Datasets:

hwang2006

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datasets-github-issues-2024-08-23

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7119). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005156 / 0.011353 (-0.006197) | 0.003365 / 0.011008 (-0.007643) | 0.063451 / 0.038508 (0.024943) | 0.029510 / 0.023109 (0.006401) | 0.244825 / 0.275898 (-0.031074) | 0.265157 / 0.323480 (-0.058323) | 0.004239 / 0.007986 (-0.003747) | 0.002732 / 0.004328 (-0.001596) | 0.050412 / 0.004250 (0.046162) | 0.043608 / 0.037052 (0.006556) | 0.256635 / 0.258489 (-0.001854) | 0.277472 / 0.293841 (-0.016369) | 0.029329 / 0.128546 (-0.099217) | 0.012318 / 0.075646 (-0.063329) | 0.204751 / 0.419271 (-0.214520) | 0.036468 / 0.043533 (-0.007065) | 0.246773 / 0.255139 (-0.008366) | 0.263932 / 0.283200 (-0.019268) | 0.017053 / 0.141683 (-0.124629) | 1.173249 / 1.452155 (-0.278905) | 1.234186 / 1.492716 (-0.258531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092398 / 0.018006 (0.074391) | 0.309473 / 0.000490 (0.308983) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018553 / 0.037411 (-0.018858) | 0.062546 / 0.014526 (0.048020) | 0.073943 / 0.176557 (-0.102613) | 0.120498 / 0.737135 (-0.616638) | 0.075185 / 0.296338 (-0.221153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296899 / 0.215209 (0.081690) | 2.919088 / 2.077655 (0.841433) | 1.533146 / 1.504120 (0.029026) | 1.395441 / 1.541195 (-0.145754) | 1.399089 / 1.468490 (-0.069401) | 0.742750 / 4.584777 (-3.842027) | 2.390317 / 3.745712 (-1.355395) | 2.883166 / 5.269862 (-2.386695) | 1.854003 / 4.565676 (-2.711674) | 0.077140 / 0.424275 (-0.347136) | 0.005176 / 0.007607 (-0.002432) | 0.349391 / 0.226044 (0.123347) | 3.466043 / 2.268929 (1.197114) | 1.870619 / 55.444624 (-53.574005) | 1.559173 / 6.876477 (-5.317303) | 1.605480 / 2.142072 (-0.536592) | 0.786753 / 4.805227 (-4.018474) | 0.134869 / 6.500664 (-6.365795) | 0.042176 / 0.075469 (-0.033293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.954256 / 1.841788 (-0.887532) | 11.194758 / 8.074308 (3.120449) | 9.129670 / 10.191392 (-1.061722) | 0.138318 / 0.680424 (-0.542106) | 0.014299 / 0.534201 (-0.519902) | 0.303704 / 0.579283 (-0.275579) | 0.262513 / 0.434364 (-0.171851) | 0.346539 / 0.540337 (-0.193798) | 0.429524 / 1.386936 (-0.957412) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005692 / 0.011353 (-0.005661) | 0.003423 / 0.011008 (-0.007586) | 0.050618 / 0.038508 (0.012110) | 0.031053 / 0.023109 (0.007944) | 0.275901 / 0.275898 (0.000003) | 0.294404 / 0.323480 (-0.029076) | 0.004303 / 0.007986 (-0.003682) | 0.002728 / 0.004328 (-0.001600) | 0.049757 / 0.004250 (0.045507) | 0.039997 / 0.037052 (0.002945) | 0.287291 / 0.258489 (0.028802) | 0.319186 / 0.293841 (0.025345) | 0.032558 / 0.128546 (-0.095988) | 0.012088 / 0.075646 (-0.063558) | 0.060746 / 0.419271 (-0.358525) | 0.034046 / 0.043533 (-0.009486) | 0.276170 / 0.255139 (0.021031) | 0.293673 / 0.283200 (0.010474) | 0.018018 / 0.141683 (-0.123665) | 1.158453 / 1.452155 (-0.293701) | 1.198599 / 1.492716 (-0.294118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093134 / 0.018006 (0.075127) | 0.304511 / 0.000490 (0.304021) | 0.000216 / 0.000200 (0.000016) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022991 / 0.037411 (-0.014421) | 0.077548 / 0.014526 (0.063022) | 0.087887 / 0.176557 (-0.088670) | 0.131786 / 0.737135 (-0.605349) | 0.088747 / 0.296338 (-0.207591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302811 / 0.215209 (0.087602) | 2.959276 / 2.077655 (0.881621) | 1.591348 / 1.504120 (0.087229) | 1.464731 / 1.541195 (-0.076464) | 1.474112 / 1.468490 (0.005622) | 0.741573 / 4.584777 (-3.843204) | 0.959229 / 3.745712 (-2.786483) | 2.895750 / 5.269862 (-2.374111) | 1.896051 / 4.565676 (-2.669625) | 0.079012 / 0.424275 (-0.345264) | 0.005494 / 0.007607 (-0.002113) | 0.355699 / 0.226044 (0.129655) | 3.524833 / 2.268929 (1.255905) | 1.972358 / 55.444624 (-53.472266) | 1.667249 / 6.876477 (-5.209228) | 1.658635 / 2.142072 (-0.483438) | 0.813184 / 4.805227 (-3.992044) | 0.134226 / 6.500664 (-6.366438) | 0.041087 / 0.075469 (-0.034382) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.038963 / 1.841788 (-0.802824) | 11.785835 / 8.074308 (3.711526) | 10.397027 / 10.191392 (0.205635) | 0.141748 / 0.680424 (-0.538676) | 0.014738 / 0.534201 (-0.519463) | 0.300056 / 0.579283 (-0.279227) | 0.127442 / 0.434364 (-0.306922) | 0.345013 / 0.540337 (-0.195324) | 0.449598 / 1.386936 (-0.937338) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70bac27ef861b2b11f581a291a6b76adeee24f98 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7118). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005674 / 0.011353 (-0.005679) | 0.003919 / 0.011008 (-0.007089) | 0.062665 / 0.038508 (0.024157) | 0.031750 / 0.023109 (0.008641) | 0.234809 / 0.275898 (-0.041089) | 0.264454 / 0.323480 (-0.059026) | 0.004265 / 0.007986 (-0.003720) | 0.002757 / 0.004328 (-0.001572) | 0.048921 / 0.004250 (0.044671) | 0.050765 / 0.037052 (0.013713) | 0.246185 / 0.258489 (-0.012305) | 0.287011 / 0.293841 (-0.006829) | 0.030754 / 0.128546 (-0.097792) | 0.012368 / 0.075646 (-0.063278) | 0.203841 / 0.419271 (-0.215431) | 0.037579 / 0.043533 (-0.005953) | 0.238165 / 0.255139 (-0.016974) | 0.264375 / 0.283200 (-0.018824) | 0.018663 / 0.141683 (-0.123020) | 1.143897 / 1.452155 (-0.308258) | 1.218130 / 1.492716 (-0.274586) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102112 / 0.018006 (0.084106) | 0.303214 / 0.000490 (0.302724) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019401 / 0.037411 (-0.018010) | 0.062444 / 0.014526 (0.047919) | 0.076497 / 0.176557 (-0.100060) | 0.122309 / 0.737135 (-0.614826) | 0.077178 / 0.296338 (-0.219160) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282931 / 0.215209 (0.067722) | 2.783587 / 2.077655 (0.705932) | 1.464076 / 1.504120 (-0.040044) | 1.333912 / 1.541195 (-0.207282) | 1.367391 / 1.468490 (-0.101099) | 0.736702 / 4.584777 (-3.848075) | 2.413625 / 3.745712 (-1.332087) | 2.949549 / 5.269862 (-2.320313) | 1.910308 / 4.565676 (-2.655369) | 0.077419 / 0.424275 (-0.346856) | 0.005159 / 0.007607 (-0.002448) | 0.345595 / 0.226044 (0.119551) | 3.433205 / 2.268929 (1.164277) | 1.844443 / 55.444624 (-53.600181) | 1.527475 / 6.876477 (-5.349002) | 1.544315 / 2.142072 (-0.597758) | 0.803942 / 4.805227 (-4.001285) | 0.134131 / 6.500664 (-6.366533) | 0.042638 / 0.075469 (-0.032831) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975158 / 1.841788 (-0.866629) | 11.726187 / 8.074308 (3.651879) | 9.403347 / 10.191392 (-0.788045) | 0.131583 / 0.680424 (-0.548840) | 0.014358 / 0.534201 (-0.519843) | 0.301360 / 0.579283 (-0.277923) | 0.266529 / 0.434364 (-0.167835) | 0.341669 / 0.540337 (-0.198668) | 0.425751 / 1.386936 (-0.961186) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005911 / 0.011353 (-0.005442) | 0.004093 / 0.011008 (-0.006915) | 0.049936 / 0.038508 (0.011428) | 0.031828 / 0.023109 (0.008719) | 0.273874 / 0.275898 (-0.002025) | 0.296871 / 0.323480 (-0.026609) | 0.004470 / 0.007986 (-0.003516) | 0.002902 / 0.004328 (-0.001426) | 0.048848 / 0.004250 (0.044597) | 0.042320 / 0.037052 (0.005268) | 0.287957 / 0.258489 (0.029468) | 0.321033 / 0.293841 (0.027192) | 0.032996 / 0.128546 (-0.095550) | 0.012244 / 0.075646 (-0.063403) | 0.060493 / 0.419271 (-0.358779) | 0.034630 / 0.043533 (-0.008902) | 0.277254 / 0.255139 (0.022115) | 0.292822 / 0.283200 (0.009623) | 0.017966 / 0.141683 (-0.123717) | 1.167432 / 1.452155 (-0.284723) | 1.231837 / 1.492716 (-0.260880) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099970 / 0.018006 (0.081964) | 0.313240 / 0.000490 (0.312750) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022928 / 0.037411 (-0.014483) | 0.077058 / 0.014526 (0.062532) | 0.090147 / 0.176557 (-0.086409) | 0.129416 / 0.737135 (-0.607720) | 0.091021 / 0.296338 (-0.205318) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300697 / 0.215209 (0.085488) | 2.944649 / 2.077655 (0.866995) | 1.609106 / 1.504120 (0.104986) | 1.483762 / 1.541195 (-0.057433) | 1.519433 / 1.468490 (0.050943) | 0.714129 / 4.584777 (-3.870648) | 0.991848 / 3.745712 (-2.753864) | 2.966340 / 5.269862 (-2.303521) | 1.905427 / 4.565676 (-2.660249) | 0.079041 / 0.424275 (-0.345234) | 0.005671 / 0.007607 (-0.001936) | 0.356037 / 0.226044 (0.129993) | 3.504599 / 2.268929 (1.235670) | 1.979207 / 55.444624 (-53.465417) | 1.695030 / 6.876477 (-5.181447) | 1.703978 / 2.142072 (-0.438095) | 0.800871 / 4.805227 (-4.004357) | 0.134414 / 6.500664 (-6.366250) | 0.041743 / 0.075469 (-0.033726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.029879 / 1.841788 (-0.811909) | 12.132252 / 8.074308 (4.057944) | 10.596576 / 10.191392 (0.405184) | 0.132237 / 0.680424 (-0.548187) | 0.016239 / 0.534201 (-0.517962) | 0.301831 / 0.579283 (-0.277452) | 0.127966 / 0.434364 (-0.306398) | 0.341081 / 0.540337 (-0.199256) | 0.448996 / 1.386936 (-0.937940) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a0fa48a68c3502edfa50273b881f909e4e6e70c \"CML watermark\")\n" ]

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[ "Hi ! I think the issue comes from the fact that you return `row` entirely, and therefore the dataset has to re-encode the audio data in `row`.\r\n\r\nCan you try this instead ?\r\n\r\n```python\r\n# map the dataset\r\ndef transcribe_audio(row):\r\n audio = row[\"audio\"] # get the audio but do nothing with it\r\n return {\"transcribed\": True}\r\n```\r\n\r\nPS: no need to iter on the dataset to trigger the `map` function on a `Dataset` - `map` runs directly when it's called (contrary to `IterableDataset` taht you can get when streaming, which are lazy)" ]

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[ "Hi ! `Sequence` has a weird behavior for dictionaries (from tensorflow-datasets), use a regular list instead:\r\n\r\n```python\r\nds = datasets.load_dataset('json', data_files=\"./temp.json\", features=datasets.Features({\r\n 'ref1': datasets.Value('string'),\r\n 'ref2': datasets.Value('string'),\r\n 'cuts': [{\r\n \"cut1\": datasets.Value(\"uint16\"),\r\n \"cut2\": datasets.Value(\"uint16\")\r\n }]\r\n}))\r\n```" ]

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[ "https://github.com/neurafusionai/Hugging_Face/blob/main/meta_opt_350m_customer_support_lora_v1.ipynb\r\n\r\ncouldnt train because of GPU\r\nI didnt pip install datasets -U\r\nbut looks like restarting worked" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7114). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005381 / 0.011353 (-0.005972) | 0.003929 / 0.011008 (-0.007079) | 0.062505 / 0.038508 (0.023997) | 0.031048 / 0.023109 (0.007938) | 0.244794 / 0.275898 (-0.031104) | 0.270997 / 0.323480 (-0.052483) | 0.003186 / 0.007986 (-0.004799) | 0.002750 / 0.004328 (-0.001579) | 0.048289 / 0.004250 (0.044039) | 0.042617 / 0.037052 (0.005565) | 0.262607 / 0.258489 (0.004118) | 0.281778 / 0.293841 (-0.012063) | 0.029426 / 0.128546 (-0.099120) | 0.012466 / 0.075646 (-0.063181) | 0.205221 / 0.419271 (-0.214051) | 0.035535 / 0.043533 (-0.007998) | 0.247866 / 0.255139 (-0.007273) | 0.269121 / 0.283200 (-0.014079) | 0.018557 / 0.141683 (-0.123125) | 1.147982 / 1.452155 (-0.304173) | 1.188998 / 1.492716 (-0.303718) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096550 / 0.018006 (0.078544) | 0.300497 / 0.000490 (0.300007) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019150 / 0.037411 (-0.018261) | 0.063518 / 0.014526 (0.048993) | 0.076643 / 0.176557 (-0.099914) | 0.122958 / 0.737135 (-0.614177) | 0.078511 / 0.296338 (-0.217828) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278163 / 0.215209 (0.062953) | 2.733514 / 2.077655 (0.655859) | 1.434335 / 1.504120 (-0.069785) | 1.318976 / 1.541195 (-0.222219) | 1.352498 / 1.468490 (-0.115992) | 0.717326 / 4.584777 (-3.867450) | 2.403683 / 3.745712 (-1.342029) | 2.930366 / 5.269862 (-2.339495) | 1.879938 / 4.565676 (-2.685739) | 0.079016 / 0.424275 (-0.345259) | 0.005156 / 0.007607 (-0.002451) | 0.331099 / 0.226044 (0.105055) | 3.305878 / 2.268929 (1.036949) | 1.804185 / 55.444624 (-53.640439) | 1.508785 / 6.876477 (-5.367692) | 1.570102 / 2.142072 (-0.571970) | 0.796348 / 4.805227 (-4.008879) | 0.135737 / 6.500664 (-6.364927) | 0.042902 / 0.075469 (-0.032567) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979923 / 1.841788 (-0.861865) | 11.656257 / 8.074308 (3.581949) | 9.745611 / 10.191392 (-0.445781) | 0.144497 / 0.680424 (-0.535927) | 0.022457 / 0.534201 (-0.511744) | 0.317251 / 0.579283 (-0.262032) | 0.264956 / 0.434364 (-0.169408) | 0.341873 / 0.540337 (-0.198464) | 0.439734 / 1.386936 (-0.947202) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006137 / 0.011353 (-0.005216) | 0.003999 / 0.011008 (-0.007009) | 0.049994 / 0.038508 (0.011486) | 0.032401 / 0.023109 (0.009292) | 0.272210 / 0.275898 (-0.003688) | 0.296038 / 0.323480 (-0.027442) | 0.004429 / 0.007986 (-0.003557) | 0.002894 / 0.004328 (-0.001434) | 0.049296 / 0.004250 (0.045045) | 0.041390 / 0.037052 (0.004337) | 0.288951 / 0.258489 (0.030462) | 0.321733 / 0.293841 (0.027892) | 0.033553 / 0.128546 (-0.094994) | 0.012122 / 0.075646 (-0.063524) | 0.060661 / 0.419271 (-0.358610) | 0.034752 / 0.043533 (-0.008781) | 0.272866 / 0.255139 (0.017727) | 0.292436 / 0.283200 (0.009237) | 0.018822 / 0.141683 (-0.122861) | 1.167758 / 1.452155 (-0.284397) | 1.207977 / 1.492716 (-0.284739) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095862 / 0.018006 (0.077855) | 0.313746 / 0.000490 (0.313256) | 0.000219 / 0.000200 (0.000020) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022940 / 0.037411 (-0.014472) | 0.076833 / 0.014526 (0.062307) | 0.088209 / 0.176557 (-0.088348) | 0.130154 / 0.737135 (-0.606981) | 0.089948 / 0.296338 (-0.206390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305393 / 0.215209 (0.090184) | 3.001629 / 2.077655 (0.923975) | 1.629378 / 1.504120 (0.125258) | 1.496022 / 1.541195 (-0.045173) | 1.542937 / 1.468490 (0.074447) | 0.734249 / 4.584777 (-3.850528) | 0.966226 / 3.745712 (-2.779486) | 3.051986 / 5.269862 (-2.217876) | 1.954694 / 4.565676 (-2.610982) | 0.081538 / 0.424275 (-0.342737) | 0.005198 / 0.007607 (-0.002409) | 0.355837 / 0.226044 (0.129793) | 3.537454 / 2.268929 (1.268525) | 2.036157 / 55.444624 (-53.408467) | 1.719255 / 6.876477 (-5.157222) | 1.744899 / 2.142072 (-0.397174) | 0.816034 / 4.805227 (-3.989193) | 0.135650 / 6.500664 (-6.365014) | 0.042206 / 0.075469 (-0.033263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.055518 / 1.841788 (-0.786269) | 12.654622 / 8.074308 (4.580313) | 10.450807 / 10.191392 (0.259415) | 0.153567 / 0.680424 (-0.526857) | 0.016114 / 0.534201 (-0.518087) | 0.301182 / 0.579283 (-0.278101) | 0.130043 / 0.434364 (-0.304321) | 0.341289 / 0.540337 (-0.199048) | 0.434573 / 1.386936 (-0.952363) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb8ae4d2c3dda8c770fe48a40195775a7b517b6b \"CML watermark\")\n" ]

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[ "That's expected behavior, it's also the same in `torch`:\r\n\r\n```python\r\n>>> list(DataLoader(list(range(5)), batch_size=10, drop_last=True))\r\n[]\r\n```" ]

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[ "@sayakpaul please advice " ]

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[ "Note that the CI before was using:\r\n- llvmlite: 0.43.0\r\n- numba: 0.60.0\r\n\r\nNow it tries to use:\r\n- llvmlite: 0.34.0\r\n- numba: 0.51.2", "The issue is because numba-0.60.0 pins numpy<2.1 and `uv` tries to install latest numpy-2.1.0 with an old numba-0.51.0 version (and llvmlite-0.34.0). See discussion in their repo:\r\n- https://github.com/numba/numba/issues/9708\r\n\r\nLatest numpy-2.1.0 will be supported by the next numba-0.61.0 release in September.\r\n\r\nNote that our CI requires numba with the \"audio\" extra:\r\n- librosa > numba" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7110). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Note that the CI error is unrelated to this PR and should be addressed in another PR. See:\r\n- #7111", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005354 / 0.011353 (-0.005999) | 0.004031 / 0.011008 (-0.006977) | 0.062470 / 0.038508 (0.023962) | 0.030882 / 0.023109 (0.007773) | 0.244816 / 0.275898 (-0.031082) | 0.264324 / 0.323480 (-0.059156) | 0.004164 / 0.007986 (-0.003822) | 0.002858 / 0.004328 (-0.001471) | 0.049008 / 0.004250 (0.044758) | 0.042139 / 0.037052 (0.005086) | 0.279496 / 0.258489 (0.021007) | 0.279408 / 0.293841 (-0.014433) | 0.029701 / 0.128546 (-0.098845) | 0.012501 / 0.075646 (-0.063145) | 0.203267 / 0.419271 (-0.216004) | 0.035964 / 0.043533 (-0.007569) | 0.239361 / 0.255139 (-0.015778) | 0.258942 / 0.283200 (-0.024257) | 0.017956 / 0.141683 (-0.123727) | 1.160468 / 1.452155 (-0.291687) | 1.203475 / 1.492716 (-0.289242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004639 / 0.018006 (-0.013367) | 0.298020 / 0.000490 (0.297530) | 0.000212 / 0.000200 (0.000012) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019371 / 0.037411 (-0.018040) | 0.063311 / 0.014526 (0.048785) | 0.076412 / 0.176557 (-0.100145) | 0.122574 / 0.737135 (-0.614561) | 0.078076 / 0.296338 (-0.218263) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275381 / 0.215209 (0.060172) | 2.713220 / 2.077655 (0.635565) | 1.441940 / 1.504120 (-0.062179) | 1.325545 / 1.541195 (-0.215650) | 1.363859 / 1.468490 (-0.104631) | 0.715147 / 4.584777 (-3.869630) | 2.356482 / 3.745712 (-1.389230) | 2.882792 / 5.269862 (-2.387069) | 1.833399 / 4.565676 (-2.732278) | 0.077872 / 0.424275 (-0.346403) | 0.005172 / 0.007607 (-0.002435) | 0.326361 / 0.226044 (0.100316) | 3.239202 / 2.268929 (0.970273) | 1.837745 / 55.444624 (-53.606879) | 1.517299 / 6.876477 (-5.359178) | 1.552938 / 2.142072 (-0.589134) | 0.801496 / 4.805227 (-4.003731) | 0.133351 / 6.500664 (-6.367314) | 0.042052 / 0.075469 (-0.033418) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957887 / 1.841788 (-0.883901) | 11.625291 / 8.074308 (3.550983) | 9.679413 / 10.191392 (-0.511979) | 0.140271 / 0.680424 (-0.540153) | 0.013991 / 0.534201 (-0.520210) | 0.299874 / 0.579283 (-0.279409) | 0.267164 / 0.434364 (-0.167200) | 0.338143 / 0.540337 (-0.202194) | 0.434105 / 1.386936 (-0.952831) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005833 / 0.011353 (-0.005520) | 0.003761 / 0.011008 (-0.007247) | 0.049699 / 0.038508 (0.011191) | 0.032786 / 0.023109 (0.009677) | 0.265100 / 0.275898 (-0.010798) | 0.291045 / 0.323480 (-0.032435) | 0.004281 / 0.007986 (-0.003705) | 0.002737 / 0.004328 (-0.001591) | 0.048524 / 0.004250 (0.044274) | 0.040783 / 0.037052 (0.003731) | 0.281122 / 0.258489 (0.022633) | 0.311349 / 0.293841 (0.017508) | 0.032143 / 0.128546 (-0.096403) | 0.011747 / 0.075646 (-0.063899) | 0.059432 / 0.419271 (-0.359840) | 0.034362 / 0.043533 (-0.009171) | 0.261061 / 0.255139 (0.005922) | 0.279536 / 0.283200 (-0.003663) | 0.019172 / 0.141683 (-0.122510) | 1.160069 / 1.452155 (-0.292086) | 1.224160 / 1.492716 (-0.268556) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093596 / 0.018006 (0.075590) | 0.302862 / 0.000490 (0.302372) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022785 / 0.037411 (-0.014626) | 0.079263 / 0.014526 (0.064737) | 0.091340 / 0.176557 (-0.085216) | 0.129453 / 0.737135 (-0.607682) | 0.091349 / 0.296338 (-0.204989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298166 / 0.215209 (0.082957) | 3.003146 / 2.077655 (0.925491) | 1.575903 / 1.504120 (0.071783) | 1.445231 / 1.541195 (-0.095963) | 1.477116 / 1.468490 (0.008625) | 0.726496 / 4.584777 (-3.858281) | 0.959827 / 3.745712 (-2.785885) | 2.941142 / 5.269862 (-2.328720) | 1.878581 / 4.565676 (-2.687096) | 0.078475 / 0.424275 (-0.345800) | 0.005137 / 0.007607 (-0.002470) | 0.352078 / 0.226044 (0.126034) | 3.486113 / 2.268929 (1.217184) | 1.965024 / 55.444624 (-53.479600) | 1.667223 / 6.876477 (-5.209254) | 1.665254 / 2.142072 (-0.476819) | 0.803543 / 4.805227 (-4.001684) | 0.133003 / 6.500664 (-6.367661) | 0.041462 / 0.075469 (-0.034008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.045534 / 1.841788 (-0.796254) | 12.124988 / 8.074308 (4.050680) | 10.418723 / 10.191392 (0.227331) | 0.142453 / 0.680424 (-0.537971) | 0.015686 / 0.534201 (-0.518515) | 0.300557 / 0.579283 (-0.278726) | 0.119851 / 0.434364 (-0.314512) | 0.342297 / 0.540337 (-0.198040) | 0.441263 / 1.386936 (-0.945673) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90b1d94ef419cb26f0bb24d982897dca39aa8a46 \"CML watermark\")\n", "lgtm!" ]

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[ "I don't reproduce, I was able to create a new repo: https://huggingface.co./datasets/severo/reproduce-datasets-issues-7108. Can you confirm it's still broken?", "I have just tried again.\r\n\r\nFirefox: The `Create dataset` doesn't work. It has worked in the past. It's my preferred browser.\r\n\r\nChrome: The `Create dataset` works.\r\n\r\nIt seems to be a Firefox specific issue.", "I have updated Firefox 129.0 (64 bit), and now the `Create dataset` is working again in Firefox.\r\n\r\nUX: It would be nice with better error messages on HuggingFace.", "maybe an issue with the cookie. cc @Wauplin @coyotte508 " ]

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[ "There seems to be a PR related to the load_dataset path that went into 2.21.0 -- https://github.com/huggingface/datasets/pull/6862/files\r\n\r\nTaking a look at it now", "+1\r\n\r\nDowngrading to 2.20.0 fixed my issue, hopefully helpful for others.", "I tried adding a simple test to `test_load.py` with the alpaca eval dataset but the test didn't fail :(. \r\n\r\nSo looks like this might have something to do with the environment? ", "There was an issue with the script of the \"tatsu-lab/alpaca_eval\" dataset.\r\n\r\nI was fixed with this PR: \r\n- [Fix FileNotFoundError](https://huggingface.co./datasets/tatsu-lab/alpaca_eval/discussions/2)\r\n\r\nIt should work now if you retry to load the dataset." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7106). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7105). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Nice\r\n\r\n<img width=\"141\" alt=\"Capture d’écran 2024-08-19 à 15 25 00\" src=\"https://github.com/user-attachments/assets/18c7b3ec-a57e-45d7-9b19-0b12df9feccd\">\r\n", "fyi the CI failure on test_py310_numpy2 is unrelated to this PR (it's a dependency install failure)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005677 / 0.011353 (-0.005676) | 0.004054 / 0.011008 (-0.006954) | 0.063101 / 0.038508 (0.024592) | 0.031665 / 0.023109 (0.008556) | 0.243332 / 0.275898 (-0.032566) | 0.271067 / 0.323480 (-0.052413) | 0.004283 / 0.007986 (-0.003703) | 0.002889 / 0.004328 (-0.001440) | 0.049269 / 0.004250 (0.045018) | 0.048707 / 0.037052 (0.011654) | 0.258599 / 0.258489 (0.000110) | 0.307715 / 0.293841 (0.013874) | 0.029850 / 0.128546 (-0.098696) | 0.012299 / 0.075646 (-0.063347) | 0.207616 / 0.419271 (-0.211656) | 0.037655 / 0.043533 (-0.005878) | 0.246602 / 0.255139 (-0.008537) | 0.268518 / 0.283200 (-0.014682) | 0.018128 / 0.141683 (-0.123555) | 1.181569 / 1.452155 (-0.270586) | 1.250641 / 1.492716 (-0.242075) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.143911 / 0.018006 (0.125905) | 0.305608 / 0.000490 (0.305118) | 0.000250 / 0.000200 (0.000050) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019208 / 0.037411 (-0.018204) | 0.062502 / 0.014526 (0.047976) | 0.075896 / 0.176557 (-0.100661) | 0.123422 / 0.737135 (-0.613713) | 0.077311 / 0.296338 (-0.219028) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283108 / 0.215209 (0.067899) | 2.783509 / 2.077655 (0.705855) | 1.466358 / 1.504120 (-0.037762) | 1.350989 / 1.541195 (-0.190206) | 1.370517 / 1.468490 (-0.097973) | 0.732706 / 4.584777 (-3.852071) | 2.366710 / 3.745712 (-1.379002) | 2.988913 / 5.269862 (-2.280949) | 1.892204 / 4.565676 (-2.673473) | 0.079077 / 0.424275 (-0.345198) | 0.005158 / 0.007607 (-0.002449) | 0.336620 / 0.226044 (0.110576) | 3.423556 / 2.268929 (1.154628) | 1.848732 / 55.444624 (-53.595892) | 1.544996 / 6.876477 (-5.331480) | 1.550051 / 2.142072 (-0.592022) | 0.798235 / 4.805227 (-4.006993) | 0.132945 / 6.500664 (-6.367719) | 0.041785 / 0.075469 (-0.033684) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963359 / 1.841788 (-0.878429) | 11.699994 / 8.074308 (3.625686) | 9.311998 / 10.191392 (-0.879394) | 0.140493 / 0.680424 (-0.539931) | 0.013834 / 0.534201 (-0.520367) | 0.302569 / 0.579283 (-0.276714) | 0.267377 / 0.434364 (-0.166987) | 0.341093 / 0.540337 (-0.199244) | 0.431941 / 1.386936 (-0.954995) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005744 / 0.011353 (-0.005608) | 0.003668 / 0.011008 (-0.007340) | 0.049837 / 0.038508 (0.011329) | 0.032051 / 0.023109 (0.008941) | 0.271725 / 0.275898 (-0.004173) | 0.302612 / 0.323480 (-0.020867) | 0.004455 / 0.007986 (-0.003531) | 0.002816 / 0.004328 (-0.001512) | 0.049036 / 0.004250 (0.044785) | 0.041233 / 0.037052 (0.004181) | 0.287900 / 0.258489 (0.029411) | 0.326204 / 0.293841 (0.032363) | 0.032027 / 0.128546 (-0.096519) | 0.012033 / 0.075646 (-0.063613) | 0.060822 / 0.419271 (-0.358449) | 0.033830 / 0.043533 (-0.009703) | 0.274855 / 0.255139 (0.019716) | 0.294191 / 0.283200 (0.010992) | 0.017979 / 0.141683 (-0.123704) | 1.151353 / 1.452155 (-0.300801) | 1.215384 / 1.492716 (-0.277333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102552 / 0.018006 (0.084546) | 0.314148 / 0.000490 (0.313658) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024565 / 0.037411 (-0.012846) | 0.076968 / 0.014526 (0.062442) | 0.087982 / 0.176557 (-0.088574) | 0.129844 / 0.737135 (-0.607292) | 0.091370 / 0.296338 (-0.204968) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296767 / 0.215209 (0.081558) | 2.910716 / 2.077655 (0.833062) | 1.579526 / 1.504120 (0.075406) | 1.453457 / 1.541195 (-0.087737) | 1.466296 / 1.468490 (-0.002194) | 0.728372 / 4.584777 (-3.856405) | 0.963852 / 3.745712 (-2.781861) | 2.946582 / 5.269862 (-2.323280) | 1.936199 / 4.565676 (-2.629478) | 0.078886 / 0.424275 (-0.345389) | 0.005537 / 0.007607 (-0.002071) | 0.346315 / 0.226044 (0.120270) | 3.440774 / 2.268929 (1.171845) | 1.937549 / 55.444624 (-53.507076) | 1.649507 / 6.876477 (-5.226970) | 1.653386 / 2.142072 (-0.488686) | 0.806598 / 4.805227 (-3.998629) | 0.133384 / 6.500664 (-6.367280) | 0.040552 / 0.075469 (-0.034917) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030515 / 1.841788 (-0.811272) | 12.129888 / 8.074308 (4.055580) | 10.287069 / 10.191392 (0.095677) | 0.141512 / 0.680424 (-0.538912) | 0.015483 / 0.534201 (-0.518718) | 0.300053 / 0.579283 (-0.279230) | 0.120825 / 0.434364 (-0.313539) | 0.342681 / 0.540337 (-0.197656) | 0.470616 / 1.386936 (-0.916320) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#28780197dd3e4c125defae29ac8ef5346c41350a \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7104). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005343 / 0.011353 (-0.006010) | 0.003562 / 0.011008 (-0.007447) | 0.062785 / 0.038508 (0.024277) | 0.031459 / 0.023109 (0.008349) | 0.246497 / 0.275898 (-0.029401) | 0.268258 / 0.323480 (-0.055222) | 0.003201 / 0.007986 (-0.004785) | 0.004153 / 0.004328 (-0.000175) | 0.049003 / 0.004250 (0.044753) | 0.042780 / 0.037052 (0.005728) | 0.263857 / 0.258489 (0.005368) | 0.278578 / 0.293841 (-0.015263) | 0.030357 / 0.128546 (-0.098190) | 0.012341 / 0.075646 (-0.063305) | 0.206010 / 0.419271 (-0.213262) | 0.036244 / 0.043533 (-0.007289) | 0.245799 / 0.255139 (-0.009340) | 0.265467 / 0.283200 (-0.017733) | 0.019473 / 0.141683 (-0.122210) | 1.147913 / 1.452155 (-0.304242) | 1.209968 / 1.492716 (-0.282749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099393 / 0.018006 (0.081387) | 0.300898 / 0.000490 (0.300408) | 0.000258 / 0.000200 (0.000058) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018888 / 0.037411 (-0.018523) | 0.062452 / 0.014526 (0.047926) | 0.073799 / 0.176557 (-0.102757) | 0.121297 / 0.737135 (-0.615839) | 0.074855 / 0.296338 (-0.221484) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283969 / 0.215209 (0.068760) | 2.808820 / 2.077655 (0.731165) | 1.446106 / 1.504120 (-0.058014) | 1.321622 / 1.541195 (-0.219573) | 1.348317 / 1.468490 (-0.120173) | 0.738369 / 4.584777 (-3.846408) | 2.349825 / 3.745712 (-1.395887) | 2.913964 / 5.269862 (-2.355897) | 1.870585 / 4.565676 (-2.695092) | 0.080141 / 0.424275 (-0.344134) | 0.005174 / 0.007607 (-0.002433) | 0.335977 / 0.226044 (0.109933) | 3.356267 / 2.268929 (1.087338) | 1.811149 / 55.444624 (-53.633475) | 1.510685 / 6.876477 (-5.365792) | 1.524960 / 2.142072 (-0.617112) | 0.803900 / 4.805227 (-4.001328) | 0.138294 / 6.500664 (-6.362370) | 0.042241 / 0.075469 (-0.033229) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975597 / 1.841788 (-0.866191) | 11.395109 / 8.074308 (3.320801) | 9.837724 / 10.191392 (-0.353668) | 0.141474 / 0.680424 (-0.538950) | 0.015075 / 0.534201 (-0.519126) | 0.304285 / 0.579283 (-0.274998) | 0.267845 / 0.434364 (-0.166519) | 0.342808 / 0.540337 (-0.197529) | 0.434299 / 1.386936 (-0.952637) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005612 / 0.011353 (-0.005741) | 0.003808 / 0.011008 (-0.007201) | 0.050533 / 0.038508 (0.012024) | 0.032635 / 0.023109 (0.009526) | 0.265522 / 0.275898 (-0.010376) | 0.289763 / 0.323480 (-0.033716) | 0.004395 / 0.007986 (-0.003590) | 0.002868 / 0.004328 (-0.001460) | 0.048443 / 0.004250 (0.044193) | 0.040047 / 0.037052 (0.002995) | 0.279013 / 0.258489 (0.020524) | 0.314499 / 0.293841 (0.020658) | 0.032321 / 0.128546 (-0.096225) | 0.011902 / 0.075646 (-0.063744) | 0.059827 / 0.419271 (-0.359445) | 0.034388 / 0.043533 (-0.009145) | 0.270660 / 0.255139 (0.015521) | 0.290776 / 0.283200 (0.007576) | 0.017875 / 0.141683 (-0.123808) | 1.188085 / 1.452155 (-0.264070) | 1.221384 / 1.492716 (-0.271332) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095619 / 0.018006 (0.077613) | 0.305331 / 0.000490 (0.304841) | 0.000217 / 0.000200 (0.000018) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022481 / 0.037411 (-0.014930) | 0.076957 / 0.014526 (0.062431) | 0.087830 / 0.176557 (-0.088726) | 0.128290 / 0.737135 (-0.608845) | 0.090565 / 0.296338 (-0.205774) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291861 / 0.215209 (0.076652) | 2.869776 / 2.077655 (0.792121) | 1.575114 / 1.504120 (0.070994) | 1.449873 / 1.541195 (-0.091322) | 1.450333 / 1.468490 (-0.018158) | 0.723319 / 4.584777 (-3.861458) | 0.972603 / 3.745712 (-2.773109) | 2.940909 / 5.269862 (-2.328953) | 1.889664 / 4.565676 (-2.676012) | 0.078654 / 0.424275 (-0.345621) | 0.005197 / 0.007607 (-0.002410) | 0.344380 / 0.226044 (0.118336) | 3.387509 / 2.268929 (1.118580) | 1.981590 / 55.444624 (-53.463034) | 1.643214 / 6.876477 (-5.233263) | 1.640435 / 2.142072 (-0.501638) | 0.802037 / 4.805227 (-4.003191) | 0.133016 / 6.500664 (-6.367648) | 0.040861 / 0.075469 (-0.034608) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026372 / 1.841788 (-0.815416) | 11.959931 / 8.074308 (3.885623) | 10.122523 / 10.191392 (-0.068869) | 0.144443 / 0.680424 (-0.535981) | 0.015629 / 0.534201 (-0.518572) | 0.304802 / 0.579283 (-0.274481) | 0.120538 / 0.434364 (-0.313826) | 0.343394 / 0.540337 (-0.196943) | 0.437544 / 1.386936 (-0.949392) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84832c07f614e5f51a762166b2fa9ac27e988173 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7103). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005255 / 0.011353 (-0.006098) | 0.003344 / 0.011008 (-0.007664) | 0.062062 / 0.038508 (0.023554) | 0.030154 / 0.023109 (0.007045) | 0.233728 / 0.275898 (-0.042170) | 0.258799 / 0.323480 (-0.064681) | 0.004105 / 0.007986 (-0.003880) | 0.002708 / 0.004328 (-0.001621) | 0.048689 / 0.004250 (0.044439) | 0.041864 / 0.037052 (0.004812) | 0.247221 / 0.258489 (-0.011268) | 0.274067 / 0.293841 (-0.019774) | 0.029108 / 0.128546 (-0.099439) | 0.011867 / 0.075646 (-0.063779) | 0.203181 / 0.419271 (-0.216090) | 0.035162 / 0.043533 (-0.008371) | 0.239723 / 0.255139 (-0.015416) | 0.256679 / 0.283200 (-0.026521) | 0.018362 / 0.141683 (-0.123321) | 1.139974 / 1.452155 (-0.312181) | 1.193946 / 1.492716 (-0.298770) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.135477 / 0.018006 (0.117471) | 0.298500 / 0.000490 (0.298011) | 0.000225 / 0.000200 (0.000025) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018743 / 0.037411 (-0.018668) | 0.062999 / 0.014526 (0.048474) | 0.073466 / 0.176557 (-0.103090) | 0.119227 / 0.737135 (-0.617908) | 0.074338 / 0.296338 (-0.222000) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280747 / 0.215209 (0.065538) | 2.750660 / 2.077655 (0.673006) | 1.461004 / 1.504120 (-0.043116) | 1.348439 / 1.541195 (-0.192756) | 1.365209 / 1.468490 (-0.103281) | 0.718416 / 4.584777 (-3.866361) | 2.333568 / 3.745712 (-1.412144) | 2.854639 / 5.269862 (-2.415223) | 1.821144 / 4.565676 (-2.744532) | 0.077234 / 0.424275 (-0.347041) | 0.005111 / 0.007607 (-0.002497) | 0.330749 / 0.226044 (0.104705) | 3.277189 / 2.268929 (1.008260) | 1.825886 / 55.444624 (-53.618739) | 1.515078 / 6.876477 (-5.361399) | 1.527288 / 2.142072 (-0.614785) | 0.786922 / 4.805227 (-4.018305) | 0.131539 / 6.500664 (-6.369125) | 0.042365 / 0.075469 (-0.033104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961809 / 1.841788 (-0.879979) | 11.184540 / 8.074308 (3.110232) | 9.473338 / 10.191392 (-0.718054) | 0.138460 / 0.680424 (-0.541964) | 0.014588 / 0.534201 (-0.519613) | 0.301503 / 0.579283 (-0.277780) | 0.261092 / 0.434364 (-0.173271) | 0.336480 / 0.540337 (-0.203857) | 0.427665 / 1.386936 (-0.959271) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005517 / 0.011353 (-0.005836) | 0.003417 / 0.011008 (-0.007591) | 0.049338 / 0.038508 (0.010830) | 0.033411 / 0.023109 (0.010302) | 0.264328 / 0.275898 (-0.011570) | 0.286750 / 0.323480 (-0.036730) | 0.004299 / 0.007986 (-0.003686) | 0.002506 / 0.004328 (-0.001823) | 0.049511 / 0.004250 (0.045260) | 0.041471 / 0.037052 (0.004418) | 0.276732 / 0.258489 (0.018243) | 0.311908 / 0.293841 (0.018067) | 0.031683 / 0.128546 (-0.096863) | 0.011700 / 0.075646 (-0.063946) | 0.060084 / 0.419271 (-0.359188) | 0.037757 / 0.043533 (-0.005776) | 0.265342 / 0.255139 (0.010203) | 0.287782 / 0.283200 (0.004583) | 0.018692 / 0.141683 (-0.122990) | 1.163462 / 1.452155 (-0.288692) | 1.219236 / 1.492716 (-0.273481) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094102 / 0.018006 (0.076096) | 0.303976 / 0.000490 (0.303487) | 0.000208 / 0.000200 (0.000008) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023252 / 0.037411 (-0.014160) | 0.076986 / 0.014526 (0.062461) | 0.088831 / 0.176557 (-0.087726) | 0.128661 / 0.737135 (-0.608475) | 0.089082 / 0.296338 (-0.207256) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297428 / 0.215209 (0.082218) | 2.951568 / 2.077655 (0.873913) | 1.597627 / 1.504120 (0.093508) | 1.466556 / 1.541195 (-0.074639) | 1.455522 / 1.468490 (-0.012968) | 0.723576 / 4.584777 (-3.861201) | 0.951113 / 3.745712 (-2.794599) | 2.889671 / 5.269862 (-2.380190) | 1.877330 / 4.565676 (-2.688347) | 0.079124 / 0.424275 (-0.345151) | 0.005146 / 0.007607 (-0.002461) | 0.344063 / 0.226044 (0.118018) | 3.432190 / 2.268929 (1.163261) | 1.927049 / 55.444624 (-53.517576) | 1.638552 / 6.876477 (-5.237924) | 1.647791 / 2.142072 (-0.494282) | 0.800526 / 4.805227 (-4.004701) | 0.131858 / 6.500664 (-6.368806) | 0.040852 / 0.075469 (-0.034618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025536 / 1.841788 (-0.816252) | 11.798302 / 8.074308 (3.723994) | 10.012051 / 10.191392 (-0.179341) | 0.137701 / 0.680424 (-0.542723) | 0.015151 / 0.534201 (-0.519050) | 0.298972 / 0.579283 (-0.280311) | 0.123816 / 0.434364 (-0.310548) | 0.337292 / 0.540337 (-0.203046) | 0.432729 / 1.386936 (-0.954207) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bececdac927160b5c7e883736d7cc79d5699ad0a \"CML watermark\")\n" ]

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[ "Hi @lajd , I was skeptical about how we are saving the shards each as their own dataset (arrow file) in the script above, and so I updated the script to try out saving the shards in a few different file formats. From the experiments I ran, I saw binary format show significantly the best performance, with arrow and parquet about the same. However, I was unable to reproduce a drastically slower iteration speed after shuffling in any case when using the revised script -- pasting below:\r\n\r\n```python\r\nimport time\r\nfrom datasets import load_dataset, Dataset, IterableDataset\r\nfrom pathlib import Path\r\nimport torch\r\nimport pandas as pd\r\nimport pickle\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\n\r\ndef generate_random_example():\r\n return {\r\n 'inputs': torch.randn(128).tolist(),\r\n 'indices': torch.randint(0, 10000, (2, 20000)).tolist(),\r\n 'values': torch.randn(20000).tolist(),\r\n }\r\n\r\n\r\ndef generate_shard_data(examples_per_shard: int = 512):\r\n return [generate_random_example() for _ in range(examples_per_shard)]\r\n\r\n\r\ndef save_shard_as_arrow(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Convert data to a Hugging Face Dataset\r\n dataset = Dataset.from_dict({\r\n 'inputs': [example['inputs'] for example in shard_data],\r\n 'indices': [example['indices'] for example in shard_data],\r\n 'values': [example['values'] for example in shard_data],\r\n })\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}\"\r\n\r\n # Save the dataset to disk using the Arrow format\r\n dataset.save_to_disk(str(shard_write_path))\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef save_shard_as_parquet(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Convert data to a pandas DataFrame for easy conversion to Parquet\r\n df = pd.DataFrame(shard_data)\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}.parquet\"\r\n\r\n # Convert DataFrame to PyArrow Table for Parquet saving\r\n table = pa.Table.from_pandas(df)\r\n\r\n # Save the table as a Parquet file\r\n pq.write_table(table, shard_write_path)\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef save_shard_as_binary(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}.bin\"\r\n\r\n # Save each example as a serialized binary object using pickle\r\n with open(shard_write_path, 'wb') as f:\r\n for example in shard_data:\r\n f.write(pickle.dumps(example))\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef generate_split_shards(save_dir, filetype=\"parquet\", num_shards: int = 16, examples_per_shard: int = 512):\r\n shard_filepaths = []\r\n for shard_idx in range(num_shards):\r\n if filetype == \"parquet\":\r\n shard_filepaths.append(save_shard_as_parquet(shard_idx, save_dir, examples_per_shard))\r\n elif filetype == \"binary\":\r\n shard_filepaths.append(save_shard_as_binary(shard_idx, save_dir, examples_per_shard))\r\n elif filetype == \"arrow\":\r\n shard_filepaths.append(save_shard_as_arrow(shard_idx, save_dir, examples_per_shard))\r\n else:\r\n raise ValueError(f\"Unsupported filetype: {filetype}. Choose either 'parquet' or 'binary'.\")\r\n return shard_filepaths\r\n\r\n\r\ndef _binary_dataset_generator(files):\r\n for filepath in files:\r\n with open(filepath, 'rb') as f:\r\n while True:\r\n try:\r\n example = pickle.load(f)\r\n yield example\r\n except EOFError:\r\n break\r\n\r\n\r\ndef load_binary_dataset(shard_filepaths):\r\n return IterableDataset.from_generator(\r\n _binary_dataset_generator, gen_kwargs={\"files\": shard_filepaths},\r\n )\r\n\r\n\r\ndef load_parquet_dataset(shard_filepaths):\r\n # Load the dataset as an IterableDataset\r\n return load_dataset(\r\n \"parquet\",\r\n data_files={split: shard_filepaths},\r\n streaming=True,\r\n split=split,\r\n )\r\n\r\n\r\ndef load_arrow_dataset(shard_filepaths):\r\n # Load the dataset as an IterableDataset\r\n shard_filepaths = [f + \"/data-00000-of-00001.arrow\" for f in shard_filepaths]\r\n return load_dataset(\r\n \"arrow\",\r\n data_files={split: shard_filepaths},\r\n streaming=True,\r\n split=split,\r\n )\r\n\r\n\r\ndef load_dataset_wrapper(filetype: str, shard_filepaths: list[str]):\r\n if filetype == \"parquet\":\r\n return load_parquet_dataset(shard_filepaths)\r\n if filetype == \"binary\":\r\n return load_binary_dataset(shard_filepaths)\r\n if filetype == \"arrow\":\r\n return load_arrow_dataset(shard_filepaths)\r\n else:\r\n raise ValueError(\"Unsupported filetype\")\r\n\r\n\r\n# Example usage:\r\nsplit = \"train\"\r\nsplit_save_dir = \"/tmp/random_split\"\r\n\r\nfiletype = \"binary\" # or \"parquet\", or \"arrow\"\r\nnum_shards = 16\r\n\r\nshard_filepaths = generate_split_shards(split_save_dir, filetype=filetype, num_shards=num_shards)\r\ndataset = load_dataset_wrapper(filetype=filetype, shard_filepaths=shard_filepaths)\r\n\r\ndataset = dataset.shuffle(buffer_size=100, seed=42)\r\n\r\nstart_time = time.time()\r\nfor count, item in enumerate(dataset):\r\n if count > 0 and count % 100 == 0:\r\n elapsed_time = time.time() - start_time\r\n iterations_per_second = count / elapsed_time\r\n print(f\"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second\")\r\n```", "update: I was able to reproduce the issue you described -- but ONLY if I do \r\n\r\n```\r\nrandom_dataset = random_dataset.with_format(\"numpy\")\r\n```\r\n\r\nIf I do this, I see similar numbers as what you reported. If I do not use numpy format, parquet and arrow are about 17 iterations per second regardless of whether or not we shuffle. Using binary, (again no numpy format tried with this yet), still shows the fastest speeds on average (shuffle and no shuffle) of about 850 it/sec.\r\n\r\nI suspect some issues with arrow and numpy being optimized for sequential reads, and shuffling cuases issuses... hmm" ]

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[ "Having looked into this further it seems the core of the issue is with two different formats in the same repo.\r\n\r\nWhen the `parquet` config is first, the `WebDataset`s are loaded as `parquet`, if the `WebDataset` configs are first, the `parquet` is loaded as `WebDataset`.\r\n\r\nA workaround in my case would be to just turn the `parquet` into a `WebDataset`, although I'd still need the Dataset Viewer config limit increasing. In other cases using the same format may not be possible.\r\n\r\nRelevant code: \r\n- [HubDatasetModuleFactoryWithoutScript](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/load.py#L964)\r\n- [get_data_patterns](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/data_files.py#L415)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7099). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005649 / 0.011353 (-0.005704) | 0.003918 / 0.011008 (-0.007091) | 0.064333 / 0.038508 (0.025825) | 0.031909 / 0.023109 (0.008800) | 0.249020 / 0.275898 (-0.026878) | 0.273563 / 0.323480 (-0.049917) | 0.004184 / 0.007986 (-0.003802) | 0.002809 / 0.004328 (-0.001519) | 0.049066 / 0.004250 (0.044816) | 0.043324 / 0.037052 (0.006272) | 0.257889 / 0.258489 (-0.000600) | 0.285410 / 0.293841 (-0.008431) | 0.030681 / 0.128546 (-0.097865) | 0.012389 / 0.075646 (-0.063258) | 0.206172 / 0.419271 (-0.213100) | 0.036500 / 0.043533 (-0.007032) | 0.253674 / 0.255139 (-0.001465) | 0.272086 / 0.283200 (-0.011114) | 0.019558 / 0.141683 (-0.122125) | 1.149501 / 1.452155 (-0.302653) | 1.198036 / 1.492716 (-0.294680) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.139977 / 0.018006 (0.121971) | 0.301149 / 0.000490 (0.300659) | 0.000253 / 0.000200 (0.000053) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019137 / 0.037411 (-0.018274) | 0.062616 / 0.014526 (0.048090) | 0.075965 / 0.176557 (-0.100591) | 0.120976 / 0.737135 (-0.616159) | 0.076384 / 0.296338 (-0.219954) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283801 / 0.215209 (0.068592) | 2.794074 / 2.077655 (0.716419) | 1.475633 / 1.504120 (-0.028487) | 1.336270 / 1.541195 (-0.204925) | 1.376159 / 1.468490 (-0.092331) | 0.718768 / 4.584777 (-3.866009) | 2.375970 / 3.745712 (-1.369742) | 2.969121 / 5.269862 (-2.300741) | 1.900236 / 4.565676 (-2.665440) | 0.082463 / 0.424275 (-0.341812) | 0.005159 / 0.007607 (-0.002448) | 0.329057 / 0.226044 (0.103012) | 3.250535 / 2.268929 (0.981607) | 1.846415 / 55.444624 (-53.598210) | 1.496622 / 6.876477 (-5.379855) | 1.538125 / 2.142072 (-0.603947) | 0.806127 / 4.805227 (-3.999101) | 0.135272 / 6.500664 (-6.365392) | 0.042668 / 0.075469 (-0.032801) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983035 / 1.841788 (-0.858753) | 11.725835 / 8.074308 (3.651527) | 9.962818 / 10.191392 (-0.228574) | 0.131928 / 0.680424 (-0.548496) | 0.015784 / 0.534201 (-0.518417) | 0.301640 / 0.579283 (-0.277643) | 0.266251 / 0.434364 (-0.168113) | 0.339723 / 0.540337 (-0.200614) | 0.443384 / 1.386936 (-0.943552) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006301 / 0.011353 (-0.005052) | 0.004346 / 0.011008 (-0.006662) | 0.051406 / 0.038508 (0.012898) | 0.032263 / 0.023109 (0.009154) | 0.273715 / 0.275898 (-0.002183) | 0.300982 / 0.323480 (-0.022498) | 0.004533 / 0.007986 (-0.003452) | 0.002911 / 0.004328 (-0.001418) | 0.050464 / 0.004250 (0.046214) | 0.041131 / 0.037052 (0.004078) | 0.289958 / 0.258489 (0.031469) | 0.328632 / 0.293841 (0.034791) | 0.033545 / 0.128546 (-0.095001) | 0.013145 / 0.075646 (-0.062501) | 0.062241 / 0.419271 (-0.357031) | 0.035095 / 0.043533 (-0.008438) | 0.273303 / 0.255139 (0.018164) | 0.293652 / 0.283200 (0.010452) | 0.019980 / 0.141683 (-0.121703) | 1.155432 / 1.452155 (-0.296722) | 1.211409 / 1.492716 (-0.281307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094885 / 0.018006 (0.076879) | 0.307423 / 0.000490 (0.306933) | 0.000254 / 0.000200 (0.000054) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023462 / 0.037411 (-0.013949) | 0.081980 / 0.014526 (0.067454) | 0.089890 / 0.176557 (-0.086666) | 0.131058 / 0.737135 (-0.606078) | 0.091873 / 0.296338 (-0.204465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298522 / 0.215209 (0.083313) | 2.981771 / 2.077655 (0.904116) | 1.632515 / 1.504120 (0.128395) | 1.502885 / 1.541195 (-0.038310) | 1.496868 / 1.468490 (0.028377) | 0.750145 / 4.584777 (-3.834632) | 0.988853 / 3.745712 (-2.756859) | 3.029162 / 5.269862 (-2.240700) | 1.952304 / 4.565676 (-2.613373) | 0.082418 / 0.424275 (-0.341857) | 0.005724 / 0.007607 (-0.001883) | 0.356914 / 0.226044 (0.130870) | 3.523804 / 2.268929 (1.254875) | 1.983254 / 55.444624 (-53.461370) | 1.673135 / 6.876477 (-5.203342) | 1.716639 / 2.142072 (-0.425433) | 0.821568 / 4.805227 (-3.983659) | 0.136113 / 6.500664 (-6.364551) | 0.041593 / 0.075469 (-0.033876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.044670 / 1.841788 (-0.797118) | 12.739375 / 8.074308 (4.665066) | 10.263619 / 10.191392 (0.072227) | 0.132811 / 0.680424 (-0.547613) | 0.015491 / 0.534201 (-0.518710) | 0.305545 / 0.579283 (-0.273738) | 0.129226 / 0.434364 (-0.305138) | 0.345532 / 0.540337 (-0.194805) | 0.460406 / 1.386936 (-0.926530) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ebec2691fb1e40145429f63375cef3f46d3011ab \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7098). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi @albertvillanova, is this PR looking okay to you? Anything else you'd like to see?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7096). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005278 / 0.011353 (-0.006075) | 0.003536 / 0.011008 (-0.007472) | 0.062604 / 0.038508 (0.024096) | 0.030704 / 0.023109 (0.007595) | 0.242178 / 0.275898 (-0.033720) | 0.264335 / 0.323480 (-0.059145) | 0.004118 / 0.007986 (-0.003868) | 0.002789 / 0.004328 (-0.001539) | 0.048813 / 0.004250 (0.044563) | 0.041787 / 0.037052 (0.004735) | 0.252369 / 0.258489 (-0.006120) | 0.280981 / 0.293841 (-0.012859) | 0.029646 / 0.128546 (-0.098900) | 0.012093 / 0.075646 (-0.063553) | 0.203036 / 0.419271 (-0.216235) | 0.035814 / 0.043533 (-0.007719) | 0.248929 / 0.255139 (-0.006210) | 0.266568 / 0.283200 (-0.016632) | 0.018761 / 0.141683 (-0.122922) | 1.188443 / 1.452155 (-0.263712) | 1.219324 / 1.492716 (-0.273392) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095256 / 0.018006 (0.077250) | 0.301069 / 0.000490 (0.300579) | 0.000219 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018541 / 0.037411 (-0.018870) | 0.067333 / 0.014526 (0.052807) | 0.075483 / 0.176557 (-0.101073) | 0.121301 / 0.737135 (-0.615834) | 0.076924 / 0.296338 (-0.219414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284722 / 0.215209 (0.069513) | 2.817656 / 2.077655 (0.740001) | 1.483827 / 1.504120 (-0.020293) | 1.363072 / 1.541195 (-0.178123) | 1.380472 / 1.468490 (-0.088018) | 0.739543 / 4.584777 (-3.845234) | 2.390699 / 3.745712 (-1.355013) | 2.980347 / 5.269862 (-2.289515) | 1.897881 / 4.565676 (-2.667795) | 0.078827 / 0.424275 (-0.345448) | 0.005193 / 0.007607 (-0.002414) | 0.342739 / 0.226044 (0.116695) | 3.370871 / 2.268929 (1.101942) | 1.846475 / 55.444624 (-53.598150) | 1.577860 / 6.876477 (-5.298617) | 1.628606 / 2.142072 (-0.513466) | 0.815686 / 4.805227 (-3.989541) | 0.134985 / 6.500664 (-6.365679) | 0.042330 / 0.075469 (-0.033139) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962530 / 1.841788 (-0.879258) | 11.271449 / 8.074308 (3.197141) | 9.615452 / 10.191392 (-0.575940) | 0.140322 / 0.680424 (-0.540101) | 0.014057 / 0.534201 (-0.520144) | 0.306212 / 0.579283 (-0.273071) | 0.266758 / 0.434364 (-0.167606) | 0.341229 / 0.540337 (-0.199108) | 0.428974 / 1.386936 (-0.957962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005980 / 0.011353 (-0.005373) | 0.003831 / 0.011008 (-0.007177) | 0.049837 / 0.038508 (0.011329) | 0.030602 / 0.023109 (0.007493) | 0.274107 / 0.275898 (-0.001791) | 0.298175 / 0.323480 (-0.025305) | 0.004492 / 0.007986 (-0.003494) | 0.002840 / 0.004328 (-0.001489) | 0.048984 / 0.004250 (0.044733) | 0.040001 / 0.037052 (0.002949) | 0.286130 / 0.258489 (0.027641) | 0.321546 / 0.293841 (0.027705) | 0.032675 / 0.128546 (-0.095871) | 0.012222 / 0.075646 (-0.063424) | 0.060321 / 0.419271 (-0.358950) | 0.034456 / 0.043533 (-0.009077) | 0.272408 / 0.255139 (0.017269) | 0.294714 / 0.283200 (0.011515) | 0.018568 / 0.141683 (-0.123115) | 1.169826 / 1.452155 (-0.282329) | 1.223906 / 1.492716 (-0.268810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093734 / 0.018006 (0.075727) | 0.305915 / 0.000490 (0.305425) | 0.000210 / 0.000200 (0.000010) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022389 / 0.037411 (-0.015022) | 0.076640 / 0.014526 (0.062114) | 0.088660 / 0.176557 (-0.087897) | 0.128998 / 0.737135 (-0.608137) | 0.090346 / 0.296338 (-0.205992) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291642 / 0.215209 (0.076433) | 2.897270 / 2.077655 (0.819615) | 1.571564 / 1.504120 (0.067444) | 1.449533 / 1.541195 (-0.091662) | 1.458744 / 1.468490 (-0.009746) | 0.725465 / 4.584777 (-3.859312) | 0.962597 / 3.745712 (-2.783115) | 3.035056 / 5.269862 (-2.234806) | 1.902542 / 4.565676 (-2.663135) | 0.079869 / 0.424275 (-0.344407) | 0.005172 / 0.007607 (-0.002435) | 0.352099 / 0.226044 (0.126055) | 3.469058 / 2.268929 (1.200129) | 1.953402 / 55.444624 (-53.491222) | 1.647182 / 6.876477 (-5.229294) | 1.686473 / 2.142072 (-0.455599) | 0.797218 / 4.805227 (-4.008009) | 0.134161 / 6.500664 (-6.366503) | 0.041563 / 0.075469 (-0.033906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.045855 / 1.841788 (-0.795933) | 12.271390 / 8.074308 (4.197082) | 10.186889 / 10.191392 (-0.004503) | 0.141141 / 0.680424 (-0.539283) | 0.015482 / 0.534201 (-0.518719) | 0.305699 / 0.579283 (-0.273584) | 0.128539 / 0.434364 (-0.305825) | 0.348492 / 0.540337 (-0.191845) | 0.444867 / 1.386936 (-0.942069) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#93dc73501298ccb1d31d854ba20fcf2c3b2fea8b \"CML watermark\")\n" ]

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