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{ "diff_url": "https://github.com/huggingface/datasets/pull/6028.diff", "html_url": "https://github.com/huggingface/datasets/pull/6028", "merged_at": "2023-07-17T17:01:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6028.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6028" }

[ "_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) | 0.006665 / 0.011353 (-0.004688) | 0.004376 / 0.011008 (-0.006633) | 0.085529 / 0.038508 (0.047021) | 0.076372 / 0.023109 (0.053263) | 0.310019 / 0.275898 (0.034121) | 0.341404 / 0.323480 (0.017924) | 0.005666 / 0.007986 (-0.002320) | 0.003763 / 0.004328 (-0.000566) | 0.064678 / 0.004250 (0.060427) | 0.059283 / 0.037052 (0.022231) | 0.316194 / 0.258489 (0.057704) | 0.349397 / 0.293841 (0.055557) | 0.031199 / 0.128546 (-0.097347) | 0.008724 / 0.075646 (-0.066923) | 0.300236 / 0.419271 (-0.119035) | 0.068872 / 0.043533 (0.025339) | 0.308521 / 0.255139 (0.053382) | 0.331292 / 0.283200 (0.048092) | 0.028236 / 0.141683 (-0.113447) | 1.501365 / 1.452155 (0.049211) | 1.554334 / 1.492716 (0.061618) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238291 / 0.018006 (0.220285) | 0.565069 / 0.000490 (0.564580) | 0.001626 / 0.000200 (0.001426) | 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) | 0.029777 / 0.037411 (-0.007634) | 0.082873 / 0.014526 (0.068347) | 0.099619 / 0.176557 (-0.076937) | 0.156572 / 0.737135 (-0.580563) | 0.099887 / 0.296338 (-0.196452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401017 / 0.215209 (0.185808) | 3.827192 / 2.077655 (1.749537) | 1.861554 / 1.504120 (0.357434) | 1.699869 / 1.541195 (0.158674) | 1.720043 / 1.468490 (0.251553) | 0.486757 / 4.584777 (-4.098020) | 3.638125 / 3.745712 (-0.107587) | 5.844959 / 5.269862 (0.575097) | 3.454901 / 4.565676 (-1.110775) | 0.057650 / 0.424275 (-0.366625) | 0.007341 / 0.007607 (-0.000266) | 0.462698 / 0.226044 (0.236654) | 4.633472 / 2.268929 (2.364544) | 2.287607 / 55.444624 (-53.157017) | 2.057318 / 6.876477 (-4.819159) | 2.203657 / 2.142072 (0.061584) | 0.598136 / 4.805227 (-4.207091) | 0.134012 / 6.500664 (-6.366653) | 0.060824 / 0.075469 (-0.014645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277752 / 1.841788 (-0.564036) | 20.013398 / 8.074308 (11.939089) | 14.372993 / 10.191392 (4.181601) | 0.169991 / 0.680424 (-0.510433) | 0.018344 / 0.534201 (-0.515857) | 0.396985 / 0.579283 (-0.182299) | 0.416289 / 0.434364 (-0.018075) | 0.458658 / 0.540337 (-0.081680) | 0.692980 / 1.386936 (-0.693956) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006689 / 0.011353 (-0.004664) | 0.004393 / 0.011008 (-0.006615) | 0.064069 / 0.038508 (0.025561) | 0.080717 / 0.023109 (0.057607) | 0.370090 / 0.275898 (0.094191) | 0.400432 / 0.323480 (0.076952) | 0.005613 / 0.007986 (-0.002372) | 0.003641 / 0.004328 (-0.000687) | 0.064771 / 0.004250 (0.060520) | 0.057555 / 0.037052 (0.020502) | 0.392156 / 0.258489 (0.133667) | 0.409842 / 0.293841 (0.116001) | 0.031500 / 0.128546 (-0.097047) | 0.008786 / 0.075646 (-0.066860) | 0.070342 / 0.419271 (-0.348929) | 0.048646 / 0.043533 (0.005113) | 0.360914 / 0.255139 (0.105775) | 0.387626 / 0.283200 (0.104426) | 0.022787 / 0.141683 (-0.118896) | 1.508915 / 1.452155 (0.056761) | 1.539719 / 1.492716 (0.047002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257985 / 0.018006 (0.239979) | 0.550990 / 0.000490 (0.550501) | 0.000407 / 0.000200 (0.000207) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030183 / 0.037411 (-0.007228) | 0.086882 / 0.014526 (0.072356) | 0.102382 / 0.176557 (-0.074175) | 0.154745 / 0.737135 (-0.582390) | 0.104008 / 0.296338 (-0.192331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426284 / 0.215209 (0.211075) | 4.240812 / 2.077655 (2.163158) | 2.261240 / 1.504120 (0.757120) | 2.085905 / 1.541195 (0.544710) | 2.160374 / 1.468490 (0.691883) | 0.481126 / 4.584777 (-4.103651) | 3.516234 / 3.745712 (-0.229478) | 3.325322 / 5.269862 (-1.944539) | 2.043307 / 4.565676 (-2.522369) | 0.056663 / 0.424275 (-0.367612) | 0.007786 / 0.007607 (0.000179) | 0.497614 / 0.226044 (0.271570) | 4.974529 / 2.268929 (2.705600) | 2.700018 / 55.444624 (-52.744606) | 2.393778 / 6.876477 (-4.482699) | 2.628202 / 2.142072 (0.486130) | 0.594316 / 4.805227 (-4.210911) | 0.147092 / 6.500664 (-6.353572) | 0.062207 / 0.075469 (-0.013262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.315676 / 1.841788 (-0.526112) | 20.749251 / 8.074308 (12.674943) | 14.371553 / 10.191392 (4.180160) | 0.170249 / 0.680424 (-0.510175) | 0.018478 / 0.534201 (-0.515722) | 0.395710 / 0.579283 (-0.183573) | 0.409706 / 0.434364 (-0.024658) | 0.463454 / 0.540337 (-0.076884) | 0.615657 / 1.386936 (-0.771279) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"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) | 0.007224 / 0.011353 (-0.004129) | 0.004506 / 0.011008 (-0.006503) | 0.096729 / 0.038508 (0.058221) | 0.082394 / 0.023109 (0.059284) | 0.390954 / 0.275898 (0.115056) | 0.416647 / 0.323480 (0.093167) | 0.005894 / 0.007986 (-0.002092) | 0.003756 / 0.004328 (-0.000572) | 0.075800 / 0.004250 (0.071549) | 0.062683 / 0.037052 (0.025631) | 0.398959 / 0.258489 (0.140470) | 0.436624 / 0.293841 (0.142783) | 0.034650 / 0.128546 (-0.093896) | 0.009655 / 0.075646 (-0.065991) | 0.315761 / 0.419271 (-0.103511) | 0.060957 / 0.043533 (0.017424) | 0.385649 / 0.255139 (0.130510) | 0.394022 / 0.283200 (0.110822) | 0.024601 / 0.141683 (-0.117082) | 1.729586 / 1.452155 (0.277431) | 1.724153 / 1.492716 (0.231437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207070 / 0.018006 (0.189063) | 0.466502 / 0.000490 (0.466012) | 0.010739 / 0.000200 (0.010540) | 0.000214 / 0.000054 (0.000160) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031633 / 0.037411 (-0.005779) | 0.095345 / 0.014526 (0.080819) | 0.105399 / 0.176557 (-0.071157) | 0.174173 / 0.737135 (-0.562962) | 0.104207 / 0.296338 (-0.192132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435312 / 0.215209 (0.220103) | 4.265600 / 2.077655 (2.187946) | 2.056500 / 1.504120 (0.552380) | 1.848023 / 1.541195 (0.306828) | 1.946156 / 1.468490 (0.477666) | 0.557788 / 4.584777 (-4.026989) | 4.070289 / 3.745712 (0.324577) | 3.608027 / 5.269862 (-1.661835) | 2.214556 / 4.565676 (-2.351121) | 0.062623 / 0.424275 (-0.361652) | 0.008083 / 0.007607 (0.000476) | 0.491782 / 0.226044 (0.265738) | 4.989963 / 2.268929 (2.721035) | 2.575867 / 55.444624 (-52.868757) | 2.208045 / 6.876477 (-4.668431) | 2.364184 / 2.142072 (0.222112) | 0.633925 / 4.805227 (-4.171302) | 0.144323 / 6.500664 (-6.356341) | 0.067505 / 0.075469 (-0.007965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.467219 / 1.841788 (-0.374569) | 22.334967 / 8.074308 (14.260659) | 15.715747 / 10.191392 (5.524355) | 0.175443 / 0.680424 (-0.504980) | 0.026165 / 0.534201 (-0.508036) | 0.490675 / 0.579283 (-0.088608) | 0.509211 / 0.434364 (0.074847) | 0.586303 / 0.540337 (0.045965) | 0.785052 / 1.386936 (-0.601884) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007893 / 0.011353 (-0.003460) | 0.004577 / 0.011008 (-0.006431) | 0.075781 / 0.038508 (0.037273) | 0.095492 / 0.023109 (0.072382) | 0.433259 / 0.275898 (0.157361) | 0.469386 / 0.323480 (0.145906) | 0.006317 / 0.007986 (-0.001669) | 0.003708 / 0.004328 (-0.000621) | 0.074417 / 0.004250 (0.070167) | 0.068605 / 0.037052 (0.031552) | 0.448701 / 0.258489 (0.190212) | 0.469131 / 0.293841 (0.175290) | 0.036647 / 0.128546 (-0.091899) | 0.010077 / 0.075646 (-0.065570) | 0.082457 / 0.419271 (-0.336815) | 0.063255 / 0.043533 (0.019722) | 0.428144 / 0.255139 (0.173005) | 0.451872 / 0.283200 (0.168672) | 0.033953 / 0.141683 (-0.107730) | 1.781752 / 1.452155 (0.329597) | 1.869014 / 1.492716 (0.376297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223596 / 0.018006 (0.205590) | 0.470307 / 0.000490 (0.469818) | 0.005059 / 0.000200 (0.004859) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038804 / 0.037411 (0.001393) | 0.117879 / 0.014526 (0.103353) | 0.140701 / 0.176557 (-0.035855) | 0.194672 / 0.737135 (-0.542463) | 0.132806 / 0.296338 (-0.163533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510109 / 0.215209 (0.294900) | 4.729457 / 2.077655 (2.651803) | 2.512113 / 1.504120 (1.007993) | 2.302553 / 1.541195 (0.761358) | 2.420462 / 1.468490 (0.951972) | 0.531682 / 4.584777 (-4.053095) | 4.061208 / 3.745712 (0.315496) | 3.588542 / 5.269862 (-1.681320) | 2.203187 / 4.565676 (-2.362489) | 0.065791 / 0.424275 (-0.358484) | 0.008839 / 0.007607 (0.001232) | 0.562041 / 0.226044 (0.335997) | 5.702340 / 2.268929 (3.433412) | 3.127609 / 55.444624 (-52.317015) | 2.823060 / 6.876477 (-4.053417) | 2.898675 / 2.142072 (0.756603) | 0.659589 / 4.805227 (-4.145638) | 0.148798 / 6.500664 (-6.351866) | 0.070787 / 0.075469 (-0.004682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.478317 / 1.841788 (-0.363471) | 21.995400 / 8.074308 (13.921092) | 16.770729 / 10.191392 (6.579337) | 0.226333 / 0.680424 (-0.454091) | 0.021835 / 0.534201 (-0.512366) | 0.460373 / 0.579283 (-0.118910) | 0.479494 / 0.434364 (0.045130) | 0.529470 / 0.540337 (-0.010868) | 0.718066 / 1.386936 (-0.668870) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"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) | 0.007824 / 0.011353 (-0.003529) | 0.004601 / 0.011008 (-0.006407) | 0.100025 / 0.038508 (0.061517) | 0.096046 / 0.023109 (0.072936) | 0.376226 / 0.275898 (0.100328) | 0.410905 / 0.323480 (0.087425) | 0.006048 / 0.007986 (-0.001938) | 0.003817 / 0.004328 (-0.000511) | 0.076624 / 0.004250 (0.072374) | 0.066390 / 0.037052 (0.029338) | 0.380098 / 0.258489 (0.121609) | 0.413603 / 0.293841 (0.119762) | 0.036546 / 0.128546 (-0.092001) | 0.009881 / 0.075646 (-0.065765) | 0.344338 / 0.419271 (-0.074934) | 0.061882 / 0.043533 (0.018350) | 0.368568 / 0.255139 (0.113429) | 0.397133 / 0.283200 (0.113934) | 0.027255 / 0.141683 (-0.114428) | 1.795099 / 1.452155 (0.342945) | 1.852443 / 1.492716 (0.359727) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247436 / 0.018006 (0.229430) | 0.494119 / 0.000490 (0.493629) | 0.004359 / 0.000200 (0.004159) | 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) | 0.034765 / 0.037411 (-0.002647) | 0.104541 / 0.014526 (0.090015) | 0.113898 / 0.176557 (-0.062659) | 0.183634 / 0.737135 (-0.553501) | 0.116423 / 0.296338 (-0.179916) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458747 / 0.215209 (0.243538) | 4.555740 / 2.077655 (2.478085) | 2.217240 / 1.504120 (0.713121) | 2.039879 / 1.541195 (0.498684) | 2.088581 / 1.468490 (0.620091) | 0.588063 / 4.584777 (-3.996714) | 4.238226 / 3.745712 (0.492514) | 4.768060 / 5.269862 (-0.501802) | 2.857117 / 4.565676 (-1.708560) | 0.068742 / 0.424275 (-0.355533) | 0.008667 / 0.007607 (0.001059) | 0.549294 / 0.226044 (0.323249) | 5.464635 / 2.268929 (3.195706) | 2.744435 / 55.444624 (-52.700189) | 2.347660 / 6.876477 (-4.528816) | 2.616816 / 2.142072 (0.474743) | 0.703701 / 4.805227 (-4.101526) | 0.159749 / 6.500664 (-6.340915) | 0.071990 / 0.075469 (-0.003479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486599 / 1.841788 (-0.355188) | 22.745438 / 8.074308 (14.671130) | 16.822332 / 10.191392 (6.630940) | 0.184730 / 0.680424 (-0.495694) | 0.021267 / 0.534201 (-0.512934) | 0.467108 / 0.579283 (-0.112176) | 0.472674 / 0.434364 (0.038311) | 0.548094 / 0.540337 (0.007756) | 0.735885 / 1.386936 (-0.651051) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007746 / 0.011353 (-0.003607) | 0.004585 / 0.011008 (-0.006423) | 0.076943 / 0.038508 (0.038435) | 0.087473 / 0.023109 (0.064363) | 0.480099 / 0.275898 (0.204201) | 0.495271 / 0.323480 (0.171791) | 0.006348 / 0.007986 (-0.001638) | 0.003902 / 0.004328 (-0.000426) | 0.077586 / 0.004250 (0.073335) | 0.066467 / 0.037052 (0.029415) | 0.468741 / 0.258489 (0.210252) | 0.506778 / 0.293841 (0.212937) | 0.036877 / 0.128546 (-0.091669) | 0.010102 / 0.075646 (-0.065545) | 0.084419 / 0.419271 (-0.334852) | 0.058721 / 0.043533 (0.015188) | 0.453633 / 0.255139 (0.198494) | 0.481171 / 0.283200 (0.197971) | 0.028716 / 0.141683 (-0.112967) | 1.853048 / 1.452155 (0.400893) | 1.885847 / 1.492716 (0.393130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192136 / 0.018006 (0.174130) | 0.484481 / 0.000490 (0.483991) | 0.002951 / 0.000200 (0.002751) | 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) | 0.037949 / 0.037411 (0.000538) | 0.108364 / 0.014526 (0.093838) | 0.119542 / 0.176557 (-0.057014) | 0.188542 / 0.737135 (-0.548593) | 0.122011 / 0.296338 (-0.174327) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483135 / 0.215209 (0.267926) | 4.849715 / 2.077655 (2.772060) | 2.497736 / 1.504120 (0.993616) | 2.314243 / 1.541195 (0.773048) | 2.412739 / 1.468490 (0.944249) | 0.564137 / 4.584777 (-4.020639) | 4.242273 / 3.745712 (0.496561) | 6.337843 / 5.269862 (1.067982) | 3.923250 / 4.565676 (-0.642426) | 0.066464 / 0.424275 (-0.357811) | 0.009217 / 0.007607 (0.001610) | 0.575667 / 0.226044 (0.349623) | 5.746187 / 2.268929 (3.477258) | 3.069655 / 55.444624 (-52.374969) | 2.674798 / 6.876477 (-4.201679) | 2.956535 / 2.142072 (0.814463) | 0.701043 / 4.805227 (-4.104185) | 0.157241 / 6.500664 (-6.343423) | 0.073175 / 0.075469 (-0.002294) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609943 / 1.841788 (-0.231844) | 23.478594 / 8.074308 (15.404286) | 17.454437 / 10.191392 (7.263045) | 0.186422 / 0.680424 (-0.494002) | 0.021703 / 0.534201 (-0.512498) | 0.471704 / 0.579283 (-0.107579) | 0.480553 / 0.434364 (0.046189) | 0.552881 / 0.540337 (0.012544) | 0.722515 / 1.386936 (-0.664421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"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) | 0.007542 / 0.011353 (-0.003811) | 0.004692 / 0.011008 (-0.006316) | 0.099155 / 0.038508 (0.060647) | 0.089365 / 0.023109 (0.066256) | 0.370870 / 0.275898 (0.094972) | 0.422152 / 0.323480 (0.098673) | 0.006223 / 0.007986 (-0.001763) | 0.003852 / 0.004328 (-0.000476) | 0.075438 / 0.004250 (0.071188) | 0.065973 / 0.037052 (0.028921) | 0.381513 / 0.258489 (0.123024) | 0.416196 / 0.293841 (0.122355) | 0.035483 / 0.128546 (-0.093063) | 0.009884 / 0.075646 (-0.065762) | 0.341290 / 0.419271 (-0.077982) | 0.060546 / 0.043533 (0.017014) | 0.365101 / 0.255139 (0.109962) | 0.391058 / 0.283200 (0.107859) | 0.026325 / 0.141683 (-0.115358) | 1.815168 / 1.452155 (0.363013) | 1.834711 / 1.492716 (0.341994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222177 / 0.018006 (0.204171) | 0.501151 / 0.000490 (0.500662) | 0.010202 / 0.000200 (0.010002) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034043 / 0.037411 (-0.003368) | 0.097884 / 0.014526 (0.083358) | 0.114022 / 0.176557 (-0.062534) | 0.186200 / 0.737135 (-0.550935) | 0.115555 / 0.296338 (-0.180783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485857 / 0.215209 (0.270648) | 4.959263 / 2.077655 (2.881608) | 2.501085 / 1.504120 (0.996965) | 2.234660 / 1.541195 (0.693465) | 2.238585 / 1.468490 (0.770095) | 0.645431 / 4.584777 (-3.939345) | 4.434311 / 3.745712 (0.688599) | 4.771491 / 5.269862 (-0.498371) | 2.778963 / 4.565676 (-1.786714) | 0.075615 / 0.424275 (-0.348660) | 0.009502 / 0.007607 (0.001895) | 0.546539 / 0.226044 (0.320495) | 5.464242 / 2.268929 (3.195314) | 2.894101 / 55.444624 (-52.550524) | 2.513761 / 6.876477 (-4.362715) | 2.719843 / 2.142072 (0.577770) | 0.678828 / 4.805227 (-4.126399) | 0.157839 / 6.500664 (-6.342825) | 0.071305 / 0.075469 (-0.004164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.496879 / 1.841788 (-0.344909) | 22.214452 / 8.074308 (14.140144) | 17.707541 / 10.191392 (7.516149) | 0.197008 / 0.680424 (-0.483416) | 0.024883 / 0.534201 (-0.509318) | 0.493611 / 0.579283 (-0.085672) | 0.500677 / 0.434364 (0.066313) | 0.569381 / 0.540337 (0.029044) | 0.773950 / 1.386936 (-0.612986) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007337 / 0.011353 (-0.004015) | 0.004572 / 0.011008 (-0.006436) | 0.091123 / 0.038508 (0.052615) | 0.079762 / 0.023109 (0.056652) | 0.450527 / 0.275898 (0.174629) | 0.525097 / 0.323480 (0.201617) | 0.005873 / 0.007986 (-0.002112) | 0.003797 / 0.004328 (-0.000532) | 0.076259 / 0.004250 (0.072009) | 0.062745 / 0.037052 (0.025692) | 0.465553 / 0.258489 (0.207064) | 0.546026 / 0.293841 (0.252186) | 0.035638 / 0.128546 (-0.092909) | 0.010086 / 0.075646 (-0.065560) | 0.109269 / 0.419271 (-0.310002) | 0.056765 / 0.043533 (0.013233) | 0.440887 / 0.255139 (0.185748) | 0.513325 / 0.283200 (0.230125) | 0.027206 / 0.141683 (-0.114476) | 1.863564 / 1.452155 (0.411409) | 1.918206 / 1.492716 (0.425490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266479 / 0.018006 (0.248473) | 0.487971 / 0.000490 (0.487481) | 0.012246 / 0.000200 (0.012046) | 0.000119 / 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) | 0.035281 / 0.037411 (-0.002130) | 0.102991 / 0.014526 (0.088465) | 0.114638 / 0.176557 (-0.061919) | 0.184117 / 0.737135 (-0.553018) | 0.117943 / 0.296338 (-0.178396) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.497897 / 0.215209 (0.282688) | 4.973806 / 2.077655 (2.896151) | 2.596146 / 1.504120 (1.092026) | 2.419694 / 1.541195 (0.878499) | 2.525784 / 1.468490 (1.057294) | 0.568021 / 4.584777 (-4.016756) | 4.296431 / 3.745712 (0.550719) | 3.690682 / 5.269862 (-1.579179) | 2.345965 / 4.565676 (-2.219712) | 0.066859 / 0.424275 (-0.357416) | 0.009093 / 0.007607 (0.001486) | 0.582616 / 0.226044 (0.356571) | 5.826528 / 2.268929 (3.557600) | 3.253222 / 55.444624 (-52.191403) | 2.798447 / 6.876477 (-4.078030) | 3.054609 / 2.142072 (0.912537) | 0.678816 / 4.805227 (-4.126411) | 0.157966 / 6.500664 (-6.342698) | 0.073797 / 0.075469 (-0.001672) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599480 / 1.841788 (-0.242308) | 23.249738 / 8.074308 (15.175430) | 16.965406 / 10.191392 (6.774014) | 0.171390 / 0.680424 (-0.509034) | 0.021810 / 0.534201 (-0.512391) | 0.483339 / 0.579283 (-0.095944) | 0.496615 / 0.434364 (0.062251) | 0.583786 / 0.540337 (0.043448) | 0.741699 / 1.386936 (-0.645237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"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) | 0.006054 / 0.011353 (-0.005299) | 0.003706 / 0.011008 (-0.007302) | 0.080060 / 0.038508 (0.041552) | 0.061479 / 0.023109 (0.038370) | 0.327981 / 0.275898 (0.052083) | 0.356930 / 0.323480 (0.033450) | 0.004671 / 0.007986 (-0.003315) | 0.002901 / 0.004328 (-0.001428) | 0.062425 / 0.004250 (0.058174) | 0.046310 / 0.037052 (0.009258) | 0.323657 / 0.258489 (0.065168) | 0.370130 / 0.293841 (0.076289) | 0.027151 / 0.128546 (-0.101395) | 0.007850 / 0.075646 (-0.067797) | 0.262300 / 0.419271 (-0.156971) | 0.045456 / 0.043533 (0.001923) | 0.325569 / 0.255139 (0.070430) | 0.352962 / 0.283200 (0.069762) | 0.020156 / 0.141683 (-0.121527) | 1.429404 / 1.452155 (-0.022750) | 1.615032 / 1.492716 (0.122316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187309 / 0.018006 (0.169303) | 0.428848 / 0.000490 (0.428358) | 0.003599 / 0.000200 (0.003399) | 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) | 0.023260 / 0.037411 (-0.014151) | 0.072467 / 0.014526 (0.057941) | 0.082398 / 0.176557 (-0.094159) | 0.142573 / 0.737135 (-0.594562) | 0.082570 / 0.296338 (-0.213768) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426503 / 0.215209 (0.211294) | 4.267875 / 2.077655 (2.190220) | 2.189762 / 1.504120 (0.685642) | 2.027992 / 1.541195 (0.486798) | 2.053211 / 1.468490 (0.584721) | 0.503850 / 4.584777 (-4.080927) | 3.086444 / 3.745712 (-0.659268) | 3.319492 / 5.269862 (-1.950370) | 2.070714 / 4.565676 (-2.494962) | 0.057591 / 0.424275 (-0.366684) | 0.006407 / 0.007607 (-0.001200) | 0.501145 / 0.226044 (0.275100) | 5.017753 / 2.268929 (2.748825) | 2.643145 / 55.444624 (-52.801479) | 2.327440 / 6.876477 (-4.549037) | 2.460250 / 2.142072 (0.318178) | 0.589397 / 4.805227 (-4.215830) | 0.124948 / 6.500664 (-6.375716) | 0.060450 / 0.075469 (-0.015020) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279870 / 1.841788 (-0.561918) | 18.115908 / 8.074308 (10.041600) | 13.570032 / 10.191392 (3.378640) | 0.132981 / 0.680424 (-0.547442) | 0.016942 / 0.534201 (-0.517259) | 0.333591 / 0.579283 (-0.245692) | 0.358844 / 0.434364 (-0.075520) | 0.395748 / 0.540337 (-0.144590) | 0.546213 / 1.386936 (-0.840723) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006062 / 0.011353 (-0.005291) | 0.003673 / 0.011008 (-0.007336) | 0.064726 / 0.038508 (0.026218) | 0.061854 / 0.023109 (0.038745) | 0.385343 / 0.275898 (0.109445) | 0.441284 / 0.323480 (0.117805) | 0.004830 / 0.007986 (-0.003156) | 0.002909 / 0.004328 (-0.001420) | 0.063874 / 0.004250 (0.059624) | 0.049331 / 0.037052 (0.012278) | 0.418484 / 0.258489 (0.159995) | 0.451397 / 0.293841 (0.157556) | 0.027665 / 0.128546 (-0.100881) | 0.008088 / 0.075646 (-0.067558) | 0.069625 / 0.419271 (-0.349646) | 0.043437 / 0.043533 (-0.000095) | 0.359789 / 0.255139 (0.104650) | 0.430206 / 0.283200 (0.147007) | 0.022308 / 0.141683 (-0.119375) | 1.461030 / 1.452155 (0.008875) | 1.513683 / 1.492716 (0.020966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230958 / 0.018006 (0.212952) | 0.417553 / 0.000490 (0.417063) | 0.000802 / 0.000200 (0.000602) | 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) | 0.025421 / 0.037411 (-0.011991) | 0.077156 / 0.014526 (0.062630) | 0.087533 / 0.176557 (-0.089024) | 0.138048 / 0.737135 (-0.599087) | 0.089358 / 0.296338 (-0.206981) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439172 / 0.215209 (0.223963) | 4.409509 / 2.077655 (2.331854) | 2.491270 / 1.504120 (0.987150) | 2.308446 / 1.541195 (0.767252) | 2.378440 / 1.468490 (0.909950) | 0.499834 / 4.584777 (-4.084943) | 3.083168 / 3.745712 (-0.662544) | 2.867543 / 5.269862 (-2.402318) | 1.876354 / 4.565676 (-2.689323) | 0.057092 / 0.424275 (-0.367183) | 0.006955 / 0.007607 (-0.000653) | 0.513799 / 0.226044 (0.287754) | 5.126660 / 2.268929 (2.857731) | 2.917348 / 55.444624 (-52.527277) | 2.508035 / 6.876477 (-4.368441) | 2.698089 / 2.142072 (0.556016) | 0.586828 / 4.805227 (-4.218399) | 0.124740 / 6.500664 (-6.375924) | 0.062276 / 0.075469 (-0.013193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291624 / 1.841788 (-0.550164) | 18.199968 / 8.074308 (10.125660) | 13.888139 / 10.191392 (3.696747) | 0.162955 / 0.680424 (-0.517469) | 0.017343 / 0.534201 (-0.516858) | 0.334683 / 0.579283 (-0.244600) | 0.352708 / 0.434364 (-0.081656) | 0.400629 / 0.540337 (-0.139708) | 0.539497 / 1.386936 (-0.847439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"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) | 0.007500 / 0.011353 (-0.003853) | 0.004498 / 0.011008 (-0.006510) | 0.100239 / 0.038508 (0.061731) | 0.083424 / 0.023109 (0.060315) | 0.366664 / 0.275898 (0.090766) | 0.406641 / 0.323480 (0.083161) | 0.004577 / 0.007986 (-0.003409) | 0.004809 / 0.004328 (0.000480) | 0.076898 / 0.004250 (0.072647) | 0.064021 / 0.037052 (0.026969) | 0.375836 / 0.258489 (0.117347) | 0.413008 / 0.293841 (0.119167) | 0.036010 / 0.128546 (-0.092537) | 0.009655 / 0.075646 (-0.065991) | 0.342595 / 0.419271 (-0.076677) | 0.061846 / 0.043533 (0.018313) | 0.376543 / 0.255139 (0.121404) | 0.395858 / 0.283200 (0.112659) | 0.026792 / 0.141683 (-0.114891) | 1.775569 / 1.452155 (0.323414) | 1.865077 / 1.492716 (0.372360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221521 / 0.018006 (0.203514) | 0.474604 / 0.000490 (0.474114) | 0.004354 / 0.000200 (0.004154) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032947 / 0.037411 (-0.004464) | 0.100454 / 0.014526 (0.085928) | 0.111955 / 0.176557 (-0.064602) | 0.179752 / 0.737135 (-0.557383) | 0.114282 / 0.296338 (-0.182056) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458261 / 0.215209 (0.243052) | 4.563536 / 2.077655 (2.485881) | 2.231928 / 1.504120 (0.727808) | 2.036751 / 1.541195 (0.495556) | 2.170413 / 1.468490 (0.701923) | 0.570825 / 4.584777 (-4.013952) | 4.505762 / 3.745712 (0.760050) | 5.033461 / 5.269862 (-0.236401) | 2.704989 / 4.565676 (-1.860687) | 0.067011 / 0.424275 (-0.357264) | 0.008568 / 0.007607 (0.000961) | 0.545151 / 0.226044 (0.319106) | 5.438984 / 2.268929 (3.170055) | 2.771818 / 55.444624 (-52.672806) | 2.393082 / 6.876477 (-4.483395) | 2.467173 / 2.142072 (0.325101) | 0.678849 / 4.805227 (-4.126379) | 0.160480 / 6.500664 (-6.340184) | 0.073681 / 0.075469 (-0.001788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.532272 / 1.841788 (-0.309516) | 22.548741 / 8.074308 (14.474433) | 17.091044 / 10.191392 (6.899652) | 0.172100 / 0.680424 (-0.508324) | 0.022220 / 0.534201 (-0.511981) | 0.467871 / 0.579283 (-0.111412) | 0.491135 / 0.434364 (0.056771) | 0.548433 / 0.540337 (0.008096) | 0.733340 / 1.386936 (-0.653596) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007593 / 0.011353 (-0.003760) | 0.004656 / 0.011008 (-0.006352) | 0.076940 / 0.038508 (0.038431) | 0.085183 / 0.023109 (0.062073) | 0.447178 / 0.275898 (0.171280) | 0.469545 / 0.323480 (0.146065) | 0.006023 / 0.007986 (-0.001962) | 0.003808 / 0.004328 (-0.000520) | 0.076767 / 0.004250 (0.072517) | 0.065713 / 0.037052 (0.028661) | 0.445573 / 0.258489 (0.187084) | 0.481689 / 0.293841 (0.187848) | 0.036893 / 0.128546 (-0.091654) | 0.009976 / 0.075646 (-0.065670) | 0.084443 / 0.419271 (-0.334829) | 0.058829 / 0.043533 (0.015297) | 0.429291 / 0.255139 (0.174152) | 0.454016 / 0.283200 (0.170816) | 0.027289 / 0.141683 (-0.114394) | 1.806786 / 1.452155 (0.354632) | 1.887680 / 1.492716 (0.394964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241012 / 0.018006 (0.223006) | 0.470629 / 0.000490 (0.470139) | 0.003213 / 0.000200 (0.003013) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036896 / 0.037411 (-0.000515) | 0.106932 / 0.014526 (0.092406) | 0.120333 / 0.176557 (-0.056223) | 0.186271 / 0.737135 (-0.550865) | 0.121581 / 0.296338 (-0.174758) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507782 / 0.215209 (0.292573) | 5.062932 / 2.077655 (2.985278) | 2.689539 / 1.504120 (1.185419) | 2.482978 / 1.541195 (0.941784) | 2.561320 / 1.468490 (1.092830) | 0.570664 / 4.584777 (-4.014113) | 4.346051 / 3.745712 (0.600339) | 6.479374 / 5.269862 (1.209513) | 4.096483 / 4.565676 (-0.469194) | 0.067564 / 0.424275 (-0.356711) | 0.009147 / 0.007607 (0.001540) | 0.596059 / 0.226044 (0.370015) | 5.963223 / 2.268929 (3.694295) | 3.201039 / 55.444624 (-52.243585) | 2.816581 / 6.876477 (-4.059896) | 3.047821 / 2.142072 (0.905748) | 0.687749 / 4.805227 (-4.117478) | 0.158174 / 6.500664 (-6.342490) | 0.073329 / 0.075469 (-0.002140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.601346 / 1.841788 (-0.240441) | 23.712210 / 8.074308 (15.637902) | 16.567272 / 10.191392 (6.375880) | 0.224745 / 0.680424 (-0.455679) | 0.021662 / 0.534201 (-0.512539) | 0.471427 / 0.579283 (-0.107856) | 0.498751 / 0.434364 (0.064387) | 0.572047 / 0.540337 (0.031710) | 0.821868 / 1.386936 (-0.565068) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"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) | 0.006371 / 0.011353 (-0.004981) | 0.003749 / 0.011008 (-0.007259) | 0.084155 / 0.038508 (0.045647) | 0.072450 / 0.023109 (0.049340) | 0.308002 / 0.275898 (0.032104) | 0.340471 / 0.323480 (0.016991) | 0.005054 / 0.007986 (-0.002931) | 0.003176 / 0.004328 (-0.001152) | 0.064867 / 0.004250 (0.060616) | 0.054305 / 0.037052 (0.017252) | 0.321047 / 0.258489 (0.062558) | 0.345999 / 0.293841 (0.052158) | 0.030507 / 0.128546 (-0.098039) | 0.008299 / 0.075646 (-0.067347) | 0.287682 / 0.419271 (-0.131590) | 0.052048 / 0.043533 (0.008515) | 0.308322 / 0.255139 (0.053183) | 0.333220 / 0.283200 (0.050020) | 0.022698 / 0.141683 (-0.118985) | 1.474033 / 1.452155 (0.021879) | 1.544790 / 1.492716 (0.052074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200612 / 0.018006 (0.182606) | 0.450934 / 0.000490 (0.450445) | 0.005383 / 0.000200 (0.005183) | 0.000200 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027759 / 0.037411 (-0.009652) | 0.080935 / 0.014526 (0.066409) | 0.093041 / 0.176557 (-0.083516) | 0.148643 / 0.737135 (-0.588492) | 0.093463 / 0.296338 (-0.202876) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.381653 / 0.215209 (0.166444) | 3.810699 / 2.077655 (1.733044) | 1.866858 / 1.504120 (0.362738) | 1.716985 / 1.541195 (0.175790) | 1.788071 / 1.468490 (0.319581) | 0.481130 / 4.584777 (-4.103647) | 3.529798 / 3.745712 (-0.215914) | 3.982037 / 5.269862 (-1.287824) | 2.324866 / 4.565676 (-2.240811) | 0.056767 / 0.424275 (-0.367508) | 0.007306 / 0.007607 (-0.000301) | 0.459472 / 0.226044 (0.233428) | 4.602808 / 2.268929 (2.333879) | 2.332014 / 55.444624 (-53.112610) | 2.044858 / 6.876477 (-4.831619) | 2.204165 / 2.142072 (0.062093) | 0.577946 / 4.805227 (-4.227281) | 0.130900 / 6.500664 (-6.369764) | 0.059054 / 0.075469 (-0.016415) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245211 / 1.841788 (-0.596576) | 19.176397 / 8.074308 (11.102089) | 13.995280 / 10.191392 (3.803888) | 0.171743 / 0.680424 (-0.508681) | 0.018038 / 0.534201 (-0.516163) | 0.392338 / 0.579283 (-0.186945) | 0.419370 / 0.434364 (-0.014994) | 0.477829 / 0.540337 (-0.062508) | 0.677409 / 1.386936 (-0.709527) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006513 / 0.011353 (-0.004840) | 0.003984 / 0.011008 (-0.007024) | 0.064516 / 0.038508 (0.026008) | 0.070504 / 0.023109 (0.047395) | 0.384509 / 0.275898 (0.108611) | 0.410564 / 0.323480 (0.087084) | 0.005310 / 0.007986 (-0.002675) | 0.003268 / 0.004328 (-0.001061) | 0.064684 / 0.004250 (0.060433) | 0.055367 / 0.037052 (0.018315) | 0.399108 / 0.258489 (0.140619) | 0.422740 / 0.293841 (0.128900) | 0.031624 / 0.128546 (-0.096922) | 0.008617 / 0.075646 (-0.067030) | 0.070929 / 0.419271 (-0.348342) | 0.049146 / 0.043533 (0.005613) | 0.385492 / 0.255139 (0.130353) | 0.407434 / 0.283200 (0.124234) | 0.021972 / 0.141683 (-0.119711) | 1.496135 / 1.452155 (0.043980) | 1.533739 / 1.492716 (0.041023) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226218 / 0.018006 (0.208211) | 0.443176 / 0.000490 (0.442686) | 0.000376 / 0.000200 (0.000176) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030315 / 0.037411 (-0.007097) | 0.086416 / 0.014526 (0.071890) | 0.097725 / 0.176557 (-0.078831) | 0.150407 / 0.737135 (-0.586728) | 0.099914 / 0.296338 (-0.196424) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409807 / 0.215209 (0.194598) | 4.099086 / 2.077655 (2.021431) | 2.103160 / 1.504120 (0.599040) | 1.927927 / 1.541195 (0.386733) | 1.977751 / 1.468490 (0.509261) | 0.476995 / 4.584777 (-4.107781) | 3.521835 / 3.745712 (-0.223877) | 3.237695 / 5.269862 (-2.032167) | 1.995953 / 4.565676 (-2.569724) | 0.056208 / 0.424275 (-0.368068) | 0.007660 / 0.007607 (0.000053) | 0.483537 / 0.226044 (0.257492) | 4.833974 / 2.268929 (2.565046) | 2.589115 / 55.444624 (-52.855510) | 2.228076 / 6.876477 (-4.648401) | 2.395271 / 2.142072 (0.253198) | 0.577534 / 4.805227 (-4.227694) | 0.131432 / 6.500664 (-6.369232) | 0.060999 / 0.075469 (-0.014471) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356043 / 1.841788 (-0.485745) | 19.470401 / 8.074308 (11.396093) | 14.091266 / 10.191392 (3.899874) | 0.166809 / 0.680424 (-0.513615) | 0.018782 / 0.534201 (-0.515419) | 0.394916 / 0.579283 (-0.184367) | 0.411378 / 0.434364 (-0.022986) | 0.466886 / 0.540337 (-0.073451) | 0.617369 / 1.386936 (-0.769567) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"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) | 0.007590 / 0.011353 (-0.003762) | 0.004068 / 0.011008 (-0.006941) | 0.105479 / 0.038508 (0.066971) | 0.085614 / 0.023109 (0.062505) | 0.384325 / 0.275898 (0.108427) | 0.467867 / 0.323480 (0.144387) | 0.004652 / 0.007986 (-0.003333) | 0.005445 / 0.004328 (0.001117) | 0.079604 / 0.004250 (0.075353) | 0.066031 / 0.037052 (0.028978) | 0.426184 / 0.258489 (0.167695) | 0.480712 / 0.293841 (0.186871) | 0.037837 / 0.128546 (-0.090709) | 0.009765 / 0.075646 (-0.065882) | 0.351316 / 0.419271 (-0.067955) | 0.063634 / 0.043533 (0.020101) | 0.420297 / 0.255139 (0.165158) | 0.449169 / 0.283200 (0.165969) | 0.030947 / 0.141683 (-0.110736) | 1.840184 / 1.452155 (0.388029) | 1.934074 / 1.492716 (0.441357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223483 / 0.018006 (0.205477) | 0.521086 / 0.000490 (0.520596) | 0.000379 / 0.000200 (0.000179) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032011 / 0.037411 (-0.005400) | 0.101474 / 0.014526 (0.086948) | 0.108652 / 0.176557 (-0.067904) | 0.173340 / 0.737135 (-0.563796) | 0.114186 / 0.296338 (-0.182153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478020 / 0.215209 (0.262811) | 4.645400 / 2.077655 (2.567746) | 2.590763 / 1.504120 (1.086643) | 2.383002 / 1.541195 (0.841807) | 2.482550 / 1.468490 (1.014060) | 0.572417 / 4.584777 (-4.012360) | 4.233436 / 3.745712 (0.487724) | 4.858823 / 5.269862 (-0.411038) | 2.838913 / 4.565676 (-1.726764) | 0.070010 / 0.424275 (-0.354265) | 0.009602 / 0.007607 (0.001995) | 0.538735 / 0.226044 (0.312691) | 5.534340 / 2.268929 (3.265411) | 2.915006 / 55.444624 (-52.529619) | 2.625132 / 6.876477 (-4.251345) | 2.537838 / 2.142072 (0.395766) | 0.667870 / 4.805227 (-4.137357) | 0.146330 / 6.500664 (-6.354334) | 0.071631 / 0.075469 (-0.003838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.594686 / 1.841788 (-0.247101) | 22.311113 / 8.074308 (14.236804) | 17.603983 / 10.191392 (7.412591) | 0.195995 / 0.680424 (-0.484428) | 0.022254 / 0.534201 (-0.511947) | 0.479661 / 0.579283 (-0.099622) | 0.463626 / 0.434364 (0.029262) | 0.483465 / 0.540337 (-0.056873) | 0.676141 / 1.386936 (-0.710795) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006146 / 0.011353 (-0.005207) | 0.004856 / 0.011008 (-0.006152) | 0.067506 / 0.038508 (0.028998) | 0.073968 / 0.023109 (0.050859) | 0.470013 / 0.275898 (0.194115) | 0.479022 / 0.323480 (0.155542) | 0.005972 / 0.007986 (-0.002014) | 0.003846 / 0.004328 (-0.000483) | 0.075141 / 0.004250 (0.070890) | 0.058597 / 0.037052 (0.021544) | 0.481454 / 0.258489 (0.222965) | 0.515634 / 0.293841 (0.221793) | 0.034979 / 0.128546 (-0.093567) | 0.010385 / 0.075646 (-0.065261) | 0.072649 / 0.419271 (-0.346622) | 0.058183 / 0.043533 (0.014650) | 0.462138 / 0.255139 (0.206999) | 0.476093 / 0.283200 (0.192893) | 0.032918 / 0.141683 (-0.108765) | 1.820530 / 1.452155 (0.368375) | 1.626360 / 1.492716 (0.133644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208970 / 0.018006 (0.190964) | 0.492478 / 0.000490 (0.491988) | 0.005487 / 0.000200 (0.005287) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037896 / 0.037411 (0.000484) | 0.089752 / 0.014526 (0.075227) | 0.107445 / 0.176557 (-0.069111) | 0.181260 / 0.737135 (-0.555876) | 0.105700 / 0.296338 (-0.190639) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495031 / 0.215209 (0.279821) | 4.806939 / 2.077655 (2.729284) | 2.227928 / 1.504120 (0.723808) | 2.067117 / 1.541195 (0.525922) | 2.348982 / 1.468490 (0.880492) | 0.567201 / 4.584777 (-4.017576) | 4.166592 / 3.745712 (0.420880) | 3.654329 / 5.269862 (-1.615533) | 2.331092 / 4.565676 (-2.234584) | 0.062212 / 0.424275 (-0.362063) | 0.008775 / 0.007607 (0.001168) | 0.515413 / 0.226044 (0.289369) | 5.449300 / 2.268929 (3.180371) | 3.206574 / 55.444624 (-52.238050) | 2.600455 / 6.876477 (-4.276022) | 3.041162 / 2.142072 (0.899089) | 0.681899 / 4.805227 (-4.123328) | 0.155400 / 6.500664 (-6.345265) | 0.073933 / 0.075469 (-0.001537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.572329 / 1.841788 (-0.269459) | 23.638519 / 8.074308 (15.564211) | 17.145663 / 10.191392 (6.954271) | 0.232690 / 0.680424 (-0.447734) | 0.028620 / 0.534201 (-0.505581) | 0.488105 / 0.579283 (-0.091178) | 0.490365 / 0.434364 (0.056001) | 0.599501 / 0.540337 (0.059164) | 0.708101 / 1.386936 (-0.678835) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"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) | 0.005947 / 0.011353 (-0.005406) | 0.003577 / 0.011008 (-0.007431) | 0.081631 / 0.038508 (0.043122) | 0.058651 / 0.023109 (0.035541) | 0.342742 / 0.275898 (0.066843) | 0.384130 / 0.323480 (0.060650) | 0.004620 / 0.007986 (-0.003366) | 0.002885 / 0.004328 (-0.001444) | 0.063698 / 0.004250 (0.059448) | 0.048953 / 0.037052 (0.011901) | 0.367880 / 0.258489 (0.109391) | 0.407050 / 0.293841 (0.113209) | 0.027242 / 0.128546 (-0.101305) | 0.007914 / 0.075646 (-0.067733) | 0.262156 / 0.419271 (-0.157116) | 0.044750 / 0.043533 (0.001218) | 0.351613 / 0.255139 (0.096474) | 0.380284 / 0.283200 (0.097084) | 0.020080 / 0.141683 (-0.121603) | 1.498101 / 1.452155 (0.045946) | 1.543608 / 1.492716 (0.050892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180014 / 0.018006 (0.162008) | 0.436172 / 0.000490 (0.435682) | 0.003694 / 0.000200 (0.003494) | 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) | 0.024389 / 0.037411 (-0.013022) | 0.072874 / 0.014526 (0.058348) | 0.083469 / 0.176557 (-0.093088) | 0.144600 / 0.737135 (-0.592536) | 0.084229 / 0.296338 (-0.212110) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.391636 / 0.215209 (0.176427) | 3.906941 / 2.077655 (1.829286) | 1.901944 / 1.504120 (0.397825) | 1.762702 / 1.541195 (0.221507) | 1.817970 / 1.468490 (0.349480) | 0.500345 / 4.584777 (-4.084432) | 3.011351 / 3.745712 (-0.734361) | 4.417763 / 5.269862 (-0.852098) | 2.689744 / 4.565676 (-1.875933) | 0.057765 / 0.424275 (-0.366511) | 0.006412 / 0.007607 (-0.001195) | 0.468156 / 0.226044 (0.242112) | 4.664975 / 2.268929 (2.396047) | 2.323355 / 55.444624 (-53.121270) | 1.984280 / 6.876477 (-4.892197) | 2.165215 / 2.142072 (0.023142) | 0.586950 / 4.805227 (-4.218278) | 0.124363 / 6.500664 (-6.376301) | 0.060702 / 0.075469 (-0.014767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238870 / 1.841788 (-0.602917) | 18.587360 / 8.074308 (10.513052) | 13.831674 / 10.191392 (3.640282) | 0.143542 / 0.680424 (-0.536882) | 0.016913 / 0.534201 (-0.517288) | 0.332314 / 0.579283 (-0.246969) | 0.345419 / 0.434364 (-0.088945) | 0.381257 / 0.540337 (-0.159081) | 0.537844 / 1.386936 (-0.849092) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006294 / 0.011353 (-0.005059) | 0.003714 / 0.011008 (-0.007294) | 0.062684 / 0.038508 (0.024176) | 0.063520 / 0.023109 (0.040411) | 0.389591 / 0.275898 (0.113693) | 0.444278 / 0.323480 (0.120798) | 0.004825 / 0.007986 (-0.003160) | 0.003010 / 0.004328 (-0.001318) | 0.062767 / 0.004250 (0.058517) | 0.051739 / 0.037052 (0.014686) | 0.434299 / 0.258489 (0.175810) | 0.452003 / 0.293841 (0.158162) | 0.027375 / 0.128546 (-0.101171) | 0.008135 / 0.075646 (-0.067511) | 0.067401 / 0.419271 (-0.351871) | 0.042752 / 0.043533 (-0.000780) | 0.367633 / 0.255139 (0.112494) | 0.433039 / 0.283200 (0.149840) | 0.021086 / 0.141683 (-0.120597) | 1.488024 / 1.452155 (0.035870) | 1.507767 / 1.492716 (0.015050) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230046 / 0.018006 (0.212040) | 0.428085 / 0.000490 (0.427595) | 0.002188 / 0.000200 (0.001988) | 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) | 0.026705 / 0.037411 (-0.010706) | 0.082466 / 0.014526 (0.067940) | 0.089378 / 0.176557 (-0.087179) | 0.147287 / 0.737135 (-0.589849) | 0.090426 / 0.296338 (-0.205913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430882 / 0.215209 (0.215672) | 4.296224 / 2.077655 (2.218569) | 2.229982 / 1.504120 (0.725862) | 2.048506 / 1.541195 (0.507311) | 2.129514 / 1.468490 (0.661024) | 0.502964 / 4.584777 (-4.081813) | 3.048125 / 3.745712 (-0.697587) | 4.208636 / 5.269862 (-1.061226) | 2.594015 / 4.565676 (-1.971661) | 0.057967 / 0.424275 (-0.366308) | 0.006875 / 0.007607 (-0.000732) | 0.513872 / 0.226044 (0.287828) | 5.126435 / 2.268929 (2.857506) | 2.691278 / 55.444624 (-52.753346) | 2.361723 / 6.876477 (-4.514754) | 2.511213 / 2.142072 (0.369141) | 0.593558 / 4.805227 (-4.211670) | 0.129332 / 6.500664 (-6.371332) | 0.064051 / 0.075469 (-0.011418) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289049 / 1.841788 (-0.552739) | 18.912363 / 8.074308 (10.838055) | 14.226500 / 10.191392 (4.035108) | 0.131392 / 0.680424 (-0.549032) | 0.016750 / 0.534201 (-0.517451) | 0.330078 / 0.579283 (-0.249205) | 0.347588 / 0.434364 (-0.086776) | 0.383234 / 0.540337 (-0.157103) | 0.510967 / 1.386936 (-0.875969) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"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) | 0.005974 / 0.011353 (-0.005379) | 0.003691 / 0.011008 (-0.007317) | 0.079410 / 0.038508 (0.040902) | 0.061769 / 0.023109 (0.038660) | 0.323310 / 0.275898 (0.047412) | 0.354325 / 0.323480 (0.030845) | 0.004794 / 0.007986 (-0.003191) | 0.002899 / 0.004328 (-0.001430) | 0.062104 / 0.004250 (0.057854) | 0.048973 / 0.037052 (0.011921) | 0.326497 / 0.258489 (0.068008) | 0.361347 / 0.293841 (0.067506) | 0.026741 / 0.128546 (-0.101805) | 0.007936 / 0.075646 (-0.067710) | 0.259168 / 0.419271 (-0.160104) | 0.044859 / 0.043533 (0.001327) | 0.319342 / 0.255139 (0.064203) | 0.343711 / 0.283200 (0.060511) | 0.022298 / 0.141683 (-0.119384) | 1.451595 / 1.452155 (-0.000560) | 1.573730 / 1.492716 (0.081014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.173086 / 0.018006 (0.155080) | 0.432400 / 0.000490 (0.431910) | 0.003739 / 0.000200 (0.003539) | 0.000073 / 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) | 0.024477 / 0.037411 (-0.012934) | 0.073463 / 0.014526 (0.058937) | 0.083410 / 0.176557 (-0.093146) | 0.144760 / 0.737135 (-0.592376) | 0.084199 / 0.296338 (-0.212140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388251 / 0.215209 (0.173042) | 3.875375 / 2.077655 (1.797720) | 1.875515 / 1.504120 (0.371395) | 1.729282 / 1.541195 (0.188087) | 1.784732 / 1.468490 (0.316242) | 0.496985 / 4.584777 (-4.087792) | 3.030276 / 3.745712 (-0.715436) | 2.813192 / 5.269862 (-2.456669) | 1.868647 / 4.565676 (-2.697030) | 0.057376 / 0.424275 (-0.366899) | 0.006463 / 0.007607 (-0.001144) | 0.462153 / 0.226044 (0.236108) | 4.586583 / 2.268929 (2.317654) | 2.287730 / 55.444624 (-53.156894) | 1.972177 / 6.876477 (-4.904299) | 2.151592 / 2.142072 (0.009520) | 0.587169 / 4.805227 (-4.218058) | 0.127063 / 6.500664 (-6.373601) | 0.060297 / 0.075469 (-0.015172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267651 / 1.841788 (-0.574136) | 18.426011 / 8.074308 (10.351703) | 14.050470 / 10.191392 (3.859078) | 0.148063 / 0.680424 (-0.532361) | 0.017112 / 0.534201 (-0.517089) | 0.330051 / 0.579283 (-0.249232) | 0.358730 / 0.434364 (-0.075634) | 0.392365 / 0.540337 (-0.147972) | 0.534650 / 1.386936 (-0.852286) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005936 / 0.011353 (-0.005417) | 0.003652 / 0.011008 (-0.007356) | 0.063066 / 0.038508 (0.024558) | 0.060617 / 0.023109 (0.037507) | 0.388293 / 0.275898 (0.112395) | 0.411422 / 0.323480 (0.087942) | 0.004691 / 0.007986 (-0.003295) | 0.002857 / 0.004328 (-0.001472) | 0.064198 / 0.004250 (0.059947) | 0.049124 / 0.037052 (0.012071) | 0.403601 / 0.258489 (0.145112) | 0.413619 / 0.293841 (0.119778) | 0.027279 / 0.128546 (-0.101267) | 0.008072 / 0.075646 (-0.067575) | 0.067890 / 0.419271 (-0.351381) | 0.041866 / 0.043533 (-0.001667) | 0.393438 / 0.255139 (0.138299) | 0.402865 / 0.283200 (0.119666) | 0.023381 / 0.141683 (-0.118302) | 1.496324 / 1.452155 (0.044170) | 1.538080 / 1.492716 (0.045364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212065 / 0.018006 (0.194059) | 0.410511 / 0.000490 (0.410021) | 0.001236 / 0.000200 (0.001036) | 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) | 0.026012 / 0.037411 (-0.011399) | 0.076592 / 0.014526 (0.062066) | 0.085963 / 0.176557 (-0.090594) | 0.137803 / 0.737135 (-0.599332) | 0.087594 / 0.296338 (-0.208745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434283 / 0.215209 (0.219074) | 4.345478 / 2.077655 (2.267824) | 2.400954 / 1.504120 (0.896834) | 2.282024 / 1.541195 (0.740829) | 2.414247 / 1.468490 (0.945757) | 0.501855 / 4.584777 (-4.082922) | 3.059433 / 3.745712 (-0.686279) | 2.811288 / 5.269862 (-2.458574) | 1.856839 / 4.565676 (-2.708838) | 0.058017 / 0.424275 (-0.366258) | 0.006844 / 0.007607 (-0.000763) | 0.515376 / 0.226044 (0.289332) | 5.148775 / 2.268929 (2.879847) | 2.930807 / 55.444624 (-52.513817) | 2.520532 / 6.876477 (-4.355944) | 2.746299 / 2.142072 (0.604227) | 0.590102 / 4.805227 (-4.215125) | 0.125747 / 6.500664 (-6.374917) | 0.061873 / 0.075469 (-0.013597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306247 / 1.841788 (-0.535541) | 18.366048 / 8.074308 (10.291740) | 13.855617 / 10.191392 (3.664225) | 0.150124 / 0.680424 (-0.530300) | 0.017189 / 0.534201 (-0.517012) | 0.336285 / 0.579283 (-0.242998) | 0.344985 / 0.434364 (-0.089379) | 0.397973 / 0.540337 (-0.142364) | 0.536142 / 1.386936 (-0.850794) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"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) | 0.006401 / 0.011353 (-0.004952) | 0.003789 / 0.011008 (-0.007219) | 0.079516 / 0.038508 (0.041008) | 0.068279 / 0.023109 (0.045170) | 0.295691 / 0.275898 (0.019793) | 0.327208 / 0.323480 (0.003728) | 0.005070 / 0.007986 (-0.002915) | 0.003044 / 0.004328 (-0.001285) | 0.061411 / 0.004250 (0.057161) | 0.053227 / 0.037052 (0.016175) | 0.297368 / 0.258489 (0.038879) | 0.334740 / 0.293841 (0.040899) | 0.029459 / 0.128546 (-0.099087) | 0.008080 / 0.075646 (-0.067566) | 0.267344 / 0.419271 (-0.151927) | 0.049877 / 0.043533 (0.006344) | 0.293853 / 0.255139 (0.038714) | 0.319819 / 0.283200 (0.036620) | 0.022593 / 0.141683 (-0.119089) | 1.459054 / 1.452155 (0.006900) | 1.471250 / 1.492716 (-0.021466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194326 / 0.018006 (0.176320) | 0.443565 / 0.000490 (0.443075) | 0.003745 / 0.000200 (0.003545) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026640 / 0.037411 (-0.010772) | 0.077630 / 0.014526 (0.063104) | 0.089364 / 0.176557 (-0.087192) | 0.147327 / 0.737135 (-0.589809) | 0.089603 / 0.296338 (-0.206735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.373758 / 0.215209 (0.158549) | 3.746778 / 2.077655 (1.669123) | 1.814991 / 1.504120 (0.310871) | 1.645650 / 1.541195 (0.104455) | 1.690752 / 1.468490 (0.222262) | 0.472117 / 4.584777 (-4.112660) | 3.457346 / 3.745712 (-0.288367) | 3.138869 / 5.269862 (-2.130993) | 1.934924 / 4.565676 (-2.630753) | 0.055709 / 0.424275 (-0.368566) | 0.006680 / 0.007607 (-0.000927) | 0.446874 / 0.226044 (0.220829) | 4.458409 / 2.268929 (2.189480) | 2.253932 / 55.444624 (-53.190693) | 2.007240 / 6.876477 (-4.869237) | 2.081687 / 2.142072 (-0.060386) | 0.563379 / 4.805227 (-4.241848) | 0.128694 / 6.500664 (-6.371970) | 0.057409 / 0.075469 (-0.018060) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212231 / 1.841788 (-0.629556) | 18.519121 / 8.074308 (10.444813) | 13.582243 / 10.191392 (3.390851) | 0.142488 / 0.680424 (-0.537936) | 0.017421 / 0.534201 (-0.516780) | 0.366864 / 0.579283 (-0.212419) | 0.401467 / 0.434364 (-0.032897) | 0.443659 / 0.540337 (-0.096679) | 0.618854 / 1.386936 (-0.768082) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006121 / 0.011353 (-0.005232) | 0.003690 / 0.011008 (-0.007318) | 0.060340 / 0.038508 (0.021832) | 0.067215 / 0.023109 (0.044106) | 0.382846 / 0.275898 (0.106948) | 0.415774 / 0.323480 (0.092294) | 0.004868 / 0.007986 (-0.003118) | 0.003108 / 0.004328 (-0.001221) | 0.060572 / 0.004250 (0.056321) | 0.050453 / 0.037052 (0.013401) | 0.400494 / 0.258489 (0.142005) | 0.424368 / 0.293841 (0.130527) | 0.030279 / 0.128546 (-0.098267) | 0.008151 / 0.075646 (-0.067495) | 0.066707 / 0.419271 (-0.352564) | 0.046118 / 0.043533 (0.002585) | 0.386697 / 0.255139 (0.131558) | 0.410156 / 0.283200 (0.126957) | 0.020688 / 0.141683 (-0.120995) | 1.418162 / 1.452155 (-0.033993) | 1.463057 / 1.492716 (-0.029659) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216081 / 0.018006 (0.198075) | 0.440541 / 0.000490 (0.440051) | 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) | 0.027763 / 0.037411 (-0.009648) | 0.082316 / 0.014526 (0.067791) | 0.094086 / 0.176557 (-0.082471) | 0.144738 / 0.737135 (-0.592398) | 0.094837 / 0.296338 (-0.201501) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396277 / 0.215209 (0.181068) | 3.958791 / 2.077655 (1.881136) | 2.021367 / 1.504120 (0.517247) | 1.860112 / 1.541195 (0.318917) | 1.886032 / 1.468490 (0.417541) | 0.468536 / 4.584777 (-4.116241) | 3.417950 / 3.745712 (-0.327762) | 4.849991 / 5.269862 (-0.419871) | 2.773935 / 4.565676 (-1.791742) | 0.055813 / 0.424275 (-0.368462) | 0.007053 / 0.007607 (-0.000554) | 0.470167 / 0.226044 (0.244122) | 4.702969 / 2.268929 (2.434041) | 2.474161 / 55.444624 (-52.970464) | 2.171256 / 6.876477 (-4.705220) | 2.315373 / 2.142072 (0.173301) | 0.589195 / 4.805227 (-4.216032) | 0.128237 / 6.500664 (-6.372427) | 0.058641 / 0.075469 (-0.016828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292947 / 1.841788 (-0.548841) | 18.851300 / 8.074308 (10.776992) | 14.089764 / 10.191392 (3.898372) | 0.164853 / 0.680424 (-0.515571) | 0.017281 / 0.534201 (-0.516920) | 0.359112 / 0.579283 (-0.220171) | 0.386696 / 0.434364 (-0.047668) | 0.428222 / 0.540337 (-0.112115) | 0.568659 / 1.386936 (-0.818277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"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) | 0.006051 / 0.011353 (-0.005301) | 0.003654 / 0.011008 (-0.007355) | 0.080081 / 0.038508 (0.041572) | 0.062925 / 0.023109 (0.039815) | 0.358097 / 0.275898 (0.082199) | 0.405728 / 0.323480 (0.082248) | 0.005359 / 0.007986 (-0.002627) | 0.002820 / 0.004328 (-0.001508) | 0.063108 / 0.004250 (0.058858) | 0.049627 / 0.037052 (0.012575) | 0.397870 / 0.258489 (0.139381) | 0.437157 / 0.293841 (0.143316) | 0.027707 / 0.128546 (-0.100839) | 0.007911 / 0.075646 (-0.067735) | 0.260991 / 0.419271 (-0.158280) | 0.044771 / 0.043533 (0.001238) | 0.340230 / 0.255139 (0.085091) | 0.384925 / 0.283200 (0.101725) | 0.021369 / 0.141683 (-0.120314) | 1.431439 / 1.452155 (-0.020715) | 1.478794 / 1.492716 (-0.013922) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182626 / 0.018006 (0.164620) | 0.435551 / 0.000490 (0.435061) | 0.003015 / 0.000200 (0.002815) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024703 / 0.037411 (-0.012708) | 0.073640 / 0.014526 (0.059114) | 0.084598 / 0.176557 (-0.091959) | 0.145810 / 0.737135 (-0.591325) | 0.085125 / 0.296338 (-0.211213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394539 / 0.215209 (0.179330) | 3.945882 / 2.077655 (1.868227) | 1.947166 / 1.504120 (0.443046) | 1.763305 / 1.541195 (0.222111) | 1.816208 / 1.468490 (0.347718) | 0.498880 / 4.584777 (-4.085897) | 3.098283 / 3.745712 (-0.647429) | 2.823474 / 5.269862 (-2.446388) | 1.873993 / 4.565676 (-2.691684) | 0.058097 / 0.424275 (-0.366179) | 0.006488 / 0.007607 (-0.001119) | 0.466711 / 0.226044 (0.240667) | 4.671520 / 2.268929 (2.402592) | 2.363381 / 55.444624 (-53.081243) | 2.052092 / 6.876477 (-4.824385) | 2.209212 / 2.142072 (0.067140) | 0.594650 / 4.805227 (-4.210577) | 0.125604 / 6.500664 (-6.375060) | 0.061511 / 0.075469 (-0.013958) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226564 / 1.841788 (-0.615224) | 18.583605 / 8.074308 (10.509297) | 13.993091 / 10.191392 (3.801699) | 0.146185 / 0.680424 (-0.534239) | 0.016839 / 0.534201 (-0.517362) | 0.334116 / 0.579283 (-0.245167) | 0.360780 / 0.434364 (-0.073584) | 0.386008 / 0.540337 (-0.154329) | 0.643278 / 1.386936 (-0.743658) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.003658 / 0.011008 (-0.007350) | 0.063250 / 0.038508 (0.024742) | 0.063542 / 0.023109 (0.040433) | 0.366845 / 0.275898 (0.090947) | 0.409794 / 0.323480 (0.086314) | 0.005678 / 0.007986 (-0.002308) | 0.003061 / 0.004328 (-0.001268) | 0.063561 / 0.004250 (0.059311) | 0.052648 / 0.037052 (0.015596) | 0.378096 / 0.258489 (0.119607) | 0.410706 / 0.293841 (0.116865) | 0.027668 / 0.128546 (-0.100878) | 0.008045 / 0.075646 (-0.067601) | 0.068290 / 0.419271 (-0.350981) | 0.042602 / 0.043533 (-0.000930) | 0.364976 / 0.255139 (0.109837) | 0.395599 / 0.283200 (0.112400) | 0.022733 / 0.141683 (-0.118950) | 1.522473 / 1.452155 (0.070319) | 1.515891 / 1.492716 (0.023175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232554 / 0.018006 (0.214547) | 0.420702 / 0.000490 (0.420213) | 0.002161 / 0.000200 (0.001961) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026276 / 0.037411 (-0.011135) | 0.078504 / 0.014526 (0.063978) | 0.088989 / 0.176557 (-0.087567) | 0.144044 / 0.737135 (-0.593091) | 0.091074 / 0.296338 (-0.205265) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420189 / 0.215209 (0.204980) | 4.189596 / 2.077655 (2.111941) | 2.316425 / 1.504120 (0.812305) | 2.186877 / 1.541195 (0.645682) | 2.259065 / 1.468490 (0.790575) | 0.502827 / 4.584777 (-4.081950) | 3.135266 / 3.745712 (-0.610446) | 2.838808 / 5.269862 (-2.431053) | 1.876519 / 4.565676 (-2.689158) | 0.057802 / 0.424275 (-0.366473) | 0.006824 / 0.007607 (-0.000784) | 0.500213 / 0.226044 (0.274168) | 4.999798 / 2.268929 (2.730869) | 2.627713 / 55.444624 (-52.816911) | 2.344263 / 6.876477 (-4.532214) | 2.415449 / 2.142072 (0.273376) | 0.593082 / 4.805227 (-4.212145) | 0.125787 / 6.500664 (-6.374877) | 0.062699 / 0.075469 (-0.012770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308219 / 1.841788 (-0.533569) | 18.703099 / 8.074308 (10.628791) | 13.976234 / 10.191392 (3.784842) | 0.144037 / 0.680424 (-0.536387) | 0.016592 / 0.534201 (-0.517609) | 0.333078 / 0.579283 (-0.246206) | 0.342317 / 0.434364 (-0.092047) | 0.396837 / 0.540337 (-0.143500) | 0.532641 / 1.386936 (-0.854295) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I'm aware of this behavior, which is tricky to solve due to fsspec's hidden file handling (see https://github.com/huggingface/datasets/issues/4115#issuecomment-1108819538). I've tested some regex patterns to address this, and they seem to work (will push them on Monday; btw they don't break any of fsspec's tests, so maybe we can contribute this as an enhancement to them). Also, perhaps we should include the files starting with `__` in the results again (we hadn't had issues with this pattern before). WDYT?", "I see. Feel free to merge this one if it's good for you btw :)\r\n\r\n> Also, perhaps we should include the files starting with __ in the results again (we hadn't had issues with this pattern before)\r\n\r\nThe point was mainly to ignore `__pycache__` directories for example. Also also for consistency with the iter_files/iter_archive which are already ignoring them", "Very elegant solution! Feel free to merge if the CI is green after adding the tests.", "CI failure is unrelated to this PR" ]

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[ "Hi @lhoestq, thanks for the review.\r\nI have made all the changes, PTAL! :) " ]

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[ "It's an issue with the streaming mode:\r\n\r\n```python\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('nli_tr', name='snli_tr',split='test', streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/nli_tr/c2ddd0c0a70caddac6a81c2dae5ca7939f00060d517d08f1983927818dba6521/nli_tr.py\", line 155, in _generate_examples\r\n with codecs.open(filepath, encoding=\"utf-8\") as f:\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/codecs.py\", line 905, in open\r\n file = builtins.open(filename, mode, buffering)\r\nFileNotFoundError: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_test.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip'\r\n```\r\n\r\nNote that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. `nli_tr` is above the limit, hence the error.", "cc @huggingface/datasets ", "Apparently there is an issue with the data source URLs: Server Not Found\r\n- https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip\r\n\r\nWe are contacting the authors to ask them: \r\n@e-budur you are one of the authors: are you aware of the issue with the URLs of your data ?", "Reported to their repo:\r\n- https://github.com/boun-tabi/NLI-TR/issues/9", "The server issue was temporary and is now resolved.", "Once we have implemented support for streaming, the viewer works: https://huggingface.co/datasets/nli_tr" ]

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[ "I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Remaining CI failures are unrelated to this fix, merging" ]

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[ "Added dummy data and encoding to open(). Now everything should be fine, hopefully :)" ]

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[ "The docs for this PR live [here](https://huggingface.co/docs/datasets/pr_3882). All of your documentation changes will be reflected on that endpoint." ]

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[ "> Nice thanks !\r\n> \r\n> I left a few comments\r\n> \r\n> Also it looks like this PR includes changes about other files than the ones for ethos\r\n> \r\n> Can you create another branch and another PR please ?\r\n\r\n@lhoestq Should I close this PR? The new one is the: #1453", "You can create another PR and close this one if you don't mind", "> You can create another PR and close this one if you don't mind\r\n\r\nPerfect! You should see the #1453 PR for the fixed version! Thanks" ]

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

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[ "This dataset has files in ASF/AMC format. [ The skeleton file is the ASF file (Acclaim Skeleton File). The motion file is the AMC file (Acclaim Motion Capture data). ] \r\n\r\nSome questions : \r\n1. How do we go about representing these features using datasets.Features and generate examples ?\r\n2. The dataset download link for ASF/AMC files does not have metadata information, for eg : category and subcategory information. We will need to crawl the website for this information. The authors mention \"Please don't crawl this database for all motions.\" Can we mail the authors for this information ?\r\nThe dataset structure is as follows : \r\n```\r\nsubjects\r\n\t- 01\r\n\t\t- 01_01.amc\r\n\t\t- 01_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 01.asf\r\n\t- 02\r\n\t\t- 02_01.amc\r\n\t\t- 02_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 02.asf\r\n```\r\nThere is no metadata regarding the category, sub-category and motion description.\r\n\r\nNeed your inputs. @mariosasko / @lhoestq \r\nThank you.\r\n", "Hi @dnaveenr! Thanks for working on this!\r\n\r\n1. We can use the `Sequence(Value(\"string\"))` feature type for the subject's AMC files and `Value(\"string\")` for the subject's ASF file (`Value(\"string\")` represents the file paths) + the types for categories/subcategories and descriptions.\r\n2. We can use this URL to download the motion descriptions: http://mocap.cs.cmu.edu/search.php?subjectnumber=<subject_number>&motion=%%%&maincat=%&subcat=%&subtext=yes where `subject_number` is the number between 1 and 144. And to get categories/subcategories, feel free to contact the authors (they state in the FAQ they are happy to help) and ask them if they can provide the mapping from categories/subcategories to the AMC files to avoid crawling. You can also mention that your goal is to make their dataset more accessible by adding its loading script to the Hub.\r\n\r\nThe AMC files are also available in the tvd, c3d, mpg and avi formats (the links are in the [FAQ](http://mocap.cs.cmu.edu/faqs.php) section), so it would be nice to have one config for each of these additional formats. \r\n\r\nAnd additionally, we can add a `Data Preprocessing` section to the card where we explain how to load/process the files. I can help with that.", "Hi @mariosasko ,\r\n\r\n1. Thanks for this, so we can add the file paths.\r\n2. Yes, I had already mailed the authors a couple of days back actually, asking for the metadata details[ i.e category, sub-category and motion description] . They are yet to respond though, I will wait for a couple of days and try to follow up with them again. :) Else we can use the workaround solution.\r\n\r\nYes. Supporting all the formats would be helpful. \r\n\r\n> And additionally, we can add a Data Preprocessing section to the card where we explain how to load/process the files. I can help with that.\r\n\r\nOkay. Got it." ]

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[ "We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think ?", "Now that I've heard you explain ClassLabel, that makes a lot of sense! While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible? I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`? It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept? Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel. I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts? I'm not sure if this is overly cautious. Whichever approach you think is better, I'd be happy to take it on!\r\n", "I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python/numpy. To do so we just have to adapt the python/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)" ]

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[ "Hi ! I think the issue you linked is closed and suggests to use `remove_columns`.\r\n\r\nMoreover if you end up with a dataset with an unnecessarily nested data, please modify your processing functions to not output nested data, or use `map(..., batched=True)` if you function take batches as input", "Hi @lhoestq , you are right about the issues this pull has steered beyond that issue. I created this [colab notebook](https://colab.research.google.com/drive/16aLu6QrDSV_aUYRdpufl5E4iS08qkUGj?usp=sharing) to present the error. I tried using batch and that won't resolve it either. I'm looking into that error right now.", "I think you just need to pass one example at a time to your tokenizer, this way you don't end up with nested data:\r\n```python\r\n\r\ndef preprocessFunction(row):\r\n collatedContext = tokenizer.eos_token.join([row[\"context\"+str(i+1)] for i in range(int(AMT_OF_CONTEXT))])\r\n response = row[\"response\"]\r\n tokenizedContext = tokenizer(\r\n collatedContext, max_length=max_context_length, truncation=True # don't pass as a list here\r\n )\r\n with tokenizer.as_target_tokenizer():\r\n tokenized_response = tokenizer(\r\n response, max_length=max_response_length, truncation=True # don't pass a a list here\r\n )\r\n tokenizedContext[\"labels\"] = tokenized_response[\"input_ids\"]\r\n return tokenizedContext\r\n```", "Yes that is correct, the purpose of this pull is to advise of a more general solution like with `def remove_excess_nesting(self)` or maybe automate the solution (stas00 advised not to automate it as it could \"not be backwards compatible\").", "I'm not sure I understand how having `remove_excess_nesting` would make more sense than just fixing the preprocessFunction to simply not return nested samples, can you elaborate ?", "Figuring out the issue can be a bit difficult to figure out. Only until I added batch does it make a little more sense with the error\r\n\r\n> sequence item 0: expected str instance, list found\r\n\r\nbut batch was never intended.\r\n\r\nWhen you run the colab you will notice that only until collating do you learn there is this error. So i figured it would be better to address it during at the `DatasetDict` level.\r\nI think it would be ideal if the user could be notified at the preprocess function.", "I'm not arguing that `remove_excess_nesting` is the right solution but what I aim to address is dealing with unnecessary nesting as early as possible.", "> When you run the colab you will notice that only until collating do you learn there is this error.\r\n\r\nI think users can just check the `dataset.features` and they would notice that the data are nested\r\n```python\r\n{\r\n 'input_ids': Sequence(Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), length=-1, id=None)\r\n ...\r\n}\r\n```\r\n\r\nSometime nested data are intentional, so you can't know in advance if it's a user's mistake or something planned.", "Yes, I understand, it could be intentional and only the collator has problems with it. So, it is not worth handling it any differently in any other non-erroneous data. \r\n\r\nThat being said do you think there is any use for the `remove_excess_nesting` method? Or maybe it should be applied in a different way? If not feel free to close this PR. ", "I think users can write it and use `map` themselves if needed, it is pretty straightforward to implement.\r\n\r\nI'm closing this PR if you don't mind, and thank you for the discussion :)", "No problem @lhoestq , thanks for walking me through it." ]

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[ "Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train\").take(3)`\r\n\r\n\r\n", "Thank you for your quick response!" ]

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[ "I don't know the main problem but it looks like, it is ignoring the last directory in your case. So, create a directory called 'zzz' in the same folder as train, validation and test. if it doesn't work, create a directory called \"aaa\". It worked for me.\r\n", "@SamSamhuns could you please try to load it with the current main-branch version of `datasets`? I suppose the problem is that it tries to get splits names from filenames in this case, ignoring directories names, but `val` wasn't in keywords at that time, but it was fixed recently in this PR https://github.com/huggingface/datasets/pull/4844. ", "I have a similar problem.\r\nWhen I try to create `data_infos.json` using `datasets-cli test Peter.py --save_infos --all_configs` I get an error:\r\n`ValueError: Unknown split \"test\". Should be one of ['train'].`\r\n\r\nThe `data_infos.json` is created perfectly fine when I use only one split - `datasets.Split.TRAIN`\r\n\r\n@polinaeterna Could you help here please?\r\n\r\nYou can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)", "@skalinin It seems the `dataset_infos.json` of your dataset is missing the info on the test split (and `datasets-cli` doesn't ignore the cached infos at the moment, which is a known bug), so your issue is not related to this one. I think you can fix your issue by deleting all the cached `dataset_infos.json` (in the local repo and in `~/.cache/huggingface/modules`) before running the `datasets-cli test` command. Let us know if that doesn't help, and I can try to generate it myself.", "This code indeed behaves as expected on `main`. But suppose the `val_234.png` is renamed to some other value not containing one of [these](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L31) keywords, in that case, this issue becomes relevant again because the real cause of it is the order in which we check the predefined split patterns to assign data files to each split - first we assign data files based on filenames, and only if this fails meaning not a single split found (`val` is not recognized here in the older versions of `datasets`, which results in an empty `validation` split), do we assign based on directory names.\r\n\r\n@polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if `data_dir` is specified (or if `load_dataset(data_dir)` is called)? ", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nyes that makes sense !", "Looks like the `val/validation` dir name issue is fixed with the current main-branch version of the `datasets` repository. \r\n\r\n> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nI agree with this as well. I would expect higher precedence to the directory name over the file name. Right now if I place a single file named `train_00001.jpg` under the `validation` directory, `load_dataset` cannot find the validation split.", "Thanks for the reply\r\n\r\nI've created a separate [issue](https://github.com/huggingface/datasets/issues/4982#issue-1375604693) for my problem.", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nSounds good to me! opened a PR: https://github.com/huggingface/datasets/pull/4985", "Hi there @polinaeterna @mariosasko ! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!", "hi @shaneacton ! could you please show your dataset structure?", "Hi there @polinaeterna . My local CSV files are stored as follows:\r\nbinding:\r\n---------- tune.csv\r\n---------- public_data:\r\n--------------------------- train.csv\r\n\r\n`self.list_shards(split)` sucessfully finds the relevant data files", "@shaneacton do you have `validation.csv`/`val.csv`/`valid.csv`/`dev.csv` file in your data folder? I can't find it in the structure you provided", "@polinaeterna no, does the name of the split need to match the name of the file exactly?\r\n\r\nBut my train file is not actually named 'train.py' its called 'XXXXXXXXX_train_XXXXXXXX.csv'\r\nAnd the code works fine for train, but fails for validation.\r\nDoes the file name need to _contain_ the split name?", "@shaneacton what files do you expect to be included in \"validation\" split? yes, you should somehow indicate that a file belongs to a certain split - either by including split name in a filename or by putting it into a folder with split name, you can also check out [this documentation page](https://huggingface.co/docs/datasets/main/en/repository_structure) :)\r\nby default all the data goes to a single `train` split", "@polinaeterna I have specified my train/test/tune files via the `split_to_filepattern` argument when initialising my `FileDataSource` class. This is how `list_shards` is able to find the right files.\r\nAfter your last message, I have tried renaminig my data files to simply `train.csv` and `validation.csv`, however I am still getting the same error: `Unknown split \"validation\". Should be one of ['train']`", "@polinaeterna I have solved the issue. The solution was to call:\r\n`load_dataset(\"csv\", data_files={split: files}, split=split)`" ]

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[ "Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n", "Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed)." ]

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[ "Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp1252`, which causes the problem.", "Awesome, thank you. 😃 ", "@sgraaf, I have just merged the fix in the master branch.\r\n\r\nYou can either:\r\n- install `datasets` from source code\r\n- wait until we make the next release of `datasets`\r\n- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https://www.python.org/dev/peps/pep-0540) either by passing the command-line option `-X utf8` or by setting the environment variable `PYTHONUTF8=1`." ]

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[ "I don't think you need to generate new dummy data, since they're in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn[\"names\"] = turn[\"NAMES\"]\r\nE TypeError: tuple indices must be integers or slices, not str\r\n```\r\n\r\nDo you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?", "> ``` \r\n> \r\n> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?\r\n> ```\r\n\r\nThis is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row[\"TURNS]\"` each `turn` is essentially a dictionary. Not sure why it's being considered a tuple here." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\n```\r\nDownloading and preparing dataset tedlium/release1 to /home/sanchitgandhi/cache/tedlium/release1/1.0.1/5a9fcb97b4b52d5a1c9dc7bde4b1d5994cd89c4a3425ea36c789bf6096fee4f0...\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/load.py\", line 1703, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 1240, in _download_and_prepare\r\n raise MissingBeamOptions(\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n `load_dataset('tedlium', 'release1', beam_runner='DirectRunner')`\r\n```\r\nSpecifying the `beam_runner='DirectRunner'` works:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache', beam_runner='DirectRunner')\r\n```", "Extra Python imports/Linux packages:\r\n```\r\npip install pydub\r\nsudo apt install ffmpeg\r\n```", "Script heavily inspired by the TF datasets script at: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/tedlium.py\r\n\r\nThe TF datasets script uses the module AudioSegment from the package `pydub` (https://github.com/jiaaro/pydub), which is used to to open the audio files (stored in .sph format):\r\nhttps://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L167-L170\r\nThis package requires the pip install of `pydub` and the system installation of `ffmpeg`: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nThe TF datasets script also uses `_build_pcollection`:\r\nhttps://github.com/huggingface/datasets/blob/8afbbb6fe66b40d05574e2e72e65e974c72ae769/datasets/tedlium/tedlium.py#L200-L206\r\nHowever, I was advised against using `beam` logic. Thus, I have reverted to generating the examples file-by-file: https://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L112-L138\r\n\r\nI am now able to generate examples by running the `load_dataset` command:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\nHere, generating examples is **extremely** slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?", "> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nIt's ok, windows users will have have a bad time but I'm not sure we can do much about it.\r\n\r\n> Here, generating examples is extremely slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?\r\n\r\nNot at the moment. For such cases we advise hosting the dataset ourselves in a processed format. The license doesn't allow this since the license is \"NoDerivatives\". Currently the only way to parallelize it is by keeping is as a beam dataset and let users pay Google Dataflow to process it (or use spark or whatever).", "Thanks for your super speedy reply @lhoestq!\r\n\r\nI’ve uploaded the script and README.md to the org here: https://huggingface.co/datasets/LIUM/tedlium\r\nIs any modification of the script required to be able to use it from the Hub? When I run:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntedlium = load_dataset(\"LIUM/tedlium\", \"release1\") # for Release 1\r\n```\r\nI get the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\nInput In [2], in <cell line: 1>()\r\n----> 1 load_dataset(\"LIUM/tedlium\", \"release1\")\r\n\r\nFile ~/datasets/src/datasets/load.py:1676, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n 1673 ignore_verifications = ignore_verifications or save_infos\r\n 1675 # Create a dataset builder\r\n-> 1676 builder_instance = load_dataset_builder(\r\n 1677 path=path,\r\n 1678 name=name,\r\n 1679 data_dir=data_dir,\r\n 1680 data_files=data_files,\r\n 1681 cache_dir=cache_dir,\r\n 1682 features=features,\r\n 1683 download_config=download_config,\r\n 1684 download_mode=download_mode,\r\n 1685 revision=revision,\r\n 1686 use_auth_token=use_auth_token,\r\n 1687 **config_kwargs,\r\n 1688 )\r\n 1690 # Return iterable dataset in case of streaming\r\n 1691 if streaming:\r\n\r\nFile ~/datasets/src/datasets/load.py:1502, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n 1500 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1501 download_config.use_auth_token = use_auth_token\r\n-> 1502 dataset_module = dataset_module_factory(\r\n 1503 path,\r\n 1504 revision=revision,\r\n 1505 download_config=download_config,\r\n 1506 download_mode=download_mode,\r\n 1507 data_dir=data_dir,\r\n 1508 data_files=data_files,\r\n 1509 )\r\n 1511 # Get dataset builder class from the processing script\r\n 1512 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:1254, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1249 if isinstance(e1, FileNotFoundError):\r\n 1250 raise FileNotFoundError(\r\n 1251 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1252 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1253 ) from None\r\n-> 1254 raise e1 from None\r\n 1255 else:\r\n 1256 raise FileNotFoundError(\r\n 1257 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory.\"\r\n 1258 )\r\n\r\nFile ~/datasets/src/datasets/load.py:1227, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1225 raise e\r\n 1226 if filename in [sibling.rfilename for sibling in dataset_info.siblings]:\r\n-> 1227 return HubDatasetModuleFactoryWithScript(\r\n 1228 path,\r\n 1229 revision=revision,\r\n 1230 download_config=download_config,\r\n 1231 download_mode=download_mode,\r\n 1232 dynamic_modules_path=dynamic_modules_path,\r\n 1233 ).get_module()\r\n 1234 else:\r\n 1235 return HubDatasetModuleFactoryWithoutScript(\r\n 1236 path,\r\n 1237 revision=revision,\r\n (...)\r\n 1241 download_mode=download_mode,\r\n 1242 ).get_module()\r\n\r\nFile ~/datasets/src/datasets/load.py:940, in HubDatasetModuleFactoryWithScript.get_module(self)\r\n 938 def get_module(self) -> DatasetModule:\r\n 939 # get script and other files\r\n--> 940 local_path = self.download_loading_script()\r\n 941 dataset_infos_path = self.download_dataset_infos_file()\r\n 942 imports = get_imports(local_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:918, in HubDatasetModuleFactoryWithScript.download_loading_script(self)\r\n 917 def download_loading_script(self) -> str:\r\n--> 918 file_path = hf_hub_url(path=self.name, name=self.name.split(\"/\")[1] + \".py\", revision=self.revision)\r\n 919 download_config = self.download_config.copy()\r\n 920 if download_config.download_desc is None:\r\n\r\nTypeError: hf_hub_url() got an unexpected keyword argument 'name'\r\n```\r\n\r\nNote that I am able to load the dataset from the `datasets` repo with the following lines of code:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```", "What version of `datasets` do you have ?\r\nUpdating `datasets` should fix the error ;)\r\n", "> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\n`soundfile`, which is a required audio dependency, should also work with `.sph` files, no?", "> `soundfile`, which is a required audio dependency, should also work with `.sph` files, no?\r\n\r\nAwesome, thanks for the pointer @mariosasko! Switched `pydub` to `soundfile`, and having specifying the `dtype` argument in `soundfile.read` as `np.int16`, the arrays match with those from `pydub` ✅\r\n\r\nI also did some heavy optimising of the script with the processing of the `.stm` and `.sph` files - it now runs 2000x faster than before, so there probably isn't a need to upload the data to the Hub @lhoestq. The total processing time is just ~2mins now 🚀\r\n", "TEDLIUM completed and uploaded to the HF Hub: https://huggingface.co/datasets/LIUM/tedlium", "Awesome !" ]

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[ "Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate archives ащк different splits I suppose it can (and will) be implemented someday. \r\n\r\n\r\nBtw for quick check of the dataset you can use [streaming](https://huggingface.co/docs/datasets/stream):\r\n```python\r\ncv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)\r\ncv = iter(cv)\r\nprint(next(cv))\r\n\r\n>> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',\r\n>> 'path': None,\r\n>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',\r\n>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,\r\n>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),\r\n>> ...}\r\n\r\n```", "thank you for the answer but am not sure if this will not be helpful, as we\nneed maybe just 10% of the datasets for some experiment\n\ncan we get just a portion of the dataset with stream?\n\n\nis there really no solution? :(\n\nAm Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n***@***.***>:\n\n> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n> hard to implement because quite often datasets data is being hosted in a\n> single archive for all splits :( So we have to download the whole archive\n> to split it into splits. This is the case for CommonVoice too.\n>\n> However, for cases when data is distributed in separate archives in\n> different splits I suppose it can be implemented someday.\n>\n> Btw for quick check of the dataset you can use streaming\n> <https://huggingface.co/docs/datasets/stream>:\n>\n> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "maybe it would be nice if you guys ould do some sort of shard before\nloading the dataset, so users can download just chunks of data :)\n\nI think this would be very helpful\n\nAm Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> thank you for the answer but am not sure if this will not be helpful, as\n> we need maybe just 10% of the datasets for some experiment\n>\n> can we get just a portion of the dataset with stream?\n>\n>\n> is there really no solution? :(\n>\n> Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n> ***@***.***>:\n>\n>> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n>> hard to implement because quite often datasets data is being hosted in a\n>> single archive for all splits :( So we have to download the whole archive\n>> to split it into splits. This is the case for CommonVoice too.\n>>\n>> However, for cases when data is distributed in separate archives in\n>> different splits I suppose it can be implemented someday.\n>>\n>> Btw for quick check of the dataset you can use streaming\n>> <https://huggingface.co/docs/datasets/stream>:\n>>\n>> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n>> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>>\n>> —\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n>> .\n>> You are receiving this because you were mentioned.Message ID:\n>> ***@***.***>\n>>\n>\n", "+1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set", "Hey, I'm also interested in that as a feature. I'm having the same problem with Common Voice 13.0. The dataset is super big but I only want the test data to benchmark multilingual models, but I don't have much Terabytes to store all the dataset..." ]

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[ "Thanks @sumanthd17 that fixed it. " ]

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[ "Thanks for reporting. This looks like a very bad issue. I'm looking into it", "I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch", "Thanks!!! works perfectly in the bleading edge master version", "Closed by #1899" ]

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

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[ "Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps://github.com/huggingface/datasets/blob/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152/src/datasets/utils/filelock.py#L135-L135", "Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.", "Snap, encountered when trying to run [this example from PyTorch Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html):\r\n\r\n```py\r\nimport torch\r\n\r\nimport flash\r\nfrom flash.audio import SpeechRecognition, SpeechRecognitionData\r\nfrom flash.core.data.utils import download_data\r\n\r\n# 1. Create the DataModule\r\ndownload_data(\"https://pl-flash-data.s3.amazonaws.com/timit_data.zip\", \"./data\")\r\n\r\ndatamodule = SpeechRecognitionData.from_json(\r\n input_fields=\"file\",\r\n target_fields=\"text\",\r\n train_file=\"data/timit/train.json\",\r\n test_file=\"data/timit/test.json\",\r\n)\r\n```\r\n\r\nGave this traceback:\r\n\r\n```py\r\nTraceback (most recent call last):\r\n File \"lf_ft.py\", line 10, in <module>\r\n datamodule = SpeechRecognitionData.from_json(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py\", line 1005, in from_json\r\n return cls.from_data_source(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py\", line 571, in from_data_source\r\n train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py\", line 307, in to_datasets\r\n train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py\", line 344, in generate_dataset\r\n data = load_data(data, mock_dataset)\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py\", line 103, in load_data\r\n dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py\", line 1599, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py\", line 1457, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py\", line 285, in __init__\r\n with FileLock(lock_path):\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'\r\n```\r\n\r\nMy home directory is encrypted, therefore the maximum length is 143 ([source 1](https://github.com/ray-project/ray/issues/1463#issuecomment-425674521), [source 2](https://stackoverflow.com/a/6571568/2668831))\r\n\r\nFrom what I've read I think the error is in reference to the file name (just the final part of the path) which is 145 chars long:\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock\")\r\n145\r\n```\r\n\r\nI also have a file in this directory (i.e. whose length is not a problem):\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_librispeech_asr_clean_2.1.0_468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1.lock\")\r\n137\r\n```", "Perhaps this could be exposed as a config setting so you could change it manually?\r\n\r\nhttps://github.com/huggingface/datasets/blob/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152/src/datasets/utils/filelock.py#L135-L135\r\n\r\nRather than hard-code 255, default it to 255, and allow it to be changed, the same way is done for `datasets.config.IN_MEMORY_MAX_SIZE`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/12b7e13bc568b9f92705f64b249e148f3bc9a9ea/src/datasets/config.py#L171-L173\r\n\r\nIn fact there already appears to be an existing variable to do so:\r\n\r\nhttps://github.com/huggingface/datasets/blob/12b7e13bc568b9f92705f64b249e148f3bc9a9ea/src/datasets/config.py#L187\r\n\r\nIt's used here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/efe89edd36e4ffa562fc3eebaf07a5fec26e6dac/src/datasets/builder.py#L163-L165\r\n\r\nPerhaps it could be set based on a test (trying to create a 255 char length named lock file and seeing if it fails)", "Just fixed it, sending a PR :smile:", "Hi @lmmx @gar1t ,\r\n\r\nit would be helpful if you could run the following code and copy-paste the output here:\r\n```python\r\nimport datasets\r\nimport os\r\nos.statvfs(datasets.config.HF_DATASETS_CACHE)\r\n```", "`os.statvfs_result(f_bsize=4096, f_frsize=4096, f_blocks=240046344, f_bfree=96427610, f_bavail=84216487, f_files=61038592, f_ffree=58216027, f_favail=58216027, f_flag=4102, f_namemax=143)`", "Hi @lmmx,\r\n\r\nThanks for providing the result of the command. I've opened a PR, and it would be great if you could verify that the fix works on your system. To install the version of the datasets with the fix, please run the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@fix-2924\r\n```\r\n\r\nBtw, I saw your PR, and I appreciate your effort. However, my approach is a bit simpler for the end-user, so that's why I decided to fix the issue myself.", "No problem Mario I didn't know that was where that value was recorded so I learnt something :smiley: I just wanted to get a local version working, of course you should implement whatever fix is best for HF. Yes can confirm this fixes it too. Thanks!", "Hello @mariosasko \r\n\r\nHas this fix shown up in the 2.10.1 version of huggingface datasets?" ]

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

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[ "The dataset maintainers (https://huggingface.co/datasets/ubuntu_dialogs_corpus) decided to forbid the dataset from being downloaded automatically (https://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download), and the dataset viewer respects this.\r\nWe will try to improve the error display though. Thanks for reporting." ]

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[ "Thank you, @albertvillanova!", "Got it. Thanks for explaining this, @albertvillanova!\r\n\r\n> On the other hand, the tests are not passing because the dummy data should also be fixed. Once done, this PR will be able to be merged into master.\r\n\r\nWill do this 👍", "Hi ! I think we need to fix the issue for every dataset. This can be done simply by fixing how we handle Google Drive links, see my comment https://github.com/huggingface/datasets/pull/3775#issuecomment-1050970157", "Hi @lhoestq! I think @albertvillanova has already fixed this in #3787", "Cool ! I missed this one :) thanks", "No problem!", "Hi, @AngadSethi, I think that once:\r\n- #3787 \r\n\r\nwas merged, issue:\r\n- #3784 \r\n\r\nwas also fixed.\r\n\r\nTherefore, I think this PR is no longer necessary. I'm closing it. Let me know if you agree.", "Yes, absolutely @albertvillanova! I agree :)" ]

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[ "To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.", "Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`..." ]

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

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

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[ "Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?", "@albertvillanova Thanks! It works well now. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "cc: @albertvillanova just FYI" ]

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[ "I understand that the issue is that a remote file (URL) is being loaded as a local file. Right @albertvillanova @lhoestq?\r\n\r\n```\r\nMessage: [Errno 2] No such file or directory: 'https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2015Task12Corrected/train/restaurants_train.xml'\r\n```", "`xml.dom.minidom.parse` is not supported in streaming mode. I opened a PR here to fix it:\r\nhttps://huggingface.co/datasets/Yaxin/SemEval2015/discussions/1\r\n\r\nPlease review the PR @WithYouTo and let me know if it works !", "Additionally, I'm also patching our library, so that we support streaming datasets that use `xml.dom.minidom.parse`." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I've disabled the `test_dummy_dataset_serialize_s3` tests in the Linux CI to avoid the failures (these tests only fail on Windows in 3.6). These failures are unrelated to this PR's changes, and I would like to address this in a new PR.", "[This comment](https://github.com/pytorch/audio/issues/2363#issuecomment-1179089175) explains the issue with MP3 decoding in `torchaudio` in the latest release (supports Python 3.7+). I fixed CI by pinning `torchaudio` to `<0.12.0`. Another way to fix this issue is by installing `ffmpeg` with conda or using the unofficial GH action. But I don't think it's worth making CI more complex, considering we can wait for the soundfile release, which should bring MP3 decoding, and drop the `torchaudio` dependency then.", "Yay for dropping Python 3.6!", "I think we can merge in this state. Also, if an env has Python version < 3.7 installed, we raise a warning, so I don't think we even need to create (and pin) an issue to notify the contributors of this change." ]

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[ "AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"/path/to/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"/path/to/file.json\", \"w+\") as f:\r\n json.dump(py_dict, f)\r\n```", "Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.", "Hi! `datasets` now supports `Dataset.to_csv` and `Dataset.to_json` for saving data in a human readable format." ]

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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) | 0.006498 / 0.011353 (-0.004855) | 0.004406 / 0.011008 (-0.006602) | 0.097136 / 0.038508 (0.058628) | 0.027711 / 0.023109 (0.004601) | 0.303092 / 0.275898 (0.027194) | 0.336804 / 0.323480 (0.013324) | 0.004838 / 0.007986 (-0.003148) | 0.004533 / 0.004328 (0.000204) | 0.075062 / 0.004250 (0.070812) | 0.035105 / 0.037052 (-0.001947) | 0.310245 / 0.258489 (0.051756) | 0.347086 / 0.293841 (0.053245) | 0.030867 / 0.128546 (-0.097679) | 0.011436 / 0.075646 (-0.064211) | 0.320728 / 0.419271 (-0.098544) | 0.042303 / 0.043533 (-0.001230) | 0.308177 / 0.255139 (0.053038) | 0.333673 / 0.283200 (0.050473) | 0.084736 / 0.141683 (-0.056947) | 1.477391 / 1.452155 (0.025237) | 1.530399 / 1.492716 (0.037682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212698 / 0.018006 (0.194692) | 0.409098 / 0.000490 (0.408608) | 0.004202 / 0.000200 (0.004002) | 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) | 0.022725 / 0.037411 (-0.014686) | 0.095866 / 0.014526 (0.081340) | 0.104153 / 0.176557 (-0.072404) | 0.162964 / 0.737135 (-0.574171) | 0.106505 / 0.296338 (-0.189834) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431336 / 0.215209 (0.216127) | 4.283290 / 2.077655 (2.205635) | 1.982418 / 1.504120 (0.478298) | 1.762104 / 1.541195 (0.220909) | 1.807528 / 1.468490 (0.339038) | 0.695507 / 4.584777 (-3.889270) | 3.376299 / 3.745712 (-0.369413) | 1.856642 / 5.269862 (-3.413219) | 1.154258 / 4.565676 (-3.411419) | 0.082749 / 0.424275 (-0.341526) | 0.012289 / 0.007607 (0.004682) | 0.525842 / 0.226044 (0.299798) | 5.285764 / 2.268929 (3.016835) | 2.389926 / 55.444624 (-53.054698) | 2.021830 / 6.876477 (-4.854646) | 2.107460 / 2.142072 (-0.034612) | 0.808118 / 4.805227 (-3.997109) | 0.150791 / 6.500664 (-6.349873) | 0.065825 / 0.075469 (-0.009644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206939 / 1.841788 (-0.634849) | 13.795902 / 8.074308 (5.721594) | 14.107950 / 10.191392 (3.916558) | 0.144300 / 0.680424 (-0.536124) | 0.016478 / 0.534201 (-0.517723) | 0.379395 / 0.579283 (-0.199888) | 0.388437 / 0.434364 (-0.045927) | 0.451443 / 0.540337 (-0.088894) | 0.523142 / 1.386936 (-0.863794) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006503 / 0.011353 (-0.004850) | 0.004578 / 0.011008 (-0.006430) | 0.076278 / 0.038508 (0.037770) | 0.028052 / 0.023109 (0.004943) | 0.337873 / 0.275898 (0.061975) | 0.371368 / 0.323480 (0.047888) | 0.005086 / 0.007986 (-0.002899) | 0.003354 / 0.004328 (-0.000975) | 0.076876 / 0.004250 (0.072625) | 0.039146 / 0.037052 (0.002093) | 0.340299 / 0.258489 (0.081810) | 0.381209 / 0.293841 (0.087368) | 0.031771 / 0.128546 (-0.096775) | 0.011670 / 0.075646 (-0.063976) | 0.085156 / 0.419271 (-0.334116) | 0.041990 / 0.043533 (-0.001543) | 0.338644 / 0.255139 (0.083505) | 0.362461 / 0.283200 (0.079262) | 0.089772 / 0.141683 (-0.051911) | 1.480341 / 1.452155 (0.028187) | 1.562815 / 1.492716 (0.070099) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205700 / 0.018006 (0.187694) | 0.402206 / 0.000490 (0.401716) | 0.001212 / 0.000200 (0.001012) | 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) | 0.025172 / 0.037411 (-0.012240) | 0.100959 / 0.014526 (0.086433) | 0.108464 / 0.176557 (-0.068093) | 0.161321 / 0.737135 (-0.575814) | 0.114245 / 0.296338 (-0.182093) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437425 / 0.215209 (0.222216) | 4.362212 / 2.077655 (2.284557) | 2.068815 / 1.504120 (0.564695) | 1.864089 / 1.541195 (0.322894) | 1.909038 / 1.468490 (0.440548) | 0.696097 / 4.584777 (-3.888680) | 3.358628 / 3.745712 (-0.387084) | 2.999085 / 5.269862 (-2.270777) | 1.533917 / 4.565676 (-3.031760) | 0.083010 / 0.424275 (-0.341266) | 0.012372 / 0.007607 (0.004765) | 0.539926 / 0.226044 (0.313882) | 5.438326 / 2.268929 (3.169397) | 2.498581 / 55.444624 (-52.946043) | 2.153359 / 6.876477 (-4.723117) | 2.177891 / 2.142072 (0.035819) | 0.803169 / 4.805227 (-4.002059) | 0.151079 / 6.500664 (-6.349585) | 0.065981 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336682 / 1.841788 (-0.505106) | 14.133055 / 8.074308 (6.058747) | 14.033972 / 10.191392 (3.842580) | 0.152109 / 0.680424 (-0.528315) | 0.016475 / 0.534201 (-0.517726) | 0.387808 / 0.579283 (-0.191475) | 0.378347 / 0.434364 (-0.056017) | 0.484732 / 0.540337 (-0.055606) | 0.569907 / 1.386936 (-0.817029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1c4ec00511868bd881e84a6f7e0333648d833b8e \"CML watermark\")\n" ]

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[ "Indeed. Probably we will want to have a similar method directly in the library", "Related: #166 " ]

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[ "Could you rebase from master just to make sure we won't break anything for `fever` pls @mariamabarham ?" ]

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[ "@mariosasko I've raised a PR #5076 against this issue. Please help to review. Thanks." ]

[]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008191 / 0.011353 (-0.003162) | 0.004669 / 0.011008 (-0.006339) | 0.101315 / 0.038508 (0.062807) | 0.090235 / 0.023109 (0.067126) | 0.381265 / 0.275898 (0.105367) | 0.418266 / 0.323480 (0.094786) | 0.006292 / 0.007986 (-0.001693) | 0.003979 / 0.004328 (-0.000349) | 0.075946 / 0.004250 (0.071696) | 0.070678 / 0.037052 (0.033625) | 0.378006 / 0.258489 (0.119517) | 0.425825 / 0.293841 (0.131984) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.345687 / 0.419271 (-0.073584) | 0.063846 / 0.043533 (0.020313) | 0.386003 / 0.255139 (0.130864) | 0.400875 / 0.283200 (0.117675) | 0.027806 / 0.141683 (-0.113877) | 1.814810 / 1.452155 (0.362655) | 1.879897 / 1.492716 (0.387180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218684 / 0.018006 (0.200677) | 0.501715 / 0.000490 (0.501225) | 0.004808 / 0.000200 (0.004608) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035494 / 0.037411 (-0.001917) | 0.100949 / 0.014526 (0.086423) | 0.114639 / 0.176557 (-0.061917) | 0.188908 / 0.737135 (-0.548227) | 0.115794 / 0.296338 (-0.180545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462537 / 0.215209 (0.247328) | 4.612469 / 2.077655 (2.534814) | 2.298065 / 1.504120 (0.793945) | 2.088738 / 1.541195 (0.547543) | 2.188072 / 1.468490 (0.719582) | 0.565412 / 4.584777 (-4.019364) | 4.180394 / 3.745712 (0.434681) | 3.848696 / 5.269862 (-1.421165) | 2.391381 / 4.565676 (-2.174296) | 0.067647 / 0.424275 (-0.356628) | 0.008847 / 0.007607 (0.001240) | 0.553288 / 0.226044 (0.327243) | 5.517962 / 2.268929 (3.249033) | 2.866622 / 55.444624 (-52.578002) | 2.439025 / 6.876477 (-4.437452) | 2.740156 / 2.142072 (0.598084) | 0.694796 / 4.805227 (-4.110431) | 0.159022 / 6.500664 (-6.341642) | 0.074471 / 0.075469 (-0.000998) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534979 / 1.841788 (-0.306808) | 23.297273 / 8.074308 (15.222965) | 16.859178 / 10.191392 (6.667786) | 0.207594 / 0.680424 (-0.472830) | 0.021990 / 0.534201 (-0.512211) | 0.472059 / 0.579283 (-0.107224) | 0.497632 / 0.434364 (0.063268) | 0.565672 / 0.540337 (0.025335) | 0.772485 / 1.386936 (-0.614451) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007777 / 0.011353 (-0.003576) | 0.004679 / 0.011008 (-0.006329) | 0.077317 / 0.038508 (0.038809) | 0.087433 / 0.023109 (0.064324) | 0.437389 / 0.275898 (0.161491) | 0.479562 / 0.323480 (0.156082) | 0.006137 / 0.007986 (-0.001849) | 0.003938 / 0.004328 (-0.000390) | 0.074769 / 0.004250 (0.070518) | 0.066605 / 0.037052 (0.029553) | 0.454865 / 0.258489 (0.196376) | 0.485103 / 0.293841 (0.191262) | 0.036540 / 0.128546 (-0.092006) | 0.009983 / 0.075646 (-0.065664) | 0.083566 / 0.419271 (-0.335706) | 0.059527 / 0.043533 (0.015994) | 0.449154 / 0.255139 (0.194015) | 0.462542 / 0.283200 (0.179342) | 0.027581 / 0.141683 (-0.114102) | 1.776720 / 1.452155 (0.324565) | 1.847920 / 1.492716 (0.355204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246792 / 0.018006 (0.228786) | 0.494513 / 0.000490 (0.494024) | 0.004376 / 0.000200 (0.004176) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037837 / 0.037411 (0.000426) | 0.112752 / 0.014526 (0.098226) | 0.121742 / 0.176557 (-0.054815) | 0.189365 / 0.737135 (-0.547770) | 0.124366 / 0.296338 (-0.171973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492890 / 0.215209 (0.277681) | 4.920270 / 2.077655 (2.842615) | 2.565350 / 1.504120 (1.061230) | 2.378679 / 1.541195 (0.837484) | 2.483794 / 1.468490 (1.015304) | 0.579623 / 4.584777 (-4.005154) | 4.195924 / 3.745712 (0.450212) | 3.903382 / 5.269862 (-1.366479) | 2.466884 / 4.565676 (-2.098793) | 0.064145 / 0.424275 (-0.360130) | 0.008695 / 0.007607 (0.001088) | 0.579300 / 0.226044 (0.353256) | 5.809064 / 2.268929 (3.540136) | 3.145393 / 55.444624 (-52.299232) | 2.832760 / 6.876477 (-4.043717) | 3.020460 / 2.142072 (0.878388) | 0.700235 / 4.805227 (-4.104992) | 0.161262 / 6.500664 (-6.339402) | 0.076484 / 0.075469 (0.001015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606504 / 1.841788 (-0.235284) | 23.747863 / 8.074308 (15.673555) | 17.281712 / 10.191392 (7.090320) | 0.203874 / 0.680424 (-0.476550) | 0.021839 / 0.534201 (-0.512362) | 0.472365 / 0.579283 (-0.106918) | 0.475150 / 0.434364 (0.040786) | 0.571713 / 0.540337 (0.031376) | 0.759210 / 1.386936 (-0.627726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n", "> Some questions: won't this have an impact on downloading time, once we do not longer compress the payload? What is the advantage of this approach over the one with block_size: 0?\r\n\r\nSurely, but this prevents random access which is needed at multiple places in the code (eg to check the compression type).\r\nGithub isn't a good place for big files anyway so we should be fine" ]

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[ "Hi @zixiliuUSC, \r\n\r\nAs explained in the documentation (https://huggingface.co/docs/datasets/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:\r\n```python\r\nds = load_dataset('json', data_files='my_file.json')\r\n```" ]

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[ "There are currently 402 datasets with deprecated \"languages\" or \"licenses\".", "hey @albertvillanova ,i would love to work on this issue if you like.", "Hi @ayushthe1, thanks for your offer.\r\n\r\nBut as you can see, I self-assigned this issue.\r\n\r\nI have already fixed 200 out of the 402 datasets. My script is still running and fixing the rest.\r\n\r\nFor example: https://huggingface.co/datasets/fhamborg/news_sentiment_newsmtsc/discussions/2/files", "Thanks for your time. Will try next time. 😇", "@ayushthe1 feel free to take one of the non-assigned open issues: https://github.com/huggingface/datasets/issues", "This is done." ]

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[ "Actually, I found the answer [here](https://github.com/facebookresearch/faiss/wiki/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.", "@lhoestq @patrickvonplaten \r\n\r\nI also found another short bug in the retrieval part. Especially, when retrieving documents. If Faiss returns the -1 as the index, the retriever will always use the last element in the dataset.\r\n\r\nplease check [def get_doc_dicts function](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L222)\r\n\r\n\r\nDoes the use of the HNSW guarantee to retrieve valid indexes always? \r\n\r\n", "Hi !\r\nNo it happens sometimes to return -1, especially if your dataset is small.\r\nIf your dataset is big enough it shouldn't happen in my experience.\r\n\r\nIdeally we should ignore all the -1 that are returned. It should be possible to change that in RAG's code ", "I also checked with some indexes it returns more -1s. Specially with IVF\nwhen nprobr is very low. It doesn't happen when using HNSW though. But at\nthe moment if it happens, dataset will always return the last element.\nMaybe we should change it to repeat the most last valid retrieved doc id.\nWhat do you think?\n\nOn Wed, Apr 7, 2021, 21:09 Quentin Lhoest ***@***.***> wrote:\n\n> Hi !\n> No it happens sometimes to return -1, especially if your dataset is small.\n> If your dataset is big enough it shouldn't happen.\n>\n> Ideally we should ignore all the -1 that are returned. It should be\n> possible to change that in RAG's code\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2175#issuecomment-814746509>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGTENOTLBEZTXEO2RS3THQOMPANCNFSM42PRVYDA>\n> .\n>\n", "That would be an easy way to workaround this issue. Feel free to open a PR on `transformers` and ping me ! :)", "Sure. Will push everything together with RAG end to end. :) thanks a lot.\n\nOn Wed, Apr 7, 2021, 21:16 Quentin Lhoest ***@***.***> wrote:\n\n> That would be an easy way to workaround this issue. Feel free to open a PR\n> on transformers and ping me ! :)\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2175#issuecomment-814752589>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGWLROCGARKN7WOJYSTTHQPH5ANCNFSM42PRVYDA>\n> .\n>\n" ]

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[ "Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https://github.com/earwig/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal tool", "Ok, thanks! I'll try the Wiki40b dataset.", "If anyone else is concerned about this, `wiki40b` does indeed seem very well cleaned." ]

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[ "Currently, it does not work with https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/common_voice_7_0.py#L146 (which was the goal), because `dl_manager.download_config.use_auth_token` is ignored, and the authentication is required to be use `huggingface-cli login`.\r\nIn my use case (dataset viewer), I'd prefer to use a specific \"User Token Access\", with only the \"read\" role (https://huggingface.co/settings/token).\r\n\r\nSee https://github.com/huggingface/datasets-preview-backend/issues/74#issuecomment-1007316853 for the context", "> Simply passing download_config is ok :)\r\n\r\nhmm, I prefer only passing use_auth_token. But the question is more: is it correct, in the (convoluted) case if `download_config.use_auth_token` exists and is different from `use_auth_token`? Which one should be used?", "If both are passed, `use_auth_token` should have the priority (more specific parameters have the higher priority)" ]

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[ "I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?", "Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed." ]

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[ "I think its better to create a new branch with this fix. I forgot I was still using the old branch." ]