135395 / 0.014526 (0.120869) | 0.147230 / 0.176557 (-0.029326) | 0.205847 / 0.737135 (-0.531288) | 0.151496 / 0.296338 (-0.144842) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.514097 / 0.215209 (0.298887) | 5.134093 / 2.077655 (3.056438) | 2.496775 / 1.504120 (0.992655) | 2.268078 / 1.541195 (0.726883) | 2.342153 / 1.468490 (0.873663) | 0.623130 / 4.584777 (-3.961647) | 4.601787 / 3.745712 (0.856075) | 3.414249 / 5.269862 (-1.855613) | 1.849603 / 4.565676 (-2.716073) | 0.078350 / 0.424275 (-0.345925) | 0.013785 / 0.007607 (0.006178) | 0.638783 / 0.226044 (0.412739) | 6.378356 / 2.268929 (4.109427) | 3.072867 / 55.444624 (-52.371757) | 2.668123 / 6.876477 (-4.208354) | 2.693905 / 2.142072 (0.551833) | 0.764583 / 4.805227 (-4.040644) | 0.166854 / 6.500664 (-6.333810) | 0.076883 / 0.075469 (0.001414) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502003 / 1.841788 (-0.339784) | 18.674205 / 8.074308 (10.599897) | 16.837759 / 10.191392 (6.646367) | 0.176995 / 0.680424 (-0.503428) | 0.020126 / 0.534201 (-0.514075) | 0.464480 / 0.579283 (-0.114803) | 0.516477 / 0.434364 (0.082113) | 0.549818 / 0.540337 (0.009481) | 0.659927 / 1.386936 (-0.727009) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "@alvarobartt Yes, I'll ping you for a review once it's ready!" ]

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

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

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

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[ "The \"exact match\" deduplication will be possible when we resolve https://github.com/huggingface/datasets/issues/2514 (first, https://github.com/apache/arrow/issues/30950 needs to be addressed on the Arrow side). In the meantime, you can use Polars or DuckDB (e.g., via [datasets-sql](https://github.com/mariosasko/datasets_sql)).\r\n\r\nFuzzy deduplication is out-of-scope for now ([splink](https://github.com/moj-analytical-services/splink) is probably the best tool for it).", "This library can be an intermediate solution : https://github.com/ChenghaoMou/text-dedup/tree/main", "I have been using polars to remove duplicates but it would be nice to do it directly in pyarrow.\r\n\r\nFor example,\r\n\r\n1. Read dataset with pyarrow\r\n2. Use scan_pyarrow_dataset() with Polars to create a LazyFrame\r\n3. Use sort and unique to remove duplicates based on a subset of columns\r\n4. Convert to table and save data with ds.write_dataset()\r\n\r\nThere are times where that workflow makes perfect sense because I do additional transformations with Polars. Most of the time I am simply just reading dataset A and writing dataset B without duplicates though, and I wish I could use a pyarrow scanner or table directly. " ]

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

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

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[ "Hi! You can refer to [this doc](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) to see the intended usage (basically, it skips the Arrow -> Parquet conversion step in `ds = load_dataset(...); ds.to_parquet(\"path/to/parquet\")`) and allows writing Parquet to remote storage unlike `to_parquet`).\r\n\r\n> I guess I'd expect as_dataset to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with load_dataset to no avail, probably due to misunderstandings on my part).\r\n\r\n`as_dataset` does not work with `file_format=\"parquet\"` files as Parquet files cannot be memory-mapped, so I think we should just raise an error in that case.\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007049 / 0.011353 (-0.004304) | 0.005034 / 0.011008 (-0.005974) | 0.097737 / 0.038508 (0.059229) | 0.033280 / 0.023109 (0.010170) | 0.301017 / 0.275898 (0.025119) | 0.336593 / 0.323480 (0.013113) | 0.005567 / 0.007986 (-0.002419) | 0.005384 / 0.004328 (0.001056) | 0.072980 / 0.004250 (0.068730) | 0.045030 / 0.037052 (0.007978) | 0.303280 / 0.258489 (0.044791) | 0.367528 / 0.293841 (0.073687) | 0.034131 / 0.128546 (-0.094415) | 0.012118 / 0.075646 (-0.063528) | 0.331677 / 0.419271 (-0.087594) | 0.049211 / 0.043533 (0.005678) | 0.297535 / 0.255139 (0.042396) | 0.318136 / 0.283200 (0.034936) | 0.101574 / 0.141683 (-0.040109) | 1.472769 / 1.452155 (0.020615) | 1.541724 / 1.492716 (0.049007) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014646 / 0.018006 (-0.003360) | 0.439050 / 0.000490 (0.438560) | 0.008575 / 0.000200 (0.008375) | 0.000297 / 0.000054 (0.000242) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027591 / 0.037411 (-0.009820) | 0.111639 / 0.014526 (0.097113) | 0.117098 / 0.176557 (-0.059458) | 0.173281 / 0.737135 (-0.563855) | 0.123197 / 0.296338 (-0.173141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397507 / 0.215209 (0.182298) | 3.971457 / 2.077655 (1.893803) | 1.781158 / 1.504120 (0.277038) | 1.590419 / 1.541195 (0.049224) | 1.716374 / 1.468490 (0.247884) | 0.687150 / 4.584777 (-3.897627) | 3.691009 / 3.745712 (-0.054703) | 2.050900 / 5.269862 (-3.218961) | 1.304893 / 4.565676 (-3.260784) | 0.084507 / 0.424275 (-0.339768) | 0.012231 / 0.007607 (0.004624) | 0.493033 / 0.226044 (0.266988) | 4.929957 / 2.268929 (2.661028) | 2.209069 / 55.444624 (-53.235555) | 1.885992 / 6.876477 (-4.990485) | 2.007004 / 2.142072 (-0.135069) | 0.827265 / 4.805227 (-3.977963) | 0.168225 / 6.500664 (-6.332439) | 0.064988 / 0.075469 (-0.010481) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.182341 / 1.841788 (-0.659447) | 14.691983 / 8.074308 (6.617674) | 14.350720 / 10.191392 (4.159328) | 0.164307 / 0.680424 (-0.516117) | 0.017480 / 0.534201 (-0.516720) | 0.421843 / 0.579283 (-0.157441) | 0.417481 / 0.434364 (-0.016883) | 0.496587 / 0.540337 (-0.043751) | 0.581208 / 1.386936 (-0.805728) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007070 / 0.011353 (-0.004283) | 0.005083 / 0.011008 (-0.005926) | 0.075009 / 0.038508 (0.036500) | 0.032343 / 0.023109 (0.009234) | 0.366788 / 0.275898 (0.090890) | 0.392273 / 0.323480 (0.068794) | 0.005512 / 0.007986 (-0.002474) | 0.003999 / 0.004328 (-0.000329) | 0.073743 / 0.004250 (0.069492) | 0.046203 / 0.037052 (0.009151) | 0.367874 / 0.258489 (0.109385) | 0.409154 / 0.293841 (0.115313) | 0.035227 / 0.128546 (-0.093319) | 0.012223 / 0.075646 (-0.063424) | 0.087149 / 0.419271 (-0.332122) | 0.045648 / 0.043533 (0.002115) | 0.362414 / 0.255139 (0.107275) | 0.379970 / 0.283200 (0.096770) | 0.100631 / 0.141683 (-0.041052) | 1.439733 / 1.452155 (-0.012422) | 1.506266 / 1.492716 (0.013550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227071 / 0.018006 (0.209065) | 0.451243 / 0.000490 (0.450753) | 0.000406 / 0.000200 (0.000206) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028952 / 0.037411 (-0.008459) | 0.111934 / 0.014526 (0.097408) | 0.124080 / 0.176557 (-0.052477) | 0.174022 / 0.737135 (-0.563113) | 0.126811 / 0.296338 (-0.169527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436423 / 0.215209 (0.221214) | 4.331959 / 2.077655 (2.254304) | 2.111914 / 1.504120 (0.607794) | 1.921338 / 1.541195 (0.380143) | 1.994425 / 1.468490 (0.525935) | 0.699164 / 4.584777 (-3.885613) | 3.722143 / 3.745712 (-0.023569) | 3.516538 / 5.269862 (-1.753323) | 1.867245 / 4.565676 (-2.698431) | 0.085923 / 0.424275 (-0.338352) | 0.012059 / 0.007607 (0.004452) | 0.586147 / 0.226044 (0.360102) | 5.395823 / 2.268929 (3.126894) | 2.594430 / 55.444624 (-52.850194) | 2.275021 / 6.876477 (-4.601456) | 2.347810 / 2.142072 (0.205737) | 0.835118 / 4.805227 (-3.970109) | 0.167089 / 6.500664 (-6.333575) | 0.064893 / 0.075469 (-0.010576) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291423 / 1.841788 (-0.550365) | 14.992696 / 8.074308 (6.918388) | 13.307842 / 10.191392 (3.116450) | 0.163799 / 0.680424 (-0.516625) | 0.017315 / 0.534201 (-0.516886) | 0.461319 / 0.579283 (-0.117965) | 0.430474 / 0.434364 (-0.003889) | 0.568115 / 0.540337 (0.027777) | 0.647909 / 1.386936 (-0.739027) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5161c9ecdcdde9cc99c7f212da13523d5ba6bdb \"CML watermark\")\n" ]

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

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

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[ "Hi @PhilippeMoussalli thanks for opening a detailed issue. It seems the issue is more related to the `datasets` library so I'll ping @lhoestq @mariosasko on this one :) \n\n(edit: also can one of you move the issue to the datasets repo? Thanks in advance 🙏)", "Hi ! The `Image()` info is stored in the **schema metadata**. More precisely there should be a \"huggingface\" field in the schema metadata that contains the `datasets` feature type of each column.\r\n\r\nTo fix your issue, you can use the same schema as the original Parquet files to write the new ones. You can also get the schema with metadata from a `Features` object, e.g.\r\n\r\n```python\r\nfrom datasets import Features, Image, Value\r\n\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\nprint(schema.metadata)\r\n# {b'huggingface': b'{\"info\": {\"features\": {\"image\": {\"_type\": \"Image\"}, \"text\": {\"dtype\": \"string\", \"_type\": \"Value\"}}}}'}\r\n```", "It appears that the parquet files at `hf://datasets/lambdalabs/pokemon-blip-captions` don't have this metadata, and it is defined in the dataset_infos.json instead (legacy).\r\n\r\nYou can get the right schema with the HF metadata this way:\r\n\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nfeatures = load_dataset_builder(\"lambdalabs/pokemon-blip-captions\").info.features\r\nschema = features.arrow_schema\r\n```", "Btw in the future we might add support for an dedicated Image extension type in Arrow so that you won't need to add the schema metadata anymore ;)", "Thanks @Wauplin @lhoestq for the quick reply :)! \r\n\r\nI tried your approach by passing the huggingface schema to the dask writer \r\n\r\n```\r\nfrom datasets import Features, Image, Value\r\ndf = dd.read_parquet(f\"hf://datasets/lambdalabs/pokemon-blip-captions\",index=False)\r\nfeatures = Features({\"image\": Image(), \"text\": Value(\"string\")})\r\nschema = features.arrow_schema\r\ndd.to_parquet(df, path = \"hf://datasets/philippemo/dummy_dataset/data\", schema=schema)\r\n```\r\nAt first it didn't work as I was not able to visualize the images, so then I manually added the `dataset_infos.json` from the example dataset and it worked :)\r\n\r\nHowever, It's not very ideal since there are some metadata in that file that need to be computed in order to load the data properly such as `num_of_bytes` and `num_examples` which might be unknown in my use case. \r\n\r\n\r\n\r\nDo you have any pointers there? you mentioned that `datasets_info.json` will be deprecated/legacy. Could you point me to some example image datasets on the hub that are stored as parquet and don't have the `datasets_info.json`?\r\n\r\n", "You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;)\r\nI could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n\r\nWhat made you think it didn't work ?", "> You don't need the dataset_infos.json file as long as you have the schema with HF metadata ;) I could also check that it works fine myself on the git revision without the dataset_infos.json file.\r\n> \r\n> What made you think it didn't work ?\r\n\r\nThose are two identical dataset repos where both were pushed with dask with the specified schema you mentioned above. I then uploaded the `dataset_infos.json` manually taken from the original example dataset into one of them. \r\n\r\n* **With schema**: https://huggingface.co/datasets/philippemo/dummy_dataset_with_schema\r\n* **Without schema**: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nYou can see that in the examples without schema the images fail to render properly. When loaded with `datasets` they return an dict and not a Pillow Image ", "I see ! I think it's a bug on our side - it should work without the metadata - let me investigate", "Alright, it's fixed: https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nIt shows the image correctly now - even without the extra metadata :)", "Thanks @lhoestq! \r\nI tested pushing a dataset again without the metadata and it works perfectly! \r\nI appreciate the help", "Hi @lhoestq, \r\n\r\nI'v tried pushing another dataset again and I think the issue reappeared again: \r\n\r\n```\r\ndf = dd.read_parquet(f\"hf://datasets/lambdalabs/pokemon-blip-captions\")\r\nfeatures = datasets.Features({\"image\": datasets.Image(), \"text\": datasets.Value(\"string\")})\r\nschema = features.arrow_schema\r\ndd.to_parquet(df, path = \"hf://datasets/philippemo/dummy_dataset_without_schema_12_06/data\", schema=schema)\r\n```\r\n\r\nHere is the dataset: \r\n https://huggingface.co/datasets/philippemo/dummy_dataset_without_schema_12_06\r\nThe one that was working 2 weeks ago still seems to be intact though, it might be that It rendered properly when it was initially submitted and after this something was reverted from your side:\r\nhttps://huggingface.co/datasets/philippemo/dummy_dataset_without_schema\r\n\r\nIt's weird because nothing really changed from the implementation, might be another issue in the hub backend. Do you have any pointers on how to resolve this? ", "We're doing some changes in the way we're handling image parquet datasets right now. We'll include the fix from https://github.com/huggingface/datasets/pull/5921 in the new datasets-server version in the coming days", "alright thanks for the update :), would that be part of the new release of datasets or is it something separate? if so, where can I track it? ", "Once the new version of `datasets` is released (tomorrow probably) we'll open an issue on https://github.com/huggingface/datasets-server to update to this version :)", "Alright we did the update :) This is fixed for good now", "Yes thanks 🎉🎉🎉" ]

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

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006598 / 0.011353 (-0.004755) | 0.004565 / 0.011008 (-0.006443) | 0.099063 / 0.038508 (0.060555) | 0.028334 / 0.023109 (0.005225) | 0.323539 / 0.275898 (0.047641) | 0.372462 / 0.323480 (0.048982) | 0.005120 / 0.007986 (-0.002865) | 0.004797 / 0.004328 (0.000468) | 0.076862 / 0.004250 (0.072611) | 0.038021 / 0.037052 (0.000968) | 0.337801 / 0.258489 (0.079312) | 0.374601 / 0.293841 (0.080760) | 0.031158 / 0.128546 (-0.097389) | 0.011672 / 0.075646 (-0.063974) | 0.324913 / 0.419271 (-0.094359) | 0.051702 / 0.043533 (0.008169) | 0.339440 / 0.255139 (0.084301) | 0.372502 / 0.283200 (0.089303) | 0.097590 / 0.141683 (-0.044093) | 1.534238 / 1.452155 (0.082083) | 1.599701 / 1.492716 (0.106985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204101 / 0.018006 (0.186095) | 0.416981 / 0.000490 (0.416491) | 0.003436 / 0.000200 (0.003236) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023527 / 0.037411 (-0.013885) | 0.095748 / 0.014526 (0.081222) | 0.104498 / 0.176557 (-0.072059) | 0.164000 / 0.737135 (-0.573135) | 0.109170 / 0.296338 (-0.187168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418239 / 0.215209 (0.203030) | 4.153959 / 2.077655 (2.076305) | 1.856687 / 1.504120 (0.352567) | 1.657818 / 1.541195 (0.116623) | 1.715146 / 1.468490 (0.246656) | 0.700673 / 4.584777 (-3.884103) | 3.401060 / 3.745712 (-0.344652) | 2.891045 / 5.269862 (-2.378816) | 1.519433 / 4.565676 (-3.046243) | 0.083151 / 0.424275 (-0.341124) | 0.012352 / 0.007607 (0.004745) | 0.523901 / 0.226044 (0.297856) | 5.288871 / 2.268929 (3.019943) | 2.322806 / 55.444624 (-53.121818) | 1.982223 / 6.876477 (-4.894253) | 2.074883 / 2.142072 (-0.067189) | 0.812400 / 4.805227 (-3.992827) | 0.152183 / 6.500664 (-6.348481) | 0.066538 / 0.075469 (-0.008931) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223220 / 1.841788 (-0.618567) | 14.024391 / 8.074308 (5.950083) | 14.166657 / 10.191392 (3.975265) | 0.146017 / 0.680424 (-0.534407) | 0.016698 / 0.534201 (-0.517503) | 0.380779 / 0.579283 (-0.198504) | 0.387113 / 0.434364 (-0.047251) | 0.446329 / 0.540337 (-0.094009) | 0.523819 / 1.386936 (-0.863118) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006803 / 0.011353 (-0.004549) | 0.004554 / 0.011008 (-0.006454) | 0.077406 / 0.038508 (0.038897) | 0.028495 / 0.023109 (0.005386) | 0.358847 / 0.275898 (0.082949) | 0.393256 / 0.323480 (0.069776) | 0.005317 / 0.007986 (-0.002669) | 0.004690 / 0.004328 (0.000362) | 0.075842 / 0.004250 (0.071592) | 0.041985 / 0.037052 (0.004933) | 0.367546 / 0.258489 (0.109057) | 0.408019 / 0.293841 (0.114178) | 0.030712 / 0.128546 (-0.097834) | 0.011756 / 0.075646 (-0.063891) | 0.086002 / 0.419271 (-0.333269) | 0.038949 / 0.043533 (-0.004583) | 0.361045 / 0.255139 (0.105906) | 0.381728 / 0.283200 (0.098528) | 0.090692 / 0.141683 (-0.050991) | 1.493251 / 1.452155 (0.041097) | 1.584566 / 1.492716 (0.091850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217470 / 0.018006 (0.199463) | 0.429955 / 0.000490 (0.429465) | 0.000394 / 0.000200 (0.000194) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026223 / 0.037411 (-0.011189) | 0.102570 / 0.014526 (0.088045) | 0.110848 / 0.176557 (-0.065709) | 0.162413 / 0.737135 (-0.574722) | 0.114579 / 0.296338 (-0.181760) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464957 / 0.215209 (0.249748) | 4.656597 / 2.077655 (2.578942) | 2.279755 / 1.504120 (0.775636) | 2.230263 / 1.541195 (0.689068) | 2.341540 / 1.468490 (0.873050) | 0.699505 / 4.584777 (-3.885272) | 3.389003 / 3.745712 (-0.356709) | 1.867526 / 5.269862 (-3.402336) | 1.167171 / 4.565676 (-3.398506) | 0.083451 / 0.424275 (-0.340824) | 0.012348 / 0.007607 (0.004741) | 0.584205 / 0.226044 (0.358161) | 5.853623 / 2.268929 (3.584694) | 2.646650 / 55.444624 (-52.797974) | 2.286504 / 6.876477 (-4.589973) | 2.327536 / 2.142072 (0.185464) | 0.811209 / 4.805227 (-3.994018) | 0.151842 / 6.500664 (-6.348822) | 0.067783 / 0.075469 (-0.007686) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330427 / 1.841788 (-0.511360) | 14.668981 / 8.074308 (6.594673) | 13.321154 / 10.191392 (3.129762) | 0.164383 / 0.680424 (-0.516040) | 0.016667 / 0.534201 (-0.517534) | 0.383439 / 0.579283 (-0.195844) | 0.392988 / 0.434364 (-0.041376) | 0.443318 / 0.540337 (-0.097020) | 0.537849 / 1.386936 (-0.849087) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e99bd4583bd636074b1826e2d0581161807480f1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006379 / 0.011353 (-0.004974) | 0.004691 / 0.011008 (-0.006317) | 0.098047 / 0.038508 (0.059539) | 0.028126 / 0.023109 (0.005017) | 0.327143 / 0.275898 (0.051245) | 0.362482 / 0.323480 (0.039002) | 0.004953 / 0.007986 (-0.003033) | 0.003386 / 0.004328 (-0.000943) | 0.076222 / 0.004250 (0.071971) | 0.037583 / 0.037052 (0.000531) | 0.329661 / 0.258489 (0.071172) | 0.365945 / 0.293841 (0.072104) | 0.030455 / 0.128546 (-0.098091) | 0.011397 / 0.075646 (-0.064249) | 0.323889 / 0.419271 (-0.095383) | 0.043719 / 0.043533 (0.000186) | 0.331499 / 0.255139 (0.076360) | 0.359357 / 0.283200 (0.076158) | 0.088904 / 0.141683 (-0.052779) | 1.458584 / 1.452155 (0.006429) | 1.549375 / 1.492716 (0.056658) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195808 / 0.018006 (0.177802) | 0.411148 / 0.000490 (0.410659) | 0.003602 / 0.000200 (0.003402) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023278 / 0.037411 (-0.014133) | 0.097317 / 0.014526 (0.082791) | 0.102669 / 0.176557 (-0.073888) | 0.168203 / 0.737135 (-0.568933) | 0.105205 / 0.296338 (-0.191133) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424800 / 0.215209 (0.209591) | 4.228444 / 2.077655 (2.150790) | 1.895544 / 1.504120 (0.391424) | 1.698793 / 1.541195 (0.157598) | 1.717931 / 1.468490 (0.249441) | 0.702251 / 4.584777 (-3.882526) | 3.407013 / 3.745712 (-0.338699) | 2.784634 / 5.269862 (-2.485228) | 1.491317 / 4.565676 (-3.074359) | 0.082926 / 0.424275 (-0.341350) | 0.012320 / 0.007607 (0.004713) | 0.524188 / 0.226044 (0.298143) | 5.249798 / 2.268929 (2.980870) | 2.358953 / 55.444624 (-53.085672) | 1.985922 / 6.876477 (-4.890555) | 2.034293 / 2.142072 (-0.107779) | 0.815671 / 4.805227 (-3.989556) | 0.152583 / 6.500664 (-6.348081) | 0.066687 / 0.075469 (-0.008782) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210901 / 1.841788 (-0.630886) | 13.621765 / 8.074308 (5.547457) | 14.213215 / 10.191392 (4.021823) | 0.143346 / 0.680424 (-0.537078) | 0.016904 / 0.534201 (-0.517297) | 0.379795 / 0.579283 (-0.199489) | 0.381287 / 0.434364 (-0.053077) | 0.449086 / 0.540337 (-0.091251) | 0.538792 / 1.386936 (-0.848144) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006207 / 0.011353 (-0.005146) | 0.004404 / 0.011008 (-0.006604) | 0.076363 / 0.038508 (0.037854) | 0.027335 / 0.023109 (0.004226) | 0.370967 / 0.275898 (0.095069) | 0.401936 / 0.323480 (0.078456) | 0.004835 / 0.007986 (-0.003151) | 0.004559 / 0.004328 (0.000231) | 0.074964 / 0.004250 (0.070713) | 0.038254 / 0.037052 (0.001202) | 0.374799 / 0.258489 (0.116310) | 0.425191 / 0.293841 (0.131350) | 0.035290 / 0.128546 (-0.093256) | 0.011379 / 0.075646 (-0.064267) | 0.085911 / 0.419271 (-0.333360) | 0.043073 / 0.043533 (-0.000460) | 0.373557 / 0.255139 (0.118418) | 0.395179 / 0.283200 (0.111979) | 0.098602 / 0.141683 (-0.043081) | 1.467234 / 1.452155 (0.015079) | 1.571868 / 1.492716 (0.079152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221848 / 0.018006 (0.203842) | 0.394943 / 0.000490 (0.394454) | 0.002983 / 0.000200 (0.002783) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024385 / 0.037411 (-0.013027) | 0.100087 / 0.014526 (0.085561) | 0.104897 / 0.176557 (-0.071660) | 0.156150 / 0.737135 (-0.580985) | 0.109113 / 0.296338 (-0.187226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441995 / 0.215209 (0.226786) | 4.415423 / 2.077655 (2.337769) | 2.148791 / 1.504120 (0.644671) | 1.947061 / 1.541195 (0.405866) | 1.954807 / 1.468490 (0.486317) | 0.690245 / 4.584777 (-3.894532) | 3.372766 / 3.745712 (-0.372946) | 1.851073 / 5.269862 (-3.418789) | 1.155558 / 4.565676 (-3.410118) | 0.082796 / 0.424275 (-0.341479) | 0.012845 / 0.007607 (0.005238) | 0.548173 / 0.226044 (0.322129) | 5.530984 / 2.268929 (3.262056) | 2.665360 / 55.444624 (-52.779264) | 2.324266 / 6.876477 (-4.552211) | 2.329397 / 2.142072 (0.187324) | 0.801481 / 4.805227 (-4.003746) | 0.152145 / 6.500664 (-6.348519) | 0.067915 / 0.075469 (-0.007554) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291488 / 1.841788 (-0.550299) | 13.912143 / 8.074308 (5.837835) | 12.975493 / 10.191392 (2.784101) | 0.129915 / 0.680424 (-0.550509) | 0.016516 / 0.534201 (-0.517685) | 0.386979 / 0.579283 (-0.192304) | 0.389163 / 0.434364 (-0.045201) | 0.443324 / 0.540337 (-0.097014) | 0.533744 / 1.386936 (-0.853192) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb48834fc2aa45cad73fe70a7ecaa0dd6015b8d0 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5867). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008635 / 0.011353 (-0.002717) | 0.006014 / 0.011008 (-0.004995) | 0.116314 / 0.038508 (0.077806) | 0.041113 / 0.023109 (0.018004) | 0.358564 / 0.275898 (0.082666) | 0.397547 / 0.323480 (0.074067) | 0.007012 / 0.007986 (-0.000974) | 0.004638 / 0.004328 (0.000310) | 0.086509 / 0.004250 (0.082259) | 0.056731 / 0.037052 (0.019678) | 0.358859 / 0.258489 (0.100370) | 0.425339 / 0.293841 (0.131498) | 0.041780 / 0.128546 (-0.086767) | 0.014203 / 0.075646 (-0.061443) | 0.398240 / 0.419271 (-0.021031) | 0.060180 / 0.043533 (0.016647) | 0.352887 / 0.255139 (0.097748) | 0.381793 / 0.283200 (0.098594) | 0.148578 / 0.141683 (0.006895) | 1.749483 / 1.452155 (0.297328) | 1.869765 / 1.492716 (0.377049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244435 / 0.018006 (0.226428) | 0.499545 / 0.000490 (0.499055) | 0.004576 / 0.000200 (0.004376) | 0.000147 / 0.000054 (0.000093) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031163 / 0.037411 (-0.006249) | 0.131082 / 0.014526 (0.116556) | 0.137442 / 0.176557 (-0.039114) | 0.203783 / 0.737135 (-0.533352) | 0.144068 / 0.296338 (-0.152270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503587 / 0.215209 (0.288378) | 5.011953 / 2.077655 (2.934299) | 2.366968 / 1.504120 (0.862848) | 2.130914 / 1.541195 (0.589719) | 2.243560 / 1.468490 (0.775070) | 0.856719 / 4.584777 (-3.728058) | 4.707445 / 3.745712 (0.961733) | 2.506166 / 5.269862 (-2.763696) | 1.590400 / 4.565676 (-2.975277) | 0.102075 / 0.424275 (-0.322200) | 0.014499 / 0.007607 (0.006892) | 0.624966 / 0.226044 (0.398922) | 6.197671 / 2.268929 (3.928742) | 2.898481 / 55.444624 (-52.546143) | 2.499590 / 6.876477 (-4.376886) | 2.649690 / 2.142072 (0.507617) | 1.012542 / 4.805227 (-3.792685) | 0.202833 / 6.500664 (-6.297831) | 0.078033 / 0.075469 (0.002564) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.448321 / 1.841788 (-0.393467) | 18.084909 / 8.074308 (10.010601) | 17.383027 / 10.191392 (7.191635) | 0.212167 / 0.680424 (-0.468256) | 0.020754 / 0.534201 (-0.513447) | 0.514653 / 0.579283 (-0.064630) | 0.543307 / 0.434364 (0.108944) | 0.653066 / 0.540337 (0.112728) | 0.745773 / 1.386936 (-0.641164) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008576 / 0.011353 (-0.002777) | 0.005834 / 0.011008 (-0.005174) | 0.089842 / 0.038508 (0.051334) | 0.040035 / 0.023109 (0.016926) | 0.449329 / 0.275898 (0.173431) | 0.471572 / 0.323480 (0.148092) | 0.006771 / 0.007986 (-0.001215) | 0.006129 / 0.004328 (0.001800) | 0.090370 / 0.004250 (0.086119) | 0.056924 / 0.037052 (0.019872) | 0.455134 / 0.258489 (0.196645) | 0.502670 / 0.293841 (0.208829) | 0.041689 / 0.128546 (-0.086857) | 0.014447 / 0.075646 (-0.061200) | 0.104528 / 0.419271 (-0.314744) | 0.055535 / 0.043533 (0.012003) | 0.450667 / 0.255139 (0.195528) | 0.453108 / 0.283200 (0.169908) | 0.119296 / 0.141683 (-0.022387) | 1.747359 / 1.452155 (0.295204) | 1.839421 / 1.492716 (0.346705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314910 / 0.018006 (0.296904) | 0.495575 / 0.000490 (0.495085) | 0.054702 / 0.000200 (0.054503) | 0.000505 / 0.000054 (0.000450) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033991 / 0.037411 (-0.003420) | 0.133268 / 0.014526 (0.118742) | 0.142286 / 0.176557 (-0.034271) | 0.200562 / 0.737135 (-0.536573) | 0.147161 / 0.296338 (-0.149178) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520288 / 0.215209 (0.305079) | 5.227684 / 2.077655 (3.150029) | 2.553330 / 1.504120 (1.049210) | 2.324338 / 1.541195 (0.783143) | 2.406790 / 1.468490 (0.938300) | 0.850404 / 4.584777 (-3.734373) | 4.612156 / 3.745712 (0.866444) | 2.592546 / 5.269862 (-2.677316) | 1.708984 / 4.565676 (-2.856692) | 0.103751 / 0.424275 (-0.320524) | 0.014379 / 0.007607 (0.006772) | 0.634661 / 0.226044 (0.408616) | 6.344939 / 2.268929 (4.076010) | 3.179807 / 55.444624 (-52.264817) | 2.831856 / 6.876477 (-4.044621) | 2.866729 / 2.142072 (0.724656) | 0.994519 / 4.805227 (-3.810708) | 0.201566 / 6.500664 (-6.299098) | 0.078902 / 0.075469 (0.003433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538738 / 1.841788 (-0.303049) | 18.746367 / 8.074308 (10.672059) | 16.504763 / 10.191392 (6.313371) | 0.197898 / 0.680424 (-0.482526) | 0.020469 / 0.534201 (-0.513732) | 0.529106 / 0.579283 (-0.050177) | 0.536891 / 0.434364 (0.102527) | 0.600947 / 0.540337 (0.060610) | 0.701713 / 1.386936 (-0.685223) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3054f66b4765a520e6fe165c44a4307d40775229 \"CML watermark\")\n" ]

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

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[ "_The documentation is not available anymore as the PR was closed or merged._", "If it's easy to keep supporting it we can keep it no ? There are many datasets on the hub that implement the tasks templates in dataset scripts and it's maybe easier to keep task templates than opening PRs to those datasets.", "do we know if people use the tasks api?\r\n\r\nedit: i mean, i'm fine with removing it if it's not used much, especially considering that it's not documented well.", "@lhoestq \r\n\r\nLess than 80 public datasets (all canonical) implement `task_templates`, so updating them should be easy.\r\n\r\nPS: I skipped gated datasets when checking for the presence of `task_templates`, but it's safe to assume their contribution to the total count is insignificant.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006480 / 0.011353 (-0.004872) | 0.003904 / 0.011008 (-0.007104) | 0.084287 / 0.038508 (0.045779) | 0.071438 / 0.023109 (0.048329) | 0.309823 / 0.275898 (0.033925) | 0.341038 / 0.323480 (0.017558) | 0.005163 / 0.007986 (-0.002822) | 0.003291 / 0.004328 (-0.001037) | 0.064473 / 0.004250 (0.060222) | 0.053385 / 0.037052 (0.016332) | 0.323561 / 0.258489 (0.065072) | 0.346332 / 0.293841 (0.052491) | 0.030588 / 0.128546 (-0.097958) | 0.008342 / 0.075646 (-0.067305) | 0.287205 / 0.419271 (-0.132067) | 0.051953 / 0.043533 (0.008420) | 0.310925 / 0.255139 (0.055786) | 0.344443 / 0.283200 (0.061244) | 0.022754 / 0.141683 (-0.118928) | 1.459648 / 1.452155 (0.007494) | 1.528413 / 1.492716 (0.035697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206404 / 0.018006 (0.188398) | 0.461864 / 0.000490 (0.461374) | 0.004501 / 0.000200 (0.004302) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026891 / 0.037411 (-0.010520) | 0.081206 / 0.014526 (0.066680) | 0.093648 / 0.176557 (-0.082908) | 0.148491 / 0.737135 (-0.588645) | 0.093874 / 0.296338 (-0.202464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401715 / 0.215209 (0.186506) | 4.018597 / 2.077655 (1.940943) | 2.029735 / 1.504120 (0.525615) | 1.860069 / 1.541195 (0.318875) | 1.935712 / 1.468490 (0.467222) | 0.485896 / 4.584777 (-4.098881) | 3.638177 / 3.745712 (-0.107535) | 5.124058 / 5.269862 (-0.145804) | 3.099666 / 4.565676 (-1.466011) | 0.057173 / 0.424275 (-0.367102) | 0.007240 / 0.007607 (-0.000367) | 0.478758 / 0.226044 (0.252713) | 4.798471 / 2.268929 (2.529543) | 2.502980 / 55.444624 (-52.941645) | 2.170650 / 6.876477 (-4.705827) | 2.381394 / 2.142072 (0.239321) | 0.578766 / 4.805227 (-4.226462) | 0.132342 / 6.500664 (-6.368322) | 0.059759 / 0.075469 (-0.015710) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249238 / 1.841788 (-0.592549) | 19.224673 / 8.074308 (11.150365) | 13.786894 / 10.191392 (3.595502) | 0.164633 / 0.680424 (-0.515791) | 0.018065 / 0.534201 (-0.516136) | 0.390589 / 0.579283 (-0.188694) | 0.408993 / 0.434364 (-0.025370) | 0.457001 / 0.540337 (-0.083336) | 0.625327 / 1.386936 (-0.761609) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006827 / 0.011353 (-0.004526) | 0.004007 / 0.011008 (-0.007001) | 0.065239 / 0.038508 (0.026731) | 0.079829 / 0.023109 (0.056719) | 0.400323 / 0.275898 (0.124425) | 0.434158 / 0.323480 (0.110678) | 0.005314 / 0.007986 (-0.002671) | 0.003354 / 0.004328 (-0.000974) | 0.065044 / 0.004250 (0.060794) | 0.060315 / 0.037052 (0.023262) | 0.401513 / 0.258489 (0.143024) | 0.441119 / 0.293841 (0.147278) | 0.031783 / 0.128546 (-0.096763) | 0.008608 / 0.075646 (-0.067038) | 0.071755 / 0.419271 (-0.347517) | 0.048816 / 0.043533 (0.005283) | 0.393896 / 0.255139 (0.138757) | 0.412156 / 0.283200 (0.128956) | 0.024410 / 0.141683 (-0.117272) | 1.515159 / 1.452155 (0.063005) | 1.562217 / 1.492716 (0.069501) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229993 / 0.018006 (0.211987) | 0.449898 / 0.000490 (0.449409) | 0.000376 / 0.000200 (0.000176) | 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) | 0.030297 / 0.037411 (-0.007115) | 0.086737 / 0.014526 (0.072212) | 0.098312 / 0.176557 (-0.078244) | 0.152890 / 0.737135 (-0.584246) | 0.099335 / 0.296338 (-0.197003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415786 / 0.215209 (0.200577) | 4.137606 / 2.077655 (2.059952) | 2.120082 / 1.504120 (0.615963) | 1.943984 / 1.541195 (0.402789) | 2.040821 / 1.468490 (0.572331) | 0.479273 / 4.584777 (-4.105504) | 3.563854 / 3.745712 (-0.181858) | 3.396071 / 5.269862 (-1.873790) | 2.011302 / 4.565676 (-2.554374) | 0.057202 / 0.424275 (-0.367073) | 0.007338 / 0.007607 (-0.000269) | 0.488378 / 0.226044 (0.262333) | 4.881615 / 2.268929 (2.612686) | 2.669685 / 55.444624 (-52.774939) | 2.258236 / 6.876477 (-4.618241) | 2.343303 / 2.142072 (0.201230) | 0.606762 / 4.805227 (-4.198466) | 0.133190 / 6.500664 (-6.367475) | 0.062971 / 0.075469 (-0.012498) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345215 / 1.841788 (-0.496573) | 20.023713 / 8.074308 (11.949405) | 14.555777 / 10.191392 (4.364385) | 0.162388 / 0.680424 (-0.518036) | 0.018528 / 0.534201 (-0.515673) | 0.393055 / 0.579283 (-0.186229) | 0.411820 / 0.434364 (-0.022544) | 0.461705 / 0.540337 (-0.078633) | 0.629395 / 1.386936 (-0.757541) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f54f2ff4c68a00242789e9890e3b46cab320448 \"CML watermark\")\n", "Ok ! I also know https://huggingface.co/datasets/hf-internal-testing/cats_vs_dogs_sample/blob/main/cats_vs_dogs_sample.py that needs to be updated as well", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009100 / 0.011353 (-0.002253) | 0.005158 / 0.011008 (-0.005850) | 0.109291 / 0.038508 (0.070782) | 0.086053 / 0.023109 (0.062943) | 0.469859 / 0.275898 (0.193961) | 0.476142 / 0.323480 (0.152662) | 0.006739 / 0.007986 (-0.001247) | 0.005077 / 0.004328 (0.000748) | 0.078193 / 0.004250 (0.073943) | 0.065956 / 0.037052 (0.028904) | 0.490323 / 0.258489 (0.231834) | 0.497418 / 0.293841 (0.203577) | 0.060562 / 0.128546 (-0.067984) | 0.016321 / 0.075646 (-0.059325) | 0.379703 / 0.419271 (-0.039568) | 0.087335 / 0.043533 (0.043802) | 0.488240 / 0.255139 (0.233101) | 0.497391 / 0.283200 (0.214191) | 0.040699 / 0.141683 (-0.100984) | 1.778925 / 1.452155 (0.326770) | 1.856436 / 1.492716 (0.363720) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236428 / 0.018006 (0.218422) | 0.551950 / 0.000490 (0.551460) | 0.007400 / 0.000200 (0.007201) | 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) | 0.028461 / 0.037411 (-0.008950) | 0.093441 / 0.014526 (0.078915) | 0.103868 / 0.176557 (-0.072688) | 0.176269 / 0.737135 (-0.560867) | 0.107760 / 0.296338 (-0.188578) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.593382 / 0.215209 (0.378173) | 5.863711 / 2.077655 (3.786057) | 2.493777 / 1.504120 (0.989657) | 2.088547 / 1.541195 (0.547352) | 2.173147 / 1.468490 (0.704656) | 0.875661 / 4.584777 (-3.709116) | 5.209023 / 3.745712 (1.463310) | 4.483261 / 5.269862 (-0.786600) | 2.843288 / 4.565676 (-1.722388) | 0.098488 / 0.424275 (-0.325787) | 0.008371 / 0.007607 (0.000764) | 0.668413 / 0.226044 (0.442368) | 6.709802 / 2.268929 (4.440873) | 3.132453 / 55.444624 (-52.312172) | 2.428736 / 6.876477 (-4.447741) | 2.560867 / 2.142072 (0.418794) | 0.983550 / 4.805227 (-3.821677) | 0.207072 / 6.500664 (-6.293592) | 0.073786 / 0.075469 (-0.001683) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625871 / 1.841788 (-0.215917) | 23.481015 / 8.074308 (15.406707) | 20.556677 / 10.191392 (10.365285) | 0.238147 / 0.680424 (-0.442277) | 0.029453 / 0.534201 (-0.504748) | 0.464589 / 0.579283 (-0.114695) | 0.599129 / 0.434364 (0.164765) | 0.550146 / 0.540337 (0.009808) | 0.794646 / 1.386936 (-0.592290) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008613 / 0.011353 (-0.002739) | 0.004979 / 0.011008 (-0.006030) | 0.078095 / 0.038508 (0.039587) | 0.080285 / 0.023109 (0.057176) | 0.482881 / 0.275898 (0.206983) | 0.520442 / 0.323480 (0.196962) | 0.006241 / 0.007986 (-0.001744) | 0.003964 / 0.004328 (-0.000364) | 0.080027 / 0.004250 (0.075777) | 0.065209 / 0.037052 (0.028157) | 0.476113 / 0.258489 (0.217623) | 0.535383 / 0.293841 (0.241542) | 0.053084 / 0.128546 (-0.075462) | 0.014284 / 0.075646 (-0.061362) | 0.083859 / 0.419271 (-0.335413) | 0.061024 / 0.043533 (0.017492) | 0.477810 / 0.255139 (0.222671) | 0.508718 / 0.283200 (0.225518) | 0.036602 / 0.141683 (-0.105081) | 1.810422 / 1.452155 (0.358267) | 1.832833 / 1.492716 (0.340117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281443 / 0.018006 (0.263437) | 0.568249 / 0.000490 (0.567760) | 0.000493 / 0.000200 (0.000293) | 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) | 0.033302 / 0.037411 (-0.004110) | 0.100433 / 0.014526 (0.085907) | 0.105465 / 0.176557 (-0.071091) | 0.161986 / 0.737135 (-0.575149) | 0.115736 / 0.296338 (-0.180603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.622892 / 0.215209 (0.407683) | 6.144361 / 2.077655 (4.066706) | 2.849443 / 1.504120 (1.345323) | 2.544097 / 1.541195 (1.002902) | 2.579859 / 1.468490 (1.111369) | 0.826078 / 4.584777 (-3.758699) | 5.021808 / 3.745712 (1.276096) | 4.694784 / 5.269862 (-0.575077) | 2.796263 / 4.565676 (-1.769413) | 0.090983 / 0.424275 (-0.333292) | 0.008445 / 0.007607 (0.000838) | 0.744675 / 0.226044 (0.518631) | 7.662989 / 2.268929 (5.394060) | 3.665611 / 55.444624 (-51.779013) | 2.942836 / 6.876477 (-3.933641) | 2.874402 / 2.142072 (0.732329) | 1.010097 / 4.805227 (-3.795130) | 0.218008 / 6.500664 (-6.282656) | 0.087359 / 0.075469 (0.011890) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655631 / 1.841788 (-0.186157) | 23.539596 / 8.074308 (15.465288) | 20.909512 / 10.191392 (10.718120) | 0.202092 / 0.680424 (-0.478332) | 0.029807 / 0.534201 (-0.504394) | 0.487591 / 0.579283 (-0.091692) | 0.573719 / 0.434364 (0.139355) | 0.531168 / 0.540337 (-0.009170) | 0.742375 / 1.386936 (-0.644561) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa231a7be55c6bca2bede8af4ac6da63c3162116 \"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) | 0.006247 / 0.011353 (-0.005106) | 0.003650 / 0.011008 (-0.007358) | 0.079655 / 0.038508 (0.041147) | 0.060279 / 0.023109 (0.037170) | 0.309033 / 0.275898 (0.033135) | 0.338479 / 0.323480 (0.014999) | 0.004651 / 0.007986 (-0.003335) | 0.002849 / 0.004328 (-0.001480) | 0.062852 / 0.004250 (0.058602) | 0.049230 / 0.037052 (0.012178) | 0.312502 / 0.258489 (0.054012) | 0.354558 / 0.293841 (0.060717) | 0.027497 / 0.128546 (-0.101049) | 0.007885 / 0.075646 (-0.067762) | 0.260232 / 0.419271 (-0.159040) | 0.045459 / 0.043533 (0.001926) | 0.311629 / 0.255139 (0.056490) | 0.367806 / 0.283200 (0.084606) | 0.020875 / 0.141683 (-0.120808) | 1.423802 / 1.452155 (-0.028352) | 1.497729 / 1.492716 (0.005013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185629 / 0.018006 (0.167623) | 0.441421 / 0.000490 (0.440931) | 0.004847 / 0.000200 (0.004647) | 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) | 0.022428 / 0.037411 (-0.014984) | 0.073375 / 0.014526 (0.058849) | 0.083194 / 0.176557 (-0.093363) | 0.143984 / 0.737135 (-0.593151) | 0.084128 / 0.296338 (-0.212211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397220 / 0.215209 (0.182010) | 3.954394 / 2.077655 (1.876740) | 1.920638 / 1.504120 (0.416518) | 1.744284 / 1.541195 (0.203089) | 1.802623 / 1.468490 (0.334133) | 0.501988 / 4.584777 (-4.082789) | 3.096071 / 3.745712 (-0.649642) | 4.648267 / 5.269862 (-0.621595) | 2.770995 / 4.565676 (-1.794682) | 0.057513 / 0.424275 (-0.366762) | 0.006315 / 0.007607 (-0.001292) | 0.467683 / 0.226044 (0.241639) | 4.683959 / 2.268929 (2.415031) | 2.384980 / 55.444624 (-53.059645) | 2.030894 / 6.876477 (-4.845583) | 2.148374 / 2.142072 (0.006302) | 0.585142 / 4.805227 (-4.220085) | 0.123173 / 6.500664 (-6.377491) | 0.059140 / 0.075469 (-0.016329) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244707 / 1.841788 (-0.597080) | 18.176043 / 8.074308 (10.101735) | 13.742770 / 10.191392 (3.551378) | 0.149692 / 0.680424 (-0.530732) | 0.016591 / 0.534201 (-0.517610) | 0.342138 / 0.579283 (-0.237145) | 0.353931 / 0.434364 (-0.080433) | 0.392317 / 0.540337 (-0.148020) | 0.524011 / 1.386936 (-0.862925) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005937 / 0.011353 (-0.005416) | 0.003609 / 0.011008 (-0.007399) | 0.061729 / 0.038508 (0.023221) | 0.057844 / 0.023109 (0.034735) | 0.418051 / 0.275898 (0.142153) | 0.453014 / 0.323480 (0.129534) | 0.004530 / 0.007986 (-0.003456) | 0.002861 / 0.004328 (-0.001468) | 0.062236 / 0.004250 (0.057986) | 0.048612 / 0.037052 (0.011560) | 0.418487 / 0.258489 (0.159998) | 0.455114 / 0.293841 (0.161273) | 0.027419 / 0.128546 (-0.101127) | 0.007919 / 0.075646 (-0.067728) | 0.066940 / 0.419271 (-0.352331) | 0.041816 / 0.043533 (-0.001717) | 0.419788 / 0.255139 (0.164649) | 0.439682 / 0.283200 (0.156483) | 0.020902 / 0.141683 (-0.120781) | 1.473993 / 1.452155 (0.021838) | 1.532438 / 1.492716 (0.039722) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228766 / 0.018006 (0.210760) | 0.412189 / 0.000490 (0.411699) | 0.000371 / 0.000200 (0.000171) | 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) | 0.026139 / 0.037411 (-0.011272) | 0.076626 / 0.014526 (0.062100) | 0.088262 / 0.176557 (-0.088295) | 0.143096 / 0.737135 (-0.594039) | 0.089642 / 0.296338 (-0.206696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423030 / 0.215209 (0.207821) | 4.218333 / 2.077655 (2.140679) | 2.280943 / 1.504120 (0.776823) | 2.051746 / 1.541195 (0.510551) | 2.101085 / 1.468490 (0.632595) | 0.495860 / 4.584777 (-4.088917) | 3.108065 / 3.745712 (-0.637647) | 2.944188 / 5.269862 (-2.325673) | 1.833693 / 4.565676 (-2.731984) | 0.057509 / 0.424275 (-0.366766) | 0.006406 / 0.007607 (-0.001201) | 0.497208 / 0.226044 (0.271164) | 4.974972 / 2.268929 (2.706044) | 2.786639 / 55.444624 (-52.657985) | 2.423815 / 6.876477 (-4.452662) | 2.446377 / 2.142072 (0.304305) | 0.584521 / 4.805227 (-4.220706) | 0.124129 / 6.500664 (-6.376535) | 0.061373 / 0.075469 (-0.014096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307076 / 1.841788 (-0.534711) | 18.443873 / 8.074308 (10.369565) | 13.835730 / 10.191392 (3.644338) | 0.159795 / 0.680424 (-0.520629) | 0.016643 / 0.534201 (-0.517558) | 0.334300 / 0.579283 (-0.244983) | 0.347136 / 0.434364 (-0.087228) | 0.394633 / 0.540337 (-0.145704) | 0.552445 / 1.386936 (-0.834491) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8cfc0262363ea8cbd8c78537a09f851ec6ec30f5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007273 / 0.011353 (-0.004080) | 0.004704 / 0.011008 (-0.006304) | 0.105857 / 0.038508 (0.067349) | 0.062493 / 0.023109 (0.039384) | 0.325704 / 0.275898 (0.049806) | 0.355795 / 0.323480 (0.032315) | 0.005552 / 0.007986 (-0.002433) | 0.003543 / 0.004328 (-0.000785) | 0.068098 / 0.004250 (0.063848) | 0.049563 / 0.037052 (0.012511) | 0.362956 / 0.258489 (0.104467) | 0.376047 / 0.293841 (0.082206) | 0.039272 / 0.128546 (-0.089275) | 0.011521 / 0.075646 (-0.064125) | 0.291899 / 0.419271 (-0.127373) | 0.056916 / 0.043533 (0.013383) | 0.365352 / 0.255139 (0.110213) | 0.357251 / 0.283200 (0.074051) | 0.031670 / 0.141683 (-0.110013) | 1.533294 / 1.452155 (0.081140) | 1.566580 / 1.492716 (0.073864) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219812 / 0.018006 (0.201805) | 0.499808 / 0.000490 (0.499318) | 0.000343 / 0.000200 (0.000143) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024011 / 0.037411 (-0.013400) | 0.079686 / 0.014526 (0.065161) | 0.087925 / 0.176557 (-0.088631) | 0.149065 / 0.737135 (-0.588071) | 0.088514 / 0.296338 (-0.207824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495003 / 0.215209 (0.279794) | 5.106371 / 2.077655 (3.028717) | 2.285497 / 1.504120 (0.781377) | 2.056052 / 1.541195 (0.514858) | 2.024913 / 1.468490 (0.556423) | 0.726048 / 4.584777 (-3.858729) | 4.873945 / 3.745712 (1.128233) | 7.488671 / 5.269862 (2.218809) | 4.361208 / 4.565676 (-0.204469) | 0.089014 / 0.424275 (-0.335261) | 0.007178 / 0.007607 (-0.000429) | 0.633669 / 0.226044 (0.407625) | 6.328154 / 2.268929 (4.059226) | 3.071598 / 55.444624 (-52.373026) | 2.416077 / 6.876477 (-4.460399) | 2.431033 / 2.142072 (0.288961) | 0.918167 / 4.805227 (-3.887060) | 0.193829 / 6.500664 (-6.306836) | 0.073446 / 0.075469 (-0.002023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344994 / 1.841788 (-0.496793) | 19.911699 / 8.074308 (11.837391) | 17.182697 / 10.191392 (6.991305) | 0.216932 / 0.680424 (-0.463492) | 0.025415 / 0.534201 (-0.508786) | 0.416806 / 0.579283 (-0.162477) | 0.524934 / 0.434364 (0.090570) | 0.510783 / 0.540337 (-0.029554) | 0.687856 / 1.386936 (-0.699081) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008469 / 0.011353 (-0.002884) | 0.003797 / 0.011008 (-0.007211) | 0.067276 / 0.038508 (0.028768) | 0.066825 / 0.023109 (0.043716) | 0.394976 / 0.275898 (0.119078) | 0.432563 / 0.323480 (0.109083) | 0.006003 / 0.007986 (-0.001982) | 0.003399 / 0.004328 (-0.000930) | 0.070899 / 0.004250 (0.066649) | 0.050940 / 0.037052 (0.013887) | 0.378291 / 0.258489 (0.119802) | 0.429889 / 0.293841 (0.136048) | 0.043245 / 0.128546 (-0.085302) | 0.012182 / 0.075646 (-0.063465) | 0.074560 / 0.419271 (-0.344711) | 0.065290 / 0.043533 (0.021757) | 0.371209 / 0.255139 (0.116070) | 0.389731 / 0.283200 (0.106532) | 0.045729 / 0.141683 (-0.095954) | 1.451785 / 1.452155 (-0.000370) | 1.598539 / 1.492716 (0.105822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261357 / 0.018006 (0.243351) | 0.520142 / 0.000490 (0.519653) | 0.008305 / 0.000200 (0.008105) | 0.000089 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026492 / 0.037411 (-0.010919) | 0.082430 / 0.014526 (0.067904) | 0.095979 / 0.176557 (-0.080578) | 0.151752 / 0.737135 (-0.585383) | 0.090086 / 0.296338 (-0.206252) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.535967 / 0.215209 (0.320758) | 5.228605 / 2.077655 (3.150950) | 2.395078 / 1.504120 (0.890959) | 2.185500 / 1.541195 (0.644306) | 2.219456 / 1.468490 (0.750966) | 0.764794 / 4.584777 (-3.819983) | 4.796617 / 3.745712 (1.050905) | 4.143450 / 5.269862 (-1.126411) | 2.527391 / 4.565676 (-2.038286) | 0.081418 / 0.424275 (-0.342857) | 0.007170 / 0.007607 (-0.000437) | 0.706071 / 0.226044 (0.480026) | 6.501060 / 2.268929 (4.232131) | 3.176315 / 55.444624 (-52.268309) | 2.443245 / 6.876477 (-4.433232) | 2.517832 / 2.142072 (0.375759) | 0.916254 / 4.805227 (-3.888973) | 0.184282 / 6.500664 (-6.316382) | 0.062613 / 0.075469 (-0.012857) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.444283 / 1.841788 (-0.397504) | 20.227311 / 8.074308 (12.153003) | 17.512856 / 10.191392 (7.321464) | 0.219556 / 0.680424 (-0.460868) | 0.024705 / 0.534201 (-0.509496) | 0.423215 / 0.579283 (-0.156068) | 0.513103 / 0.434364 (0.078739) | 0.473853 / 0.540337 (-0.066485) | 0.738165 / 1.386936 (-0.648771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b65660b7c6e853391991734210e38f805459b0ed \"CML watermark\")\n" ]

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

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